[
  {
    "path": ".github/ISSUE_TEMPLATE/error-report.md",
    "content": "---\nname: Error report\nabout: 'Create a report to help us improve '\ntitle: ''\nlabels: ''\nassignees: ''\n\n---\n\n**Describe the Error**\nA clear and concise description of what the bug is.\n\n**To Reproduce**\nSteps to reproduce the behavior:\n1. Go to '...'\n2. Click on '....'\n3. Scroll down to '....'\n4. See error\n\n**Expected behavior**\nA clear and concise description of what you expected to happen.\n\n**Screenshots**\nIf applicable, add screenshots to help explain your problem.\n\n**Desktop (please complete the following information):**\n - OS: [e.g. iOS]\n - Browser [e.g. chrome, safari]\n - Version [e.g. 22]\n\n**Smartphone (please complete the following information):**\n - Device: [e.g. iPhone6]\n - OS: [e.g. iOS8.1]\n - Browser [e.g. stock browser, safari]\n - Version [e.g. 22]\n\n**Additional context**\nAdd any other context about the problem here.\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/README.md",
    "content": "## Supervised Machine Learning : Regression and Classification\n\n\n\n- [Week 1](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1)\n\n    - [Practice quiz: Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Practice%20quiz%20-%20Regression)\n    - [Practice quiz: Supervised vs unsupervised learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Practice%20quiz%20-%20Supervised%20vs%20unsupervised%20learning)\n    - [Practice quiz: Train the model with gradient descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Practice%20quiz%20-%20Train%20the%20model%20with%20gradient%20descent)\n  - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs)\n    - [Model Representation](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs/C1_W1_Lab03_Model_Representation_Soln.ipynb)\n    - [Cost Function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs/C1_W1_Lab04_Cost_function_Soln.ipynb)\n    - [Gradient Descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs/C1_W1_Lab05_Gradient_Descent_Soln.ipynb)\n\n<br/>\n\n- [Week 2](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2) \n\n    - [Practice quiz: Gradient descent in practice](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Practice%20quiz%20-%20Gradient%20descent%20in%20practice)\n    - [Practice quiz: Multiple linear regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Practice%20quiz%20-%20Multiple%20linear%20regression)\n    - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs)\n      - [Numpy Vectorization](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb)\n      - [Multi Variate Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab02_Multiple_Variable_Soln.ipynb)\n      - [Feature Scaling](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb)\n      - [Feature Engineering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb)\n      - [Sklearn Gradient Descent](/C1%20-%20Supervised%20Machine%20Learning%3A%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n      - [Sklearn Normal Method](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/C1W2A1)\n      - [Linear Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/C1W2A1/C1_W2_Linear_Regression.ipynb)\n\n<br/>\n\n- [Week 3](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3)\n\n    - [Practice quiz: Cost function for logistic regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Practice%20quiz%20-%20Cost%20function%20for%20logistic%20regression)\n    - [Practice quiz: Gradient descent for logistic regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Practice%20quiz%20-%20Gradient%20descent%20for%20logistic%20regression)\n    - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs)\n        - [Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab01_Classification_Soln.ipynb)\n        - [Sigmoid Function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab02_Sigmoid_function_Soln.ipynb)\n        - [Decision Boundary](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab03_Decision_Boundary_Soln.ipynb)\n        - [Logistic Loss](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab04_LogisticLoss_Soln.ipynb)\n        - [Cost Function](/C1%20-%20Supervised%20Machine%20Learning:%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab05_Cost_Function_Soln.ipynb)\n        - [Gradient Descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab06_Gradient_Descent_Soln.ipynb)\n        - [Scikit Learn - Logistic Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab07_Scikit_Learn_Soln.ipynb)\n        - [Overfitting](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab08_Overfitting_Soln.ipynb)\n        - [Regularization](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab09_Regularization_Soln.ipynb)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/C1W3A1)\n      - [Logistic Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/C1W3A1/C1_W3_Logistic_Regression.ipynb)\n\n#### [Certificate Of Completion](https://coursera.org/share/195768f3c1a83e42298d3f61dae99d01)\n\n<br/>\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Optional Labs/C1_W1_Lab01_Python_Jupyter_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab:  Brief Introduction to Python and Jupyter Notebooks\\n\",\n    \"Welcome to the first optional lab! \\n\",\n    \"Optional labs are available to:\\n\",\n    \"- provide information - like this notebook\\n\",\n    \"- reinforce lecture material with hands-on examples\\n\",\n    \"- provide working examples of routines used in the graded labs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- Get a brief introduction to Jupyter notebooks\\n\",\n    \"- Take a tour of Jupyter notebooks\\n\",\n    \"- Learn the difference between markdown cells and code cells\\n\",\n    \"- Practice some basic python\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The easiest way to become familiar with Jupyter notebooks is to take the tour available above in the Help menu:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C1W1L1_Tour.PNG\\\"  alt='missing' width=\\\"400\\\"  ><center/>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Jupyter notebooks have two types of cells that are used in this course. Cells such as this which contain documentation called `Markdown Cells`. The name is derived from the simple formatting language used in the cells. You will not be required to produce markdown cells. Its useful to understand the `cell pulldown` shown in graphic below. Occasionally, a cell will end up in the wrong mode and you may need to restore it to the right state:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<figure>\\n\",\n    \"   <img src=\\\"./images/C1W1L1_Markdown.PNG\\\"  alt='missing' width=\\\"400\\\"  >\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The other type of cell is the `code cell` where you will write your code:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"This is  code cell\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#This is  a 'Code' Cell\\n\",\n    \"print(\\\"This is  code cell\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Python\\n\",\n    \"You can write your code in the code cells. \\n\",\n    \"To run the code, select the cell and either\\n\",\n    \"- hold the shift-key down and hit 'enter' or 'return'\\n\",\n    \"- click the 'run' arrow above\\n\",\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C1W1L1_Run.PNG\\\"  width=\\\"400\\\"  >\\n\",\n    \"<figure/>\\n\",\n    \"\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Print statement\\n\",\n    \"Print statements will generally use the python f-string style.  \\n\",\n    \"Try creating your own print in the following cell.  \\n\",\n    \"Try both methods of running the cell.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"f strings allow you to embed variables right in the strings!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# print statements\\n\",\n    \"variable = \\\"right in the strings!\\\"\\n\",\n    \"print(f\\\"f strings allow you to embed variables {variable}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Congratulations!\\n\",\n    \"You now know how to find your way around a Jupyter Notebook.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Optional Labs/C1_W1_Lab03_Model_Representation_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Model Representation\\n\",\n    \"\\n\",\n    \"<figure>\\n\",\n    \" <img src=\\\"./images/C1_W1_L3_S1_Lecture_b.png\\\"   style=\\\"width:600px;height:200px;\\\">\\n\",\n    \"</figure>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab you will:\\n\",\n    \"- Learn to implement the model $f_{w,b}$ for linear regression with one variable\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Notation\\n\",\n    \"Here is a summary of some of the notation you will encounter.  \\n\",\n    \"\\n\",\n    \"|General <img width=70/> <br />  Notation  <img width=70/> | Description<img width=350/>| Python (if applicable) |\\n\",\n    \"|: ------------|: ------------------------------------------------------------||\\n\",\n    \"| $a$ | scalar, non bold                                                      ||\\n\",\n    \"| $\\\\mathbf{a}$ | vector, bold                                                      ||\\n\",\n    \"| **Regression** |         |    |     |\\n\",\n    \"|  $\\\\mathbf{x}$ | Training Example feature values (in this lab - Size (1000 sqft))  | `x_train` |   \\n\",\n    \"|  $\\\\mathbf{y}$  | Training Example  targets (in this lab Price (1000s of dollars)).  | `y_train` \\n\",\n    \"|  $x^{(i)}$, $y^{(i)}$ | $i_{th}$Training Example | `x_i`, `y_i`|\\n\",\n    \"| m | Number of training examples | `m`|\\n\",\n    \"|  $w$  |  parameter: weight,                                 | `w`    |\\n\",\n    \"|  $b$           |  parameter: bias                                           | `b`    |     \\n\",\n    \"| $f_{w,b}(x^{(i)})$ | The result of the model evaluation at $x^{(i)}$ parameterized by $w,b$: $f_{w,b}(x^{(i)}) = wx^{(i)}+b$  | `f_wb` | \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Tools\\n\",\n    \"In this lab you will make use of: \\n\",\n    \"- NumPy, a popular library for scientific computing\\n\",\n    \"- Matplotlib, a popular library for plotting data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Problem Statement\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W1_L3_S1_trainingdata.png\\\"    style=\\\" width:380px; padding: 10px;  \\\" /> \\n\",\n    \"\\n\",\n    \"As in the lecture, you will use the motivating example of housing price prediction.  \\n\",\n    \"This lab will use a simple data set with only two data points - a house with 1000 square feet(sqft) sold for \\\\\\\\$300,000 and a house with 2000 square feet sold for \\\\\\\\$500,000. These two points will constitute our *data or training set*. In this lab, the units of size are 1000 sqft and the units of price are 1000s of dollars.\\n\",\n    \"\\n\",\n    \"| Size (1000 sqft)     | Price (1000s of dollars) |\\n\",\n    \"| -------------------| ------------------------ |\\n\",\n    \"| 1.0               | 300                      |\\n\",\n    \"| 2.0               | 500                      |\\n\",\n    \"\\n\",\n    \"You would like to fit a linear regression model (shown above as the blue straight line) through these two points, so you can then predict price for other houses - say, a house with 1200 sqft.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Please run the following code cell to create your `x_train` and `y_train` variables. The data is stored in one-dimensional NumPy arrays.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x_train = [1. 2.]\\n\",\n      \"y_train = [300. 500.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# x_train is the input variable (size in 1000 square feet)\\n\",\n    \"# y_train is the target (price in 1000s of dollars)\\n\",\n    \"x_train = np.array([1.0, 2.0])\\n\",\n    \"y_train = np.array([300.0, 500.0])\\n\",\n    \"print(f\\\"x_train = {x_train}\\\")\\n\",\n    \"print(f\\\"y_train = {y_train}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \">**Note**: The course will frequently utilize the python 'f-string' output formatting described [here](https://docs.python.org/3/tutorial/inputoutput.html) when printing. The content between the curly braces is evaluated when producing the output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Number of training examples `m`\\n\",\n    \"You will use `m` to denote the number of training examples. Numpy arrays have a `.shape` parameter. `x_train.shape` returns a python tuple with an entry for each dimension. `x_train.shape[0]` is the length of the array and number of examples as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x_train.shape: (2,)\\n\",\n      \"Number of training examples is: 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# m is the number of training examples\\n\",\n    \"print(f\\\"x_train.shape: {x_train.shape}\\\")\\n\",\n    \"m = x_train.shape[0]\\n\",\n    \"print(f\\\"Number of training examples is: {m}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"One can also use the Python `len()` function as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of training examples is: 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# m is the number of training examples\\n\",\n    \"m = len(x_train)\\n\",\n    \"print(f\\\"Number of training examples is: {m}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Training example `x_i, y_i`\\n\",\n    \"\\n\",\n    \"You will use (x$^{(i)}$, y$^{(i)}$) to denote the $i^{th}$ training example. Since Python is zero indexed, (x$^{(0)}$, y$^{(0)}$) is (1.0, 300.0) and (x$^{(1)}$, y$^{(1)}$) is (2.0, 500.0). \\n\",\n    \"\\n\",\n    \"To access a value in a Numpy array, one indexes the array with the desired offset. For example the syntax to access location zero of `x_train` is `x_train[0]`.\\n\",\n    \"Run the next code block below to get the $i^{th}$ training example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(x^(0), y^(0)) = (1.0, 300.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"i = 0 # Change this to 1 to see (x^1, y^1)\\n\",\n    \"\\n\",\n    \"x_i = x_train[i]\\n\",\n    \"y_i = y_train[i]\\n\",\n    \"print(f\\\"(x^({i}), y^({i})) = ({x_i}, {y_i})\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Plotting the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"You can plot these two points using the `scatter()` function in the `matplotlib` library, as shown in the cell below. \\n\",\n    \"- The function arguments `marker` and `c` show the points as red crosses (the default is blue dots).\\n\",\n    \"\\n\",\n    \"You can use other functions in the `matplotlib` library to set the title and labels to display\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot the data points\\n\",\n    \"plt.scatter(x_train, y_train, marker='x', c='r')\\n\",\n    \"# Set the title\\n\",\n    \"plt.title(\\\"Housing Prices\\\")\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Price (in 1000s of dollars)')\\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Size (1000 sqft)')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Model function\\n\",\n    \"\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W1_L3_S1_model.png\\\"     style=\\\" width:380px; padding: 10px; \\\" > As described in lecture, the model function for linear regression (which is a function that maps from `x` to `y`) is represented as \\n\",\n    \"\\n\",\n    \"$$ f_{w,b}(x^{(i)}) = wx^{(i)} + b \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"The formula above is how you can represent straight lines - different values of $w$ and $b$ give you different straight lines on the plot. <br/> <br/> <br/> <br/> <br/> \\n\",\n    \"\\n\",\n    \"Let's try to get a better intuition for this through the code blocks below. Let's start with $w = 100$ and $b = 100$. \\n\",\n    \"\\n\",\n    \"**Note: You can come back to this cell to adjust the model's w and b parameters**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"w: 100\\n\",\n      \"b: 100\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w = 100\\n\",\n    \"b = 100\\n\",\n    \"print(f\\\"w: {w}\\\")\\n\",\n    \"print(f\\\"b: {b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Now, let's compute the value of $f_{w,b}(x^{(i)})$ for your two data points. You can explicitly write this out for each data point as - \\n\",\n    \"\\n\",\n    \"for $x^{(0)}$, `f_wb = w * x[0] + b`\\n\",\n    \"\\n\",\n    \"for $x^{(1)}$, `f_wb = w * x[1] + b`\\n\",\n    \"\\n\",\n    \"For a large number of data points, this can get unwieldy and repetitive. So instead, you can calculate the function output in a `for` loop as shown in the `compute_model_output` function below.\\n\",\n    \"> **Note**: The argument description `(ndarray (m,))` describes a Numpy n-dimensional array of shape (m,). `(scalar)` describes an argument without dimensions, just a magnitude.  \\n\",\n    \"> **Note**: `np.zero(n)` will return a one-dimensional numpy array with $n$ entries   \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_model_output(x, w, b):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the prediction of a linear model\\n\",\n    \"    Args:\\n\",\n    \"      x (ndarray (m,)): Data, m examples \\n\",\n    \"      w,b (scalar)    : model parameters  \\n\",\n    \"    Returns\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = x.shape[0]\\n\",\n    \"    f_wb = np.zeros(m)\\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb[i] = w * x[i] + b\\n\",\n    \"        \\n\",\n    \"    return f_wb\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Now let's call the `compute_model_output` function and plot the output..\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"tmp_f_wb = compute_model_output(x_train, w, b,)\\n\",\n    \"\\n\",\n    \"# Plot our model prediction\\n\",\n    \"plt.plot(x_train, tmp_f_wb, c='b',label='Our Prediction')\\n\",\n    \"\\n\",\n    \"# Plot the data points\\n\",\n    \"plt.scatter(x_train, y_train, marker='x', c='r',label='Actual Values')\\n\",\n    \"\\n\",\n    \"# Set the title\\n\",\n    \"plt.title(\\\"Housing Prices\\\")\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Price (in 1000s of dollars)')\\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Size (1000 sqft)')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"As you can see, setting $w = 100$ and $b = 100$ does *not* result in a line that fits our data. \\n\",\n    \"\\n\",\n    \"### Challenge\\n\",\n    \"Try experimenting with different values of $w$ and $b$. What should the values be for a line that fits our data?\\n\",\n    \"\\n\",\n    \"#### Tip:\\n\",\n    \"You can use your mouse to click on the triangle to the left of the green \\\"Hints\\\" below to reveal some hints for choosing b and w.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"<summary>\\n\",\n    \"    <font size='3', color='darkgreen'><b>Hints</b></font>\\n\",\n    \"</summary>\\n\",\n    \"    <p>\\n\",\n    \"    <ul>\\n\",\n    \"        <li>Try $w = 200$ and $b = 100$ </li>\\n\",\n    \"    </ul>\\n\",\n    \"    </p>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Prediction\\n\",\n    \"Now that we have a model, we can use it to make our original prediction. Let's predict the price of a house with 1200 sqft. Since the units of $x$ are in 1000's of sqft, $x$ is 1.2.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"$340 thousand dollars\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w = 200                         \\n\",\n    \"b = 100    \\n\",\n    \"x_i = 1.2\\n\",\n    \"cost_1200sqft = w * x_i + b    \\n\",\n    \"\\n\",\n    \"print(f\\\"${cost_1200sqft:.0f} thousand dollars\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Congratulations!\\n\",\n    \"In this lab you have learned:\\n\",\n    \" - Linear regression builds a model which establishes a relationship between features and targets\\n\",\n    \"     - In the example above, the feature was house size and the target was house price\\n\",\n    \"     - for simple linear regression, the model has two parameters $w$ and $b$ whose values are 'fit' using *training data*.\\n\",\n    \"     - once a model's parameters have been determined, the model can be used to make predictions on novel data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Optional Labs/C1_W1_Lab04_Cost_function_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional  Lab: Cost Function \\n\",\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C1_W1_L3_S2_Lecture_b.png\\\"  style=\\\"width:1000px;height:200px;\\\" ></center>\\n\",\n    \"</figure>\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab you will:\\n\",\n    \"- you will implement and explore the `cost` function for linear regression with one variable. \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Tools\\n\",\n    \"In this lab we will make use of: \\n\",\n    \"- NumPy, a popular library for scientific computing\\n\",\n    \"- Matplotlib, a popular library for plotting data\\n\",\n    \"- local plotting routines in the lab_utils_uni.py file in the local directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_uni import plt_intuition, plt_stationary, plt_update_onclick, soup_bowl\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Problem Statement\\n\",\n    \"\\n\",\n    \"You would like a model which can predict housing prices given the size of the house.  \\n\",\n    \"Let's use the same two data points as before the previous lab- a house with 1000 square feet sold for \\\\\\\\$300,000 and a house with 2000 square feet sold for \\\\\\\\$500,000.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"| Size (1000 sqft)     | Price (1000s of dollars) |\\n\",\n    \"| -------------------| ------------------------ |\\n\",\n    \"| 1                 | 300                      |\\n\",\n    \"| 2                  | 500                      |\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_train = np.array([1.0, 2.0])           #(size in 1000 square feet)\\n\",\n    \"y_train = np.array([300.0, 500.0])           #(price in 1000s of dollars)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Computing Cost\\n\",\n    \"The term 'cost' in this assignment might be a little confusing since the data is housing cost. Here, cost is a measure how well our model is predicting the target price of the house. The term 'price' is used for housing data.\\n\",\n    \"\\n\",\n    \"The equation for cost with one variable is:\\n\",\n    \"  $$J(w,b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})^2 \\\\tag{1}$$ \\n\",\n    \" \\n\",\n    \"where \\n\",\n    \"  $$f_{w,b}(x^{(i)}) = wx^{(i)} + b \\\\tag{2}$$\\n\",\n    \"  \\n\",\n    \"- $f_{w,b}(x^{(i)})$ is our prediction for example $i$ using parameters $w,b$.  \\n\",\n    \"- $(f_{w,b}(x^{(i)}) -y^{(i)})^2$ is the squared difference between the target value and the prediction.   \\n\",\n    \"- These differences are summed over all the $m$ examples and divided by `2m` to produce the cost, $J(w,b)$.  \\n\",\n    \">Note, in lecture summation ranges are typically from 1 to m, while code will be from 0 to m-1.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The code below calculates cost by looping over each example. In each loop:\\n\",\n    \"- `f_wb`, a prediction is calculated\\n\",\n    \"- the difference between the target and the prediction is calculated and squared.\\n\",\n    \"- this is added to the total cost.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost(x, y, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost function for linear regression.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      x (ndarray (m,)): Data, m examples \\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w,b (scalar)    : model parameters  \\n\",\n    \"    \\n\",\n    \"    Returns\\n\",\n    \"        total_cost (float): The cost of using w,b as the parameters for linear regression\\n\",\n    \"               to fit the data points in x and y\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # number of training examples\\n\",\n    \"    m = x.shape[0] \\n\",\n    \"    \\n\",\n    \"    cost_sum = 0 \\n\",\n    \"    for i in range(m): \\n\",\n    \"        f_wb = w * x[i] + b   \\n\",\n    \"        cost = (f_wb - y[i]) ** 2  \\n\",\n    \"        cost_sum = cost_sum + cost  \\n\",\n    \"    total_cost = (1 / (2 * m)) * cost_sum  \\n\",\n    \"\\n\",\n    \"    return total_cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Cost Function Intuition\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W1_Lab02_GoalOfRegression.PNG\\\"    style=\\\" width:380px; padding: 10px;  \\\" /> Your goal is to find a model $f_{w,b}(x) = wx + b$, with parameters $w,b$,  which will accurately predict house values given an input $x$. The cost is a measure of how accurate the model is on the training data.\\n\",\n    \"\\n\",\n    \"The cost equation (1) above shows that if $w$ and $b$ can be selected such that the predictions $f_{w,b}(x)$ match the target data $y$, the $(f_{w,b}(x^{(i)}) - y^{(i)})^2 $ term will be zero and the cost minimized. In this simple two point example, you can achieve this!\\n\",\n    \"\\n\",\n    \"In the previous lab, you determined that $b=100$ provided an optimal solution so let's set $b$ to 100 and focus on $w$.\\n\",\n    \"\\n\",\n    \"<br/>\\n\",\n    \"Below, use the slider control to select the value of $w$ that minimizes cost. It can take a few seconds for the plot to update.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"interactive(children=(IntSlider(value=150, description='w', max=400, step=10), Output()), _dom_classes=('widge…\",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"edfdeb101ed24a93bfe261e6233f0071\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_intuition(x_train,y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The plot contains a few points that are worth mentioning.\\n\",\n    \"- cost is minimized when $w = 200$, which matches results from the previous lab\\n\",\n    \"- Because the difference between the target and pediction is squared in the cost equation, the cost increases rapidly when $w$ is either too large or too small.\\n\",\n    \"- Using the `w` and `b` selected by minimizing cost results in a line which is a perfect fit to the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Cost Function Visualization- 3D\\n\",\n    \"\\n\",\n    \"You can see how cost varies with respect to *both* `w` and `b` by plotting in 3D or using a contour plot.   \\n\",\n    \"It is worth noting that some of the plotting in this course can become quite involved. The plotting routines are provided and while it can be instructive to read through the code to become familiar with the methods, it is not needed to complete the course successfully. The routines are in lab_utils_uni.py in the local directory.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Larger Data Set\\n\",\n    \"It's use instructive to view a scenario with a few more data points. This data set includes data points that do not fall on the same line. What does that mean for the cost equation? Can we find $w$, and $b$ that will give us a cost of 0? \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_train = np.array([1.0, 1.7, 2.0, 2.5, 3.0, 3.2])\\n\",\n    \"y_train = np.array([250, 300, 480,  430,   630, 730,])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"In the contour plot, click on a point to select `w` and `b` to achieve the lowest cost. Use the contours to guide your selections. Note, it can take a few seconds to update the graph. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\",\n      \"image/png\": 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\",\n 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' width=900.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"191ddac06b504f7b815bb4ebd6455afd\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close('all') \\n\",\n    \"fig, ax, dyn_items = plt_stationary(x_train, y_train)\\n\",\n    \"updater = plt_update_onclick(fig, ax, x_train, y_train, dyn_items)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Above, note the dashed lines in the left plot. These represent the portion of the cost contributed by each example in your training set. In this case, values of approximately $w=209$ and $b=2.4$ provide low cost. Note that, because our training examples are not on a line, the minimum cost is not zero.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Convex Cost surface\\n\",\n    \"The fact that the cost function squares the loss ensures that the 'error surface' is convex like a soup bowl. It will always have a minimum that can be reached by following the gradient in all dimensions. In the previous plot, because the $w$ and $b$ dimensions scale differently, this is not easy to recognize. The following plot, where $w$ and $b$ are symmetric, was shown in lecture:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\",\n      \"image/png\": 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width=800.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"3dba37647ea9464aba900e726d0bc246\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"soup_bowl()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Congratulations!\\n\",\n    \"You have learned the following:\\n\",\n    \" - The cost equation provides a measure of how well your predictions match your training data.\\n\",\n    \" - Minimizing the cost can provide optimal values of $w$, $b$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Optional Labs/C1_W1_Lab05_Gradient_Descent_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Gradient Descent for Linear Regression\\n\",\n    \"\\n\",\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C1_W1_L4_S1_Lecture_GD.png\\\"  style=\\\"width:800px;height:200px;\\\" ></center>\\n\",\n    \"</figure>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- automate the process of optimizing $w$ and $b$ using gradient descent.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Tools\\n\",\n    \"In this lab, we will make use of: \\n\",\n    \"- NumPy, a popular library for scientific computing\\n\",\n    \"- Matplotlib, a popular library for plotting data\\n\",\n    \"- plotting routines in the lab_utils.py file in the local directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import math, copy\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"from lab_utils_uni import plt_house_x, plt_contour_wgrad, plt_divergence, plt_gradients\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40291_2\\\"></a>\\n\",\n    \"# Problem Statement\\n\",\n    \"\\n\",\n    \"Let's use the same two data points as before - a house with 1000 square feet sold for \\\\\\\\$300,000 and a house with 2000 square feet sold for \\\\\\\\$500,000.\\n\",\n    \"\\n\",\n    \"| Size (1000 sqft)     | Price (1000s of dollars) |\\n\",\n    \"| ----------------| ------------------------ |\\n\",\n    \"| 1               | 300                      |\\n\",\n    \"| 2               | 500                      |\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Load our data set\\n\",\n    \"x_train = np.array([1.0, 2.0])   #features\\n\",\n    \"y_train = np.array([300.0, 500.0])   #target value\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40291_2.0.1\\\"></a>\\n\",\n    \"### Compute_Cost\\n\",\n    \"This was developed in the last lab. We'll need it again here.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#Function to calculate the cost\\n\",\n    \"def compute_cost(x, y, w, b):\\n\",\n    \"   \\n\",\n    \"    m = x.shape[0] \\n\",\n    \"    cost = 0\\n\",\n    \"    \\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb = w * x[i] + b\\n\",\n    \"        cost = cost + (f_wb - y[i])**2\\n\",\n    \"    total_cost = 1 / (2 * m) * cost\\n\",\n    \"\\n\",\n    \"    return total_cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40291_2.1\\\"></a>\\n\",\n    \"## Gradient descent summary\\n\",\n    \"So far in this course, you have developed a linear model that predicts $f_{w,b}(x^{(i)})$:\\n\",\n    \"$$f_{w,b}(x^{(i)}) = wx^{(i)} + b \\\\tag{1}$$\\n\",\n    \"In linear regression, you utilize input training data to fit the parameters $w$,$b$ by minimizing a measure of the error between our predictions $f_{w,b}(x^{(i)})$ and the actual data $y^{(i)}$. The measure is called the $cost$, $J(w,b)$. In training you measure the cost over all of our training samples $x^{(i)},y^{(i)}$\\n\",\n    \"$$J(w,b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})^2\\\\tag{2}$$ \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"\\n\",\n    \"In lecture, *gradient descent* was described as:\\n\",\n    \"\\n\",\n    \"$$\\\\begin{align*} \\\\text{repeat}&\\\\text{ until convergence:} \\\\; \\\\lbrace \\\\newline\\n\",\n    \"\\\\;  w &= w -  \\\\alpha \\\\frac{\\\\partial J(w,b)}{\\\\partial w} \\\\tag{3}  \\\\; \\\\newline \\n\",\n    \" b &= b -  \\\\alpha \\\\frac{\\\\partial J(w,b)}{\\\\partial b}  \\\\newline \\\\rbrace\\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"where, parameters $w$, $b$ are updated simultaneously.  \\n\",\n    \"The gradient is defined as:\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\frac{\\\\partial J(w,b)}{\\\\partial w}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})x^{(i)} \\\\tag{4}\\\\\\\\\\n\",\n    \"  \\\\frac{\\\\partial J(w,b)}{\\\\partial b}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)}) \\\\tag{5}\\\\\\\\\\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"Here *simultaniously* means that you calculate the partial derivatives for all the parameters before updating any of the parameters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40291_2.2\\\"></a>\\n\",\n    \"## Implement Gradient Descent\\n\",\n    \"You will implement gradient descent algorithm for one feature. You will need three functions. \\n\",\n    \"- `compute_gradient` implementing equation (4) and (5) above\\n\",\n    \"- `compute_cost` implementing equation (2) above (code from previous lab)\\n\",\n    \"- `gradient_descent`, utilizing compute_gradient and compute_cost\\n\",\n    \"\\n\",\n    \"Conventions:\\n\",\n    \"- The naming of python variables containing partial derivatives follows this pattern,$\\\\frac{\\\\partial J(w,b)}{\\\\partial b}$  will be `dj_db`.\\n\",\n    \"- w.r.t is With Respect To, as in partial derivative of $J(wb)$ With Respect To $b$.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40291_2.3\\\"></a>\\n\",\n    \"### compute_gradient\\n\",\n    \"<a name='ex-01'></a>\\n\",\n    \"`compute_gradient`  implements (4) and (5) above and returns $\\\\frac{\\\\partial J(w,b)}{\\\\partial w}$,$\\\\frac{\\\\partial J(w,b)}{\\\\partial b}$. The embedded comments describe the operations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_gradient(x, y, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \"    Args:\\n\",\n    \"      x (ndarray (m,)): Data, m examples \\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w,b (scalar)    : model parameters  \\n\",\n    \"    Returns\\n\",\n    \"      dj_dw (scalar): The gradient of the cost w.r.t. the parameters w\\n\",\n    \"      dj_db (scalar): The gradient of the cost w.r.t. the parameter b     \\n\",\n    \"     \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Number of training examples\\n\",\n    \"    m = x.shape[0]    \\n\",\n    \"    dj_dw = 0\\n\",\n    \"    dj_db = 0\\n\",\n    \"    \\n\",\n    \"    for i in range(m):  \\n\",\n    \"        f_wb = w * x[i] + b \\n\",\n    \"        dj_dw_i = (f_wb - y[i]) * x[i] \\n\",\n    \"        dj_db_i = f_wb - y[i] \\n\",\n    \"        dj_db += dj_db_i\\n\",\n    \"        dj_dw += dj_dw_i \\n\",\n    \"    dj_dw = dj_dw / m \\n\",\n    \"    dj_db = dj_db / m \\n\",\n    \"        \\n\",\n    \"    return dj_dw, dj_db\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<br/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W1_Lab03_lecture_slopes.PNG\\\"   style=\\\"width:340px;\\\" > The lectures described how gradient descent utilizes the partial derivative of the cost with respect to a parameter at a point to update that parameter.   \\n\",\n    \"Let's use our `compute_gradient` function to find and plot some partial derivatives of our cost function relative to one of the parameters, $w_0$.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 864x288 with 2 Axes>\",\n      \"image/png\": 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_gradients(x_train,y_train, compute_cost, compute_gradient)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Above, the left plot shows $\\\\frac{\\\\partial J(w,b)}{\\\\partial w}$ or the slope of the cost curve relative to $w$ at three points. On the right side of the plot, the derivative is positive, while on the left it is negative. Due to the 'bowl shape', the derivatives will always lead gradient descent toward the bottom where the gradient is zero.\\n\",\n    \" \\n\",\n    \"The left plot has fixed $b=100$. Gradient descent will utilize both $\\\\frac{\\\\partial J(w,b)}{\\\\partial w}$ and $\\\\frac{\\\\partial J(w,b)}{\\\\partial b}$ to update parameters. The 'quiver plot' on the right provides a means of viewing the gradient of both parameters. The arrow sizes reflect the magnitude of the gradient at that point. The direction and slope of the arrow reflects the ratio of $\\\\frac{\\\\partial J(w,b)}{\\\\partial w}$ and $\\\\frac{\\\\partial J(w,b)}{\\\\partial b}$ at that point.\\n\",\n    \"Note that the gradient points *away* from the minimum. Review equation (3) above. The scaled gradient is *subtracted* from the current value of $w$ or $b$. This moves the parameter in a direction that will reduce cost.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40291_2.5\\\"></a>\\n\",\n    \"###  Gradient Descent\\n\",\n    \"Now that gradients can be computed,  gradient descent, described in equation (3) above can be implemented below in `gradient_descent`. The details of the implementation are described in the comments. Below, you will utilize this function to find optimal values of $w$ and $b$ on the training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def gradient_descent(x, y, w_in, b_in, alpha, num_iters, cost_function, gradient_function): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Performs gradient descent to fit w,b. Updates w,b by taking \\n\",\n    \"    num_iters gradient steps with learning rate alpha\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      x (ndarray (m,))  : Data, m examples \\n\",\n    \"      y (ndarray (m,))  : target values\\n\",\n    \"      w_in,b_in (scalar): initial values of model parameters  \\n\",\n    \"      alpha (float):     Learning rate\\n\",\n    \"      num_iters (int):   number of iterations to run gradient descent\\n\",\n    \"      cost_function:     function to call to produce cost\\n\",\n    \"      gradient_function: function to call to produce gradient\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      w (scalar): Updated value of parameter after running gradient descent\\n\",\n    \"      b (scalar): Updated value of parameter after running gradient descent\\n\",\n    \"      J_history (List): History of cost values\\n\",\n    \"      p_history (list): History of parameters [w,b] \\n\",\n    \"      \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    w = copy.deepcopy(w_in) # avoid modifying global w_in\\n\",\n    \"    # An array to store cost J and w's at each iteration primarily for graphing later\\n\",\n    \"    J_history = []\\n\",\n    \"    p_history = []\\n\",\n    \"    b = b_in\\n\",\n    \"    w = w_in\\n\",\n    \"    \\n\",\n    \"    for i in range(num_iters):\\n\",\n    \"        # Calculate the gradient and update the parameters using gradient_function\\n\",\n    \"        dj_dw, dj_db = gradient_function(x, y, w , b)     \\n\",\n    \"\\n\",\n    \"        # Update Parameters using equation (3) above\\n\",\n    \"        b = b - alpha * dj_db                            \\n\",\n    \"        w = w - alpha * dj_dw                            \\n\",\n    \"\\n\",\n    \"        # Save cost J at each iteration\\n\",\n    \"        if i<100000:      # prevent resource exhaustion \\n\",\n    \"            J_history.append( cost_function(x, y, w , b))\\n\",\n    \"            p_history.append([w,b])\\n\",\n    \"        # Print cost every at intervals 10 times or as many iterations if < 10\\n\",\n    \"        if i% math.ceil(num_iters/10) == 0:\\n\",\n    \"            print(f\\\"Iteration {i:4}: Cost {J_history[-1]:0.2e} \\\",\\n\",\n    \"                  f\\\"dj_dw: {dj_dw: 0.3e}, dj_db: {dj_db: 0.3e}  \\\",\\n\",\n    \"                  f\\\"w: {w: 0.3e}, b:{b: 0.5e}\\\")\\n\",\n    \" \\n\",\n    \"    return w, b, J_history, p_history #return w and J,w history for graphing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration    0: Cost 7.93e+04  dj_dw: -6.500e+02, dj_db: -4.000e+02   w:  6.500e+00, b: 4.00000e+00\\n\",\n      \"Iteration 1000: Cost 3.41e+00  dj_dw: -3.712e-01, dj_db:  6.007e-01   w:  1.949e+02, b: 1.08228e+02\\n\",\n      \"Iteration 2000: Cost 7.93e-01  dj_dw: -1.789e-01, dj_db:  2.895e-01   w:  1.975e+02, b: 1.03966e+02\\n\",\n      \"Iteration 3000: Cost 1.84e-01  dj_dw: -8.625e-02, dj_db:  1.396e-01   w:  1.988e+02, b: 1.01912e+02\\n\",\n      \"Iteration 4000: Cost 4.28e-02  dj_dw: -4.158e-02, dj_db:  6.727e-02   w:  1.994e+02, b: 1.00922e+02\\n\",\n      \"Iteration 5000: Cost 9.95e-03  dj_dw: -2.004e-02, dj_db:  3.243e-02   w:  1.997e+02, b: 1.00444e+02\\n\",\n      \"Iteration 6000: Cost 2.31e-03  dj_dw: -9.660e-03, dj_db:  1.563e-02   w:  1.999e+02, b: 1.00214e+02\\n\",\n      \"Iteration 7000: Cost 5.37e-04  dj_dw: -4.657e-03, dj_db:  7.535e-03   w:  1.999e+02, b: 1.00103e+02\\n\",\n      \"Iteration 8000: Cost 1.25e-04  dj_dw: -2.245e-03, dj_db:  3.632e-03   w:  2.000e+02, b: 1.00050e+02\\n\",\n      \"Iteration 9000: Cost 2.90e-05  dj_dw: -1.082e-03, dj_db:  1.751e-03   w:  2.000e+02, b: 1.00024e+02\\n\",\n      \"(w,b) found by gradient descent: (199.9929,100.0116)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# initialize parameters\\n\",\n    \"w_init = 0\\n\",\n    \"b_init = 0\\n\",\n    \"# some gradient descent settings\\n\",\n    \"iterations = 10000\\n\",\n    \"tmp_alpha = 1.0e-2\\n\",\n    \"# run gradient descent\\n\",\n    \"w_final, b_final, J_hist, p_hist = gradient_descent(x_train ,y_train, w_init, b_init, tmp_alpha, \\n\",\n    \"                                                    iterations, compute_cost, compute_gradient)\\n\",\n    \"print(f\\\"(w,b) found by gradient descent: ({w_final:8.4f},{b_final:8.4f})\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W1_Lab03_lecture_learningrate.PNG\\\"  style=\\\"width:340px; padding: 15px; \\\" > \\n\",\n    \"Take a moment and note some characteristics of the gradient descent process printed above.  \\n\",\n    \"\\n\",\n    \"- The cost starts large and rapidly declines as described in the slide from the lecture.\\n\",\n    \"- The partial derivatives, `dj_dw`, and `dj_db` also get smaller, rapidly at first and then more slowly. As shown in the diagram from the lecture, as the process nears the 'bottom of the bowl' progress is slower due to the smaller value of the derivative at that point.\\n\",\n    \"- progress slows though the learning rate, alpha, remains fixed\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Cost versus iterations of gradient descent \\n\",\n    \"A plot of cost versus iterations is a useful measure of progress in gradient descent. Cost should always decrease in successful runs. The change in cost is so rapid initially, it is useful to plot the initial decent on a different scale than the final descent. In the plots below, note the scale of cost on the axes and the iteration step.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 864x288 with 2 Axes>\",\n      \"image/png\": 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot cost versus iteration  \\n\",\n    \"fig, (ax1, ax2) = plt.subplots(1, 2, constrained_layout=True, figsize=(12,4))\\n\",\n    \"ax1.plot(J_hist[:100])\\n\",\n    \"ax2.plot(1000 + np.arange(len(J_hist[1000:])), J_hist[1000:])\\n\",\n    \"ax1.set_title(\\\"Cost vs. iteration(start)\\\");  ax2.set_title(\\\"Cost vs. iteration (end)\\\")\\n\",\n    \"ax1.set_ylabel('Cost')            ;  ax2.set_ylabel('Cost') \\n\",\n    \"ax1.set_xlabel('iteration step')  ;  ax2.set_xlabel('iteration step') \\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Predictions\\n\",\n    \"Now that you have discovered the optimal values for the parameters $w$ and $b$, you can now use the model to predict housing values based on our learned parameters. As expected, the predicted values are nearly the same as the training values for the same housing. Further, the value not in the prediction is in line with the expected value.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1000 sqft house prediction 300.0 Thousand dollars\\n\",\n      \"1200 sqft house prediction 340.0 Thousand dollars\\n\",\n      \"2000 sqft house prediction 500.0 Thousand dollars\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"1000 sqft house prediction {w_final*1.0 + b_final:0.1f} Thousand dollars\\\")\\n\",\n    \"print(f\\\"1200 sqft house prediction {w_final*1.2 + b_final:0.1f} Thousand dollars\\\")\\n\",\n    \"print(f\\\"2000 sqft house prediction {w_final*2.0 + b_final:0.1f} Thousand dollars\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40291_2.6\\\"></a>\\n\",\n    \"## Plotting\\n\",\n    \"You can show the progress of gradient descent during its execution by plotting the cost over iterations on a contour plot of the cost(w,b). \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 864x432 with 1 Axes>\",\n      \"image/png\": 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(1,1, figsize=(12, 6))\\n\",\n    \"plt_contour_wgrad(x_train, y_train, p_hist, ax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Above, the contour plot shows the $cost(w,b)$ over a range of $w$ and $b$. Cost levels are represented by the rings. Overlayed, using red arrows, is the path of gradient descent. Here are some things to note:\\n\",\n    \"- The path makes steady (monotonic) progress toward its goal.\\n\",\n    \"- initial steps are much larger than the steps near the goal.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Zooming in**, we can see that final steps of gradient descent. Note the distance between steps shrinks as the gradient approaches zero.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 864x288 with 1 Axes>\",\n      \"image/png\": 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(1,1, figsize=(12, 4))\\n\",\n    \"plt_contour_wgrad(x_train, y_train, p_hist, ax, w_range=[180, 220, 0.5], b_range=[80, 120, 0.5],\\n\",\n    \"            contours=[1,5,10,20],resolution=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40291_2.7.1\\\"></a>\\n\",\n    \"### Increased Learning Rate\\n\",\n    \"\\n\",\n    \"<figure>\\n\",\n    \" <img align=\\\"left\\\", src=\\\"./images/C1_W1_Lab03_alpha_too_big.PNG\\\"   style=\\\"width:340px;height:240px;\\\" >\\n\",\n    \"</figure>\\n\",\n    \"In the lecture, there was a discussion related to the proper value of the learning rate, $\\\\alpha$ in equation(3). The larger $\\\\alpha$ is, the faster gradient descent will converge to a solution. But, if it is too large, gradient descent will diverge. Above you have an example of a solution which converges nicely.\\n\",\n    \"\\n\",\n    \"Let's try increasing the value of  $\\\\alpha$ and see what happens:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration    0: Cost 2.58e+05  dj_dw: -6.500e+02, dj_db: -4.000e+02   w:  5.200e+02, b: 3.20000e+02\\n\",\n      \"Iteration    1: Cost 7.82e+05  dj_dw:  1.130e+03, dj_db:  7.000e+02   w: -3.840e+02, b:-2.40000e+02\\n\",\n      \"Iteration    2: Cost 2.37e+06  dj_dw: -1.970e+03, dj_db: -1.216e+03   w:  1.192e+03, b: 7.32800e+02\\n\",\n      \"Iteration    3: Cost 7.19e+06  dj_dw:  3.429e+03, dj_db:  2.121e+03   w: -1.551e+03, b:-9.63840e+02\\n\",\n      \"Iteration    4: Cost 2.18e+07  dj_dw: -5.974e+03, dj_db: -3.691e+03   w:  3.228e+03, b: 1.98886e+03\\n\",\n      \"Iteration    5: Cost 6.62e+07  dj_dw:  1.040e+04, dj_db:  6.431e+03   w: -5.095e+03, b:-3.15579e+03\\n\",\n      \"Iteration    6: Cost 2.01e+08  dj_dw: -1.812e+04, dj_db: -1.120e+04   w:  9.402e+03, b: 5.80237e+03\\n\",\n      \"Iteration    7: Cost 6.09e+08  dj_dw:  3.156e+04, dj_db:  1.950e+04   w: -1.584e+04, b:-9.80139e+03\\n\",\n      \"Iteration    8: Cost 1.85e+09  dj_dw: -5.496e+04, dj_db: -3.397e+04   w:  2.813e+04, b: 1.73730e+04\\n\",\n      \"Iteration    9: Cost 5.60e+09  dj_dw:  9.572e+04, dj_db:  5.916e+04   w: -4.845e+04, b:-2.99567e+04\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# initialize parameters\\n\",\n    \"w_init = 0\\n\",\n    \"b_init = 0\\n\",\n    \"# set alpha to a large value\\n\",\n    \"iterations = 10\\n\",\n    \"tmp_alpha = 8.0e-1\\n\",\n    \"# run gradient descent\\n\",\n    \"w_final, b_final, J_hist, p_hist = gradient_descent(x_train ,y_train, w_init, b_init, tmp_alpha, \\n\",\n    \"                                                    iterations, compute_cost, compute_gradient)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Above, $w$ and $b$ are bouncing back and forth between positive and negative with the absolute value increasing with each iteration. Further, each iteration $\\\\frac{\\\\partial J(w,b)}{\\\\partial w}$ changes sign and cost is increasing rather than decreasing. This is a clear sign that the *learning rate is too large* and the solution is diverging. \\n\",\n    \"Let's visualize this with a plot.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 864x360 with 2 Axes>\",\n      \"image/png\": 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_divergence(p_hist, J_hist,x_train, y_train)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Above, the left graph shows $w$'s progression over the first few steps of gradient descent. $w$ oscillates from positive to negative and cost grows rapidly. Gradient Descent is operating on both $w$ and $b$ simultaneously, so one needs the 3-D plot on the right for the complete picture.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"\\n\",\n    \"## Congratulations!\\n\",\n    \"In this lab you:\\n\",\n    \"- delved into the details of gradient descent for a single variable.\\n\",\n    \"- developed a routine to compute the gradient\\n\",\n    \"- visualized what the gradient is\\n\",\n    \"- completed a gradient descent routine\\n\",\n    \"- utilized gradient descent to find parameters\\n\",\n    \"- examined the impact of sizing the learning rate\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"40291\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.10\"\n  },\n  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Optional Labs/data.txt",
    "content": "2104,3,399900\r\n1600,3,329900\r\n2400,3,369000\r\n1416,2,232000\r\n3000,4,539900\r\n1985,4,299900\r\n1534,3,314900\r\n1427,3,198999\r\n1380,3,212000\r\n1494,3,242500\r\n1940,4,239999\r\n2000,3,347000\r\n1890,3,329999\r\n4478,5,699900\r\n1268,3,259900\r\n2300,4,449900\r\n1320,2,299900\r\n1236,3,199900\r\n2609,4,499998\r\n3031,4,599000\r\n1767,3,252900\r\n1888,2,255000\r\n1604,3,242900\r\n1962,4,259900\r\n3890,3,573900\r\n1100,3,249900\r\n1458,3,464500\r\n2526,3,469000\r\n2200,3,475000\r\n2637,3,299900\r\n1839,2,349900\r\n1000,1,169900\r\n2040,4,314900\r\n3137,3,579900\r\n1811,4,285900\r\n1437,3,249900\r\n1239,3,229900\r\n2132,4,345000\r\n4215,4,549000\r\n2162,4,287000\r\n1664,2,368500\r\n2238,3,329900\r\n2567,4,314000\r\n1200,3,299000\r\n852,2,179900\r\n1852,4,299900\r\n1203,3,239500"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Optional Labs/deeplearning.mplstyle",
    "content": "# see https://matplotlib.org/stable/tutorials/introductory/customizing.html\nlines.linewidth: 4\nlines.solid_capstyle: butt\n\nlegend.fancybox: true\n\n# Verdana\" for non-math text,\n# Cambria Math\n\n#Blue (Crayon-Aqua) 0096FF\n#Dark Red C00000\n#Orange (Apple Orange) FF9300\n#Black 000000\n#Magenta FF40FF\n#Purple 7030A0\n\naxes.prop_cycle: cycler('color', ['0096FF', 'FF9300', 'FF40FF', '7030A0', 'C00000'])\n#axes.facecolor: f0f0f0 # grey\naxes.facecolor: ffffff  # white\naxes.labelsize: large\naxes.axisbelow: true\naxes.grid: False\naxes.edgecolor: f0f0f0\naxes.linewidth: 3.0\naxes.titlesize: x-large\n\npatch.edgecolor: f0f0f0\npatch.linewidth: 0.5\n\nsvg.fonttype: path\n\ngrid.linestyle: -\ngrid.linewidth: 1.0\ngrid.color: cbcbcb\n\nxtick.major.size: 0\nxtick.minor.size: 0\nytick.major.size: 0\nytick.minor.size: 0\n\nsavefig.edgecolor: f0f0f0\nsavefig.facecolor: f0f0f0\n\n#figure.subplot.left: 0.08\n#figure.subplot.right: 0.95\n#figure.subplot.bottom: 0.07\n\n#figure.facecolor: f0f0f0  # grey\nfigure.facecolor: ffffff  # white\n\n## ***************************************************************************\n## * FONT                                                                    *\n## ***************************************************************************\n## The font properties used by `text.Text`.\n## See https://matplotlib.org/api/font_manager_api.html for more information\n## on font properties.  The 6 font properties used for font matching are\n## given below with their default values.\n##\n## The font.family property can take either a concrete font name (not supported\n## when rendering text with usetex), or one of the following five generic\n## values:\n##     - 'serif' (e.g., Times),\n##     - 'sans-serif' (e.g., Helvetica),\n##     - 'cursive' (e.g., Zapf-Chancery),\n##     - 'fantasy' (e.g., Western), and\n##     - 'monospace' (e.g., Courier).\n## Each of these values has a corresponding default list of font names\n## (font.serif, etc.); the first available font in the list is used.  Note that\n## for font.serif, font.sans-serif, and font.monospace, the first element of\n## the list (a DejaVu font) will always be used because DejaVu is shipped with\n## Matplotlib and is thus guaranteed to be available; the other entries are\n## left as examples of other possible values.\n##\n## The font.style property has three values: normal (or roman), italic\n## or oblique.  The oblique style will be used for italic, if it is not\n## present.\n##\n## The font.variant property has two values: normal or small-caps.  For\n## TrueType fonts, which are scalable fonts, small-caps is equivalent\n## to using a font size of 'smaller', or about 83%% of the current font\n## size.\n##\n## The font.weight property has effectively 13 values: normal, bold,\n## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as\n## 400, and bold is 700.  bolder and lighter are relative values with\n## respect to the current weight.\n##\n## The font.stretch property has 11 values: ultra-condensed,\n## extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This\n## property is not currently implemented.\n##\n## The font.size property is the default font size for text, given in points.\n## 10 pt is the standard value.\n##\n## Note that font.size controls default text sizes.  To configure\n## special text sizes tick labels, axes, labels, title, etc., see the rc\n## settings for axes and ticks.  Special text sizes can be defined\n## relative to font.size, using the following values: xx-small, x-small,\n## small, medium, large, x-large, xx-large, larger, or smaller\n\n\nfont.family:  sans-serif\nfont.style:   normal\nfont.variant: normal\nfont.weight:  normal\nfont.stretch: normal\nfont.size:    8.0\n\nfont.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\nfont.sans-serif: Verdana, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\nfont.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive\nfont.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy\nfont.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n\n\n## ***************************************************************************\n## * TEXT                                                                    *\n## ***************************************************************************\n## The text properties used by `text.Text`.\n## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\n## for more information on text properties\n#text.color: black\n\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Optional Labs/lab_utils_common.py",
    "content": "\"\"\" \nlab_utils_common.py\n    functions common to all optional labs, Course 1, Week 2 \n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nplt.style.use('./deeplearning.mplstyle')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0';\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0')\n\n\n##########################################################\n# Regression Routines\n##########################################################\n\n#Function to calculate the cost\ndef compute_cost_matrix(X, y, w, b, verbose=False):\n    \"\"\"\n    Computes the gradient for linear regression\n     Args:\n      X (ndarray (m,n)): Data, m examples with n features\n      y (ndarray (m,)) : target values\n      w (ndarray (n,)) : model parameters  \n      b (scalar)       : model parameter\n      verbose : (Boolean) If true, print out intermediate value f_wb\n    Returns\n      cost: (scalar)\n    \"\"\"\n    m = X.shape[0]\n\n    # calculate f_wb for all examples.\n    f_wb = X @ w + b\n    # calculate cost\n    total_cost = (1/(2*m)) * np.sum((f_wb-y)**2)\n\n    if verbose: print(\"f_wb:\")\n    if verbose: print(f_wb)\n\n    return total_cost\n\ndef compute_gradient_matrix(X, y, w, b):\n    \"\"\"\n    Computes the gradient for linear regression\n\n    Args:\n      X (ndarray (m,n)): Data, m examples with n features\n      y (ndarray (m,)) : target values\n      w (ndarray (n,)) : model parameters  \n      b (scalar)       : model parameter\n    Returns\n      dj_dw (ndarray (n,1)): The gradient of the cost w.r.t. the parameters w.\n      dj_db (scalar):        The gradient of the cost w.r.t. the parameter b.\n\n    \"\"\"\n    m,n = X.shape\n    f_wb = X @ w + b\n    e   = f_wb - y\n    dj_dw  = (1/m) * (X.T @ e)\n    dj_db  = (1/m) * np.sum(e)\n\n    return dj_db,dj_dw\n\n\n# Loop version of multi-variable compute_cost\ndef compute_cost(X, y, w, b):\n    \"\"\"\n    compute cost\n    Args:\n      X (ndarray (m,n)): Data, m examples with n features\n      y (ndarray (m,)) : target values\n      w (ndarray (n,)) : model parameters  \n      b (scalar)       : model parameter\n    Returns\n      cost (scalar)    : cost\n    \"\"\"\n    m = X.shape[0]\n    cost = 0.0\n    for i in range(m):\n        f_wb_i = np.dot(X[i],w) + b           #(n,)(n,)=scalar\n        cost = cost + (f_wb_i - y[i])**2\n    cost = cost/(2*m)\n    return cost \n\ndef compute_gradient(X, y, w, b):\n    \"\"\"\n    Computes the gradient for linear regression\n    Args:\n      X (ndarray (m,n)): Data, m examples with n features\n      y (ndarray (m,)) : target values\n      w (ndarray (n,)) : model parameters  \n      b (scalar)       : model parameter\n    Returns\n      dj_dw (ndarray Shape (n,)): The gradient of the cost w.r.t. the parameters w.\n      dj_db (scalar):             The gradient of the cost w.r.t. the parameter b.\n    \"\"\"\n    m,n = X.shape           #(number of examples, number of features)\n    dj_dw = np.zeros((n,))\n    dj_db = 0.\n\n    for i in range(m):\n        err = (np.dot(X[i], w) + b) - y[i]\n        for j in range(n):\n            dj_dw[j] = dj_dw[j] + err * X[i,j]\n        dj_db = dj_db + err\n    dj_dw = dj_dw/m\n    dj_db = dj_db/m\n\n    return dj_db,dj_dw\n\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Optional Labs/lab_utils_uni.py",
    "content": "\"\"\" \nlab_utils_uni.py\n    routines used in Course 1, Week2, labs1-3 dealing with single variables (univariate)\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator\nfrom matplotlib.gridspec import GridSpec\nfrom matplotlib.colors import LinearSegmentedColormap\nfrom ipywidgets import interact\nfrom lab_utils_common import compute_cost\nfrom lab_utils_common import dlblue, dlorange, dldarkred, dlmagenta, dlpurple, dlcolors\n\nplt.style.use('./deeplearning.mplstyle')\nn_bin = 5\ndlcm = LinearSegmentedColormap.from_list(\n        'dl_map', dlcolors, N=n_bin)\n\n##########################################################\n# Plotting Routines\n##########################################################\n\ndef plt_house_x(X, y,f_wb=None, ax=None):\n    ''' plot house with aXis '''\n    if not ax:\n        fig, ax = plt.subplots(1,1)\n    ax.scatter(X, y, marker='x', c='r', label=\"Actual Value\")\n\n    ax.set_title(\"Housing Prices\")\n    ax.set_ylabel('Price (in 1000s of dollars)')\n    ax.set_xlabel(f'Size (1000 sqft)')\n    if f_wb is not None:\n        ax.plot(X, f_wb,  c=dlblue, label=\"Our Prediction\")\n    ax.legend()\n\n\ndef mk_cost_lines(x,y,w,b, ax):\n    ''' makes vertical cost lines'''\n    cstr = \"cost = (1/m)*(\"\n    ctot = 0\n    label = 'cost for point'\n    addedbreak = False\n    for p in zip(x,y):\n        f_wb_p = w*p[0]+b\n        c_p = ((f_wb_p - p[1])**2)/2\n        c_p_txt = c_p\n        ax.vlines(p[0], p[1],f_wb_p, lw=3, color=dlpurple, ls='dotted', label=label)\n        label='' #just one\n        cxy = [p[0], p[1] + (f_wb_p-p[1])/2]\n        ax.annotate(f'{c_p_txt:0.0f}', xy=cxy, xycoords='data',color=dlpurple,\n            xytext=(5, 0), textcoords='offset points')\n        cstr += f\"{c_p_txt:0.0f} +\"\n        if len(cstr) > 38 and addedbreak is False:\n            cstr += \"\\n\"\n            addedbreak = True\n        ctot += c_p\n    ctot = ctot/(len(x))\n    cstr = cstr[:-1] + f\") = {ctot:0.0f}\"\n    ax.text(0.15,0.02,cstr, transform=ax.transAxes, color=dlpurple)\n\n##########\n# Cost lab\n##########\n\n\ndef plt_intuition(x_train, y_train):\n\n    w_range = np.array([200-200,200+200])\n    tmp_b = 100\n\n    w_array = np.arange(*w_range, 5)\n    cost = np.zeros_like(w_array)\n    for i in range(len(w_array)):\n        tmp_w = w_array[i]\n        cost[i] = compute_cost(x_train, y_train, tmp_w, tmp_b)\n\n    @interact(w=(*w_range,10),continuous_update=False)\n    def func( w=150):\n        f_wb = np.dot(x_train, w) + tmp_b\n\n        fig, ax = plt.subplots(1, 2, constrained_layout=True, figsize=(8,4))\n        fig.canvas.toolbar_position = 'bottom'\n\n        mk_cost_lines(x_train, y_train, w, tmp_b, ax[0])\n        plt_house_x(x_train, y_train, f_wb=f_wb, ax=ax[0])\n\n        ax[1].plot(w_array, cost)\n        cur_cost = compute_cost(x_train, y_train, w, tmp_b)\n        ax[1].scatter(w,cur_cost, s=100, color=dldarkred, zorder= 10, label= f\"cost at w={w}\")\n        ax[1].hlines(cur_cost, ax[1].get_xlim()[0],w, lw=4, color=dlpurple, ls='dotted')\n        ax[1].vlines(w, ax[1].get_ylim()[0],cur_cost, lw=4, color=dlpurple, ls='dotted')\n        ax[1].set_title(\"Cost vs. w, (b fixed at 100)\")\n        ax[1].set_ylabel('Cost')\n        ax[1].set_xlabel('w')\n        ax[1].legend(loc='upper center')\n        fig.suptitle(f\"Minimize Cost: Current Cost = {cur_cost:0.0f}\", fontsize=12)\n        plt.show()\n\n# this is the 2D cost curve with interactive slider\ndef plt_stationary(x_train, y_train):\n    # setup figure\n    fig = plt.figure( figsize=(9,8))\n    #fig = plt.figure(constrained_layout=True,  figsize=(12,10))\n    fig.set_facecolor('#ffffff') #white\n    fig.canvas.toolbar_position = 'top'\n    #gs = GridSpec(2, 2, figure=fig, wspace = 0.01)\n    gs = GridSpec(2, 2, figure=fig)\n    ax0 = fig.add_subplot(gs[0, 0])\n    ax1 = fig.add_subplot(gs[0, 1])\n    ax2 = fig.add_subplot(gs[1, :],  projection='3d')\n    ax = np.array([ax0,ax1,ax2])\n\n    #setup useful ranges and common linspaces\n    w_range = np.array([200-300.,200+300])\n    b_range = np.array([50-300., 50+300])\n    b_space  = np.linspace(*b_range, 100)\n    w_space  = np.linspace(*w_range, 100)\n\n    # get cost for w,b ranges for contour and 3D\n    tmp_b,tmp_w = np.meshgrid(b_space,w_space)\n    z=np.zeros_like(tmp_b)\n    for i in range(tmp_w.shape[0]):\n        for j in range(tmp_w.shape[1]):\n            z[i,j] = compute_cost(x_train, y_train, tmp_w[i][j], tmp_b[i][j] )\n            if z[i,j] == 0: z[i,j] = 1e-6\n\n    w0=200;b=-100    #initial point\n    ### plot model w cost ###\n    f_wb = np.dot(x_train,w0) + b\n    mk_cost_lines(x_train,y_train,w0,b,ax[0])\n    plt_house_x(x_train, y_train, f_wb=f_wb, ax=ax[0])\n\n    ### plot contour ###\n    CS = ax[1].contour(tmp_w, tmp_b, np.log(z),levels=12, linewidths=2, alpha=0.7,colors=dlcolors)\n    ax[1].set_title('Cost(w,b)')\n    ax[1].set_xlabel('w', fontsize=10)\n    ax[1].set_ylabel('b', fontsize=10)\n    ax[1].set_xlim(w_range) ; ax[1].set_ylim(b_range)\n    cscat  = ax[1].scatter(w0,b, s=100, color=dlblue, zorder= 10, label=\"cost with \\ncurrent w,b\")\n    chline = ax[1].hlines(b, ax[1].get_xlim()[0],w0, lw=4, color=dlpurple, ls='dotted')\n    cvline = ax[1].vlines(w0, ax[1].get_ylim()[0],b, lw=4, color=dlpurple, ls='dotted')\n    ax[1].text(0.5,0.95,\"Click to choose w,b\",  bbox=dict(facecolor='white', ec = 'black'), fontsize = 10,\n                transform=ax[1].transAxes, verticalalignment = 'center', horizontalalignment= 'center')\n\n    #Surface plot of the cost function J(w,b)\n    ax[2].plot_surface(tmp_w, tmp_b, z,  cmap = dlcm, alpha=0.3, antialiased=True)\n    ax[2].plot_wireframe(tmp_w, tmp_b, z, color='k', alpha=0.1)\n    plt.xlabel(\"$w$\")\n    plt.ylabel(\"$b$\")\n    ax[2].zaxis.set_rotate_label(False)\n    ax[2].xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax[2].yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax[2].zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax[2].set_zlabel(\"J(w, b)\\n\\n\", rotation=90)\n    plt.title(\"Cost(w,b) \\n [You can rotate this figure]\", size=12)\n    ax[2].view_init(30, -120)\n\n    return fig,ax, [cscat, chline, cvline]\n\n\n#https://matplotlib.org/stable/users/event_handling.html\nclass plt_update_onclick:\n    def __init__(self, fig, ax, x_train,y_train, dyn_items):\n        self.fig = fig\n        self.ax = ax\n        self.x_train = x_train\n        self.y_train = y_train\n        self.dyn_items = dyn_items\n        self.cid = fig.canvas.mpl_connect('button_press_event', self)\n\n    def __call__(self, event):\n        if event.inaxes == self.ax[1]:\n            ws = event.xdata\n            bs = event.ydata\n            cst = compute_cost(self.x_train, self.y_train, ws, bs)\n\n            # clear and redraw line plot\n            self.ax[0].clear()\n            f_wb = np.dot(self.x_train,ws) + bs\n            mk_cost_lines(self.x_train,self.y_train,ws,bs,self.ax[0])\n            plt_house_x(self.x_train, self.y_train, f_wb=f_wb, ax=self.ax[0])\n\n            # remove lines and re-add on countour plot and 3d plot\n            for artist in self.dyn_items:\n                artist.remove()\n\n            a = self.ax[1].scatter(ws,bs, s=100, color=dlblue, zorder= 10, label=\"cost with \\ncurrent w,b\")\n            b = self.ax[1].hlines(bs, self.ax[1].get_xlim()[0],ws, lw=4, color=dlpurple, ls='dotted')\n            c = self.ax[1].vlines(ws, self.ax[1].get_ylim()[0],bs, lw=4, color=dlpurple, ls='dotted')\n            d = self.ax[1].annotate(f\"Cost: {cst:.0f}\", xy= (ws, bs), xytext = (4,4), textcoords = 'offset points',\n                               bbox=dict(facecolor='white'), size = 10)\n\n            #Add point in 3D surface plot\n            e = self.ax[2].scatter3D(ws, bs,cst , marker='X', s=100)\n\n            self.dyn_items = [a,b,c,d,e]\n            self.fig.canvas.draw()\n\n\ndef soup_bowl():\n    \"\"\" Create figure and plot with a 3D projection\"\"\"\n    fig = plt.figure(figsize=(8,8))\n\n    #Plot configuration\n    ax = fig.add_subplot(111, projection='3d')\n    ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.zaxis.set_rotate_label(False)\n    ax.view_init(45, -120)\n\n    #Useful linearspaces to give values to the parameters w and b\n    w = np.linspace(-20, 20, 100)\n    b = np.linspace(-20, 20, 100)\n\n    #Get the z value for a bowl-shaped cost function\n    z=np.zeros((len(w), len(b)))\n    j=0\n    for x in w:\n        i=0\n        for y in b:\n            z[i,j] = x**2 + y**2\n            i+=1\n        j+=1\n\n    #Meshgrid used for plotting 3D functions\n    W, B = np.meshgrid(w, b)\n\n    #Create the 3D surface plot of the bowl-shaped cost function\n    ax.plot_surface(W, B, z, cmap = \"Spectral_r\", alpha=0.7, antialiased=False)\n    ax.plot_wireframe(W, B, z, color='k', alpha=0.1)\n    ax.set_xlabel(\"$w$\")\n    ax.set_ylabel(\"$b$\")\n    ax.set_zlabel(\"$J(w,b)$\", rotation=90)\n    ax.set_title(\"$J(w,b)$\\n [You can rotate this figure]\", size=15)\n\n    plt.show()\n\ndef inbounds(a,b,xlim,ylim):\n    xlow,xhigh = xlim\n    ylow,yhigh = ylim\n    ax, ay = a\n    bx, by = b\n    if (ax > xlow and ax < xhigh) and (bx > xlow and bx < xhigh) \\\n        and (ay > ylow and ay < yhigh) and (by > ylow and by < yhigh):\n        return True\n    return False\n\ndef plt_contour_wgrad(x, y, hist, ax, w_range=[-100, 500, 5], b_range=[-500, 500, 5],\n                contours = [0.1,50,1000,5000,10000,25000,50000],\n                      resolution=5, w_final=200, b_final=100,step=10 ):\n    b0,w0 = np.meshgrid(np.arange(*b_range),np.arange(*w_range))\n    z=np.zeros_like(b0)\n    for i in range(w0.shape[0]):\n        for j in range(w0.shape[1]):\n            z[i][j] = compute_cost(x, y, w0[i][j], b0[i][j] )\n\n    CS = ax.contour(w0, b0, z, contours, linewidths=2,\n                   colors=[dlblue, dlorange, dldarkred, dlmagenta, dlpurple])\n    ax.clabel(CS, inline=1, fmt='%1.0f', fontsize=10)\n    ax.set_xlabel(\"w\");  ax.set_ylabel(\"b\")\n    ax.set_title('Contour plot of cost J(w,b), vs b,w with path of gradient descent')\n    w = w_final; b=b_final\n    ax.hlines(b, ax.get_xlim()[0],w, lw=2, color=dlpurple, ls='dotted')\n    ax.vlines(w, ax.get_ylim()[0],b, lw=2, color=dlpurple, ls='dotted')\n\n    base = hist[0]\n    for point in hist[0::step]:\n        edist = np.sqrt((base[0] - point[0])**2 + (base[1] - point[1])**2)\n        if(edist > resolution or point==hist[-1]):\n            if inbounds(point,base, ax.get_xlim(),ax.get_ylim()):\n                plt.annotate('', xy=point, xytext=base,xycoords='data',\n                         arrowprops={'arrowstyle': '->', 'color': 'r', 'lw': 3},\n                         va='center', ha='center')\n            base=point\n    return\n\n\ndef plt_divergence(p_hist, J_hist, x_train,y_train):\n\n    x=np.zeros(len(p_hist))\n    y=np.zeros(len(p_hist))\n    v=np.zeros(len(p_hist))\n    for i in range(len(p_hist)):\n        x[i] = p_hist[i][0]\n        y[i] = p_hist[i][1]\n        v[i] = J_hist[i]\n\n    fig = plt.figure(figsize=(12,5))\n    plt.subplots_adjust( wspace=0 )\n    gs = fig.add_gridspec(1, 5)\n    fig.suptitle(f\"Cost escalates when learning rate is too large\")\n    #===============\n    #  First subplot\n    #===============\n    ax = fig.add_subplot(gs[:2], )\n\n    # Print w vs cost to see minimum\n    fix_b = 100\n    w_array = np.arange(-70000, 70000, 1000, dtype=\"int64\")\n    cost = np.zeros_like(w_array,float)\n\n    for i in range(len(w_array)):\n        tmp_w = w_array[i]\n        cost[i] = compute_cost(x_train, y_train, tmp_w, fix_b)\n\n    ax.plot(w_array, cost)\n    ax.plot(x,v, c=dlmagenta)\n    ax.set_title(\"Cost vs w, b set to 100\")\n    ax.set_ylabel('Cost')\n    ax.set_xlabel('w')\n    ax.xaxis.set_major_locator(MaxNLocator(2))\n\n    #===============\n    # Second Subplot\n    #===============\n\n    tmp_b,tmp_w = np.meshgrid(np.arange(-35000, 35000, 500),np.arange(-70000, 70000, 500))\n    tmp_b = tmp_b.astype('int64')\n    tmp_w = tmp_w.astype('int64')\n    z=np.zeros_like(tmp_b,float)\n    for i in range(tmp_w.shape[0]):\n        for j in range(tmp_w.shape[1]):\n            z[i][j] = compute_cost(x_train, y_train, tmp_w[i][j], tmp_b[i][j] )\n\n    ax = fig.add_subplot(gs[2:], projection='3d')\n    ax.plot_surface(tmp_w, tmp_b, z,  alpha=0.3, color=dlblue)\n    ax.xaxis.set_major_locator(MaxNLocator(2))\n    ax.yaxis.set_major_locator(MaxNLocator(2))\n\n    ax.set_xlabel('w', fontsize=16)\n    ax.set_ylabel('b', fontsize=16)\n    ax.set_zlabel('\\ncost', fontsize=16)\n    plt.title('Cost vs (b, w)')\n    # Customize the view angle\n    ax.view_init(elev=20., azim=-65)\n    ax.plot(x, y, v,c=dlmagenta)\n\n    return\n\n# draw derivative line\n# y = m*(x - x1) + y1\ndef add_line(dj_dx, x1, y1, d, ax):\n    x = np.linspace(x1-d, x1+d,50)\n    y = dj_dx*(x - x1) + y1\n    ax.scatter(x1, y1, color=dlblue, s=50)\n    ax.plot(x, y, '--', c=dldarkred,zorder=10, linewidth = 1)\n    xoff = 30 if x1 == 200 else 10\n    ax.annotate(r\"$\\frac{\\partial J}{\\partial w}$ =%d\" % dj_dx, fontsize=14,\n                xy=(x1, y1), xycoords='data',\n            xytext=(xoff, 10), textcoords='offset points',\n            arrowprops=dict(arrowstyle=\"->\"),\n            horizontalalignment='left', verticalalignment='top')\n\ndef plt_gradients(x_train,y_train, f_compute_cost, f_compute_gradient):\n    #===============\n    #  First subplot\n    #===============\n    fig,ax = plt.subplots(1,2,figsize=(12,4))\n\n    # Print w vs cost to see minimum\n    fix_b = 100\n    w_array = np.linspace(-100, 500, 50)\n    w_array = np.linspace(0, 400, 50)\n    cost = np.zeros_like(w_array)\n\n    for i in range(len(w_array)):\n        tmp_w = w_array[i]\n        cost[i] = f_compute_cost(x_train, y_train, tmp_w, fix_b)\n    ax[0].plot(w_array, cost,linewidth=1)\n    ax[0].set_title(\"Cost vs w, with gradient; b set to 100\")\n    ax[0].set_ylabel('Cost')\n    ax[0].set_xlabel('w')\n\n    # plot lines for fixed b=100\n    for tmp_w in [100,200,300]:\n        fix_b = 100\n        dj_dw,dj_db = f_compute_gradient(x_train, y_train, tmp_w, fix_b )\n        j = f_compute_cost(x_train, y_train, tmp_w, fix_b)\n        add_line(dj_dw, tmp_w, j, 30, ax[0])\n\n    #===============\n    # Second Subplot\n    #===============\n\n    tmp_b,tmp_w = np.meshgrid(np.linspace(-200, 200, 10), np.linspace(-100, 600, 10))\n    U = np.zeros_like(tmp_w)\n    V = np.zeros_like(tmp_b)\n    for i in range(tmp_w.shape[0]):\n        for j in range(tmp_w.shape[1]):\n            U[i][j], V[i][j] = f_compute_gradient(x_train, y_train, tmp_w[i][j], tmp_b[i][j] )\n    X = tmp_w\n    Y = tmp_b\n    n=-2\n    color_array = np.sqrt(((V-n)/2)**2 + ((U-n)/2)**2)\n\n    ax[1].set_title('Gradient shown in quiver plot')\n    Q = ax[1].quiver(X, Y, U, V, color_array, units='width', )\n    ax[1].quiverkey(Q, 0.9, 0.9, 2, r'$2 \\frac{m}{s}$', labelpos='E',coordinates='figure')\n    ax[1].set_xlabel(\"w\"); ax[1].set_ylabel(\"b\")\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Practice quiz - Regression/README.md",
    "content": "![](/C1%20-%20Supervised%20Machine%20Learning:%20Regression%20and%20Classification/week1/Practice%20quiz:%20Regression/ss1.png)\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Practice quiz - Supervised vs unsupervised learning/README.md",
    "content": "![](/C1%20-%20Supervised%20Machine%20Learning%3A%20Regression%20and%20Classification/week1/Practice%20quiz%3A%20Supervised%20vs%20unsupervised%20learning/ss1.png)"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/Practice quiz - Train the model with gradient descent/README.md",
    "content": "![](/C1%20-%20Supervised%20Machine%20Learning%3A%20Regression%20and%20Classification/week1/Practice%20quiz%3A%20Train%20the%20model%20with%20gradient%20descent/ss1.png)"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week1/README.md",
    "content": "### Week 1 Solutions \n\n<br></br>\n\n- [Practice quiz: Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Practice%20quiz%20-%20Regression)\n\n- [Practice quiz: Supervised vs unsupervised learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Practice%20quiz%20-%20Supervised%20vs%20unsupervised%20learning)\n\n- [Practice quiz: Train the model with gradient descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Practice%20quiz%20-%20Train%20the%20model%20with%20gradient%20descent)\n- [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs)\n    - [Model Representation](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs/C1_W1_Lab03_Model_Representation_Soln.ipynb)\n    - [Cost Function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs/C1_W1_Lab04_Cost_function_Soln.ipynb)\n    - [Gradient Descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs/C1_W1_Lab05_Gradient_Descent_Soln.ipynb)\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/C1W2A1/C1_W2_Linear_Regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Practice Lab: Linear Regression\\n\",\n    \"\\n\",\n    \"Welcome to your first practice lab! In this lab, you will implement linear regression with one variable to predict profits for a restaurant franchise.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Linear regression with one variable ](#2)\\n\",\n    \"  - [ 2.1 Problem Statement](#2.1)\\n\",\n    \"  - [ 2.2  Dataset](#2.2)\\n\",\n    \"  - [ 2.3 Refresher on linear regression](#2.3)\\n\",\n    \"  - [ 2.4  Compute Cost](#2.4)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 2.5 Gradient descent ](#2.5)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"  - [ 2.6 Learning parameters using batch gradient descent ](#2.6)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](www.numpy.org) is the fundamental package for working with matrices in Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\\n\",\n    \"- ``utils.py`` contains helper functions for this assignment. You do not need to modify code in this file.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import *\\n\",\n    \"import copy\\n\",\n    \"import math\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2 -  Problem Statement\\n\",\n    \"\\n\",\n    \"Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet.\\n\",\n    \"- You would like to expand your business to cities that may give your restaurant higher profits.\\n\",\n    \"- The chain already has restaurants in various cities and you have data for profits and populations from the cities.\\n\",\n    \"- You also have data on cities that are candidates for a new restaurant. \\n\",\n    \"    - For these cities, you have the city population.\\n\",\n    \"    \\n\",\n    \"Can you use the data to help you identify which cities may potentially give your business higher profits?\\n\",\n    \"\\n\",\n    \"## 3 - Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_data()` function shown below loads the data into variables `x_train` and `y_train`\\n\",\n    \"  - `x_train` is the population of a city\\n\",\n    \"  - `y_train` is the profit of a restaurant in that city. A negative value for profit indicates a loss.   \\n\",\n    \"  - Both `X_train` and `y_train` are numpy arrays.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load the dataset\\n\",\n    \"x_train, y_train = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"Before starting on any task, it is useful to get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to just print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints the variable `x_train` and the type of the variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Type of x_train: <class 'numpy.ndarray'>\\n\",\n      \"First five elements of x_train are:\\n\",\n      \" [6.1101 5.5277 8.5186 7.0032 5.8598]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# print x_train\\n\",\n    \"print(\\\"Type of x_train:\\\",type(x_train))\\n\",\n    \"print(\\\"First five elements of x_train are:\\\\n\\\", x_train[:5]) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`x_train` is a numpy array that contains decimal values that are all greater than zero.\\n\",\n    \"- These values represent the city population times 10,000\\n\",\n    \"- For example, 6.1101 means that the population for that city is 61,101\\n\",\n    \"  \\n\",\n    \"Now, let's print `y_train`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Type of y_train: <class 'numpy.ndarray'>\\n\",\n      \"First five elements of y_train are:\\n\",\n      \" [17.592   9.1302 13.662  11.854   6.8233]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# print y_train\\n\",\n    \"print(\\\"Type of y_train:\\\",type(y_train))\\n\",\n    \"print(\\\"First five elements of y_train are:\\\\n\\\", y_train[:5])  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Similarly, `y_train` is a numpy array that has decimal values, some negative, some positive.\\n\",\n    \"- These represent your restaurant's average monthly profits in each city, in units of \\\\$10,000.\\n\",\n    \"  - For example, 17.592 represents \\\\$175,920 in average monthly profits for that city.\\n\",\n    \"  - -2.6807 represents -\\\\$26,807 in average monthly loss for that city.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions.\\n\",\n    \"\\n\",\n    \"Please print the shape of `x_train` and `y_train` and see how many training examples you have in your dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of x_train is: (97,)\\n\",\n      \"The shape of y_train is:  (97,)\\n\",\n      \"Number of training examples (m): 97\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of x_train is:', x_train.shape)\\n\",\n    \"print ('The shape of y_train is: ', y_train.shape)\\n\",\n    \"print ('Number of training examples (m):', len(x_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The city population array has 97 data points, and the monthly average profits also has 97 data points. These are NumPy 1D arrays.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Visualize your data\\n\",\n    \"\\n\",\n    \"It is often useful to understand the data by visualizing it. \\n\",\n    \"- For this dataset, you can use a scatter plot to visualize the data, since it has only two properties to plot (profit and population). \\n\",\n    \"- Many other problems that you will encounter in real life have more than two properties (for example, population, average household income, monthly profits, monthly sales).When you have more than two properties, you can still use a scatter plot to see the relationship between each pair of properties.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Create a scatter plot of the data. To change the markers to red \\\"x\\\",\\n\",\n    \"# we used the 'marker' and 'c' parameters\\n\",\n    \"plt.scatter(x_train, y_train, marker='x', c='r') \\n\",\n    \"\\n\",\n    \"# Set the title\\n\",\n    \"plt.title(\\\"Profits vs. Population per city\\\")\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Profit in $10,000')\\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Population of City in 10,000s')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Your goal is to build a linear regression model to fit this data.\\n\",\n    \"- With this model, you can then input a new city's population, and have the model estimate your restaurant's potential monthly profits for that city.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Refresher on linear regression\\n\",\n    \"\\n\",\n    \"In this practice lab, you will fit the linear regression parameters $(w,b)$ to your dataset.\\n\",\n    \"- The model function for linear regression, which is a function that maps from `x` (city population) to `y` (your restaurant's monthly profit for that city) is represented as \\n\",\n    \"    $$f_{w,b}(x) = wx + b$$\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"- To train a linear regression model, you want to find the best $(w,b)$ parameters that fit your dataset.  \\n\",\n    \"\\n\",\n    \"    - To compare how one choice of $(w,b)$ is better or worse than another choice, you can evaluate it with a cost function $J(w,b)$\\n\",\n    \"      - $J$ is a function of $(w,b)$. That is, the value of the cost $J(w,b)$ depends on the value of $(w,b)$.\\n\",\n    \"  \\n\",\n    \"    - The choice of $(w,b)$ that fits your data the best is the one that has the smallest cost $J(w,b)$.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- To find the values $(w,b)$ that gets the smallest possible cost $J(w,b)$, you can use a method called **gradient descent**. \\n\",\n    \"  - With each step of gradient descent, your parameters $(w,b)$ come closer to the optimal values that will achieve the lowest cost $J(w,b)$.\\n\",\n    \"  \\n\",\n    \"\\n\",\n    \"- The trained linear regression model can then take the input feature $x$ (city population) and output a prediction $f_{w,b}(x)$ (predicted monthly profit for a restaurant in that city).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"5\\\"></a>\\n\",\n    \"## 5 - Compute Cost\\n\",\n    \"\\n\",\n    \"Gradient descent involves repeated steps to adjust the value of your parameter $(w,b)$ to gradually get a smaller and smaller cost $J(w,b)$.\\n\",\n    \"- At each step of gradient descent, it will be helpful for you to monitor your progress by computing the cost $J(w,b)$ as $(w,b)$ gets updated. \\n\",\n    \"- In this section, you will implement a function to calculate $J(w,b)$ so that you can check the progress of your gradient descent implementation.\\n\",\n    \"\\n\",\n    \"#### Cost function\\n\",\n    \"As you may recall from the lecture, for one variable, the cost function for linear regression $J(w,b)$ is defined as\\n\",\n    \"\\n\",\n    \"$$J(w,b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})^2$$ \\n\",\n    \"\\n\",\n    \"- You can think of $f_{w,b}(x^{(i)})$ as the model's prediction of your restaurant's profit, as opposed to $y^{(i)}$, which is the actual profit that is recorded in the data.\\n\",\n    \"- $m$ is the number of training examples in the dataset\\n\",\n    \"\\n\",\n    \"#### Model prediction\\n\",\n    \"\\n\",\n    \"- For linear regression with one variable, the prediction of the model $f_{w,b}$ for an example $x^{(i)}$ is representented as:\\n\",\n    \"\\n\",\n    \"$$ f_{w,b}(x^{(i)}) = wx^{(i)} + b$$\\n\",\n    \"\\n\",\n    \"This is the equation for a line, with an intercept $b$ and a slope $w$\\n\",\n    \"\\n\",\n    \"#### Implementation\\n\",\n    \"\\n\",\n    \"Please complete the `compute_cost()` function below to compute the cost $J(w,b)$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Complete the `compute_cost` below to:\\n\",\n    \"\\n\",\n    \"* Iterate over the training examples, and for each example, compute:\\n\",\n    \"    * The prediction of the model for that example \\n\",\n    \"    $$\\n\",\n    \"    f_{wb}(x^{(i)}) =  wx^{(i)} + b \\n\",\n    \"    $$\\n\",\n    \"   \\n\",\n    \"    * The cost for that example  $$cost^{(i)} =  (f_{wb} - y^{(i)})^2$$\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"* Return the total cost over all examples\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} cost^{(i)}$$\\n\",\n    \"  * Here, $m$ is the number of training examples and $\\\\sum$ is the summation operator\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED FUNCTION: compute_cost\\n\",\n    \"\\n\",\n    \"def compute_cost(x, y, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost function for linear regression.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        x (ndarray): Shape (m,) Input to the model (Population of cities) \\n\",\n    \"        y (ndarray): Shape (m,) Label (Actual profits for the cities)\\n\",\n    \"        w, b (scalar): Parameters of the model\\n\",\n    \"    \\n\",\n    \"    Returns\\n\",\n    \"        total_cost (float): The cost of using w,b as the parameters for linear regression\\n\",\n    \"               to fit the data points in x and y\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # number of training examples\\n\",\n    \"    m = x.shape[0] \\n\",\n    \"    \\n\",\n    \"    # You need to return this variable correctly\\n\",\n    \"    total_cost = 0\\n\",\n    \"\\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    cost=0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb = w*x[i]+b\\n\",\n    \"        cost += (f_wb - y[i])**2\\n\",\n    \"    \\n\",\n    \"    total_cost = cost/(2*m)\\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"\\n\",\n    \"    return total_cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * You can represent a summation operator eg: $h = \\\\sum\\\\limits_{i = 0}^{m-1} 2i$ in code as follows:\\n\",\n    \"     ```python \\n\",\n    \"    h = 0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        h = h + 2*i\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"   * In this case, you can iterate over all the examples in `x` using a for loop and add the `cost` from each iteration to a variable (`cost_sum`) initialized outside the loop.\\n\",\n    \"\\n\",\n    \"   * Then, you can return the `total_cost` as `cost_sum` divided by `2m`.\\n\",\n    \"     \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_cost(x, y, w, b):\\n\",\n    \"        # number of training examples\\n\",\n    \"        m = x.shape[0] \\n\",\n    \"    \\n\",\n    \"        # You need to return this variable correctly\\n\",\n    \"        total_cost = 0\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ###  \\n\",\n    \"        # Variable to keep track of sum of cost from each example\\n\",\n    \"        cost_sum = 0\\n\",\n    \"    \\n\",\n    \"        # Loop over training examples\\n\",\n    \"        for i in range(m):\\n\",\n    \"            # Your code here to get the prediction f_wb for the ith example\\n\",\n    \"            f_wb = \\n\",\n    \"            # Your code here to get the cost associated with the ith example\\n\",\n    \"            cost = \\n\",\n    \"        \\n\",\n    \"            # Add to sum of cost for each example\\n\",\n    \"            cost_sum = cost_sum + cost \\n\",\n    \"\\n\",\n    \"        # Get the total cost as the sum divided by (2*m)\\n\",\n    \"        total_cost = (1 / (2 * m)) * cost_sum\\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"\\n\",\n    \"        return total_cost\\n\",\n    \"    ```\\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `f_wb` and `cost`.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; For scalars $a$, $b$ and $c$ (<code>x[i]</code>, <code>w</code> and <code>b</code> are all scalars), you can calculate the equation $h = ab + c$ in code as <code>h = a * b + c</code>\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute f_wb as <code>f_wb = w * x[i] + b </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate cost</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can calculate the square of a variable z as z**2\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate cost</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute cost as <code>cost = (f_wb - y[i]) ** 2</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can check if your implementation was correct by running the following test code:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'numpy.float64'>\\n\",\n      \"Cost at initial w (zeros): 75.203\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute cost with some initial values for paramaters w, b\\n\",\n    \"initial_w = 2\\n\",\n    \"initial_b = 1\\n\",\n    \"\\n\",\n    \"cost = compute_cost(x_train, y_train, initial_w, initial_b)\\n\",\n    \"print(type(cost))\\n\",\n    \"print(f'Cost at initial w (zeros): {cost:.3f}')\\n\",\n    \"\\n\",\n    \"# Public tests\\n\",\n    \"from public_tests import *\\n\",\n    \"compute_cost_test(compute_cost)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Cost at initial w (zeros):<b> 75.203 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"6\\\"></a>\\n\",\n    \"## 6 - Gradient descent \\n\",\n    \"\\n\",\n    \"In this section, you will implement the gradient for parameters $w, b$ for linear regression. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As described in the lecture videos, the gradient descent algorithm is:\\n\",\n    \"\\n\",\n    \"$$\\\\begin{align*}& \\\\text{repeat until convergence:} \\\\; \\\\lbrace \\\\newline \\\\; & \\\\phantom {0000} b := b -  \\\\alpha \\\\frac{\\\\partial J(w,b)}{\\\\partial b} \\\\newline       \\\\; & \\\\phantom {0000} w := w -  \\\\alpha \\\\frac{\\\\partial J(w,b)}{\\\\partial w} \\\\tag{1}  \\\\; & \\n\",\n    \"\\\\newline & \\\\rbrace\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"where, parameters $w, b$ are both updated simultaniously and where  \\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(w,b)}{\\\\partial b}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)}) \\\\tag{2}\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(w,b)}{\\\\partial w}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) -y^{(i)})x^{(i)} \\\\tag{3}\\n\",\n    \"$$\\n\",\n    \"* m is the number of training examples in the dataset\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"*  $f_{w,b}(x^{(i)})$ is the model's prediction, while $y^{(i)}$, is the target value\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"You will implement a function called `compute_gradient` which calculates $\\\\frac{\\\\partial J(w)}{\\\\partial w}$, $\\\\frac{\\\\partial J(w)}{\\\\partial b}$ \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Please complete the `compute_gradient` function to:\\n\",\n    \"\\n\",\n    \"* Iterate over the training examples, and for each example, compute:\\n\",\n    \"    * The prediction of the model for that example \\n\",\n    \"    $$\\n\",\n    \"    f_{wb}(x^{(i)}) =  wx^{(i)} + b \\n\",\n    \"    $$\\n\",\n    \"   \\n\",\n    \"    * The gradient for the parameters $w, b$ from that example \\n\",\n    \"        $$\\n\",\n    \"        \\\\frac{\\\\partial J(w,b)}{\\\\partial b}^{(i)}  =  (f_{w,b}(x^{(i)}) - y^{(i)}) \\n\",\n    \"        $$\\n\",\n    \"        $$\\n\",\n    \"        \\\\frac{\\\\partial J(w,b)}{\\\\partial w}^{(i)}  =  (f_{w,b}(x^{(i)}) -y^{(i)})x^{(i)} \\n\",\n    \"        $$\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"* Return the total gradient update from all the examples\\n\",\n    \"    $$\\n\",\n    \"    \\\\frac{\\\\partial J(w,b)}{\\\\partial b}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} \\\\frac{\\\\partial J(w,b)}{\\\\partial b}^{(i)}\\n\",\n    \"    $$\\n\",\n    \"    \\n\",\n    \"    $$\\n\",\n    \"    \\\\frac{\\\\partial J(w,b)}{\\\\partial w}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} \\\\frac{\\\\partial J(w,b)}{\\\\partial w}^{(i)} \\n\",\n    \"    $$\\n\",\n    \"  * Here, $m$ is the number of training examples and $\\\\sum$ is the summation operator\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: compute_gradient\\n\",\n    \"def compute_gradient(x, y, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \"    Args:\\n\",\n    \"      x (ndarray): Shape (m,) Input to the model (Population of cities) \\n\",\n    \"      y (ndarray): Shape (m,) Label (Actual profits for the cities)\\n\",\n    \"      w, b (scalar): Parameters of the model  \\n\",\n    \"    Returns\\n\",\n    \"      dj_dw (scalar): The gradient of the cost w.r.t. the parameters w\\n\",\n    \"      dj_db (scalar): The gradient of the cost w.r.t. the parameter b     \\n\",\n    \"     \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Number of training examples\\n\",\n    \"    m = x.shape[0]\\n\",\n    \"    \\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    dj_dw = 0\\n\",\n    \"    dj_db = 0\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb = w*x[i]+b\\n\",\n    \"        dj_db += f_wb - y[i]\\n\",\n    \"        dj_dw += (f_wb - y[i])*x[i]\\n\",\n    \"    dj_dw /= m\\n\",\n    \"    dj_db /= m\\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"        \\n\",\n    \"    return dj_dw, dj_db\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"       \\n\",\n    \"    * You can represent a summation operator eg: $h = \\\\sum\\\\limits_{i = 0}^{m-1} 2i$ in code as follows:\\n\",\n    \"     ```python \\n\",\n    \"    h = 0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        h = h + 2*i\\n\",\n    \"    ```\\n\",\n    \"    \\n\",\n    \"    * In this case, you can iterate over all the examples in `x` using a for loop and for each example, keep adding the gradient from that example to the variables `dj_dw` and `dj_db` which are initialized outside the loop. \\n\",\n    \"\\n\",\n    \"   * Then, you can return `dj_dw` and `dj_db` both divided by `m`.    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_gradient(x, y, w, b): \\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        Computes the gradient for linear regression \\n\",\n    \"        Args:\\n\",\n    \"          x (ndarray): Shape (m,) Input to the model (Population of cities) \\n\",\n    \"          y (ndarray): Shape (m,) Label (Actual profits for the cities)\\n\",\n    \"          w, b (scalar): Parameters of the model  \\n\",\n    \"        Returns\\n\",\n    \"          dj_dw (scalar): The gradient of the cost w.r.t. the parameters w\\n\",\n    \"          dj_db (scalar): The gradient of the cost w.r.t. the parameter b     \\n\",\n    \"         \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"        # Number of training examples\\n\",\n    \"        m = x.shape[0]\\n\",\n    \"    \\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        dj_dw = 0\\n\",\n    \"        dj_db = 0\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        # Loop over examples\\n\",\n    \"        for i in range(m):  \\n\",\n    \"            # Your code here to get prediction f_wb for the ith example\\n\",\n    \"            f_wb = \\n\",\n    \"            \\n\",\n    \"            # Your code here to get the gradient for w from the ith example \\n\",\n    \"            dj_dw_i = \\n\",\n    \"        \\n\",\n    \"            # Your code here to get the gradient for b from the ith example \\n\",\n    \"            dj_db_i = \\n\",\n    \"     \\n\",\n    \"            # Update dj_db : In Python, a += 1  is the same as a = a + 1\\n\",\n    \"            dj_db += dj_db_i\\n\",\n    \"        \\n\",\n    \"            # Update dj_dw\\n\",\n    \"            dj_dw += dj_dw_i\\n\",\n    \"    \\n\",\n    \"        # Divide both dj_dw and dj_db by m\\n\",\n    \"        dj_dw = dj_dw / m\\n\",\n    \"        dj_db = dj_db / m\\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"        \\n\",\n    \"        return dj_dw, dj_db\\n\",\n    \"    ```\\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `f_wb` and `cost`.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You did this in the previous exercise! For scalars $a$, $b$ and $c$ (<code>x[i]</code>, <code>w</code> and <code>b</code> are all scalars), you can calculate the equation $h = ab + c$ in code as <code>h = a * b + c</code>\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute f_wb as <code>f_wb = w * x[i] + b </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_dw_i</b></font></summary>\\n\",\n    \"           &emsp; &emsp; For scalars $a$, $b$ and $c$ (<code>f_wb</code>, <code>y[i]</code> and <code>x[i]</code> are all scalars), you can calculate the equation $h = (a - b)c$ in code as <code>h = (a-b)*c</code>\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute dj_dw_i as <code>dj_dw_i = (f_wb - y[i]) * x[i] </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_db_i</b></font></summary>\\n\",\n    \"             &emsp; &emsp; You can compute dj_db_i as <code> dj_db_i = f_wb - y[i] </code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cells below to check your implementation of the `compute_gradient` function with two different initializations of the parameters $w$,$b$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Gradient at initial w, b (zeros): -65.32884974555672 -5.83913505154639\\n\",\n      \"Using X with shape (4, 1)\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute and display gradient with w initialized to zeroes\\n\",\n    \"initial_w = 0\\n\",\n    \"initial_b = 0\\n\",\n    \"\\n\",\n    \"tmp_dj_dw, tmp_dj_db = compute_gradient(x_train, y_train, initial_w, initial_b)\\n\",\n    \"print('Gradient at initial w, b (zeros):', tmp_dj_dw, tmp_dj_db)\\n\",\n    \"\\n\",\n    \"compute_gradient_test(compute_gradient)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's run the gradient descent algorithm implemented above on our dataset.\\n\",\n    \"\\n\",\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Gradient at initial , b (zeros)<b></td>\\n\",\n    \"    <td> -65.32884975 -5.83913505154639</td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Gradient at test w, b: -47.41610118114435 -4.007175051546391\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute and display cost and gradient with non-zero w\\n\",\n    \"test_w = 0.2\\n\",\n    \"test_b = 0.2\\n\",\n    \"tmp_dj_dw, tmp_dj_db = compute_gradient(x_train, y_train, test_w, test_b)\\n\",\n    \"\\n\",\n    \"print('Gradient at test w, b:', tmp_dj_dw, tmp_dj_db)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Gradient at test w<b></td>\\n\",\n    \"    <td> -47.41610118 -4.007175051546391</td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.6\\\"></a>\\n\",\n    \"### 2.6 Learning parameters using batch gradient descent \\n\",\n    \"\\n\",\n    \"You will now find the optimal parameters of a linear regression model by using batch gradient descent. Recall batch refers to running all the examples in one iteration.\\n\",\n    \"- You don't need to implement anything for this part. Simply run the cells below. \\n\",\n    \"\\n\",\n    \"- A good way to verify that gradient descent is working correctly is to look\\n\",\n    \"at the value of $J(w,b)$ and check that it is decreasing with each step. \\n\",\n    \"\\n\",\n    \"- Assuming you have implemented the gradient and computed the cost correctly and you have an appropriate value for the learning rate alpha, $J(w,b)$ should never increase and should converge to a steady value by the end of the algorithm.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def gradient_descent(x, y, w_in, b_in, cost_function, gradient_function, alpha, num_iters): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Performs batch gradient descent to learn theta. Updates theta by taking \\n\",\n    \"    num_iters gradient steps with learning rate alpha\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      x :    (ndarray): Shape (m,)\\n\",\n    \"      y :    (ndarray): Shape (m,)\\n\",\n    \"      w_in, b_in : (scalar) Initial values of parameters of the model\\n\",\n    \"      cost_function: function to compute cost\\n\",\n    \"      gradient_function: function to compute the gradient\\n\",\n    \"      alpha : (float) Learning rate\\n\",\n    \"      num_iters : (int) number of iterations to run gradient descent\\n\",\n    \"    Returns\\n\",\n    \"      w : (ndarray): Shape (1,) Updated values of parameters of the model after\\n\",\n    \"          running gradient descent\\n\",\n    \"      b : (scalar)                Updated value of parameter of the model after\\n\",\n    \"          running gradient descent\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # number of training examples\\n\",\n    \"    m = len(x)\\n\",\n    \"    \\n\",\n    \"    # An array to store cost J and w's at each iteration — primarily for graphing later\\n\",\n    \"    J_history = []\\n\",\n    \"    w_history = []\\n\",\n    \"    w = copy.deepcopy(w_in)  #avoid modifying global w within function\\n\",\n    \"    b = b_in\\n\",\n    \"    \\n\",\n    \"    for i in range(num_iters):\\n\",\n    \"\\n\",\n    \"        # Calculate the gradient and update the parameters\\n\",\n    \"        dj_dw, dj_db = gradient_function(x, y, w, b )  \\n\",\n    \"\\n\",\n    \"        # Update Parameters using w, b, alpha and gradient\\n\",\n    \"        w = w - alpha * dj_dw               \\n\",\n    \"        b = b - alpha * dj_db               \\n\",\n    \"\\n\",\n    \"        # Save cost J at each iteration\\n\",\n    \"        if i<100000:      # prevent resource exhaustion \\n\",\n    \"            cost =  cost_function(x, y, w, b)\\n\",\n    \"            J_history.append(cost)\\n\",\n    \"\\n\",\n    \"        # Print cost every at intervals 10 times or as many iterations if < 10\\n\",\n    \"        if i% math.ceil(num_iters/10) == 0:\\n\",\n    \"            w_history.append(w)\\n\",\n    \"            print(f\\\"Iteration {i:4}: Cost {float(J_history[-1]):8.2f}   \\\")\\n\",\n    \"        \\n\",\n    \"    return w, b, J_history, w_history #return w and J,w history for graphing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's run the gradient descent algorithm above to learn the parameters for our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration    0: Cost     6.74   \\n\",\n      \"Iteration  150: Cost     5.31   \\n\",\n      \"Iteration  300: Cost     4.96   \\n\",\n      \"Iteration  450: Cost     4.76   \\n\",\n      \"Iteration  600: Cost     4.64   \\n\",\n      \"Iteration  750: Cost     4.57   \\n\",\n      \"Iteration  900: Cost     4.53   \\n\",\n      \"Iteration 1050: Cost     4.51   \\n\",\n      \"Iteration 1200: Cost     4.50   \\n\",\n      \"Iteration 1350: Cost     4.49   \\n\",\n      \"w,b found by gradient descent: 1.166362350335582 -3.63029143940436\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# initialize fitting parameters. Recall that the shape of w is (n,)\\n\",\n    \"initial_w = 0.\\n\",\n    \"initial_b = 0.\\n\",\n    \"\\n\",\n    \"# some gradient descent settings\\n\",\n    \"iterations = 1500\\n\",\n    \"alpha = 0.01\\n\",\n    \"\\n\",\n    \"w,b,_,_ = gradient_descent(x_train ,y_train, initial_w, initial_b, \\n\",\n    \"                     compute_cost, compute_gradient, alpha, iterations)\\n\",\n    \"print(\\\"w,b found by gradient descent:\\\", w, b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b> w, b found by gradient descent<b></td>\\n\",\n    \"    <td> 1.16636235 -3.63029143940436</td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will now use the final parameters from gradient descent to plot the linear fit. \\n\",\n    \"\\n\",\n    \"Recall that we can get the prediction for a single example $f(x^{(i)})= wx^{(i)}+b$. \\n\",\n    \"\\n\",\n    \"To calculate the predictions on the entire dataset, we can loop through all the training examples and calculate the prediction for each example. This is shown in the code block below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"m = x_train.shape[0]\\n\",\n    \"predicted = np.zeros(m)\\n\",\n    \"\\n\",\n    \"for i in range(m):\\n\",\n    \"    predicted[i] = w * x_train[i] + b\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will now plot the predicted values to see the linear fit.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 0, 'Population of City in 10,000s')\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot the linear fit\\n\",\n    \"plt.plot(x_train, predicted, c = \\\"b\\\")\\n\",\n    \"\\n\",\n    \"# Create a scatter plot of the data. \\n\",\n    \"plt.scatter(x_train, y_train, marker='x', c='r') \\n\",\n    \"\\n\",\n    \"# Set the title\\n\",\n    \"plt.title(\\\"Profits vs. Population per city\\\")\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Profit in $10,000')\\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Population of City in 10,000s')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Your final values of $w,b$ can also be used to make predictions on profits. Let's predict what the profit would be in areas of 35,000 and 70,000 people. \\n\",\n    \"\\n\",\n    \"- The model takes in population of a city in 10,000s as input. \\n\",\n    \"\\n\",\n    \"- Therefore, 35,000 people can be translated into an input to the model as `np.array([3.5])`\\n\",\n    \"\\n\",\n    \"- Similarly, 70,000 people can be translated into an input to the model as `np.array([7.])`\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"For population = 35,000, we predict a profit of $4519.77\\n\",\n      \"For population = 70,000, we predict a profit of $45342.45\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"predict1 = 3.5 * w + b\\n\",\n    \"print('For population = 35,000, we predict a profit of $%.2f' % (predict1*10000))\\n\",\n    \"\\n\",\n    \"predict2 = 7.0 * w + b\\n\",\n    \"print('For population = 70,000, we predict a profit of $%.2f' % (predict2*10000))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b> For population = 35,000, we predict a profit of<b></td>\\n\",\n    \"    <td> $4519.77 </td> \\n\",\n    \"  </tr>\\n\",\n    \"  \\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b> For population = 70,000, we predict a profit of<b></td>\\n\",\n    \"    <td> $45342.45 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/C1W2A1/data/ex1data1.txt",
    "content": "6.1101,17.592\n5.5277,9.1302\n8.5186,13.662\n7.0032,11.854\n5.8598,6.8233\n8.3829,11.886\n7.4764,4.3483\n8.5781,12\n6.4862,6.5987\n5.0546,3.8166\n5.7107,3.2522\n14.164,15.505\n5.734,3.1551\n8.4084,7.2258\n5.6407,0.71618\n5.3794,3.5129\n6.3654,5.3048\n5.1301,0.56077\n6.4296,3.6518\n7.0708,5.3893\n6.1891,3.1386\n20.27,21.767\n5.4901,4.263\n6.3261,5.1875\n5.5649,3.0825\n18.945,22.638\n12.828,13.501\n10.957,7.0467\n13.176,14.692\n22.203,24.147\n5.2524,-1.22\n6.5894,5.9966\n9.2482,12.134\n5.8918,1.8495\n8.2111,6.5426\n7.9334,4.5623\n8.0959,4.1164\n5.6063,3.3928\n12.836,10.117\n6.3534,5.4974\n5.4069,0.55657\n6.8825,3.9115\n11.708,5.3854\n5.7737,2.4406\n7.8247,6.7318\n7.0931,1.0463\n5.0702,5.1337\n5.8014,1.844\n11.7,8.0043\n5.5416,1.0179\n7.5402,6.7504\n5.3077,1.8396\n7.4239,4.2885\n7.6031,4.9981\n6.3328,1.4233\n6.3589,-1.4211\n6.2742,2.4756\n5.6397,4.6042\n9.3102,3.9624\n9.4536,5.4141\n8.8254,5.1694\n5.1793,-0.74279\n21.279,17.929\n14.908,12.054\n18.959,17.054\n7.2182,4.8852\n8.2951,5.7442\n10.236,7.7754\n5.4994,1.0173\n20.341,20.992\n10.136,6.6799\n7.3345,4.0259\n6.0062,1.2784\n7.2259,3.3411\n5.0269,-2.6807\n6.5479,0.29678\n7.5386,3.8845\n5.0365,5.7014\n10.274,6.7526\n5.1077,2.0576\n5.7292,0.47953\n5.1884,0.20421\n6.3557,0.67861\n9.7687,7.5435\n6.5159,5.3436\n8.5172,4.2415\n9.1802,6.7981\n6.002,0.92695\n5.5204,0.152\n5.0594,2.8214\n5.7077,1.8451\n7.6366,4.2959\n5.8707,7.2029\n5.3054,1.9869\n8.2934,0.14454\n13.394,9.0551\n5.4369,0.61705\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/C1W2A1/data/ex1data2.txt",
    "content": "2104,3,399900\n1600,3,329900\n2400,3,369000\n1416,2,232000\n3000,4,539900\n1985,4,299900\n1534,3,314900\n1427,3,198999\n1380,3,212000\n1494,3,242500\n1940,4,239999\n2000,3,347000\n1890,3,329999\n4478,5,699900\n1268,3,259900\n2300,4,449900\n1320,2,299900\n1236,3,199900\n2609,4,499998\n3031,4,599000\n1767,3,252900\n1888,2,255000\n1604,3,242900\n1962,4,259900\n3890,3,573900\n1100,3,249900\n1458,3,464500\n2526,3,469000\n2200,3,475000\n2637,3,299900\n1839,2,349900\n1000,1,169900\n2040,4,314900\n3137,3,579900\n1811,4,285900\n1437,3,249900\n1239,3,229900\n2132,4,345000\n4215,4,549000\n2162,4,287000\n1664,2,368500\n2238,3,329900\n2567,4,314000\n1200,3,299000\n852,2,179900\n1852,4,299900\n1203,3,239500\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/C1W2A1/public_tests.py",
    "content": "import numpy as np\n\ndef compute_cost_test(target):\n    # print(\"Using X with shape (4, 1)\")\n    # Case 1\n    x = np.array([2, 4, 6, 8]).T\n    y = np.array([7, 11, 15, 19]).T\n    initial_w = 2\n    initial_b = 3.0\n    cost = target(x, y, initial_w, initial_b)\n    assert cost == 0, f\"Case 1: Cost must be 0 for a perfect prediction but got {cost}\"\n    \n    # Case 2\n    x = np.array([2, 4, 6, 8]).T\n    y = np.array([7, 11, 15, 19]).T\n    initial_w = 2.0\n    initial_b = 1.0\n    cost = target(x, y, initial_w, initial_b)\n    assert cost == 2, f\"Case 2: Cost must be 2 but got {cost}\"\n    \n    # print(\"Using X with shape (5, 1)\")\n    # Case 3\n    x = np.array([1.5, 2.5, 3.5, 4.5, 1.5]).T\n    y = np.array([4, 7, 10, 13, 5]).T\n    initial_w = 1\n    initial_b = 0.0\n    cost = target(x, y, initial_w, initial_b)\n    assert np.isclose(cost, 15.325), f\"Case 3: Cost must be 15.325 for a perfect prediction but got {cost}\"\n    \n    # Case 4\n    initial_b = 1.0\n    cost = target(x, y, initial_w, initial_b)\n    assert np.isclose(cost, 10.725), f\"Case 4: Cost must be 10.725 but got {cost}\"\n    \n    # Case 5\n    y = y - 2\n    initial_b = 1.0\n    cost = target(x, y, initial_w, initial_b)\n    assert  np.isclose(cost, 4.525), f\"Case 5: Cost must be 4.525 but got {cost}\"\n    \n    print(\"\\033[92mAll tests passed!\")\n    \ndef compute_gradient_test(target):\n    print(\"Using X with shape (4, 1)\")\n    # Case 1\n    x = np.array([2, 4, 6, 8]).T\n    y = np.array([4.5, 8.5, 12.5, 16.5]).T\n    initial_w = 2.\n    initial_b = 0.5\n    dj_dw, dj_db = target(x, y, initial_w, initial_b)\n    #assert dj_dw.shape == initial_w.shape, f\"Wrong shape for dj_dw. {dj_dw} != {initial_w.shape}\"\n    assert dj_db == 0.0, f\"Case 1: dj_db is wrong: {dj_db} != 0.0\"\n    assert np.allclose(dj_dw, 0), f\"Case 1: dj_dw is wrong: {dj_dw} != [[0.0]]\"\n    \n    # Case 2 \n    x = np.array([2, 4, 6, 8]).T\n    y = np.array([4, 7, 10, 13]).T + 2\n    initial_w = 1.5\n    initial_b = 1\n    dj_dw, dj_db = target(x, y, initial_w, initial_b)\n    #assert dj_dw.shape == initial_w.shape, f\"Wrong shape for dj_dw. {dj_dw} != {initial_w.shape}\"\n    assert dj_db == -2, f\"Case 1: dj_db is wrong: {dj_db} != -2\"\n    assert np.allclose(dj_dw, -10.0), f\"Case 1: dj_dw is wrong: {dj_dw} != -10.0\"   \n    \n    print(\"\\033[92mAll tests passed!\")\n    \n\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/C1W2A1/utils.py",
    "content": "import numpy as np\n\ndef load_data():\n    data = np.loadtxt(\"data/ex1data1.txt\", delimiter=',')\n    X = data[:,0]\n    y = data[:,1]\n    return X, y\n\ndef load_data_multi():\n    data = np.loadtxt(\"data/ex1data2.txt\", delimiter=',')\n    X = data[:,:2]\n    y = data[:,2]\n    return X, y\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Python, NumPy and Vectorization\\n\",\n    \"A brief introduction to some of the scientific computing used in this course. In particular the NumPy scientific computing package and its use with python.\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [&nbsp;&nbsp;1.1 Goals](#toc_40015_1.1)\\n\",\n    \"- [&nbsp;&nbsp;1.2 Useful References](#toc_40015_1.2)\\n\",\n    \"- [2 Python and NumPy <a name='Python and NumPy'></a>](#toc_40015_2)\\n\",\n    \"- [3 Vectors](#toc_40015_3)\\n\",\n    \"- [&nbsp;&nbsp;3.1 Abstract](#toc_40015_3.1)\\n\",\n    \"- [&nbsp;&nbsp;3.2 NumPy Arrays](#toc_40015_3.2)\\n\",\n    \"- [&nbsp;&nbsp;3.3 Vector Creation](#toc_40015_3.3)\\n\",\n    \"- [&nbsp;&nbsp;3.4 Operations on Vectors](#toc_40015_3.4)\\n\",\n    \"- [4 Matrices](#toc_40015_4)\\n\",\n    \"- [&nbsp;&nbsp;4.1 Abstract](#toc_40015_4.1)\\n\",\n    \"- [&nbsp;&nbsp;4.2 NumPy Arrays](#toc_40015_4.2)\\n\",\n    \"- [&nbsp;&nbsp;4.3 Matrix Creation](#toc_40015_4.3)\\n\",\n    \"- [&nbsp;&nbsp;4.4 Operations on Matrices](#toc_40015_4.4)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np    # it is an unofficial standard to use np for numpy\\n\",\n    \"import time\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_1.1\\\"></a>\\n\",\n    \"## 1.1 Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- Review the features of NumPy and Python that are used in Course 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_1.2\\\"></a>\\n\",\n    \"## 1.2 Useful References\\n\",\n    \"- NumPy Documentation including a basic introduction: [NumPy.org](https://NumPy.org/doc/stable/)\\n\",\n    \"- A challenging feature topic: [NumPy Broadcasting](https://NumPy.org/doc/stable/user/basics.broadcasting.html)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_2\\\"></a>\\n\",\n    \"# 2 Python and NumPy <a name='Python and NumPy'></a>\\n\",\n    \"Python is the programming language we will be using in this course. It has a set of numeric data types and arithmetic operations. NumPy is a library that extends the base capabilities of python to add a richer data set including more numeric types, vectors, matrices, and many matrix functions. NumPy and python  work together fairly seamlessly. Python arithmetic operators work on NumPy data types and many NumPy functions will accept python data types.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3\\\"></a>\\n\",\n    \"# 3 Vectors\\n\",\n    \"<a name=\\\"toc_40015_3.1\\\"></a>\\n\",\n    \"## 3.1 Abstract\\n\",\n    \"<img align=\\\"right\\\" src=\\\"./images/C1_W2_Lab04_Vectors.PNG\\\" style=\\\"width:340px;\\\" >Vectors, as you will use them in this course, are ordered arrays of numbers. In notation, vectors are denoted with lower case bold letters such as $\\\\mathbf{x}$.  The elements of a vector are all the same type. A vector does not, for example, contain both characters and numbers. The number of elements in the array is often referred to as the *dimension* though mathematicians may prefer *rank*. The vector shown has a dimension of $n$. The elements of a vector can be referenced with an index. In math settings, indexes typically run from 1 to n. In computer science and these labs, indexing will typically run from 0 to n-1.  In notation, elements of a vector, when referenced individually will indicate the index in a subscript, for example, the $0^{th}$ element, of the vector $\\\\mathbf{x}$ is $x_0$. Note, the x is not bold in this case.  \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3.2\\\"></a>\\n\",\n    \"## 3.2 NumPy Arrays\\n\",\n    \"\\n\",\n    \"NumPy's basic data structure is an indexable, n-dimensional *array* containing elements of the same type (`dtype`). Right away, you may notice we have overloaded the term 'dimension'. Above, it was the number of elements in the vector, here, dimension refers to the number of indexes of an array. A one-dimensional or 1-D array has one index. In Course 1, we will represent vectors as NumPy 1-D arrays. \\n\",\n    \"\\n\",\n    \" - 1-D array, shape (n,): n elements indexed [0] through [n-1]\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3.3\\\"></a>\\n\",\n    \"## 3.3 Vector Creation\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Data creation routines in NumPy will generally have a first parameter which is the shape of the object. This can either be a single value for a 1-D result or a tuple (n,m,...) specifying the shape of the result. Below are examples of creating vectors using these routines.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"np.zeros(4) :   a = [0. 0. 0. 0.], a shape = (4,), a data type = float64\\n\",\n      \"np.zeros(4,) :  a = [0. 0. 0. 0.], a shape = (4,), a data type = float64\\n\",\n      \"np.random.random_sample(4): a = [0.16054681 0.65264985 0.98302218 0.29864795], a shape = (4,), a data type = float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# NumPy routines which allocate memory and fill arrays with value\\n\",\n    \"a = np.zeros(4);                print(f\\\"np.zeros(4) :   a = {a}, a shape = {a.shape}, a data type = {a.dtype}\\\")\\n\",\n    \"a = np.zeros((4,));             print(f\\\"np.zeros(4,) :  a = {a}, a shape = {a.shape}, a data type = {a.dtype}\\\")\\n\",\n    \"a = np.random.random_sample(4); print(f\\\"np.random.random_sample(4): a = {a}, a shape = {a.shape}, a data type = {a.dtype}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Some data creation routines do not take a shape tuple:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"np.arange(4.):     a = [0. 1. 2. 3.], a shape = (4,), a data type = float64\\n\",\n      \"np.random.rand(4): a = [0.04467047 0.14854614 0.24949402 0.02417095], a shape = (4,), a data type = float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# NumPy routines which allocate memory and fill arrays with value but do not accept shape as input argument\\n\",\n    \"a = np.arange(4.);              print(f\\\"np.arange(4.):     a = {a}, a shape = {a.shape}, a data type = {a.dtype}\\\")\\n\",\n    \"a = np.random.rand(4);          print(f\\\"np.random.rand(4): a = {a}, a shape = {a.shape}, a data type = {a.dtype}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"values can be specified manually as well. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"np.array([5,4,3,2]):  a = [5 4 3 2],     a shape = (4,), a data type = int64\\n\",\n      \"np.array([5.,4,3,2]): a = [5. 4. 3. 2.], a shape = (4,), a data type = float64\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# NumPy routines which allocate memory and fill with user specified values\\n\",\n    \"a = np.array([5,4,3,2]);  print(f\\\"np.array([5,4,3,2]):  a = {a},     a shape = {a.shape}, a data type = {a.dtype}\\\")\\n\",\n    \"a = np.array([5.,4,3,2]); print(f\\\"np.array([5.,4,3,2]): a = {a}, a shape = {a.shape}, a data type = {a.dtype}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"These have all created a one-dimensional vector  `a` with four elements. `a.shape` returns the dimensions. Here we see a.shape = `(4,)` indicating a 1-d array with 4 elements.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3.4\\\"></a>\\n\",\n    \"## 3.4 Operations on Vectors\\n\",\n    \"Let's explore some operations using vectors.\\n\",\n    \"<a name=\\\"toc_40015_3.4.1\\\"></a>\\n\",\n    \"### 3.4.1 Indexing\\n\",\n    \"Elements of vectors can be accessed via indexing and slicing. NumPy provides a very complete set of indexing and slicing capabilities. We will explore only the basics needed for the course here. Reference [Slicing and Indexing](https://NumPy.org/doc/stable/reference/arrays.indexing.html) for more details.  \\n\",\n    \"**Indexing** means referring to *an element* of an array by its position within the array.  \\n\",\n    \"**Slicing** means getting a *subset* of elements from an array based on their indices.  \\n\",\n    \"NumPy starts indexing at zero so the 3rd element of an vector $\\\\mathbf{a}$ is `a[2]`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0 1 2 3 4 5 6 7 8 9]\\n\",\n      \"a[2].shape: () a[2]  = 2, Accessing an element returns a scalar\\n\",\n      \"a[-1] = 9\\n\",\n      \"The error message you'll see is:\\n\",\n      \"index 10 is out of bounds for axis 0 with size 10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#vector indexing operations on 1-D vectors\\n\",\n    \"a = np.arange(10)\\n\",\n    \"print(a)\\n\",\n    \"\\n\",\n    \"#access an element\\n\",\n    \"print(f\\\"a[2].shape: {a[2].shape} a[2]  = {a[2]}, Accessing an element returns a scalar\\\")\\n\",\n    \"\\n\",\n    \"# access the last element, negative indexes count from the end\\n\",\n    \"print(f\\\"a[-1] = {a[-1]}\\\")\\n\",\n    \"\\n\",\n    \"#indexs must be within the range of the vector or they will produce and error\\n\",\n    \"try:\\n\",\n    \"    c = a[10]\\n\",\n    \"except Exception as e:\\n\",\n    \"    print(\\\"The error message you'll see is:\\\")\\n\",\n    \"    print(e)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3.4.2\\\"></a>\\n\",\n    \"### 3.4.2 Slicing\\n\",\n    \"Slicing creates an array of indices using a set of three values (`start:stop:step`). A subset of values is also valid. Its use is best explained by example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a         = [0 1 2 3 4 5 6 7 8 9]\\n\",\n      \"a[2:7:1] =  [2 3 4 5 6]\\n\",\n      \"a[2:7:2] =  [2 4 6]\\n\",\n      \"a[3:]    =  [3 4 5 6 7 8 9]\\n\",\n      \"a[:3]    =  [0 1 2]\\n\",\n      \"a[:]     =  [0 1 2 3 4 5 6 7 8 9]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#vector slicing operations\\n\",\n    \"a = np.arange(10)\\n\",\n    \"print(f\\\"a         = {a}\\\")\\n\",\n    \"\\n\",\n    \"#access 5 consecutive elements (start:stop:step)\\n\",\n    \"c = a[2:7:1];     print(\\\"a[2:7:1] = \\\", c)\\n\",\n    \"\\n\",\n    \"# access 3 elements separated by two \\n\",\n    \"c = a[2:7:2];     print(\\\"a[2:7:2] = \\\", c)\\n\",\n    \"\\n\",\n    \"# access all elements index 3 and above\\n\",\n    \"c = a[3:];        print(\\\"a[3:]    = \\\", c)\\n\",\n    \"\\n\",\n    \"# access all elements below index 3\\n\",\n    \"c = a[:3];        print(\\\"a[:3]    = \\\", c)\\n\",\n    \"\\n\",\n    \"# access all elements\\n\",\n    \"c = a[:];         print(\\\"a[:]     = \\\", c)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3.4.3\\\"></a>\\n\",\n    \"### 3.4.3 Single vector operations\\n\",\n    \"There are a number of useful operations that involve operations on a single vector.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a             : [1 2 3 4]\\n\",\n      \"b = -a        : [-1 -2 -3 -4]\\n\",\n      \"b = np.sum(a) : 10\\n\",\n      \"b = np.mean(a): 2.5\\n\",\n      \"b = a**2      : [ 1  4  9 16]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1,2,3,4])\\n\",\n    \"print(f\\\"a             : {a}\\\")\\n\",\n    \"# negate elements of a\\n\",\n    \"b = -a \\n\",\n    \"print(f\\\"b = -a        : {b}\\\")\\n\",\n    \"\\n\",\n    \"# sum all elements of a, returns a scalar\\n\",\n    \"b = np.sum(a) \\n\",\n    \"print(f\\\"b = np.sum(a) : {b}\\\")\\n\",\n    \"\\n\",\n    \"b = np.mean(a)\\n\",\n    \"print(f\\\"b = np.mean(a): {b}\\\")\\n\",\n    \"\\n\",\n    \"b = a**2\\n\",\n    \"print(f\\\"b = a**2      : {b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3.4.4\\\"></a>\\n\",\n    \"### 3.4.4 Vector Vector element-wise operations\\n\",\n    \"Most of the NumPy arithmetic, logical and comparison operations apply to vectors as well. These operators work on an element-by-element basis. For example \\n\",\n    \"$$ \\\\mathbf{a} + \\\\mathbf{b} = \\\\sum_{i=0}^{n-1} a_i + b_i $$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Binary operators work element wise: [0 0 6 8]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([ 1, 2, 3, 4])\\n\",\n    \"b = np.array([-1,-2, 3, 4])\\n\",\n    \"print(f\\\"Binary operators work element wise: {a + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Of course, for this to work correctly, the vectors must be of the same size:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The error message you'll see is:\\n\",\n      \"operands could not be broadcast together with shapes (4,) (2,) \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#try a mismatched vector operation\\n\",\n    \"c = np.array([1, 2])\\n\",\n    \"try:\\n\",\n    \"    d = a + c\\n\",\n    \"except Exception as e:\\n\",\n    \"    print(\\\"The error message you'll see is:\\\")\\n\",\n    \"    print(e)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3.4.5\\\"></a>\\n\",\n    \"### 3.4.5 Scalar Vector operations\\n\",\n    \"Vectors can be 'scaled' by scalar values. A scalar value is just a number. The scalar multiplies all the elements of the vector.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b = 5 * a : [ 5 10 15 20]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1, 2, 3, 4])\\n\",\n    \"\\n\",\n    \"# multiply a by a scalar\\n\",\n    \"b = 5 * a \\n\",\n    \"print(f\\\"b = 5 * a : {b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3.4.6\\\"></a>\\n\",\n    \"### 3.4.6 Vector Vector dot product\\n\",\n    \"The dot product is a mainstay of Linear Algebra and NumPy. This is an operation used extensively in this course and should be well understood. The dot product is shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<img src=\\\"./images/C1_W2_Lab04_dot_notrans.gif\\\" width=800> \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The dot product multiplies the values in two vectors element-wise and then sums the result.\\n\",\n    \"Vector dot product requires the dimensions of the two vectors to be the same. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's implement our own version of the dot product below:\\n\",\n    \"\\n\",\n    \"**Using a for loop**, implement a function which returns the dot product of two vectors. The function to return given inputs $a$ and $b$:\\n\",\n    \"$$ x = \\\\sum_{i=0}^{n-1} a_i b_i $$\\n\",\n    \"Assume both `a` and `b` are the same shape.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def my_dot(a, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"   Compute the dot product of two vectors\\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      a (ndarray (n,)):  input vector \\n\",\n    \"      b (ndarray (n,)):  input vector with same dimension as a\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"      x (scalar): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    x=0\\n\",\n    \"    for i in range(a.shape[0]):\\n\",\n    \"        x = x + a[i] * b[i]\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"my_dot(a, b) = 24\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# test 1-D\\n\",\n    \"a = np.array([1, 2, 3, 4])\\n\",\n    \"b = np.array([-1, 4, 3, 2])\\n\",\n    \"print(f\\\"my_dot(a, b) = {my_dot(a, b)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Note, the dot product is expected to return a scalar value. \\n\",\n    \"\\n\",\n    \"Let's try the same operations using `np.dot`.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"NumPy 1-D np.dot(a, b) = 24, np.dot(a, b).shape = () \\n\",\n      \"NumPy 1-D np.dot(b, a) = 24, np.dot(a, b).shape = () \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# test 1-D\\n\",\n    \"a = np.array([1, 2, 3, 4])\\n\",\n    \"b = np.array([-1, 4, 3, 2])\\n\",\n    \"c = np.dot(a, b)\\n\",\n    \"print(f\\\"NumPy 1-D np.dot(a, b) = {c}, np.dot(a, b).shape = {c.shape} \\\") \\n\",\n    \"c = np.dot(b, a)\\n\",\n    \"print(f\\\"NumPy 1-D np.dot(b, a) = {c}, np.dot(a, b).shape = {c.shape} \\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Above, you will note that the results for 1-D matched our implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_3.4.7\\\"></a>\\n\",\n    \"### 3.4.7 The Need for Speed: vector vs for loop\\n\",\n    \"We utilized the NumPy  library because it improves speed memory efficiency. Let's demonstrate:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"np.dot(a, b) =  2501072.5817\\n\",\n      \"Vectorized version duration: 29.9268 ms \\n\",\n      \"my_dot(a, b) =  2501072.5817\\n\",\n      \"loop version duration: 2137.4848 ms \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"a = np.random.rand(10000000)  # very large arrays\\n\",\n    \"b = np.random.rand(10000000)\\n\",\n    \"\\n\",\n    \"tic = time.time()  # capture start time\\n\",\n    \"c = np.dot(a, b)\\n\",\n    \"toc = time.time()  # capture end time\\n\",\n    \"\\n\",\n    \"print(f\\\"np.dot(a, b) =  {c:.4f}\\\")\\n\",\n    \"print(f\\\"Vectorized version duration: {1000*(toc-tic):.4f} ms \\\")\\n\",\n    \"\\n\",\n    \"tic = time.time()  # capture start time\\n\",\n    \"c = my_dot(a,b)\\n\",\n    \"toc = time.time()  # capture end time\\n\",\n    \"\\n\",\n    \"print(f\\\"my_dot(a, b) =  {c:.4f}\\\")\\n\",\n    \"print(f\\\"loop version duration: {1000*(toc-tic):.4f} ms \\\")\\n\",\n    \"\\n\",\n    \"del(a);del(b)  #remove these big arrays from memory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"So, vectorization provides a large speed up in this example. This is because NumPy makes better use of available data parallelism in the underlying hardware. GPU's and modern CPU's implement Single Instruction, Multiple Data (SIMD) pipelines allowing multiple operations to be issued in parallel. This is critical in Machine Learning where the data sets are often very large.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_12345_3.4.8\\\"></a>\\n\",\n    \"### 3.4.8 Vector Vector operations in Course 1\\n\",\n    \"Vector Vector operations will appear frequently in course 1. Here is why:\\n\",\n    \"- Going forward, our examples will be stored in an array, `X_train` of dimension (m,n). This will be explained more in context, but here it is important to note it is a 2 Dimensional array or matrix (see next section on matrices).\\n\",\n    \"- `w` will be a 1-dimensional vector of shape (n,).\\n\",\n    \"- we will perform operations by looping through the examples, extracting each example to work on individually by indexing X. For example:`X[i]`\\n\",\n    \"- `X[i]` returns a value of shape (n,), a 1-dimensional vector. Consequently, operations involving `X[i]` are often vector-vector.  \\n\",\n    \"\\n\",\n    \"That is a somewhat lengthy explanation, but aligning and understanding the shapes of your operands is important when performing vector operations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X[1] has shape (1,)\\n\",\n      \"w has shape (1,)\\n\",\n      \"c has shape ()\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# show common Course 1 example\\n\",\n    \"X = np.array([[1],[2],[3],[4]])\\n\",\n    \"w = np.array([2])\\n\",\n    \"c = np.dot(X[1], w)\\n\",\n    \"\\n\",\n    \"print(f\\\"X[1] has shape {X[1].shape}\\\")\\n\",\n    \"print(f\\\"w has shape {w.shape}\\\")\\n\",\n    \"print(f\\\"c has shape {c.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_4\\\"></a>\\n\",\n    \"# 4 Matrices\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_4.1\\\"></a>\\n\",\n    \"## 4.1 Abstract\\n\",\n    \"Matrices, are two dimensional arrays. The elements of a matrix are all of the same type. In notation, matrices are denoted with capitol, bold letter such as $\\\\mathbf{X}$. In this and other labs, `m` is often the number of rows and `n` the number of columns. The elements of a matrix can be referenced with a two dimensional index. In math settings, numbers in the index typically run from 1 to n. In computer science and these labs, indexing will run from 0 to n-1.  \\n\",\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C1_W2_Lab04_Matrices.PNG\\\"  alt='missing'  width=900><center/>\\n\",\n    \"    <figcaption> Generic Matrix Notation, 1st index is row, 2nd is column </figcaption>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_4.2\\\"></a>\\n\",\n    \"## 4.2 NumPy Arrays\\n\",\n    \"\\n\",\n    \"NumPy's basic data structure is an indexable, n-dimensional *array* containing elements of the same type (`dtype`). These were described earlier. Matrices have a two-dimensional (2-D) index [m,n].\\n\",\n    \"\\n\",\n    \"In Course 1, 2-D matrices are used to hold training data. Training data is $m$ examples by $n$ features creating an (m,n) array. Course 1 does not do operations directly on matrices but typically extracts an example as a vector and operates on that. Below you will review: \\n\",\n    \"- data creation\\n\",\n    \"- slicing and indexing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_4.3\\\"></a>\\n\",\n    \"## 4.3 Matrix Creation\\n\",\n    \"The same functions that created 1-D vectors will create 2-D or n-D arrays. Here are some examples\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Below, the shape tuple is provided to achieve a 2-D result. Notice how NumPy uses brackets to denote each dimension. Notice further than NumPy, when printing, will print one row per line.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a shape = (1, 5), a = [[0. 0. 0. 0. 0.]]\\n\",\n      \"a shape = (2, 1), a = [[0.]\\n\",\n      \" [0.]]\\n\",\n      \"a shape = (1, 1), a = [[0.44236513]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.zeros((1, 5))                                       \\n\",\n    \"print(f\\\"a shape = {a.shape}, a = {a}\\\")                     \\n\",\n    \"\\n\",\n    \"a = np.zeros((2, 1))                                                                   \\n\",\n    \"print(f\\\"a shape = {a.shape}, a = {a}\\\") \\n\",\n    \"\\n\",\n    \"a = np.random.random_sample((1, 1))  \\n\",\n    \"print(f\\\"a shape = {a.shape}, a = {a}\\\") \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"One can also manually specify data. Dimensions are specified with additional brackets matching the format in the printing above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" a shape = (3, 1), np.array: a = [[5]\\n\",\n      \" [4]\\n\",\n      \" [3]]\\n\",\n      \" a shape = (3, 1), np.array: a = [[5]\\n\",\n      \" [4]\\n\",\n      \" [3]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# NumPy routines which allocate memory and fill with user specified values\\n\",\n    \"a = np.array([[5], [4], [3]]);   print(f\\\" a shape = {a.shape}, np.array: a = {a}\\\")\\n\",\n    \"a = np.array([[5],   # One can also\\n\",\n    \"              [4],   # separate values\\n\",\n    \"              [3]]); #into separate rows\\n\",\n    \"print(f\\\" a shape = {a.shape}, np.array: a = {a}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_4.4\\\"></a>\\n\",\n    \"## 4.4 Operations on Matrices\\n\",\n    \"Let's explore some operations using matrices.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_4.4.1\\\"></a>\\n\",\n    \"### 4.4.1 Indexing\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Matrices include a second index. The two indexes describe [row, column]. Access can either return an element or a row/column. See below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a.shape: (3, 2), \\n\",\n      \"a= [[0 1]\\n\",\n      \" [2 3]\\n\",\n      \" [4 5]]\\n\",\n      \"\\n\",\n      \"a[2,0].shape:   (), a[2,0] = 4,     type(a[2,0]) = <class 'numpy.int64'> Accessing an element returns a scalar\\n\",\n      \"\\n\",\n      \"a[2].shape:   (2,), a[2]   = [4 5], type(a[2])   = <class 'numpy.ndarray'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#vector indexing operations on matrices\\n\",\n    \"a = np.arange(6).reshape(-1, 2)   #reshape is a convenient way to create matrices\\n\",\n    \"print(f\\\"a.shape: {a.shape}, \\\\na= {a}\\\")\\n\",\n    \"\\n\",\n    \"#access an element\\n\",\n    \"print(f\\\"\\\\na[2,0].shape:   {a[2, 0].shape}, a[2,0] = {a[2, 0]},     type(a[2,0]) = {type(a[2, 0])} Accessing an element returns a scalar\\\\n\\\")\\n\",\n    \"\\n\",\n    \"#access a row\\n\",\n    \"print(f\\\"a[2].shape:   {a[2].shape}, a[2]   = {a[2]}, type(a[2])   = {type(a[2])}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"It is worth drawing attention to the last example. Accessing a matrix by just specifying the row will return a *1-D vector*.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Reshape**  \\n\",\n    \"The previous example used [reshape](https://numpy.org/doc/stable/reference/generated/numpy.reshape.html) to shape the array.  \\n\",\n    \"`a = np.arange(6).reshape(-1, 2) `   \\n\",\n    \"This line of code first created a *1-D Vector* of six elements. It then reshaped that vector into a *2-D* array using the reshape command. This could have been written:  \\n\",\n    \"`a = np.arange(6).reshape(3, 2) `  \\n\",\n    \"To arrive at the same 3 row, 2 column array.\\n\",\n    \"The -1 argument tells the routine to compute the number of rows given the size of the array and the number of columns.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_4.4.2\\\"></a>\\n\",\n    \"### 4.4.2 Slicing\\n\",\n    \"Slicing creates an array of indices using a set of three values (`start:stop:step`). A subset of values is also valid. Its use is best explained by example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a = \\n\",\n      \"[[ 0  1  2  3  4  5  6  7  8  9]\\n\",\n      \" [10 11 12 13 14 15 16 17 18 19]]\\n\",\n      \"a[0, 2:7:1] =  [2 3 4 5 6] ,  a[0, 2:7:1].shape = (5,) a 1-D array\\n\",\n      \"a[:, 2:7:1] = \\n\",\n      \" [[ 2  3  4  5  6]\\n\",\n      \" [12 13 14 15 16]] ,  a[:, 2:7:1].shape = (2, 5) a 2-D array\\n\",\n      \"a[:,:] = \\n\",\n      \" [[ 0  1  2  3  4  5  6  7  8  9]\\n\",\n      \" [10 11 12 13 14 15 16 17 18 19]] ,  a[:,:].shape = (2, 10)\\n\",\n      \"a[1,:] =  [10 11 12 13 14 15 16 17 18 19] ,  a[1,:].shape = (10,) a 1-D array\\n\",\n      \"a[1]   =  [10 11 12 13 14 15 16 17 18 19] ,  a[1].shape   = (10,) a 1-D array\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#vector 2-D slicing operations\\n\",\n    \"a = np.arange(20).reshape(-1, 10)\\n\",\n    \"print(f\\\"a = \\\\n{a}\\\")\\n\",\n    \"\\n\",\n    \"#access 5 consecutive elements (start:stop:step)\\n\",\n    \"print(\\\"a[0, 2:7:1] = \\\", a[0, 2:7:1], \\\",  a[0, 2:7:1].shape =\\\", a[0, 2:7:1].shape, \\\"a 1-D array\\\")\\n\",\n    \"\\n\",\n    \"#access 5 consecutive elements (start:stop:step) in two rows\\n\",\n    \"print(\\\"a[:, 2:7:1] = \\\\n\\\", a[:, 2:7:1], \\\",  a[:, 2:7:1].shape =\\\", a[:, 2:7:1].shape, \\\"a 2-D array\\\")\\n\",\n    \"\\n\",\n    \"# access all elements\\n\",\n    \"print(\\\"a[:,:] = \\\\n\\\", a[:,:], \\\",  a[:,:].shape =\\\", a[:,:].shape)\\n\",\n    \"\\n\",\n    \"# access all elements in one row (very common usage)\\n\",\n    \"print(\\\"a[1,:] = \\\", a[1,:], \\\",  a[1,:].shape =\\\", a[1,:].shape, \\\"a 1-D array\\\")\\n\",\n    \"# same as\\n\",\n    \"print(\\\"a[1]   = \\\", a[1],   \\\",  a[1].shape   =\\\", a[1].shape, \\\"a 1-D array\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40015_5.0\\\"></a>\\n\",\n    \"## Congratulations!\\n\",\n    \"In this lab you mastered the features of Python and NumPy that are needed for Course 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"40015\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/C1_W2_Lab02_Multiple_Variable_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a8397a98\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Multiple Variable Linear Regression\\n\",\n    \"\\n\",\n    \"In this lab, you will extend the data structures and previously developed routines to support multiple features. Several routines are updated making the lab appear lengthy, but it makes minor adjustments to previous routines making it quick to review.\\n\",\n    \"# Outline\\n\",\n    \"- [&nbsp;&nbsp;1.1 Goals](#toc_15456_1.1)\\n\",\n    \"- [&nbsp;&nbsp;1.2 Tools](#toc_15456_1.2)\\n\",\n    \"- [&nbsp;&nbsp;1.3 Notation](#toc_15456_1.3)\\n\",\n    \"- [2 Problem Statement](#toc_15456_2)\\n\",\n    \"- [&nbsp;&nbsp;2.1 Matrix X containing our examples](#toc_15456_2.1)\\n\",\n    \"- [&nbsp;&nbsp;2.2 Parameter vector w, b](#toc_15456_2.2)\\n\",\n    \"- [3 Model Prediction With Multiple Variables](#toc_15456_3)\\n\",\n    \"- [&nbsp;&nbsp;3.1 Single Prediction element by element](#toc_15456_3.1)\\n\",\n    \"- [&nbsp;&nbsp;3.2 Single Prediction, vector](#toc_15456_3.2)\\n\",\n    \"- [4 Compute Cost With Multiple Variables](#toc_15456_4)\\n\",\n    \"- [5 Gradient Descent With Multiple Variables](#toc_15456_5)\\n\",\n    \"- [&nbsp;&nbsp;5.1 Compute Gradient with Multiple Variables](#toc_15456_5.1)\\n\",\n    \"- [&nbsp;&nbsp;5.2 Gradient Descent With Multiple Variables](#toc_15456_5.2)\\n\",\n    \"- [6 Congratulations](#toc_15456_6)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1ae3ed16\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_1.1\\\"></a>\\n\",\n    \"## 1.1 Goals\\n\",\n    \"- Extend our regression model  routines to support multiple features\\n\",\n    \"    - Extend data structures to support multiple features\\n\",\n    \"    - Rewrite prediction, cost and gradient routines to support multiple features\\n\",\n    \"    - Utilize NumPy `np.dot` to vectorize their implementations for speed and simplicity\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"15003c4e\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_1.2\\\"></a>\\n\",\n    \"## 1.2 Tools\\n\",\n    \"In this lab, we will make use of: \\n\",\n    \"- NumPy, a popular library for scientific computing\\n\",\n    \"- Matplotlib, a popular library for plotting data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"5c40e068\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import copy, math\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"np.set_printoptions(precision=2)  # reduced display precision on numpy arrays\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"50111368\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_1.3\\\"></a>\\n\",\n    \"## 1.3 Notation\\n\",\n    \"Here is a summary of some of the notation you will encounter, updated for multiple features.\\n\",\n    \"\\n\",\n    \"| General Notation | Description | Python (if applicable) |\\n\",\n    \"|:----------------:|:-----------:|:----------------------:|\\n\",\n    \"|      $a$         | scalar, non-bold | |\\n\",\n    \"|  $\\\\mathbf{a}$   | vector, bold | |\\n\",\n    \"|  $\\\\mathbf{A}$   | matrix, bold capital | |\\n\",\n    \"| **Regression** | | |\\n\",\n    \"|  $\\\\mathbf{X}$   | training example matrix | `X_train` |\\n\",\n    \"|  $\\\\mathbf{y}$   | training example targets | `y_train` |\\n\",\n    \"| $\\\\mathbf{x}^{(i)}$, $y^{(i)}$ | $i_{th}$ Training Example | `X[i]`, `y[i]` |\\n\",\n    \"|        $m$       | number of training examples | `m` |\\n\",\n    \"|        $n$       | number of features in each example | `n` |\\n\",\n    \"|  $\\\\mathbf{w}$   | parameter: weight | `w` |\\n\",\n    \"|        $b$       | parameter: bias | `b` |\\n\",\n    \"| $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)})$ | The result model evaluation at $\\\\mathbf{x}^{(i)}$ parameterized by $\\\\mathbf{w},b$: $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)}+b$ | `f_wb` |\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b5494961\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_2\\\"></a>\\n\",\n    \"# 2 Problem Statement\\n\",\n    \"\\n\",\n    \"You will use the motivating example of housing price prediction. The training dataset contains three examples with four features (size, bedrooms, floors and, age) shown in the table below.  Note that, unlike the earlier labs, size is in sqft rather than 1000 sqft. This causes an issue, which you will solve in the next lab!\\n\",\n    \"\\n\",\n    \"| Size (sqft) | Number of Bedrooms  | Number of floors | Age of  Home | Price (1000s dollars)  |   \\n\",\n    \"| ----------------| ------------------- |----------------- |--------------|-------------- |  \\n\",\n    \"| 2104            | 5                   | 1                | 45           | 460           |  \\n\",\n    \"| 1416            | 3                   | 2                | 40           | 232           |  \\n\",\n    \"| 852             | 2                   | 1                | 35           | 178           |  \\n\",\n    \"\\n\",\n    \"You will build a linear regression model using these values so you can then predict the price for other houses. For example, a house with 1200 sqft, 3 bedrooms, 1 floor, 40 years old.  \\n\",\n    \"\\n\",\n    \"Please run the following code cell to create your `X_train` and `y_train` variables.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"c2475e43\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[2104, 5, 1, 45], [1416, 3, 2, 40], [852, 2, 1, 35]])\\n\",\n    \"y_train = np.array([460, 232, 178])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"31ca6f7f\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_2.1\\\"></a>\\n\",\n    \"## 2.1 Matrix X containing our examples\\n\",\n    \"Similar to the table above, examples are stored in a NumPy matrix `X_train`. Each row of the matrix represents one example. When you have $m$ training examples ( $m$ is three in our example), and there are $n$ features (four in our example), $\\\\mathbf{X}$ is a matrix with dimensions ($m$, $n$) (m rows, n columns).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{X} = \\n\",\n    \"\\\\begin{pmatrix}\\n\",\n    \" x^{(0)}_0 & x^{(0)}_1 & \\\\cdots & x^{(0)}_{n-1} \\\\\\\\ \\n\",\n    \" x^{(1)}_0 & x^{(1)}_1 & \\\\cdots & x^{(1)}_{n-1} \\\\\\\\\\n\",\n    \" \\\\cdots \\\\\\\\\\n\",\n    \" x^{(m-1)}_0 & x^{(m-1)}_1 & \\\\cdots & x^{(m-1)}_{n-1} \\n\",\n    \"\\\\end{pmatrix}\\n\",\n    \"$$\\n\",\n    \"notation:\\n\",\n    \"- $\\\\mathbf{x}^{(i)}$ is vector containing example i. $\\\\mathbf{x}^{(i)}$ $ = (x^{(i)}_0, x^{(i)}_1, \\\\cdots,x^{(i)}_{n-1})$\\n\",\n    \"- $x^{(i)}_j$ is element j in example i. The superscript in parenthesis indicates the example number while the subscript represents an element.  \\n\",\n    \"\\n\",\n    \"Display the input data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"5d04e40c\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X Shape: (3, 4), X Type:<class 'numpy.ndarray'>)\\n\",\n      \"[[2104    5    1   45]\\n\",\n      \" [1416    3    2   40]\\n\",\n      \" [ 852    2    1   35]]\\n\",\n      \"y Shape: (3,), y Type:<class 'numpy.ndarray'>)\\n\",\n      \"[460 232 178]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# data is stored in numpy array/matrix\\n\",\n    \"print(f\\\"X Shape: {X_train.shape}, X Type:{type(X_train)})\\\")\\n\",\n    \"print(X_train)\\n\",\n    \"print(f\\\"y Shape: {y_train.shape}, y Type:{type(y_train)})\\\")\\n\",\n    \"print(y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"edf11b90\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_2.2\\\"></a>\\n\",\n    \"## 2.2 Parameter vector w, b\\n\",\n    \"\\n\",\n    \"* $\\\\mathbf{w}$ is a vector with $n$ elements.\\n\",\n    \"  - Each element contains the parameter associated with one feature.\\n\",\n    \"  - in our dataset, n is 4.\\n\",\n    \"  - notionally, we draw this as a column vector\\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{w} = \\\\begin{pmatrix}\\n\",\n    \"w_0 \\\\\\\\ \\n\",\n    \"w_1 \\\\\\\\\\n\",\n    \"\\\\cdots\\\\\\\\\\n\",\n    \"w_{n-1}\\n\",\n    \"\\\\end{pmatrix}\\n\",\n    \"$$\\n\",\n    \"* $b$ is a scalar parameter.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f9a2f0c4\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"For demonstration, $\\\\mathbf{w}$ and $b$ will be loaded with some initial selected values that are near the optimal. $\\\\mathbf{w}$ is a 1-D NumPy vector.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"6b7cefc9\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"w_init shape: (4,), b_init type: <class 'float'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"b_init = 785.1811367994083\\n\",\n    \"w_init = np.array([ 0.39133535, 18.75376741, -53.36032453, -26.42131618])\\n\",\n    \"print(f\\\"w_init shape: {w_init.shape}, b_init type: {type(b_init)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ae210bb9\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_3\\\"></a>\\n\",\n    \"# 3 Model Prediction With Multiple Variables\\n\",\n    \"The model's prediction with multiple variables is given by the linear model:\\n\",\n    \"\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}) =  w_0x_0 + w_1x_1 +... + w_{n-1}x_{n-1} + b \\\\tag{1}$$\\n\",\n    \"or in vector notation:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b  \\\\tag{2} $$ \\n\",\n    \"where $\\\\cdot$ is a vector `dot product`\\n\",\n    \"\\n\",\n    \"To demonstrate the dot product, we will implement prediction using (1) and (2).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2feb076d\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_3.1\\\"></a>\\n\",\n    \"## 3.1 Single Prediction element by element\\n\",\n    \"Our previous prediction multiplied one feature value by one parameter and added a bias parameter. A direct extension of our previous implementation of prediction to multiple features would be to implement (1) above using loop over each element, performing the multiply with its parameter and then adding the bias parameter at the end.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"27678037\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def predict_single_loop(x, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    single predict using linear regression\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      x (ndarray): Shape (n,) example with multiple features\\n\",\n    \"      w (ndarray): Shape (n,) model parameters    \\n\",\n    \"      b (scalar):  model parameter     \\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      p (scalar):  prediction\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    n = x.shape[0]\\n\",\n    \"    p = 0\\n\",\n    \"    for i in range(n):\\n\",\n    \"        p_i = x[i] * w[i]  \\n\",\n    \"        p = p + p_i         \\n\",\n    \"    p = p + b                \\n\",\n    \"    return p\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"816e2696\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x_vec shape (4,), x_vec value: [2104    5    1   45]\\n\",\n      \"f_wb shape (), prediction: 459.9999976194083\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# get a row from our training data\\n\",\n    \"x_vec = X_train[0,:]\\n\",\n    \"print(f\\\"x_vec shape {x_vec.shape}, x_vec value: {x_vec}\\\")\\n\",\n    \"\\n\",\n    \"# make a prediction\\n\",\n    \"f_wb = predict_single_loop(x_vec, w_init, b_init)\\n\",\n    \"print(f\\\"f_wb shape {f_wb.shape}, prediction: {f_wb}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"af8cc06e\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Note the shape of `x_vec`. It is a 1-D NumPy vector with 4 elements, (4,). The result, `f_wb` is a scalar.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"67793119\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_3.2\\\"></a>\\n\",\n    \"## 3.2 Single Prediction, vector\\n\",\n    \"\\n\",\n    \"Noting that equation (1) above can be implemented using the dot product as in (2) above. We can make use of vector operations to speed up predictions.\\n\",\n    \"\\n\",\n    \"Recall from the Python/Numpy lab that NumPy `np.dot()`[[link](https://numpy.org/doc/stable/reference/generated/numpy.dot.html)] can be used to perform a vector dot product. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"5d9f441e\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def predict(x, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    single predict using linear regression\\n\",\n    \"    Args:\\n\",\n    \"      x (ndarray): Shape (n,) example with multiple features\\n\",\n    \"      w (ndarray): Shape (n,) model parameters   \\n\",\n    \"      b (scalar):             model parameter \\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      p (scalar):  prediction\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    p = np.dot(x, w) + b     \\n\",\n    \"    return p    \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"3e1fe583\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"x_vec shape (4,), x_vec value: [2104    5    1   45]\\n\",\n      \"f_wb shape (), prediction: 459.99999761940825\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# get a row from our training data\\n\",\n    \"x_vec = X_train[0,:]\\n\",\n    \"print(f\\\"x_vec shape {x_vec.shape}, x_vec value: {x_vec}\\\")\\n\",\n    \"\\n\",\n    \"# make a prediction\\n\",\n    \"f_wb = predict(x_vec,w_init, b_init)\\n\",\n    \"print(f\\\"f_wb shape {f_wb.shape}, prediction: {f_wb}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"51e18ca2\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The results and shapes are the same as the previous version which used looping. Going forward, `np.dot` will be used for these operations. The prediction is now a single statement. Most routines will implement it directly rather than calling a separate predict routine.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"49051059\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_4\\\"></a>\\n\",\n    \"# 4 Compute Cost With Multiple Variables\\n\",\n    \"The equation for the cost function with multiple variables $J(\\\\mathbf{w},b)$ is:\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})^2 \\\\tag{3}$$ \\n\",\n    \"where:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b  \\\\tag{4} $$ \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"In contrast to previous labs, $\\\\mathbf{w}$ and $\\\\mathbf{x}^{(i)}$ are vectors rather than scalars supporting multiple features.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e5a93e78\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Below is an implementation of equations (3) and (4). Note that this uses a *standard pattern for this course* where a for loop over all `m` examples is used.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"12976173\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost(X, y, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    compute cost\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n)): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)) : target values\\n\",\n    \"      w (ndarray (n,)) : model parameters  \\n\",\n    \"      b (scalar)       : model parameter\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      cost (scalar): cost\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = X.shape[0]\\n\",\n    \"    cost = 0.0\\n\",\n    \"    for i in range(m):                                \\n\",\n    \"        f_wb_i = np.dot(X[i], w) + b           #(n,)(n,) = scalar (see np.dot)\\n\",\n    \"        cost = cost + (f_wb_i - y[i])**2       #scalar\\n\",\n    \"    cost = cost / (2 * m)                      #scalar    \\n\",\n    \"    return cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"a4641c44\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cost at optimal w : 1.5578904880036537e-12\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute and display cost using our pre-chosen optimal parameters. \\n\",\n    \"cost = compute_cost(X_train, y_train, w_init, b_init)\\n\",\n    \"print(f'Cost at optimal w : {cost}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bef9a962\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected Result**: Cost at optimal w : 1.5578904045996674e-12\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"42d69d8b\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_5\\\"></a>\\n\",\n    \"# 5 Gradient Descent With Multiple Variables\\n\",\n    \"Gradient descent for multiple variables:\\n\",\n    \"\\n\",\n    \"$$\\\\begin{align*} \\\\text{repeat}&\\\\text{ until convergence:} \\\\; \\\\lbrace \\\\newline\\\\;\\n\",\n    \"& w_j = w_j -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} \\\\tag{5}  \\\\; & \\\\text{for j = 0..n-1}\\\\newline\\n\",\n    \"&b\\\\ \\\\ = b -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  \\\\newline \\\\rbrace\\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"where, n is the number of features, parameters $w_j$,  $b$, are updated simultaneously and where  \\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})x_{j}^{(i)} \\\\tag{6}  \\\\\\\\\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)}) \\\\tag{7}\\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \"* m is the number of training examples in the data set\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)})$ is the model's prediction, while $y^{(i)}$ is the target value\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a6d33f60\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_5.1\\\"></a>\\n\",\n    \"## 5.1 Compute Gradient with Multiple Variables\\n\",\n    \"An implementation for calculating the equations (6) and (7) is below. There are many ways to implement this. In this version, there is an\\n\",\n    \"- outer loop over all m examples. \\n\",\n    \"    - $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ for the example can be computed directly and accumulated\\n\",\n    \"    - in a second loop over all n features:\\n\",\n    \"        - $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}$ is computed for each $w_j$.\\n\",\n    \"   \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"5a380261\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_gradient(X, y, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n)): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)) : target values\\n\",\n    \"      w (ndarray (n,)) : model parameters  \\n\",\n    \"      b (scalar)       : model parameter\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      dj_dw (ndarray (n,)): The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db (scalar):       The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m,n = X.shape           #(number of examples, number of features)\\n\",\n    \"    dj_dw = np.zeros((n,))\\n\",\n    \"    dj_db = 0.\\n\",\n    \"\\n\",\n    \"    for i in range(m):                             \\n\",\n    \"        err = (np.dot(X[i], w) + b) - y[i]   \\n\",\n    \"        for j in range(n):                         \\n\",\n    \"            dj_dw[j] = dj_dw[j] + err * X[i, j]    \\n\",\n    \"        dj_db = dj_db + err                        \\n\",\n    \"    dj_dw = dj_dw / m                                \\n\",\n    \"    dj_db = dj_db / m                                \\n\",\n    \"        \\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"3c0a185f\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dj_db at initial w,b: -1.673925169143331e-06\\n\",\n      \"dj_dw at initial w,b: \\n\",\n      \" [-2.73e-03 -6.27e-06 -2.22e-06 -6.92e-05]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Compute and display gradient \\n\",\n    \"tmp_dj_db, tmp_dj_dw = compute_gradient(X_train, y_train, w_init, b_init)\\n\",\n    \"print(f'dj_db at initial w,b: {tmp_dj_db}')\\n\",\n    \"print(f'dj_dw at initial w,b: \\\\n {tmp_dj_dw}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8e2c0375\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected Result**:   \\n\",\n    \"dj_db at initial w,b: -1.6739251122999121e-06  \\n\",\n    \"dj_dw at initial w,b:   \\n\",\n    \" [-2.73e-03 -6.27e-06 -2.22e-06 -6.92e-05]  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3650ae35\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_5.2\\\"></a>\\n\",\n    \"## 5.2 Gradient Descent With Multiple Variables\\n\",\n    \"The routine below implements equation (5) above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"462e5d1c\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def gradient_descent(X, y, w_in, b_in, cost_function, gradient_function, alpha, num_iters): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Performs batch gradient descent to learn theta. Updates theta by taking \\n\",\n    \"    num_iters gradient steps with learning rate alpha\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n))   : Data, m examples with n features\\n\",\n    \"      y (ndarray (m,))    : target values\\n\",\n    \"      w_in (ndarray (n,)) : initial model parameters  \\n\",\n    \"      b_in (scalar)       : initial model parameter\\n\",\n    \"      cost_function       : function to compute cost\\n\",\n    \"      gradient_function   : function to compute the gradient\\n\",\n    \"      alpha (float)       : Learning rate\\n\",\n    \"      num_iters (int)     : number of iterations to run gradient descent\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      w (ndarray (n,)) : Updated values of parameters \\n\",\n    \"      b (scalar)       : Updated value of parameter \\n\",\n    \"      \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # An array to store cost J and w's at each iteration primarily for graphing later\\n\",\n    \"    J_history = []\\n\",\n    \"    w = copy.deepcopy(w_in)  #avoid modifying global w within function\\n\",\n    \"    b = b_in\\n\",\n    \"    \\n\",\n    \"    for i in range(num_iters):\\n\",\n    \"\\n\",\n    \"        # Calculate the gradient and update the parameters\\n\",\n    \"        dj_db,dj_dw = gradient_function(X, y, w, b)   ##None\\n\",\n    \"\\n\",\n    \"        # Update Parameters using w, b, alpha and gradient\\n\",\n    \"        w = w - alpha * dj_dw               ##None\\n\",\n    \"        b = b - alpha * dj_db               ##None\\n\",\n    \"      \\n\",\n    \"        # Save cost J at each iteration\\n\",\n    \"        if i<100000:      # prevent resource exhaustion \\n\",\n    \"            J_history.append( cost_function(X, y, w, b))\\n\",\n    \"\\n\",\n    \"        # Print cost every at intervals 10 times or as many iterations if < 10\\n\",\n    \"        if i% math.ceil(num_iters / 10) == 0:\\n\",\n    \"            print(f\\\"Iteration {i:4d}: Cost {J_history[-1]:8.2f}   \\\")\\n\",\n    \"        \\n\",\n    \"    return w, b, J_history #return final w,b and J history for graphing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"473fe167\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"In the next cell you will test the implementation. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"ab87fade\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration    0: Cost  2529.46   \\n\",\n      \"Iteration  100: Cost   695.99   \\n\",\n      \"Iteration  200: Cost   694.92   \\n\",\n      \"Iteration  300: Cost   693.86   \\n\",\n      \"Iteration  400: Cost   692.81   \\n\",\n      \"Iteration  500: Cost   691.77   \\n\",\n      \"Iteration  600: Cost   690.73   \\n\",\n      \"Iteration  700: Cost   689.71   \\n\",\n      \"Iteration  800: Cost   688.70   \\n\",\n      \"Iteration  900: Cost   687.69   \\n\",\n      \"b,w found by gradient descent: -0.00,[ 0.2   0.   -0.01 -0.07] \\n\",\n      \"prediction: 426.19, target value: 460\\n\",\n      \"prediction: 286.17, target value: 232\\n\",\n      \"prediction: 171.47, target value: 178\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# initialize parameters\\n\",\n    \"initial_w = np.zeros_like(w_init)\\n\",\n    \"initial_b = 0.\\n\",\n    \"# some gradient descent settings\\n\",\n    \"iterations = 1000\\n\",\n    \"alpha = 5.0e-7\\n\",\n    \"# run gradient descent \\n\",\n    \"w_final, b_final, J_hist = gradient_descent(X_train, y_train, initial_w, initial_b,\\n\",\n    \"                                                    compute_cost, compute_gradient, \\n\",\n    \"                                                    alpha, iterations)\\n\",\n    \"print(f\\\"b,w found by gradient descent: {b_final:0.2f},{w_final} \\\")\\n\",\n    \"m,_ = X_train.shape\\n\",\n    \"for i in range(m):\\n\",\n    \"    print(f\\\"prediction: {np.dot(X_train[i], w_final) + b_final:0.2f}, target value: {y_train[i]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"aed3471c\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected Result**:    \\n\",\n    \"b,w found by gradient descent: -0.00,[ 0.2   0.   -0.01 -0.07]   \\n\",\n    \"prediction: 426.19, target value: 460  \\n\",\n    \"prediction: 286.17, target value: 232  \\n\",\n    \"prediction: 171.47, target value: 178  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"5be4598b\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot cost versus iteration  \\n\",\n    \"fig, (ax1, ax2) = plt.subplots(1, 2, constrained_layout=True, figsize=(12, 4))\\n\",\n    \"ax1.plot(J_hist)\\n\",\n    \"ax2.plot(100 + np.arange(len(J_hist[100:])), J_hist[100:])\\n\",\n    \"ax1.set_title(\\\"Cost vs. iteration\\\");  ax2.set_title(\\\"Cost vs. iteration (tail)\\\")\\n\",\n    \"ax1.set_ylabel('Cost')             ;  ax2.set_ylabel('Cost') \\n\",\n    \"ax1.set_xlabel('iteration step')   ;  ax2.set_xlabel('iteration step') \\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c5c5a44a\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"*These results are not inspiring*! Cost is still declining and our predictions are not very accurate. The next lab will explore how to improve on this.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6fc456c5\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"\\n\",\n    \"<a name=\\\"toc_15456_6\\\"></a>\\n\",\n    \"# 6 Congratulations!\\n\",\n    \"In this lab you:\\n\",\n    \"- Redeveloped the routines for linear regression, now with multiple variables.\\n\",\n    \"- Utilized NumPy `np.dot` to vectorize the implementations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"030caa1d\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"15456\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.3\"\n  },\n  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c8d25fe2\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Feature scaling and Learning Rate (Multi-variable)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c8b967e2\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab you will:\\n\",\n    \"- Utilize  the multiple variables routines developed in the previous lab\\n\",\n    \"- run Gradient Descent on a data set with multiple features\\n\",\n    \"- explore the impact of the *learning rate alpha* on gradient descent\\n\",\n    \"- improve performance of gradient descent by *feature scaling* using z-score normalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3dfbbe3c\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Tools\\n\",\n    \"You will utilize the functions developed in the last lab as well as matplotlib and NumPy. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"1a086458\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_multi import  load_house_data, run_gradient_descent \\n\",\n    \"from lab_utils_multi import  norm_plot, plt_equal_scale, plot_cost_i_w\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"np.set_printoptions(precision=2)\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"914339ff\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Notation\\n\",\n    \"\\n\",\n    \"| General Notation | Description | Python (if applicable) |\\n\",\n    \"|:----------------:|:-----------:|:----------------------:|\\n\",\n    \"|      $a$         | scalar, non-bold | |\\n\",\n    \"|  $\\\\mathbf{a}$   | vector, bold | |\\n\",\n    \"|  $\\\\mathbf{A}$   | matrix, bold capital | |\\n\",\n    \"| **Regression** | | |\\n\",\n    \"|  $\\\\mathbf{X}$   | training example matrix | `X_train` |\\n\",\n    \"|  $\\\\mathbf{y}$   | training example targets | `y_train` |\\n\",\n    \"| $\\\\mathbf{x}^{(i)}$, $y^{(i)}$ | $i_{th}$ Training Example | `X[i]`, `y[i]` |\\n\",\n    \"|        $m$       | number of training examples | `m` |\\n\",\n    \"|        $n$       | number of features in each example | `n` |\\n\",\n    \"|  $\\\\mathbf{w}$   | parameter: weight | `w` |\\n\",\n    \"|        $b$       | parameter: bias | `b` |\\n\",\n    \"| $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)})$ | The result of the Model evaluation at $\\\\mathbf{x}^{(i)}$ parameterized by $\\\\mathbf{w},b$: $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)}+b$ | `f_wb` |\\n\",\n    \"| $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}$ | the gradient or partial derivative of cost with respect to parameter $w_j$ | `dj_dw[j]` |\\n\",\n    \"| $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ | the gradient or partial derivative of cost with respect to parameter $b$ | `dj_db` |\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fdd8f2b7\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#  Problem Statement\\n\",\n    \"\\n\",\n    \"As in the previous labs, you will use the motivating example of housing price prediction. The training data set contains many examples with 4 features (size, bedrooms, floors and age) shown in the table below. Note, in this lab, the Size feature is in sqft while earlier labs utilized 1000 sqft.  This data set is larger than the previous lab.\\n\",\n    \"\\n\",\n    \"We would like to build a linear regression model using these values so we can then predict the price for other houses - say, a house with 1200 sqft, 3 bedrooms, 1 floor, 40 years old. \\n\",\n    \"\\n\",\n    \"##  Dataset: \\n\",\n    \"| Size (sqft) | Number of Bedrooms  | Number of floors | Age of  Home | Price (1000s dollars)  |   \\n\",\n    \"| ----------------| ------------------- |----------------- |--------------|----------------------- |  \\n\",\n    \"| 952             | 2                   | 1                | 65           | 271.5                  |  \\n\",\n    \"| 1244            | 3                   | 2                | 64           | 232                    |  \\n\",\n    \"| 1947            | 3                   | 2                | 17           | 509.8                  |  \\n\",\n    \"| ...             | ...                 | ...              | ...          | ...                    |\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"5cd0a677\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load the dataset\\n\",\n    \"X_train, y_train = load_house_data()\\n\",\n    \"X_features = ['size(sqft)','bedrooms','floors','age']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cfaef490\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's view the dataset and its features by plotting each feature versus price.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"67ce47a7\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig,ax=plt.subplots(1, 4, figsize=(12, 3), sharey=True)\\n\",\n    \"for i in range(len(ax)):\\n\",\n    \"    ax[i].scatter(X_train[:,i],y_train)\\n\",\n    \"    ax[i].set_xlabel(X_features[i])\\n\",\n    \"ax[0].set_ylabel(\\\"Price (1000's)\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"38b16741\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Plotting each feature vs. the target, price, provides some indication of which features have the strongest influence on price. Above, increasing size also increases price. Bedrooms and floors don't seem to have a strong impact on price. Newer houses have higher prices than older houses.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0dfe1e9b\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_15456_5\\\"></a>\\n\",\n    \"## Gradient Descent With Multiple Variables\\n\",\n    \"Here are the equations you developed in the last lab on gradient descent for multiple variables.:\\n\",\n    \"\\n\",\n    \"$$\\\\begin{align*} \\\\text{repeat}&\\\\text{ until convergence:} \\\\; \\\\lbrace \\\\newline\\\\;\\n\",\n    \"& w_j := w_j -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} \\\\tag{1}  \\\\; & \\\\text{for j = 0..n-1}\\\\newline\\n\",\n    \"&b\\\\ \\\\ := b -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  \\\\newline \\\\rbrace\\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"where, n is the number of features, parameters $w_j$,  $b$, are updated simultaneously and where  \\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})x_{j}^{(i)} \\\\tag{2}  \\\\\\\\\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)}) \\\\tag{3}\\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \"* m is the number of training examples in the data set\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)})$ is the model's prediction, while $y^{(i)}$ is the target value\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a921fb4d\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Learning Rate\\n\",\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C1_W2_Lab06_learningrate.PNG\\\" style=\\\"width:1200px;\\\" >\\n\",\n    \"</figure>\\n\",\n    \"The lectures discussed some of the issues related to setting the learning rate $\\\\alpha$. The learning rate controls the size of the update to the parameters. See equation (1) above. It is shared by all the parameters.  \\n\",\n    \"\\n\",\n    \"Let's run gradient descent and try a few settings of $\\\\alpha$ on our data set\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9df8e8a1\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### $\\\\alpha$ = 9.9e-7\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"fe34ec81\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration Cost          w0       w1       w2       w3       b       djdw0    djdw1    djdw2    djdw3    djdb  \\n\",\n      \"---------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|\\n\",\n      \"        0 9.55884e+04  5.5e-01  1.0e-03  5.1e-04  1.2e-02  3.6e-04 -5.5e+05 -1.0e+03 -5.2e+02 -1.2e+04 -3.6e+02\\n\",\n      \"        1 1.28213e+05 -8.8e-02 -1.7e-04 -1.0e-04 -3.4e-03 -4.8e-05  6.4e+05  1.2e+03  6.2e+02  1.6e+04  4.1e+02\\n\",\n      \"        2 1.72159e+05  6.5e-01  1.2e-03  5.9e-04  1.3e-02  4.3e-04 -7.4e+05 -1.4e+03 -7.0e+02 -1.7e+04 -4.9e+02\\n\",\n      \"        3 2.31358e+05 -2.1e-01 -4.0e-04 -2.3e-04 -7.5e-03 -1.2e-04  8.6e+05  1.6e+03  8.3e+02  2.1e+04  5.6e+02\\n\",\n      \"        4 3.11100e+05  7.9e-01  1.4e-03  7.1e-04  1.5e-02  5.3e-04 -1.0e+06 -1.8e+03 -9.5e+02 -2.3e+04 -6.6e+02\\n\",\n      \"        5 4.18517e+05 -3.7e-01 -7.1e-04 -4.0e-04 -1.3e-02 -2.1e-04  1.2e+06  2.1e+03  1.1e+03  2.8e+04  7.5e+02\\n\",\n      \"        6 5.63212e+05  9.7e-01  1.7e-03  8.7e-04  1.8e-02  6.6e-04 -1.3e+06 -2.5e+03 -1.3e+03 -3.1e+04 -8.8e+02\\n\",\n      \"        7 7.58122e+05 -5.8e-01 -1.1e-03 -6.2e-04 -1.9e-02 -3.4e-04  1.6e+06  2.9e+03  1.5e+03  3.8e+04  1.0e+03\\n\",\n      \"        8 1.02068e+06  1.2e+00  2.2e-03  1.1e-03  2.3e-02  8.3e-04 -1.8e+06 -3.3e+03 -1.7e+03 -4.2e+04 -1.2e+03\\n\",\n      \"        9 1.37435e+06 -8.7e-01 -1.7e-03 -9.1e-04 -2.7e-02 -5.2e-04  2.1e+06  3.9e+03  2.0e+03  5.1e+04  1.4e+03\\n\",\n      \"w,b found by gradient descent: w: [-0.87 -0.   -0.   -0.03], b: -0.00\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#set alpha to 9.9e-7\\n\",\n    \"_, _, hist = run_gradient_descent(X_train, y_train, 10, alpha = 9.9e-7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c96a1c4e\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"It appears the learning rate is too high.  The solution does not converge. Cost is *increasing* rather than decreasing. Let's plot the result:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"ff78ed91\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_cost_i_w(X_train, y_train, hist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c07313a8\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The plot on the right shows the value of one of the parameters, $w_0$. At each iteration, it is overshooting the optimal value and as a result, cost ends up *increasing* rather than approaching the minimum. Note that this is not a completely accurate picture as there are 4 parameters being modified each pass rather than just one. This plot is only showing $w_0$ with the other parameters fixed at benign values. In this and later plots you may notice the blue and orange lines being slightly off.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6b6ace86\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"\\n\",\n    \"### $\\\\alpha$ = 9e-7\\n\",\n    \"Let's try a bit smaller value and see what happens.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"32fe697a\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration Cost          w0       w1       w2       w3       b       djdw0    djdw1    djdw2    djdw3    djdb  \\n\",\n      \"---------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|\\n\",\n      \"        0 6.64616e+04  5.0e-01  9.1e-04  4.7e-04  1.1e-02  3.3e-04 -5.5e+05 -1.0e+03 -5.2e+02 -1.2e+04 -3.6e+02\\n\",\n      \"        1 6.18990e+04  1.8e-02  2.1e-05  2.0e-06 -7.9e-04  1.9e-05  5.3e+05  9.8e+02  5.2e+02  1.3e+04  3.4e+02\\n\",\n      \"        2 5.76572e+04  4.8e-01  8.6e-04  4.4e-04  9.5e-03  3.2e-04 -5.1e+05 -9.3e+02 -4.8e+02 -1.1e+04 -3.4e+02\\n\",\n      \"        3 5.37137e+04  3.4e-02  3.9e-05  2.8e-06 -1.6e-03  3.8e-05  4.9e+05  9.1e+02  4.8e+02  1.2e+04  3.2e+02\\n\",\n      \"        4 5.00474e+04  4.6e-01  8.2e-04  4.1e-04  8.0e-03  3.2e-04 -4.8e+05 -8.7e+02 -4.5e+02 -1.1e+04 -3.1e+02\\n\",\n      \"        5 4.66388e+04  5.0e-02  5.6e-05  2.5e-06 -2.4e-03  5.6e-05  4.6e+05  8.5e+02  4.5e+02  1.2e+04  2.9e+02\\n\",\n      \"        6 4.34700e+04  4.5e-01  7.8e-04  3.8e-04  6.4e-03  3.2e-04 -4.4e+05 -8.1e+02 -4.2e+02 -9.8e+03 -2.9e+02\\n\",\n      \"        7 4.05239e+04  6.4e-02  7.0e-05  1.2e-06 -3.3e-03  7.3e-05  4.3e+05  7.9e+02  4.2e+02  1.1e+04  2.7e+02\\n\",\n      \"        8 3.77849e+04  4.4e-01  7.5e-04  3.5e-04  4.9e-03  3.2e-04 -4.1e+05 -7.5e+02 -3.9e+02 -9.1e+03 -2.7e+02\\n\",\n      \"        9 3.52385e+04  7.7e-02  8.3e-05 -1.1e-06 -4.2e-03  8.9e-05  4.0e+05  7.4e+02  3.9e+02  1.0e+04  2.5e+02\\n\",\n      \"w,b found by gradient descent: w: [ 7.74e-02  8.27e-05 -1.06e-06 -4.20e-03], b: 0.00\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#set alpha to 9e-7\\n\",\n    \"_,_,hist = run_gradient_descent(X_train, y_train, 10, alpha = 9e-7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3cda1a4b\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Cost is decreasing throughout the run showing that alpha is not too large. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"e382eef0\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_cost_i_w(X_train, y_train, hist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9abda677\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"On the left, you see that cost is decreasing as it should. On the right, you can see that $w_0$ is still oscillating around the minimum, but it is decreasing each iteration rather than increasing. Note above that `dj_dw[0]` changes sign with each iteration as `w[0]` jumps over the optimal value.\\n\",\n    \"This alpha value will converge. You can vary the number of iterations to see how it behaves.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"88161797\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### $\\\\alpha$ = 1e-7\\n\",\n    \"Let's try a bit smaller value for $\\\\alpha$ and see what happens.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"ba5eaaab\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration Cost          w0       w1       w2       w3       b       djdw0    djdw1    djdw2    djdw3    djdb  \\n\",\n      \"---------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|\\n\",\n      \"        0 4.42313e+04  5.5e-02  1.0e-04  5.2e-05  1.2e-03  3.6e-05 -5.5e+05 -1.0e+03 -5.2e+02 -1.2e+04 -3.6e+02\\n\",\n      \"        1 2.76461e+04  9.8e-02  1.8e-04  9.2e-05  2.2e-03  6.5e-05 -4.3e+05 -7.9e+02 -4.0e+02 -9.5e+03 -2.8e+02\\n\",\n      \"        2 1.75102e+04  1.3e-01  2.4e-04  1.2e-04  2.9e-03  8.7e-05 -3.4e+05 -6.1e+02 -3.1e+02 -7.3e+03 -2.2e+02\\n\",\n      \"        3 1.13157e+04  1.6e-01  2.9e-04  1.5e-04  3.5e-03  1.0e-04 -2.6e+05 -4.8e+02 -2.4e+02 -5.6e+03 -1.8e+02\\n\",\n      \"        4 7.53002e+03  1.8e-01  3.3e-04  1.7e-04  3.9e-03  1.2e-04 -2.1e+05 -3.7e+02 -1.9e+02 -4.2e+03 -1.4e+02\\n\",\n      \"        5 5.21639e+03  2.0e-01  3.5e-04  1.8e-04  4.2e-03  1.3e-04 -1.6e+05 -2.9e+02 -1.5e+02 -3.1e+03 -1.1e+02\\n\",\n      \"        6 3.80242e+03  2.1e-01  3.8e-04  1.9e-04  4.5e-03  1.4e-04 -1.3e+05 -2.2e+02 -1.1e+02 -2.3e+03 -8.6e+01\\n\",\n      \"        7 2.93826e+03  2.2e-01  3.9e-04  2.0e-04  4.6e-03  1.4e-04 -9.8e+04 -1.7e+02 -8.6e+01 -1.7e+03 -6.8e+01\\n\",\n      \"        8 2.41013e+03  2.3e-01  4.1e-04  2.1e-04  4.7e-03  1.5e-04 -7.7e+04 -1.3e+02 -6.5e+01 -1.2e+03 -5.4e+01\\n\",\n      \"        9 2.08734e+03  2.3e-01  4.2e-04  2.1e-04  4.8e-03  1.5e-04 -6.0e+04 -1.0e+02 -4.9e+01 -7.5e+02 -4.3e+01\\n\",\n      \"w,b found by gradient descent: w: [2.31e-01 4.18e-04 2.12e-04 4.81e-03], b: 0.00\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#set alpha to 1e-7\\n\",\n    \"_,_,hist = run_gradient_descent(X_train, y_train, 10, alpha = 1e-7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1cede2ab\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Cost is decreasing throughout the run showing that $\\\\alpha$ is not too large. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"f94a757b\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_cost_i_w(X_train,y_train,hist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d26e1d32\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"On the left, you see that cost is decreasing as it should. On the right you can see that $w_0$ is decreasing without crossing the minimum. Note above that `dj_w0` is negative throughout the run. This solution will also converge, though not quite as quickly as the previous example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0097ccbf\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    },\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Feature Scaling \\n\",\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C1_W2_Lab06_featurescalingheader.PNG\\\" style=\\\"width:1200px;\\\" >\\n\",\n    \"</figure>\\n\",\n    \"The lectures described the importance of rescaling the dataset so the features have a similar range.\\n\",\n    \"If you are interested in the details of why this is the case, click on the 'details' header below. If not, the section below will walk through an implementation of how to do feature scaling.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"af7675c9\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"<summary>\\n\",\n    \"    <font size='3', color='darkgreen'><b>Details</b></font>\\n\",\n    \"</summary>\\n\",\n    \"\\n\",\n    \"Let's look again at the situation with $\\\\alpha$ = 9e-7. This is pretty close to the maximum value we can set $\\\\alpha$  to without diverging. This is a short run showing the first few iterations:\\n\",\n    \"\\n\",\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C1_W2_Lab06_ShortRun.PNG\\\" style=\\\"width:1200px;\\\" >\\n\",\n    \"</figure>\\n\",\n    \"\\n\",\n    \"Above, while cost is being decreased, its clear that $w_0$ is making more rapid progress than the other parameters due to its much larger gradient.\\n\",\n    \"\\n\",\n    \"The graphic below shows the result of a very long run with $\\\\alpha$ = 9e-7. This takes several hours.\\n\",\n    \"\\n\",\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C1_W2_Lab06_LongRun.PNG\\\" style=\\\"width:1200px;\\\" >\\n\",\n    \"</figure>\\n\",\n    \"    \\n\",\n    \"Above, you can see cost decreased slowly after its initial reduction. Notice the difference between `w0` and `w1`,`w2`,`w3` as well as  `dj_dw0` and `dj_dw1-3`. `w0` reaches its near final value very quickly and `dj_dw0` has quickly decreased to a small value showing that `w0` is near the final value. The other parameters were reduced much more slowly.\\n\",\n    \"\\n\",\n    \"Why is this?  Is there something we can improve? See below:\\n\",\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C1_W2_Lab06_scale.PNG\\\"   ></center>\\n\",\n    \"</figure>   \\n\",\n    \"\\n\",\n    \"The figure above shows why $w$'s are updated unevenly. \\n\",\n    \"- $\\\\alpha$ is shared by all parameter updates ($w$'s and $b$).\\n\",\n    \"- the common error term is multiplied by the features for the $w$'s. (not $b$).\\n\",\n    \"- the features vary significantly in magnitude making some features update much faster than others. In this case, $w_0$ is multiplied by 'size(sqft)', which is generally > 1000,  while $w_1$ is multiplied by 'number of bedrooms', which is generally 2-4. \\n\",\n    \"    \\n\",\n    \"The solution is Feature Scaling.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"78a0d10d\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The lectures discussed three different techniques: \\n\",\n    \"- Feature scaling, essentially dividing each positive feature by its maximum value, or more generally, rescale each feature by both its minimum and maximum values using (x-min)/(max-min). Both ways normalizes features to the range of -1 and 1, where the former method works for positive features which is simple and serves well for the lecture's example, and the latter method works for any features.\\n\",\n    \"- Mean normalization: $x_i := \\\\dfrac{x_i - \\\\mu_i}{max - min} $ \\n\",\n    \"- Z-score normalization which we will explore below. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9e575998\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"\\n\",\n    \"### z-score normalization \\n\",\n    \"After z-score normalization, all features will have a mean of 0 and a standard deviation of 1.\\n\",\n    \"\\n\",\n    \"To implement z-score normalization, adjust your input values as shown in this formula:\\n\",\n    \"$$x^{(i)}_j = \\\\dfrac{x^{(i)}_j - \\\\mu_j}{\\\\sigma_j} \\\\tag{4}$$ \\n\",\n    \"where $j$ selects a feature or a column in the $\\\\mathbf{X}$ matrix. $µ_j$ is the mean of all the values for feature (j) and $\\\\sigma_j$ is the standard deviation of feature (j).\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\mu_j &= \\\\frac{1}{m} \\\\sum_{i=0}^{m-1} x^{(i)}_j \\\\tag{5}\\\\\\\\\\n\",\n    \"\\\\sigma^2_j &= \\\\frac{1}{m} \\\\sum_{i=0}^{m-1} (x^{(i)}_j - \\\\mu_j)^2  \\\\tag{6}\\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \">**Implementation Note:** When normalizing the features, it is important\\n\",\n    \"to store the values used for normalization - the mean value and the standard deviation used for the computations. After learning the parameters\\n\",\n    \"from the model, we often want to predict the prices of houses we have not\\n\",\n    \"seen before. Given a new x value (living room area and number of bed-\\n\",\n    \"rooms), we must first normalize x using the mean and standard deviation\\n\",\n    \"that we had previously computed from the training set.\\n\",\n    \"\\n\",\n    \"**Implementation**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"e7b13d18\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def zscore_normalize_features(X):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    computes  X, zcore normalized by column\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n))     : input data, m examples, n features\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      X_norm (ndarray (m,n)): input normalized by column\\n\",\n    \"      mu (ndarray (n,))     : mean of each feature\\n\",\n    \"      sigma (ndarray (n,))  : standard deviation of each feature\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # find the mean of each column/feature\\n\",\n    \"    mu     = np.mean(X, axis=0)                 # mu will have shape (n,)\\n\",\n    \"    # find the standard deviation of each column/feature\\n\",\n    \"    sigma  = np.std(X, axis=0)                  # sigma will have shape (n,)\\n\",\n    \"    # element-wise, subtract mu for that column from each example, divide by std for that column\\n\",\n    \"    X_norm = (X - mu) / sigma      \\n\",\n    \"\\n\",\n    \"    return (X_norm, mu, sigma)\\n\",\n    \" \\n\",\n    \"#check our work\\n\",\n    \"#from sklearn.preprocessing import scale\\n\",\n    \"#scale(X_orig, axis=0, with_mean=True, with_std=True, copy=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2d1fa01a\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's look at the steps involved in Z-score normalization. The plot below shows the transformation step by step.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"d64c9f63\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mu     = np.mean(X_train,axis=0)   \\n\",\n    \"sigma  = np.std(X_train,axis=0) \\n\",\n    \"X_mean = (X_train - mu)\\n\",\n    \"X_norm = (X_train - mu)/sigma      \\n\",\n    \"\\n\",\n    \"fig,ax=plt.subplots(1, 3, figsize=(12, 3))\\n\",\n    \"ax[0].scatter(X_train[:,0], X_train[:,3])\\n\",\n    \"ax[0].set_xlabel(X_features[0]); ax[0].set_ylabel(X_features[3]);\\n\",\n    \"ax[0].set_title(\\\"unnormalized\\\")\\n\",\n    \"ax[0].axis('equal')\\n\",\n    \"\\n\",\n    \"ax[1].scatter(X_mean[:,0], X_mean[:,3])\\n\",\n    \"ax[1].set_xlabel(X_features[0]); ax[0].set_ylabel(X_features[3]);\\n\",\n    \"ax[1].set_title(r\\\"X - $\\\\mu$\\\")\\n\",\n    \"ax[1].axis('equal')\\n\",\n    \"\\n\",\n    \"ax[2].scatter(X_norm[:,0], X_norm[:,3])\\n\",\n    \"ax[2].set_xlabel(X_features[0]); ax[0].set_ylabel(X_features[3]);\\n\",\n    \"ax[2].set_title(r\\\"Z-score normalized\\\")\\n\",\n    \"ax[2].axis('equal')\\n\",\n    \"plt.tight_layout(rect=[0, 0.03, 1, 0.95])\\n\",\n    \"fig.suptitle(\\\"distribution of features before, during, after normalization\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"51bdd057\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The plot above shows the relationship between two of the training set parameters, \\\"age\\\" and \\\"size(sqft)\\\". *These are plotted with equal scale*. \\n\",\n    \"- Left: Unnormalized: The range of values or the variance of the 'size(sqft)' feature is much larger than that of age\\n\",\n    \"- Middle: The first step removes the mean or average value from each feature. This leaves features that are centered around zero. It's difficult to see the difference for the 'age' feature, but 'size(sqft)' is clearly around zero.\\n\",\n    \"- Right: The second step divides by the variance. This leaves both features centered at zero with a similar scale.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"995388bb\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's normalize the data and compare it to the original data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"592747d1\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X_mu = [1.42e+03 2.72e+00 1.38e+00 3.84e+01], \\n\",\n      \"X_sigma = [411.62   0.65   0.49  25.78]\\n\",\n      \"Peak to Peak range by column in Raw        X:[2.41e+03 4.00e+00 1.00e+00 9.50e+01]\\n\",\n      \"Peak to Peak range by column in Normalized X:[5.85 6.14 2.06 3.69]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# normalize the original features\\n\",\n    \"X_norm, X_mu, X_sigma = zscore_normalize_features(X_train)\\n\",\n    \"print(f\\\"X_mu = {X_mu}, \\\\nX_sigma = {X_sigma}\\\")\\n\",\n    \"print(f\\\"Peak to Peak range by column in Raw        X:{np.ptp(X_train,axis=0)}\\\")   \\n\",\n    \"print(f\\\"Peak to Peak range by column in Normalized X:{np.ptp(X_norm,axis=0)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"08f2bffb\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The peak to peak range of each column is reduced from a factor of thousands to a factor of 2-3 by normalization.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"48c76052\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 8 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 8 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig,ax=plt.subplots(1, 4, figsize=(12, 3))\\n\",\n    \"for i in range(len(ax)):\\n\",\n    \"    norm_plot(ax[i],X_train[:,i],)\\n\",\n    \"    ax[i].set_xlabel(X_features[i])\\n\",\n    \"ax[0].set_ylabel(\\\"count\\\");\\n\",\n    \"fig.suptitle(\\\"distribution of features before normalization\\\")\\n\",\n    \"plt.show()\\n\",\n    \"fig,ax=plt.subplots(1,4,figsize=(12,3))\\n\",\n    \"for i in range(len(ax)):\\n\",\n    \"    norm_plot(ax[i],X_norm[:,i],)\\n\",\n    \"    ax[i].set_xlabel(X_features[i])\\n\",\n    \"ax[0].set_ylabel(\\\"count\\\"); \\n\",\n    \"fig.suptitle(\\\"distribution of features after normalization\\\")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a95dde5a\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Notice, above, the range of the normalized data (x-axis) is centered around zero and roughly +/- 2. Most importantly, the range is similar for each feature.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ae92414b\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's re-run our gradient descent algorithm with normalized data.\\n\",\n    \"Note the **vastly larger value of alpha**. This will speed up gradient descent.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"f22af96a\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration Cost          w0       w1       w2       w3       b       djdw0    djdw1    djdw2    djdw3    djdb  \\n\",\n      \"---------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|\\n\",\n      \"        0 5.76170e+04  8.9e+00  3.0e+00  3.3e+00 -6.0e+00  3.6e+01 -8.9e+01 -3.0e+01 -3.3e+01  6.0e+01 -3.6e+02\\n\",\n      \"      100 2.21086e+02  1.1e+02 -2.0e+01 -3.1e+01 -3.8e+01  3.6e+02 -9.2e-01  4.5e-01  5.3e-01 -1.7e-01 -9.6e-03\\n\",\n      \"      200 2.19209e+02  1.1e+02 -2.1e+01 -3.3e+01 -3.8e+01  3.6e+02 -3.0e-02  1.5e-02  1.7e-02 -6.0e-03 -2.6e-07\\n\",\n      \"      300 2.19207e+02  1.1e+02 -2.1e+01 -3.3e+01 -3.8e+01  3.6e+02 -1.0e-03  5.1e-04  5.7e-04 -2.0e-04 -6.9e-12\\n\",\n      \"      400 2.19207e+02  1.1e+02 -2.1e+01 -3.3e+01 -3.8e+01  3.6e+02 -3.4e-05  1.7e-05  1.9e-05 -6.6e-06 -2.7e-13\\n\",\n      \"      500 2.19207e+02  1.1e+02 -2.1e+01 -3.3e+01 -3.8e+01  3.6e+02 -1.1e-06  5.6e-07  6.2e-07 -2.2e-07 -2.7e-13\\n\",\n      \"      600 2.19207e+02  1.1e+02 -2.1e+01 -3.3e+01 -3.8e+01  3.6e+02 -3.7e-08  1.9e-08  2.1e-08 -7.3e-09 -2.6e-13\\n\",\n      \"      700 2.19207e+02  1.1e+02 -2.1e+01 -3.3e+01 -3.8e+01  3.6e+02 -1.2e-09  6.2e-10  6.9e-10 -2.4e-10 -2.6e-13\\n\",\n      \"      800 2.19207e+02  1.1e+02 -2.1e+01 -3.3e+01 -3.8e+01  3.6e+02 -4.1e-11  2.1e-11  2.3e-11 -8.1e-12 -2.6e-13\\n\",\n      \"      900 2.19207e+02  1.1e+02 -2.1e+01 -3.3e+01 -3.8e+01  3.6e+02 -1.4e-12  6.9e-13  7.7e-13 -2.7e-13 -2.6e-13\\n\",\n      \"w,b found by gradient descent: w: [110.56 -21.27 -32.71 -37.97], b: 363.16\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w_norm, b_norm, hist = run_gradient_descent(X_norm, y_train, 1000, 1.0e-1, )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e20f2113\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The scaled features get very accurate results **much, much faster!**. Notice the gradient of each parameter is tiny by the end of this fairly short run. A learning rate of 0.1 is a good start for regression with normalized features.\\n\",\n    \"Let's plot our predictions versus the target values. Note, the prediction is made using the normalized feature while the plot is shown using the original feature values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"31845003\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x216 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#predict target using normalized features\\n\",\n    \"m = X_norm.shape[0]\\n\",\n    \"yp = np.zeros(m)\\n\",\n    \"for i in range(m):\\n\",\n    \"    yp[i] = np.dot(X_norm[i], w_norm) + b_norm\\n\",\n    \"\\n\",\n    \"    # plot predictions and targets versus original features    \\n\",\n    \"fig,ax=plt.subplots(1,4,figsize=(12, 3),sharey=True)\\n\",\n    \"for i in range(len(ax)):\\n\",\n    \"    ax[i].scatter(X_train[:,i],y_train, label = 'target')\\n\",\n    \"    ax[i].set_xlabel(X_features[i])\\n\",\n    \"    ax[i].scatter(X_train[:,i],yp,color=dlc[\\\"dlorange\\\"], label = 'predict')\\n\",\n    \"ax[0].set_ylabel(\\\"Price\\\"); ax[0].legend();\\n\",\n    \"fig.suptitle(\\\"target versus prediction using z-score normalized model\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5ad1d19c\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The results look good. A few points to note:\\n\",\n    \"- with multiple features, we can no longer have a single plot showing results versus features.\\n\",\n    \"- when generating the plot, the normalized features were used. Any predictions using the parameters learned from a normalized training set must also be normalized.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"adbe656f\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Prediction**\\n\",\n    \"The point of generating our model is to use it to predict housing prices that are not in the data set. Let's predict the price of a house with 1200 sqft, 3 bedrooms, 1 floor, 40 years old. Recall, that you must normalize the data with the mean and standard deviation derived when the training data was normalized. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"cb3b7235\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-0.53  0.43 -0.79  0.06]\\n\",\n      \" predicted price of a house with 1200 sqft, 3 bedrooms, 1 floor, 40 years old = $318709\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# First, normalize out example.\\n\",\n    \"x_house = np.array([1200, 3, 1, 40])\\n\",\n    \"x_house_norm = (x_house - X_mu) / X_sigma\\n\",\n    \"print(x_house_norm)\\n\",\n    \"x_house_predict = np.dot(x_house_norm, w_norm) + b_norm\\n\",\n    \"print(f\\\" predicted price of a house with 1200 sqft, 3 bedrooms, 1 floor, 40 years old = ${x_house_predict*1000:0.0f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e723c598\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Cost Contours**  \\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W2_Lab06_contours.PNG\\\"   style=\\\"width:240px;\\\" >Another way to view feature scaling is in terms of the cost contours. When feature scales do not match, the plot of cost versus parameters in a contour plot is asymmetric. \\n\",\n    \"\\n\",\n    \"In the plot below, the scale of the parameters is matched. The left plot is the cost contour plot of w[0], the square feet versus w[1], the number of bedrooms before normalizing the features. The plot is so asymmetric, the curves completing the contours are not visible. In contrast, when the features are normalized, the cost contour is much more symmetric. The result is that updates to parameters during gradient descent can make equal progress for each parameter. \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"f380b8f4\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_equal_scale(X_train, X_norm, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"18688194\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"\\n\",\n    \"## Congratulations!\\n\",\n    \"In this lab you:\\n\",\n    \"- utilized the routines for linear regression with multiple features you developed in previous labs\\n\",\n    \"- explored the impact of the learning rate  $\\\\alpha$ on convergence \\n\",\n    \"- discovered the value of feature scaling using z-score normalization in speeding convergence\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"755249cb\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Acknowledgments\\n\",\n    \"The housing data was derived from the [Ames Housing dataset](http://jse.amstat.org/v19n3/decock.pdf) compiled by Dean De Cock for use in data science education.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"01b8b18f\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.3\"\n  },\n  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/C1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Feature Engineering and Polynomial Regression\\n\",\n    \"\\n\",\n    \"![](./images/C1_W2_Lab07_FeatureEngLecture.PNG)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab you will:\\n\",\n    \"- explore feature engineering and polynomial regression which allows you to use the machinery of linear regression to fit very complicated, even very non-linear functions.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Tools\\n\",\n    \"You will utilize the function developed in previous labs as well as matplotlib and NumPy. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_multi import zscore_normalize_features, run_gradient_descent_feng\\n\",\n    \"np.set_printoptions(precision=2)  # reduced display precision on numpy arrays\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name='FeatureEng'></a>\\n\",\n    \"# Feature Engineering and Polynomial Regression Overview\\n\",\n    \"\\n\",\n    \"Out of the box, linear regression provides a means of building models of the form:\\n\",\n    \"$$f_{\\\\mathbf{w},b} = w_0x_0 + w_1x_1+ ... + w_{n-1}x_{n-1} + b \\\\tag{1}$$ \\n\",\n    \"What if your features/data are non-linear or are combinations of features? For example,  Housing prices do not tend to be linear with living area but penalize very small or very large houses resulting in the curves shown in the graphic above. How can we use the machinery of linear regression to fit this curve? Recall, the 'machinery' we have is the ability to modify the parameters $\\\\mathbf{w}$, $\\\\mathbf{b}$ in (1) to 'fit' the equation to the training data. However, no amount of adjusting of $\\\\mathbf{w}$,$\\\\mathbf{b}$ in (1) will achieve a fit to a non-linear curve.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name='PolynomialFeatures'></a>\\n\",\n    \"## Polynomial Features\\n\",\n    \"\\n\",\n    \"Above we were considering a scenario where the data was non-linear. Let's try using what we know so far to fit a non-linear curve. We'll start with a simple quadratic: $y = 1+x^2$\\n\",\n    \"\\n\",\n    \"You're familiar with all the routines we're using. They are available in the lab_utils.py file for review. We'll use [`np.c_[..]`](https://numpy.org/doc/stable/reference/generated/numpy.c_.html) which is a NumPy routine to concatenate along the column boundary.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration         0, Cost: 1.65756e+03\\n\",\n      \"Iteration       100, Cost: 6.94549e+02\\n\",\n      \"Iteration       200, Cost: 5.88475e+02\\n\",\n      \"Iteration       300, Cost: 5.26414e+02\\n\",\n      \"Iteration       400, Cost: 4.90103e+02\\n\",\n      \"Iteration       500, Cost: 4.68858e+02\\n\",\n      \"Iteration       600, Cost: 4.56428e+02\\n\",\n      \"Iteration       700, Cost: 4.49155e+02\\n\",\n      \"Iteration       800, Cost: 4.44900e+02\\n\",\n      \"Iteration       900, Cost: 4.42411e+02\\n\",\n      \"w,b found by gradient descent: w: [18.7], b: -52.0834\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 432x288 with 1 Axes>\",\n      \"image/png\": \"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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# create target data\\n\",\n    \"x = np.arange(0, 20, 1)\\n\",\n    \"y = 1 + x**2\\n\",\n    \"X = x.reshape(-1, 1)\\n\",\n    \"\\n\",\n    \"model_w,model_b = run_gradient_descent_feng(X,y,iterations=1000, alpha = 1e-2)\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y, marker='x', c='r', label=\\\"Actual Value\\\"); plt.title(\\\"no feature engineering\\\")\\n\",\n    \"plt.plot(x,X@model_w + model_b, label=\\\"Predicted Value\\\");  plt.xlabel(\\\"X\\\"); plt.ylabel(\\\"y\\\"); plt.legend(); plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Well, as expected, not a great fit. What is needed is something like $y= w_0x_0^2 + b$, or a **polynomial feature**.\\n\",\n    \"To accomplish this, you can modify the *input data* to *engineer* the needed features. If you swap the original data with a version that squares the $x$ value, then you can achieve $y= w_0x_0^2 + b$. Let's try it. Swap `X` for `X**2` below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# create target data\\n\",\n    \"x = np.arange(0, 20, 1)\\n\",\n    \"y = 1 + x**2\\n\",\n    \"\\n\",\n    \"# Engineer features \\n\",\n    \"X = x**2      #<-- added engineered feature\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration         0, Cost: 7.32922e+03\\n\",\n      \"Iteration      1000, Cost: 2.24844e-01\\n\",\n      \"Iteration      2000, Cost: 2.22795e-01\\n\",\n      \"Iteration      3000, Cost: 2.20764e-01\\n\",\n      \"Iteration      4000, Cost: 2.18752e-01\\n\",\n      \"Iteration      5000, Cost: 2.16758e-01\\n\",\n      \"Iteration      6000, Cost: 2.14782e-01\\n\",\n      \"Iteration      7000, Cost: 2.12824e-01\\n\",\n      \"Iteration      8000, Cost: 2.10884e-01\\n\",\n      \"Iteration      9000, Cost: 2.08962e-01\\n\",\n      \"w,b found by gradient descent: w: [1.], b: 0.0490\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 432x288 with 1 Axes>\",\n      \"image/png\": \"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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X = X.reshape(-1, 1)  #X should be a 2-D Matrix\\n\",\n    \"model_w,model_b = run_gradient_descent_feng(X, y, iterations=10000, alpha = 1e-5)\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y, marker='x', c='r', label=\\\"Actual Value\\\"); plt.title(\\\"Added x**2 feature\\\")\\n\",\n    \"plt.plot(x, np.dot(X,model_w) + model_b, label=\\\"Predicted Value\\\"); plt.xlabel(\\\"x\\\"); plt.ylabel(\\\"y\\\"); plt.legend(); plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Great! near perfect fit. Notice the values of $\\\\mathbf{w}$ and b printed right above the graph: `w,b found by gradient descent: w: [1.], b: 0.0490`. Gradient descent modified our initial values of $\\\\mathbf{w},b $ to be (1.0,0.049) or a model of $y=1*x_0^2+0.049$, very close to our target of $y=1*x_0^2+1$. If you ran it longer, it could be a better match. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Selecting Features\\n\",\n    \"<a name='GDF'></a>\\n\",\n    \"Above, we knew that an $x^2$ term was required. It may not always be obvious which features are required. One could add a variety of potential features to try and find the most useful. For example, what if we had instead tried : $y=w_0x_0 + w_1x_1^2 + w_2x_2^3+b$ ? \\n\",\n    \"\\n\",\n    \"Run the next cells. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# create target data\\n\",\n    \"x = np.arange(0, 20, 1)\\n\",\n    \"y = x**2\\n\",\n    \"\\n\",\n    \"# engineer features .\\n\",\n    \"X = np.c_[x, x**2, x**3]   #<-- added engineered feature\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration         0, Cost: 1.14029e+03\\n\",\n      \"Iteration      1000, Cost: 3.28539e+02\\n\",\n      \"Iteration      2000, Cost: 2.80443e+02\\n\",\n      \"Iteration      3000, Cost: 2.39389e+02\\n\",\n      \"Iteration      4000, Cost: 2.04344e+02\\n\",\n      \"Iteration      5000, Cost: 1.74430e+02\\n\",\n      \"Iteration      6000, Cost: 1.48896e+02\\n\",\n      \"Iteration      7000, Cost: 1.27100e+02\\n\",\n      \"Iteration      8000, Cost: 1.08495e+02\\n\",\n      \"Iteration      9000, Cost: 9.26132e+01\\n\",\n      \"w,b found by gradient descent: w: [0.08 0.54 0.03], b: 0.0106\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 432x288 with 1 Axes>\",\n      \"image/png\": \"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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"model_w,model_b = run_gradient_descent_feng(X, y, iterations=10000, alpha=1e-7)\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y, marker='x', c='r', label=\\\"Actual Value\\\"); plt.title(\\\"x, x**2, x**3 features\\\")\\n\",\n    \"plt.plot(x, X@model_w + model_b, label=\\\"Predicted Value\\\"); plt.xlabel(\\\"x\\\"); plt.ylabel(\\\"y\\\"); plt.legend(); plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Note the value of $\\\\mathbf{w}$, `[0.08 0.54 0.03]` and b is `0.0106`.This implies the model after fitting/training is:\\n\",\n    \"$$ 0.08x + 0.54x^2 + 0.03x^3 + 0.0106 $$\\n\",\n    \"Gradient descent has emphasized the data that is the best fit to the $x^2$ data by increasing the $w_1$ term relative to the others.  If you were to run for a very long time, it would continue to reduce the impact of the other terms. \\n\",\n    \">Gradient descent is picking the 'correct' features for us by emphasizing its associated parameter\\n\",\n    \"\\n\",\n    \"Let's review this idea:\\n\",\n    \"- Intially, the features were re-scaled so they are comparable to each other\\n\",\n    \"- less weight value implies less important/correct feature, and in extreme, when the weight becomes zero or very close to zero, the associated feature useful in fitting the model to the data.\\n\",\n    \"- above, after fitting, the weight associated with the $x^2$ feature is much larger than the weights for $x$ or $x^3$ as it is the most useful in fitting the data. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### An Alternate View\\n\",\n    \"Above, polynomial features were chosen based on how well they matched the target data. Another way to think about this is to note that we are still using linear regression once we have created new features. Given that, the best features will be linear relative to the target. This is best understood with an example. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# create target data\\n\",\n    \"x = np.arange(0, 20, 1)\\n\",\n    \"y = x**2\\n\",\n    \"\\n\",\n    \"# engineer features .\\n\",\n    \"X = np.c_[x, x**2, x**3]   #<-- added engineered feature\\n\",\n    \"X_features = ['x','x^2','x^3']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 864x216 with 3 Axes>\",\n      \"image/png\": 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig,ax=plt.subplots(1, 3, figsize=(12, 3), sharey=True)\\n\",\n    \"for i in range(len(ax)):\\n\",\n    \"    ax[i].scatter(X[:,i],y)\\n\",\n    \"    ax[i].set_xlabel(X_features[i])\\n\",\n    \"ax[0].set_ylabel(\\\"y\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Above, it is clear that the $x^2$ feature mapped against the target value $y$ is linear. Linear regression can then easily generate a model using that feature.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Scaling features\\n\",\n    \"As described in the last lab, if the data set has features with significantly different scales, one should apply feature scaling to speed gradient descent. In the example above, there is $x$, $x^2$ and $x^3$ which will naturally have very different scales. Let's apply Z-score normalization to our example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Peak to Peak range by column in Raw        X:[  19  361 6859]\\n\",\n      \"Peak to Peak range by column in Normalized X:[3.3  3.18 3.28]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# create target data\\n\",\n    \"x = np.arange(0,20,1)\\n\",\n    \"X = np.c_[x, x**2, x**3]\\n\",\n    \"print(f\\\"Peak to Peak range by column in Raw        X:{np.ptp(X,axis=0)}\\\")\\n\",\n    \"\\n\",\n    \"# add mean_normalization \\n\",\n    \"X = zscore_normalize_features(X)     \\n\",\n    \"print(f\\\"Peak to Peak range by column in Normalized X:{np.ptp(X,axis=0)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Now we can try again with a more aggressive value of alpha:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration         0, Cost: 9.42147e+03\\n\",\n      \"Iteration     10000, Cost: 3.90938e-01\\n\",\n      \"Iteration     20000, Cost: 2.78389e-02\\n\",\n      \"Iteration     30000, Cost: 1.98242e-03\\n\",\n      \"Iteration     40000, Cost: 1.41169e-04\\n\",\n      \"Iteration     50000, Cost: 1.00527e-05\\n\",\n      \"Iteration     60000, Cost: 7.15855e-07\\n\",\n      \"Iteration     70000, Cost: 5.09763e-08\\n\",\n      \"Iteration     80000, Cost: 3.63004e-09\\n\",\n      \"Iteration     90000, Cost: 2.58497e-10\\n\",\n      \"w,b found by gradient descent: w: [5.27e-05 1.13e+02 8.43e-05], b: 123.5000\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 432x288 with 1 Axes>\",\n      \"image/png\": \"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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(0,20,1)\\n\",\n    \"y = x**2\\n\",\n    \"\\n\",\n    \"X = np.c_[x, x**2, x**3]\\n\",\n    \"X = zscore_normalize_features(X) \\n\",\n    \"\\n\",\n    \"model_w, model_b = run_gradient_descent_feng(X, y, iterations=100000, alpha=1e-1)\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y, marker='x', c='r', label=\\\"Actual Value\\\"); plt.title(\\\"Normalized x x**2, x**3 feature\\\")\\n\",\n    \"plt.plot(x,X@model_w + model_b, label=\\\"Predicted Value\\\"); plt.xlabel(\\\"x\\\"); plt.ylabel(\\\"y\\\"); plt.legend(); plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Feature scaling allows this to converge much faster.   \\n\",\n    \"Note again the values of $\\\\mathbf{w}$. The $w_1$ term, which is the $x^2$ term is the most emphasized. Gradient descent has all but eliminated the $x^3$ term.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Complex Functions\\n\",\n    \"With feature engineering, even quite complex functions can be modeled:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration         0, Cost: 2.20188e-01\\n\",\n      \"Iteration    100000, Cost: 1.70074e-02\\n\",\n      \"Iteration    200000, Cost: 1.27603e-02\\n\",\n      \"Iteration    300000, Cost: 9.73032e-03\\n\",\n      \"Iteration    400000, Cost: 7.56440e-03\\n\",\n      \"Iteration    500000, Cost: 6.01412e-03\\n\",\n      \"Iteration    600000, Cost: 4.90251e-03\\n\",\n      \"Iteration    700000, Cost: 4.10351e-03\\n\",\n      \"Iteration    800000, Cost: 3.52730e-03\\n\",\n      \"Iteration    900000, Cost: 3.10989e-03\\n\",\n      \"w,b found by gradient descent: w: [ -1.34 -10.    24.78   5.96 -12.49 -16.26  -9.51   0.59   8.7   11.94\\n\",\n      \"   9.27   0.79 -12.82], b: -0.0073\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 432x288 with 1 Axes>\",\n      \"image/png\": 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.arange(0,20,1)\\n\",\n    \"y = np.cos(x/2)\\n\",\n    \"\\n\",\n    \"X = np.c_[x, x**2, x**3,x**4, x**5, x**6, x**7, x**8, x**9, x**10, x**11, x**12, x**13]\\n\",\n    \"X = zscore_normalize_features(X) \\n\",\n    \"\\n\",\n    \"model_w,model_b = run_gradient_descent_feng(X, y, iterations=1000000, alpha = 1e-1)\\n\",\n    \"\\n\",\n    \"plt.scatter(x, y, marker='x', c='r', label=\\\"Actual Value\\\"); plt.title(\\\"Normalized x x**2, x**3 feature\\\")\\n\",\n    \"plt.plot(x,X@model_w + model_b, label=\\\"Predicted Value\\\"); plt.xlabel(\\\"x\\\"); plt.ylabel(\\\"y\\\"); plt.legend(); plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"\\n\",\n    \"## Congratulations!\\n\",\n    \"In this lab you:\\n\",\n    \"- learned how linear regression can model complex, even highly non-linear functions using feature engineering\\n\",\n    \"- recognized that it is important to apply feature scaling when doing feature engineering\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.10\"\n  },\n  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/C1_W2_Lab05_Sklearn_GD_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Linear Regression using Scikit-Learn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"There is an open-source, commercially usable machine learning toolkit called [scikit-learn](https://scikit-learn.org/stable/index.html). This toolkit contains implementations of many of the algorithms that you will work with in this course.\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab you will:\\n\",\n    \"- Utilize  scikit-learn to implement linear regression using Gradient Descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Tools\\n\",\n    \"You will utilize functions from scikit-learn as well as matplotlib and NumPy. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.linear_model import SGDRegressor\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from lab_utils_multi import  load_house_data\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"np.set_printoptions(precision=2)\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Gradient Descent\\n\",\n    \"Scikit-learn has a gradient descent regression model [sklearn.linear_model.SGDRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#examples-using-sklearn-linear-model-sgdregressor).  Like your previous implementation of gradient descent, this model performs best with normalized inputs. [sklearn.preprocessing.StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler) will perform z-score normalization as in a previous lab. Here it is referred to as 'standard score'.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Load the data set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, y_train = load_house_data()\\n\",\n    \"X_features = ['size(sqft)','bedrooms','floors','age']\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Scale/normalize the training data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Peak to Peak range by column in Raw        X:[2.41e+03 4.00e+00 1.00e+00 9.50e+01]\\n\",\n      \"Peak to Peak range by column in Normalized X:[5.85 6.14 2.06 3.69]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scaler = StandardScaler()\\n\",\n    \"X_norm = scaler.fit_transform(X_train)\\n\",\n    \"print(f\\\"Peak to Peak range by column in Raw        X:{np.ptp(X_train,axis=0)}\\\")   \\n\",\n    \"print(f\\\"Peak to Peak range by column in Normalized X:{np.ptp(X_norm,axis=0)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Create and fit the regression model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"SGDRegressor()\\n\",\n      \"number of iterations completed: 111, number of weight updates: 10990.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sgdr = SGDRegressor(max_iter=1000)\\n\",\n    \"sgdr.fit(X_norm, y_train)\\n\",\n    \"print(sgdr)\\n\",\n    \"print(f\\\"number of iterations completed: {sgdr.n_iter_}, number of weight updates: {sgdr.t_}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### View parameters\\n\",\n    \"Note, the parameters are associated with the *normalized* input data. The fit parameters are very close to those found in the previous lab with this data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"model parameters:                   w: [109.95 -20.97 -32.35 -38.07], b:[363.15]\\n\",\n      \"model parameters from previous lab: w: [110.56 -21.27 -32.71 -37.97], b: 363.16\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"b_norm = sgdr.intercept_\\n\",\n    \"w_norm = sgdr.coef_\\n\",\n    \"print(f\\\"model parameters:                   w: {w_norm}, b:{b_norm}\\\")\\n\",\n    \"print( \\\"model parameters from previous lab: w: [110.56 -21.27 -32.71 -37.97], b: 363.16\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Make predictions\\n\",\n    \"Predict the targets of the training data. Use both the `predict` routine and compute using $w$ and $b$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"prediction using np.dot() and sgdr.predict match: True\\n\",\n      \"Prediction on training set:\\n\",\n      \"[295.17 485.84 389.62 492.  ]\\n\",\n      \"Target values \\n\",\n      \"[300.  509.8 394.  540. ]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# make a prediction using sgdr.predict()\\n\",\n    \"y_pred_sgd = sgdr.predict(X_norm)\\n\",\n    \"# make a prediction using w,b. \\n\",\n    \"y_pred = np.dot(X_norm, w_norm) + b_norm  \\n\",\n    \"print(f\\\"prediction using np.dot() and sgdr.predict match: {(y_pred == y_pred_sgd).all()}\\\")\\n\",\n    \"\\n\",\n    \"print(f\\\"Prediction on training set:\\\\n{y_pred[:4]}\\\" )\\n\",\n    \"print(f\\\"Target values \\\\n{y_train[:4]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Plot Results\\n\",\n    \"Let's plot the predictions versus the target values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"<Figure size 864x216 with 4 Axes>\",\n      \"image/png\": 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\\n\"\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot predictions and targets vs original features    \\n\",\n    \"fig,ax=plt.subplots(1,4,figsize=(12,3),sharey=True)\\n\",\n    \"for i in range(len(ax)):\\n\",\n    \"    ax[i].scatter(X_train[:,i],y_train, label = 'target')\\n\",\n    \"    ax[i].set_xlabel(X_features[i])\\n\",\n    \"    ax[i].scatter(X_train[:,i],y_pred,color=dlc[\\\"dlorange\\\"], label = 'predict')\\n\",\n    \"ax[0].set_ylabel(\\\"Price\\\"); ax[0].legend();\\n\",\n    \"fig.suptitle(\\\"target versus prediction using z-score normalized model\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you:\\n\",\n    \"- utilized an open-source machine learning toolkit, scikit-learn\\n\",\n    \"- implemented linear regression using gradient descent and feature normalization from that toolkit\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/C1_W2_Lab06_Sklearn_Normal_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Linear Regression using Scikit-Learn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"There is an open-source, commercially usable machine learning toolkit called [scikit-learn](https://scikit-learn.org/stable/index.html). This toolkit contains implementations of many of the algorithms that you will work with in this course.\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab you will:\\n\",\n    \"- Utilize  scikit-learn to implement linear regression using a close form solution based on the normal equation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Tools\\n\",\n    \"You will utilize functions from scikit-learn as well as matplotlib and NumPy. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.linear_model import LinearRegression\\n\",\n    \"from lab_utils_multi import load_house_data\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"np.set_printoptions(precision=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"toc_40291_2\\\"></a>\\n\",\n    \"# Linear Regression, closed-form solution\\n\",\n    \"Scikit-learn has the [linear regression model](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression) which implements a closed-form linear regression.\\n\",\n    \"\\n\",\n    \"Let's use the data from the early labs - a house with 1000 square feet sold for \\\\\\\\$300,000 and a house with 2000 square feet sold for \\\\\\\\$500,000.\\n\",\n    \"\\n\",\n    \"| Size (1000 sqft)     | Price (1000s of dollars) |\\n\",\n    \"| ----------------| ------------------------ |\\n\",\n    \"| 1               | 300                      |\\n\",\n    \"| 2               | 500                      |\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Load the data set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([1.0, 2.0])   #features\\n\",\n    \"y_train = np.array([300, 500])   #target value\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Create and fit the model\\n\",\n    \"The code below performs regression using scikit-learn. \\n\",\n    \"The first step creates a regression object.  \\n\",\n    \"The second step utilizes one of the methods associated with the object, `fit`. This performs regression, fitting the parameters to the input data. The toolkit expects a two-dimensional X matrix.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"LinearRegression()\"\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"linear_model = LinearRegression()\\n\",\n    \"#X must be a 2-D Matrix\\n\",\n    \"linear_model.fit(X_train.reshape(-1, 1), y_train) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### View Parameters \\n\",\n    \"The $\\\\mathbf{w}$ and $\\\\mathbf{b}$ parameters are referred to as 'coefficients' and 'intercept' in scikit-learn.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"w = [200.], b = 100.00\\n\",\n      \"'manual' prediction: f_wb = wx+b : [240100.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"b = linear_model.intercept_\\n\",\n    \"w = linear_model.coef_\\n\",\n    \"print(f\\\"w = {w:}, b = {b:0.2f}\\\")\\n\",\n    \"print(f\\\"'manual' prediction: f_wb = wx+b : {1200*w + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Make Predictions\\n\",\n    \"\\n\",\n    \"Calling the `predict` function generates predictions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Prediction on training set: [300. 500.]\\n\",\n      \"Prediction for 1200 sqft house: $240100.00\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"y_pred = linear_model.predict(X_train.reshape(-1, 1))\\n\",\n    \"\\n\",\n    \"print(\\\"Prediction on training set:\\\", y_pred)\\n\",\n    \"\\n\",\n    \"X_test = np.array([[1200]])\\n\",\n    \"print(f\\\"Prediction for 1200 sqft house: ${linear_model.predict(X_test)[0]:0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Second Example\\n\",\n    \"The second example is from an earlier lab with multiple features. The final parameter values and predictions are very close to the results from the un-normalized 'long-run' from that lab. That un-normalized run took hours to produce results, while this is nearly instantaneous. The closed-form solution work well on smaller data sets such as these but can be computationally demanding on larger data sets. \\n\",\n    \">The closed-form solution does not require normalization.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load the dataset\\n\",\n    \"X_train, y_train = load_house_data()\\n\",\n    \"X_features = ['size(sqft)','bedrooms','floors','age']\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"LinearRegression()\"\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"linear_model = LinearRegression()\\n\",\n    \"linear_model.fit(X_train, y_train) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"w = [  0.27 -32.62 -67.25  -1.47], b = 220.42\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"b = linear_model.intercept_\\n\",\n    \"w = linear_model.coef_\\n\",\n    \"print(f\\\"w = {w:}, b = {b:0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Prediction on training set:\\n\",\n      \" [295.18 485.98 389.52 492.15]\\n\",\n      \"prediction using w,b:\\n\",\n      \" [295.18 485.98 389.52 492.15]\\n\",\n      \"Target values \\n\",\n      \" [300.  509.8 394.  540. ]\\n\",\n      \" predicted price of a house with 1200 sqft, 3 bedrooms, 1 floor, 40 years old = $318709.09\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Prediction on training set:\\\\n {linear_model.predict(X_train)[:4]}\\\" )\\n\",\n    \"print(f\\\"prediction using w,b:\\\\n {(X_train @ w + b)[:4]}\\\")\\n\",\n    \"print(f\\\"Target values \\\\n {y_train[:4]}\\\")\\n\",\n    \"\\n\",\n    \"x_house = np.array([1200, 3,1, 40]).reshape(-1,4)\\n\",\n    \"x_house_predict = linear_model.predict(x_house)[0]\\n\",\n    \"print(f\\\" predicted price of a house with 1200 sqft, 3 bedrooms, 1 floor, 40 years old = ${x_house_predict*1000:0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you:\\n\",\n    \"- utilized an open-source machine learning toolkit, scikit-learn\\n\",\n    \"- implemented linear regression using a close-form solution from that toolkit\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
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    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/data/houses.txt",
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  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/deeplearning.mplstyle",
    "content": "# see https://matplotlib.org/stable/tutorials/introductory/customizing.html\nlines.linewidth: 4\nlines.solid_capstyle: butt\n\nlegend.fancybox: true\n\n# Verdana\" for non-math text,\n# Cambria Math\n\n#Blue (Crayon-Aqua) 0096FF\n#Dark Red C00000\n#Orange (Apple Orange) FF9300\n#Black 000000\n#Magenta FF40FF\n#Purple 7030A0\n\naxes.prop_cycle: cycler('color', ['0096FF', 'FF9300', 'FF40FF', '7030A0', 'C00000'])\n#axes.facecolor: f0f0f0 # grey\naxes.facecolor: ffffff  # white\naxes.labelsize: large\naxes.axisbelow: true\naxes.grid: False\naxes.edgecolor: f0f0f0\naxes.linewidth: 3.0\naxes.titlesize: x-large\n\npatch.edgecolor: f0f0f0\npatch.linewidth: 0.5\n\nsvg.fonttype: path\n\ngrid.linestyle: -\ngrid.linewidth: 1.0\ngrid.color: cbcbcb\n\nxtick.major.size: 0\nxtick.minor.size: 0\nytick.major.size: 0\nytick.minor.size: 0\n\nsavefig.edgecolor: f0f0f0\nsavefig.facecolor: f0f0f0\n\n#figure.subplot.left: 0.08\n#figure.subplot.right: 0.95\n#figure.subplot.bottom: 0.07\n\n#figure.facecolor: f0f0f0  # grey\nfigure.facecolor: ffffff  # white\n\n## ***************************************************************************\n## * FONT                                                                    *\n## ***************************************************************************\n## The font properties used by `text.Text`.\n## See https://matplotlib.org/api/font_manager_api.html for more information\n## on font properties.  The 6 font properties used for font matching are\n## given below with their default values.\n##\n## The font.family property can take either a concrete font name (not supported\n## when rendering text with usetex), or one of the following five generic\n## values:\n##     - 'serif' (e.g., Times),\n##     - 'sans-serif' (e.g., Helvetica),\n##     - 'cursive' (e.g., Zapf-Chancery),\n##     - 'fantasy' (e.g., Western), and\n##     - 'monospace' (e.g., Courier).\n## Each of these values has a corresponding default list of font names\n## (font.serif, etc.); the first available font in the list is used.  Note that\n## for font.serif, font.sans-serif, and font.monospace, the first element of\n## the list (a DejaVu font) will always be used because DejaVu is shipped with\n## Matplotlib and is thus guaranteed to be available; the other entries are\n## left as examples of other possible values.\n##\n## The font.style property has three values: normal (or roman), italic\n## or oblique.  The oblique style will be used for italic, if it is not\n## present.\n##\n## The font.variant property has two values: normal or small-caps.  For\n## TrueType fonts, which are scalable fonts, small-caps is equivalent\n## to using a font size of 'smaller', or about 83%% of the current font\n## size.\n##\n## The font.weight property has effectively 13 values: normal, bold,\n## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as\n## 400, and bold is 700.  bolder and lighter are relative values with\n## respect to the current weight.\n##\n## The font.stretch property has 11 values: ultra-condensed,\n## extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This\n## property is not currently implemented.\n##\n## The font.size property is the default font size for text, given in points.\n## 10 pt is the standard value.\n##\n## Note that font.size controls default text sizes.  To configure\n## special text sizes tick labels, axes, labels, title, etc., see the rc\n## settings for axes and ticks.  Special text sizes can be defined\n## relative to font.size, using the following values: xx-small, x-small,\n## small, medium, large, x-large, xx-large, larger, or smaller\n\n\nfont.family:  sans-serif\nfont.style:   normal\nfont.variant: normal\nfont.weight:  normal\nfont.stretch: normal\nfont.size:    12.0\n\nfont.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\nfont.sans-serif: Verdana, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\nfont.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive\nfont.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy\nfont.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n\n\n## ***************************************************************************\n## * TEXT                                                                    *\n## ***************************************************************************\n## The text properties used by `text.Text`.\n## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\n## for more information on text properties\n#text.color: black\n\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/lab_utils_common.py",
    "content": "\"\"\" \nlab_utils_common.py\n    functions common to all optional labs, Course 1, Week 2 \n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nplt.style.use('./deeplearning.mplstyle')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0';\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0')\n\n\n##########################################################\n# Regression Routines\n##########################################################\n\n#Function to calculate the cost\ndef compute_cost_matrix(X, y, w, b, verbose=False):\n    \"\"\"\n    Computes the gradient for linear regression\n     Args:\n      X (ndarray (m,n)): Data, m examples with n features\n      y (ndarray (m,)) : target values\n      w (ndarray (n,)) : model parameters  \n      b (scalar)       : model parameter\n      verbose : (Boolean) If true, print out intermediate value f_wb\n    Returns\n      cost: (scalar)\n    \"\"\"\n    m = X.shape[0]\n\n    # calculate f_wb for all examples.\n    f_wb = X @ w + b\n    # calculate cost\n    total_cost = (1/(2*m)) * np.sum((f_wb-y)**2)\n\n    if verbose: print(\"f_wb:\")\n    if verbose: print(f_wb)\n\n    return total_cost\n\ndef compute_gradient_matrix(X, y, w, b):\n    \"\"\"\n    Computes the gradient for linear regression\n\n    Args:\n      X (ndarray (m,n)): Data, m examples with n features\n      y (ndarray (m,)) : target values\n      w (ndarray (n,)) : model parameters  \n      b (scalar)       : model parameter\n    Returns\n      dj_dw (ndarray (n,1)): The gradient of the cost w.r.t. the parameters w.\n      dj_db (scalar):        The gradient of the cost w.r.t. the parameter b.\n\n    \"\"\"\n    m,n = X.shape\n    f_wb = X @ w + b\n    e   = f_wb - y\n    dj_dw  = (1/m) * (X.T @ e)\n    dj_db  = (1/m) * np.sum(e)\n\n    return dj_db,dj_dw\n\n\n# Loop version of multi-variable compute_cost\ndef compute_cost(X, y, w, b):\n    \"\"\"\n    compute cost\n    Args:\n      X (ndarray (m,n)): Data, m examples with n features\n      y (ndarray (m,)) : target values\n      w (ndarray (n,)) : model parameters  \n      b (scalar)       : model parameter\n    Returns\n      cost (scalar)    : cost\n    \"\"\"\n    m = X.shape[0]\n    cost = 0.0\n    for i in range(m):\n        f_wb_i = np.dot(X[i],w) + b           #(n,)(n,)=scalar\n        cost = cost + (f_wb_i - y[i])**2\n    cost = cost/(2*m)\n    return cost \n\ndef compute_gradient(X, y, w, b):\n    \"\"\"\n    Computes the gradient for linear regression\n    Args:\n      X (ndarray (m,n)): Data, m examples with n features\n      y (ndarray (m,)) : target values\n      w (ndarray (n,)) : model parameters  \n      b (scalar)       : model parameter\n    Returns\n      dj_dw (ndarray Shape (n,)): The gradient of the cost w.r.t. the parameters w.\n      dj_db (scalar):             The gradient of the cost w.r.t. the parameter b.\n    \"\"\"\n    m,n = X.shape           #(number of examples, number of features)\n    dj_dw = np.zeros((n,))\n    dj_db = 0.\n\n    for i in range(m):\n        err = (np.dot(X[i], w) + b) - y[i]\n        for j in range(n):\n            dj_dw[j] = dj_dw[j] + err * X[i,j]\n        dj_db = dj_db + err\n    dj_dw = dj_dw/m\n    dj_db = dj_db/m\n\n    return dj_db,dj_dw\n\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Optional Labs/lab_utils_multi.py",
    "content": "import numpy as np\nimport copy\nimport math\nfrom scipy.stats import norm\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import axes3d\nfrom matplotlib.ticker import MaxNLocator\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0'; \nplt.style.use('./deeplearning.mplstyle')\n\ndef load_data_multi():\n    data = np.loadtxt(\"data/ex1data2.txt\", delimiter=',')\n    X = data[:,:2]\n    y = data[:,2]\n    return X, y\n\n##########################################################\n# Plotting Routines\n##########################################################\n\ndef plt_house_x(X, y,f_wb=None, ax=None):\n    ''' plot house with aXis '''\n    if not ax:\n        fig, ax = plt.subplots(1,1)\n    ax.scatter(X, y, marker='x', c='r', label=\"Actual Value\")\n\n    ax.set_title(\"Housing Prices\")\n    ax.set_ylabel('Price (in 1000s of dollars)')\n    ax.set_xlabel(f'Size (1000 sqft)')\n    if f_wb is not None:\n        ax.plot(X, f_wb,  c=dlblue, label=\"Our Prediction\")\n    ax.legend()\n    \n\ndef mk_cost_lines(x,y,w,b, ax):\n    ''' makes vertical cost lines'''\n    cstr = \"cost = (1/2m)*1000*(\"\n    ctot = 0\n    label = 'cost for point'\n    for p in zip(x,y):\n        f_wb_p = w*p[0]+b\n        c_p = ((f_wb_p - p[1])**2)/2\n        c_p_txt = c_p/1000\n        ax.vlines(p[0], p[1],f_wb_p, lw=3, color=dlpurple, ls='dotted', label=label)\n        label='' #just one\n        cxy = [p[0], p[1] + (f_wb_p-p[1])/2]\n        ax.annotate(f'{c_p_txt:0.0f}', xy=cxy, xycoords='data',color=dlpurple, \n            xytext=(5, 0), textcoords='offset points')\n        cstr += f\"{c_p_txt:0.0f} +\"\n        ctot += c_p\n    ctot = ctot/(len(x))\n    cstr = cstr[:-1] + f\") = {ctot:0.0f}\"\n    ax.text(0.15,0.02,cstr, transform=ax.transAxes, color=dlpurple)\n    \n    \ndef inbounds(a,b,xlim,ylim):\n    xlow,xhigh = xlim\n    ylow,yhigh = ylim\n    ax, ay = a\n    bx, by = b\n    if (ax > xlow and ax < xhigh) and (bx > xlow and bx < xhigh) \\\n        and (ay > ylow and ay < yhigh) and (by > ylow and by < yhigh):\n        return(True)\n    else:\n        return(False)\n\nfrom mpl_toolkits.mplot3d import axes3d\ndef plt_contour_wgrad(x, y, hist, ax, w_range=[-100, 500, 5], b_range=[-500, 500, 5], \n                contours = [0.1,50,1000,5000,10000,25000,50000], \n                      resolution=5, w_final=200, b_final=100,step=10 ):\n    b0,w0 = np.meshgrid(np.arange(*b_range),np.arange(*w_range))\n    z=np.zeros_like(b0)\n    n,_ = w0.shape\n    for i in range(w0.shape[0]):\n        for j in range(w0.shape[1]):\n            z[i][j] = compute_cost(x, y, w0[i][j], b0[i][j] )\n   \n    CS = ax.contour(w0, b0, z, contours, linewidths=2,\n                   colors=[dlblue, dlorange, dldarkred, dlmagenta, dlpurple]) \n    ax.clabel(CS, inline=1, fmt='%1.0f', fontsize=10)\n    ax.set_xlabel(\"w\");  ax.set_ylabel(\"b\")\n    ax.set_title('Contour plot of cost J(w,b), vs b,w with path of gradient descent')\n    w = w_final; b=b_final\n    ax.hlines(b, ax.get_xlim()[0],w, lw=2, color=dlpurple, ls='dotted')\n    ax.vlines(w, ax.get_ylim()[0],b, lw=2, color=dlpurple, ls='dotted')\n\n    base = hist[0]\n    for point in hist[0::step]:\n        edist = np.sqrt((base[0] - point[0])**2 + (base[1] - point[1])**2)\n        if(edist > resolution or point==hist[-1]):\n            if inbounds(point,base, ax.get_xlim(),ax.get_ylim()):\n                plt.annotate('', xy=point, xytext=base,xycoords='data',\n                         arrowprops={'arrowstyle': '->', 'color': 'r', 'lw': 3},\n                         va='center', ha='center')\n            base=point\n    return\n\n\n# plots p1 vs p2. Prange is an array of entries [min, max, steps]. In feature scaling lab.\ndef plt_contour_multi(x, y, w, b, ax, prange, p1, p2, title=\"\", xlabel=\"\", ylabel=\"\"): \n    contours = [1e2, 2e2,3e2,4e2, 5e2, 6e2, 7e2,8e2,1e3, 1.25e3,1.5e3, 1e4, 1e5, 1e6, 1e7]\n    px,py = np.meshgrid(np.linspace(*(prange[p1])),np.linspace(*(prange[p2])))\n    z=np.zeros_like(px)\n    n,_ = px.shape\n    for i in range(px.shape[0]):\n        for j in range(px.shape[1]):\n            w_ij = w\n            b_ij = b\n            if p1 <= 3: w_ij[p1] = px[i,j]\n            if p1 == 4: b_ij = px[i,j]\n            if p2 <= 3: w_ij[p2] = py[i,j]\n            if p2 == 4: b_ij = py[i,j]\n                \n            z[i][j] = compute_cost(x, y, w_ij, b_ij )\n    CS = ax.contour(px, py, z, contours, linewidths=2,\n                   colors=[dlblue, dlorange, dldarkred, dlmagenta, dlpurple]) \n    ax.clabel(CS, inline=1, fmt='%1.2e', fontsize=10)\n    ax.set_xlabel(xlabel);  ax.set_ylabel(ylabel)\n    ax.set_title(title, fontsize=14)\n\n\ndef plt_equal_scale(X_train, X_norm, y_train):\n    fig,ax = plt.subplots(1,2,figsize=(12,5))\n    prange = [\n              [ 0.238-0.045, 0.238+0.045,  50],\n              [-25.77326319-0.045, -25.77326319+0.045, 50],\n              [-50000, 0,      50],\n              [-1500,  0,      50],\n              [0, 200000, 50]]\n    w_best = np.array([0.23844318, -25.77326319, -58.11084634,  -1.57727192])\n    b_best = 235\n    plt_contour_multi(X_train, y_train, w_best, b_best, ax[0], prange, 0, 1, \n                      title='Unnormalized, J(w,b), vs w[0],w[1]',\n                      xlabel= \"w[0] (size(sqft))\", ylabel=\"w[1] (# bedrooms)\")\n    #\n    w_best = np.array([111.1972, -16.75480051, -28.51530411, -37.17305735])\n    b_best = 376.949151515151\n    prange = [[ 111-50, 111+50,   75],\n              [-16.75-50,-16.75+50, 75],\n              [-28.5-8, -28.5+8,  50],\n              [-37.1-16,-37.1+16, 50],\n              [376-150, 376+150, 50]]\n    plt_contour_multi(X_norm, y_train, w_best, b_best, ax[1], prange, 0, 1, \n                      title='Normalized, J(w,b), vs w[0],w[1]',\n                      xlabel= \"w[0] (normalized size(sqft))\", ylabel=\"w[1] (normalized # bedrooms)\")\n    fig.suptitle(\"Cost contour with equal scale\", fontsize=18)\n    #plt.tight_layout(rect=(0,0,1.05,1.05))\n    fig.tight_layout(rect=(0,0,1,0.95))\n    plt.show()\n    \ndef plt_divergence(p_hist, J_hist, x_train,y_train):\n\n    x=np.zeros(len(p_hist))\n    y=np.zeros(len(p_hist))\n    v=np.zeros(len(p_hist))\n    for i in range(len(p_hist)):\n        x[i] = p_hist[i][0]\n        y[i] = p_hist[i][1]\n        v[i] = J_hist[i]\n\n    fig = plt.figure(figsize=(12,5))\n    plt.subplots_adjust( wspace=0 )\n    gs = fig.add_gridspec(1, 5)\n    fig.suptitle(f\"Cost escalates when learning rate is too large\")\n    #===============\n    #  First subplot\n    #===============\n    ax = fig.add_subplot(gs[:2], )\n\n    # Print w vs cost to see minimum\n    fix_b = 100\n    w_array = np.arange(-70000, 70000, 1000)\n    cost = np.zeros_like(w_array)\n\n    for i in range(len(w_array)):\n        tmp_w = w_array[i]\n        cost[i] = compute_cost(x_train, y_train, tmp_w, fix_b)\n\n    ax.plot(w_array, cost)\n    ax.plot(x,v, c=dlmagenta)\n    ax.set_title(\"Cost vs w, b set to 100\")\n    ax.set_ylabel('Cost')\n    ax.set_xlabel('w')\n    ax.xaxis.set_major_locator(MaxNLocator(2)) \n\n    #===============\n    # Second Subplot\n    #===============\n\n    tmp_b,tmp_w = np.meshgrid(np.arange(-35000, 35000, 500),np.arange(-70000, 70000, 500))\n    z=np.zeros_like(tmp_b)\n    for i in range(tmp_w.shape[0]):\n        for j in range(tmp_w.shape[1]):\n            z[i][j] = compute_cost(x_train, y_train, tmp_w[i][j], tmp_b[i][j] )\n\n    ax = fig.add_subplot(gs[2:], projection='3d')\n    ax.plot_surface(tmp_w, tmp_b, z,  alpha=0.3, color=dlblue)\n    ax.xaxis.set_major_locator(MaxNLocator(2)) \n    ax.yaxis.set_major_locator(MaxNLocator(2)) \n\n    ax.set_xlabel('w', fontsize=16)\n    ax.set_ylabel('b', fontsize=16)\n    ax.set_zlabel('\\ncost', fontsize=16)\n    plt.title('Cost vs (b, w)')\n    # Customize the view angle \n    ax.view_init(elev=20., azim=-65)\n    ax.plot(x, y, v,c=dlmagenta)\n    \n    return\n\n# draw derivative line\n# y = m*(x - x1) + y1\ndef add_line(dj_dx, x1, y1, d, ax):\n    x = np.linspace(x1-d, x1+d,50)\n    y = dj_dx*(x - x1) + y1\n    ax.scatter(x1, y1, color=dlblue, s=50)\n    ax.plot(x, y, '--', c=dldarkred,zorder=10, linewidth = 1)\n    xoff = 30 if x1 == 200 else 10\n    ax.annotate(r\"$\\frac{\\partial J}{\\partial w}$ =%d\" % dj_dx, fontsize=14,\n                xy=(x1, y1), xycoords='data',\n            xytext=(xoff, 10), textcoords='offset points',\n            arrowprops=dict(arrowstyle=\"->\"),\n            horizontalalignment='left', verticalalignment='top')\n\ndef plt_gradients(x_train,y_train, f_compute_cost, f_compute_gradient):\n    #===============\n    #  First subplot\n    #===============\n    fig,ax = plt.subplots(1,2,figsize=(12,4))\n\n    # Print w vs cost to see minimum\n    fix_b = 100\n    w_array = np.linspace(-100, 500, 50)\n    w_array = np.linspace(0, 400, 50)\n    cost = np.zeros_like(w_array)\n\n    for i in range(len(w_array)):\n        tmp_w = w_array[i]\n        cost[i] = f_compute_cost(x_train, y_train, tmp_w, fix_b)\n    ax[0].plot(w_array, cost,linewidth=1)\n    ax[0].set_title(\"Cost vs w, with gradient; b set to 100\")\n    ax[0].set_ylabel('Cost')\n    ax[0].set_xlabel('w')\n\n    # plot lines for fixed b=100\n    for tmp_w in [100,200,300]:\n        fix_b = 100\n        dj_dw,dj_db = f_compute_gradient(x_train, y_train, tmp_w, fix_b )\n        j = f_compute_cost(x_train, y_train, tmp_w, fix_b)\n        add_line(dj_dw, tmp_w, j, 30, ax[0])\n\n    #===============\n    # Second Subplot\n    #===============\n\n    tmp_b,tmp_w = np.meshgrid(np.linspace(-200, 200, 10), np.linspace(-100, 600, 10))\n    U = np.zeros_like(tmp_w)\n    V = np.zeros_like(tmp_b)\n    for i in range(tmp_w.shape[0]):\n        for j in range(tmp_w.shape[1]):\n            U[i][j], V[i][j] = f_compute_gradient(x_train, y_train, tmp_w[i][j], tmp_b[i][j] )\n    X = tmp_w\n    Y = tmp_b\n    n=-2\n    color_array = np.sqrt(((V-n)/2)**2 + ((U-n)/2)**2)\n\n    ax[1].set_title('Gradient shown in quiver plot')\n    Q = ax[1].quiver(X, Y, U, V, color_array, units='width', )\n    qk = ax[1].quiverkey(Q, 0.9, 0.9, 2, r'$2 \\frac{m}{s}$', labelpos='E',coordinates='figure')\n    ax[1].set_xlabel(\"w\"); ax[1].set_ylabel(\"b\")\n\ndef norm_plot(ax, data):\n    scale = (np.max(data) - np.min(data))*0.2\n    x = np.linspace(np.min(data)-scale,np.max(data)+scale,50)\n    _,bins, _ = ax.hist(data, x, color=\"xkcd:azure\")\n    #ax.set_ylabel(\"Count\")\n    \n    mu = np.mean(data); \n    std = np.std(data); \n    dist = norm.pdf(bins, loc=mu, scale = std)\n    \n    axr = ax.twinx()\n    axr.plot(bins,dist, color = \"orangered\", lw=2)\n    axr.set_ylim(bottom=0)\n    axr.axis('off')\n    \ndef plot_cost_i_w(X,y,hist):\n    ws = np.array([ p[0] for p in hist[\"params\"]])\n    rng = max(abs(ws[:,0].min()),abs(ws[:,0].max()))\n    wr = np.linspace(-rng+0.27,rng+0.27,20)\n    cst = [compute_cost(X,y,np.array([wr[i],-32, -67, -1.46]), 221) for i in range(len(wr))]\n\n    fig,ax = plt.subplots(1,2,figsize=(12,3))\n    ax[0].plot(hist[\"iter\"], (hist[\"cost\"]));  ax[0].set_title(\"Cost vs Iteration\")\n    ax[0].set_xlabel(\"iteration\"); ax[0].set_ylabel(\"Cost\")\n    ax[1].plot(wr, cst); ax[1].set_title(\"Cost vs w[0]\")\n    ax[1].set_xlabel(\"w[0]\"); ax[1].set_ylabel(\"Cost\")\n    ax[1].plot(ws[:,0],hist[\"cost\"])\n    plt.show()\n\n \n##########################################################\n# Regression Routines\n##########################################################\n\ndef compute_gradient_matrix(X, y, w, b): \n    \"\"\"\n    Computes the gradient for linear regression \n \n    Args:\n      X : (array_like Shape (m,n)) variable such as house size \n      y : (array_like Shape (m,1)) actual value \n      w : (array_like Shape (n,1)) Values of parameters of the model      \n      b : (scalar )                Values of parameter of the model      \n    Returns\n      dj_dw: (array_like Shape (n,1)) The gradient of the cost w.r.t. the parameters w. \n      dj_db: (scalar)                The gradient of the cost w.r.t. the parameter b. \n                                  \n    \"\"\"\n    m,n = X.shape\n    f_wb = X @ w + b              \n    e   = f_wb - y                \n    dj_dw  = (1/m) * (X.T @ e)    \n    dj_db  = (1/m) * np.sum(e)    \n        \n    return dj_db,dj_dw\n\n#Function to calculate the cost\ndef compute_cost_matrix(X, y, w, b, verbose=False):\n    \"\"\"\n    Computes the gradient for linear regression \n     Args:\n      X : (array_like Shape (m,n)) variable such as house size \n      y : (array_like Shape (m,)) actual value \n      w : (array_like Shape (n,)) parameters of the model \n      b : (scalar               ) parameter of the model \n      verbose : (Boolean) If true, print out intermediate value f_wb\n    Returns\n      cost: (scalar)                      \n    \"\"\" \n    m,n = X.shape\n\n    # calculate f_wb for all examples.\n    f_wb = X @ w + b  \n    # calculate cost\n    total_cost = (1/(2*m)) * np.sum((f_wb-y)**2)\n\n    if verbose: print(\"f_wb:\")\n    if verbose: print(f_wb)\n        \n    return total_cost\n\n# Loop version of multi-variable compute_cost\ndef compute_cost(X, y, w, b): \n    \"\"\"\n    compute cost\n    Args:\n      X : (ndarray): Shape (m,n) matrix of examples with multiple features\n      w : (ndarray): Shape (n)   parameters for prediction   \n      b : (scalar):              parameter  for prediction   \n    Returns\n      cost: (scalar)             cost\n    \"\"\"\n    m = X.shape[0]\n    cost = 0.0\n    for i in range(m):                                \n        f_wb_i = np.dot(X[i],w) + b       \n        cost = cost + (f_wb_i - y[i])**2              \n    cost = cost/(2*m)                                 \n    return(np.squeeze(cost)) \n\ndef compute_gradient(X, y, w, b): \n    \"\"\"\n    Computes the gradient for linear regression \n    Args:\n      X : (ndarray Shape (m,n)) matrix of examples \n      y : (ndarray Shape (m,))  target value of each example\n      w : (ndarray Shape (n,))  parameters of the model      \n      b : (scalar)              parameter of the model      \n    Returns\n      dj_dw : (ndarray Shape (n,)) The gradient of the cost w.r.t. the parameters w. \n      dj_db : (scalar)             The gradient of the cost w.r.t. the parameter b. \n    \"\"\"\n    m,n = X.shape           #(number of examples, number of features)\n    dj_dw = np.zeros((n,))\n    dj_db = 0.\n\n    for i in range(m):                             \n        err = (np.dot(X[i], w) + b) - y[i]   \n        for j in range(n):                         \n            dj_dw[j] = dj_dw[j] + err * X[i,j]    \n        dj_db = dj_db + err                        \n    dj_dw = dj_dw/m                                \n    dj_db = dj_db/m                                \n        \n    return dj_db,dj_dw\n\n#This version saves more values and is more verbose than the assigment versons\ndef gradient_descent_houses(X, y, w_in, b_in, cost_function, gradient_function, alpha, num_iters): \n    \"\"\"\n    Performs batch gradient descent to learn theta. Updates theta by taking \n    num_iters gradient steps with learning rate alpha\n    \n    Args:\n      X : (array_like Shape (m,n)    matrix of examples \n      y : (array_like Shape (m,))    target value of each example\n      w_in : (array_like Shape (n,)) Initial values of parameters of the model\n      b_in : (scalar)                Initial value of parameter of the model\n      cost_function: function to compute cost\n      gradient_function: function to compute the gradient\n      alpha : (float) Learning rate\n      num_iters : (int) number of iterations to run gradient descent\n    Returns\n      w : (array_like Shape (n,)) Updated values of parameters of the model after\n          running gradient descent\n      b : (scalar)                Updated value of parameter of the model after\n          running gradient descent\n    \"\"\"\n    \n    # number of training examples\n    m = len(X)\n    \n    # An array to store values at each iteration primarily for graphing later\n    hist={}\n    hist[\"cost\"] = []; hist[\"params\"] = []; hist[\"grads\"]=[]; hist[\"iter\"]=[];\n    \n    w = copy.deepcopy(w_in)  #avoid modifying global w within function\n    b = b_in\n    save_interval = np.ceil(num_iters/10000) # prevent resource exhaustion for long runs\n\n    print(f\"Iteration Cost          w0       w1       w2       w3       b       djdw0    djdw1    djdw2    djdw3    djdb  \")\n    print(f\"---------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|--------|\")\n\n    for i in range(num_iters):\n\n        # Calculate the gradient and update the parameters\n        dj_db,dj_dw = gradient_function(X, y, w, b)   \n\n        # Update Parameters using w, b, alpha and gradient\n        w = w - alpha * dj_dw               \n        b = b - alpha * dj_db               \n      \n        # Save cost J,w,b at each save interval for graphing\n        if i == 0 or i % save_interval == 0:     \n            hist[\"cost\"].append(cost_function(X, y, w, b))\n            hist[\"params\"].append([w,b])\n            hist[\"grads\"].append([dj_dw,dj_db])\n            hist[\"iter\"].append(i)\n\n        # Print cost every at intervals 10 times or as many iterations if < 10\n        if i% math.ceil(num_iters/10) == 0:\n            #print(f\"Iteration {i:4d}: Cost {cost_function(X, y, w, b):8.2f}   \")\n            cst = cost_function(X, y, w, b)\n            print(f\"{i:9d} {cst:0.5e} {w[0]: 0.1e} {w[1]: 0.1e} {w[2]: 0.1e} {w[3]: 0.1e} {b: 0.1e} {dj_dw[0]: 0.1e} {dj_dw[1]: 0.1e} {dj_dw[2]: 0.1e} {dj_dw[3]: 0.1e} {dj_db: 0.1e}\")\n       \n    return w, b, hist #return w,b and history for graphing\n\ndef run_gradient_descent(X,y,iterations=1000, alpha = 1e-6):\n\n    m,n = X.shape\n    # initialize parameters\n    initial_w = np.zeros(n)\n    initial_b = 0\n    # run gradient descent\n    w_out, b_out, hist_out = gradient_descent_houses(X ,y, initial_w, initial_b,\n                                               compute_cost, compute_gradient_matrix, alpha, iterations)\n    print(f\"w,b found by gradient descent: w: {w_out}, b: {b_out:0.2f}\")\n    \n    return(w_out, b_out, hist_out)\n\n# compact extaction of hist data\n#x = hist[\"iter\"]\n#J  = np.array([ p    for p in hist[\"cost\"]])\n#ws = np.array([ p[0] for p in hist[\"params\"]])\n#dj_ws = np.array([ p[0] for p in hist[\"grads\"]])\n\n#bs = np.array([ p[1] for p in hist[\"params\"]]) \n\ndef run_gradient_descent_feng(X,y,iterations=1000, alpha = 1e-6):\n    m,n = X.shape\n    # initialize parameters\n    initial_w = np.zeros(n)\n    initial_b = 0\n    # run gradient descent\n    w_out, b_out, hist_out = gradient_descent(X ,y, initial_w, initial_b,\n                                               compute_cost, compute_gradient_matrix, alpha, iterations)\n    print(f\"w,b found by gradient descent: w: {w_out}, b: {b_out:0.4f}\")\n    \n    return(w_out, b_out)\n\ndef gradient_descent(X, y, w_in, b_in, cost_function, gradient_function, alpha, num_iters): \n    \"\"\"\n    Performs batch gradient descent to learn theta. Updates theta by taking \n    num_iters gradient steps with learning rate alpha\n    \n    Args:\n      X : (array_like Shape (m,n)    matrix of examples \n      y : (array_like Shape (m,))    target value of each example\n      w_in : (array_like Shape (n,)) Initial values of parameters of the model\n      b_in : (scalar)                Initial value of parameter of the model\n      cost_function: function to compute cost\n      gradient_function: function to compute the gradient\n      alpha : (float) Learning rate\n      num_iters : (int) number of iterations to run gradient descent\n    Returns\n      w : (array_like Shape (n,)) Updated values of parameters of the model after\n          running gradient descent\n      b : (scalar)                Updated value of parameter of the model after\n          running gradient descent\n    \"\"\"\n    \n    # number of training examples\n    m = len(X)\n    \n    # An array to store values at each iteration primarily for graphing later\n    hist={}\n    hist[\"cost\"] = []; hist[\"params\"] = []; hist[\"grads\"]=[]; hist[\"iter\"]=[];\n    \n    w = copy.deepcopy(w_in)  #avoid modifying global w within function\n    b = b_in\n    save_interval = np.ceil(num_iters/10000) # prevent resource exhaustion for long runs\n\n    for i in range(num_iters):\n\n        # Calculate the gradient and update the parameters\n        dj_db,dj_dw = gradient_function(X, y, w, b)   \n\n        # Update Parameters using w, b, alpha and gradient\n        w = w - alpha * dj_dw               \n        b = b - alpha * dj_db               \n      \n        # Save cost J,w,b at each save interval for graphing\n        if i == 0 or i % save_interval == 0:     \n            hist[\"cost\"].append(cost_function(X, y, w, b))\n            hist[\"params\"].append([w,b])\n            hist[\"grads\"].append([dj_dw,dj_db])\n            hist[\"iter\"].append(i)\n\n        # Print cost every at intervals 10 times or as many iterations if < 10\n        if i% math.ceil(num_iters/10) == 0:\n            #print(f\"Iteration {i:4d}: Cost {cost_function(X, y, w, b):8.2f}   \")\n            cst = cost_function(X, y, w, b)\n            print(f\"Iteration {i:9d}, Cost: {cst:0.5e}\")\n    return w, b, hist #return w,b and history for graphing\n\ndef load_house_data():\n    data = np.loadtxt(\"./data/houses.txt\", delimiter=',', skiprows=1)\n    X = data[:,:4]\n    y = data[:,4]\n    return X, y\n\ndef zscore_normalize_features(X,rtn_ms=False):\n    \"\"\"\n    returns z-score normalized X by column\n    Args:\n      X : (numpy array (m,n)) \n    Returns\n      X_norm: (numpy array (m,n)) input normalized by column\n    \"\"\"\n    mu     = np.mean(X,axis=0)  \n    sigma  = np.std(X,axis=0)\n    X_norm = (X - mu)/sigma      \n\n    if rtn_ms:\n        return(X_norm, mu, sigma)\n    else:\n        return(X_norm)\n    \n    \n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Practice quiz - Gradient descent in practice/README.md",
    "content": "![Gradient Descent in Practice](/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Practice%20quiz%20-%20Gradient%20descent%20in%20practice/ss1.png) \n![Gradient Descent in Practice](/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Practice%20quiz%20-%20Gradient%20descent%20in%20practice/ss2.png)\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/Practice quiz - Multiple linear regression/README.md",
    "content": "![Multiple linear regression](/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Practice%20quiz%20-%20Multiple%20linear%20regression/ss1.png)\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week2/README.md",
    "content": "### Week 2 Solutions\n\n<br></br>\n\n- [Practice quiz: Gradient descent in practice](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Practice%20quiz%20-%20Gradient%20descent%20in%20practice)\n- [Practice quiz: Multiple linear regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Practice%20quiz%20-%20Multiple%20linear%20regression)\n- [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs)\n  - [Numpy Vectorization](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb)\n  - [Multi Variate Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab02_Multiple_Variable_Soln.ipynb)\n  - [Feature Scaling](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb)\n  - [Feature Engineering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb)\n  - [Sklearn Gradient Descent](/C1%20-%20Supervised%20Machine%20Learning%3A%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n  - [Sklearn Normal Method](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n- [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/C1W2A1)\n  - [Linear Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/C1W2A1/C1_W2_Linear_Regression.ipynb)"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/C1W3A1/C1_W3_Logistic_Regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Logistic Regression\\n\",\n    \"\\n\",\n    \"In this exercise, you will implement logistic regression and apply it to two different datasets. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Logistic Regression](#2)\\n\",\n    \"  - [ 2.1 Problem Statement](#2.1)\\n\",\n    \"  - [ 2.2 Loading and visualizing the data](#2.2)\\n\",\n    \"  - [ 2.3  Sigmoid function](#2.3)\\n\",\n    \"  - [ 2.4 Cost function for logistic regression](#2.4)\\n\",\n    \"  - [ 2.5 Gradient for logistic regression](#2.5)\\n\",\n    \"  - [ 2.6 Learning parameters using gradient descent ](#2.6)\\n\",\n    \"  - [ 2.7 Plotting the decision boundary](#2.7)\\n\",\n    \"  - [ 2.8 Evaluating logistic regression](#2.8)\\n\",\n    \"- [ 3 - Regularized Logistic Regression](#3)\\n\",\n    \"  - [ 3.1 Problem Statement](#3.1)\\n\",\n    \"  - [ 3.2 Loading and visualizing the data](#3.2)\\n\",\n    \"  - [ 3.3 Feature mapping](#3.3)\\n\",\n    \"  - [ 3.4 Cost function for regularized logistic regression](#3.4)\\n\",\n    \"  - [ 3.5 Gradient for regularized logistic regression](#3.5)\\n\",\n    \"  - [ 3.6 Learning parameters using gradient descent](#3.6)\\n\",\n    \"  - [ 3.7 Plotting the decision boundary](#3.7)\\n\",\n    \"  - [ 3.8 Evaluating regularized logistic regression model](#3.8)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\\n\",\n    \"-  ``utils.py`` contains helper functions for this assignment. You do not need to modify code in this file.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import *\\n\",\n    \"import copy\\n\",\n    \"import math\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Logistic Regression\\n\",\n    \"\\n\",\n    \"In this part of the exercise, you will build a logistic regression model to predict whether a student gets admitted into a university.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Problem Statement\\n\",\n    \"\\n\",\n    \"Suppose that you are the administrator of a university department and you want to determine each applicant’s chance of admission based on their results on two exams. \\n\",\n    \"* You have historical data from previous applicants that you can use as a training set for logistic regression. \\n\",\n    \"* For each training example, you have the applicant’s scores on two exams and the admissions decision. \\n\",\n    \"* Your task is to build a classification model that estimates an applicant’s probability of admission based on the scores from those two exams. \\n\",\n    \"\\n\",\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Loading and visualizing the data\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_dataset()` function shown below loads the data into variables `X_train` and `y_train`\\n\",\n    \"  - `X_train` contains exam scores on two exams for a student\\n\",\n    \"  - `y_train` is the admission decision \\n\",\n    \"      - `y_train = 1` if the student was admitted \\n\",\n    \"      - `y_train = 0` if the student was not admitted \\n\",\n    \"  - Both `X_train` and `y_train` are numpy arrays.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X_train, y_train = load_data(\\\"data/ex2data1.txt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to just print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints the first five values of `X_train` and the type of the variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"First five elements in X_train are:\\n\",\n      \" [[34.62365962 78.02469282]\\n\",\n      \" [30.28671077 43.89499752]\\n\",\n      \" [35.84740877 72.90219803]\\n\",\n      \" [60.18259939 86.3085521 ]\\n\",\n      \" [79.03273605 75.34437644]]\\n\",\n      \"Type of X_train: <class 'numpy.ndarray'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"First five elements in X_train are:\\\\n\\\", X_train[:5])\\n\",\n    \"print(\\\"Type of X_train:\\\",type(X_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now print the first five values of `y_train`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"First five elements in y_train are:\\n\",\n      \" [0. 0. 0. 1. 1.]\\n\",\n      \"Type of y_train: <class 'numpy.ndarray'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"First five elements in y_train are:\\\\n\\\", y_train[:5])\\n\",\n    \"print(\\\"Type of y_train:\\\",type(y_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions. Let's print the shape of `X_train` and `y_train` and see how many training examples we have in our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of X_train is: (100, 2)\\n\",\n      \"The shape of y_train is: (100,)\\n\",\n      \"We have m = 100 training examples\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of X_train is: ' + str(X_train.shape))\\n\",\n    \"print ('The shape of y_train is: ' + str(y_train.shape))\\n\",\n    \"print ('We have m = %d training examples' % (len(y_train)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Visualize your data\\n\",\n    \"\\n\",\n    \"Before starting to implement any learning algorithm, it is always good to visualize the data if possible.\\n\",\n    \"- The code below displays the data on a 2D plot (as shown below), where the axes are the two exam scores, and the positive and negative examples are shown with different markers.\\n\",\n    \"- We use a helper function in the ``utils.py`` file to generate this plot. \\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 1.png\\\" width=\\\"450\\\" height=\\\"450\\\">\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot examples\\n\",\n    \"plot_data(X_train, y_train[:], pos_label=\\\"Admitted\\\", neg_label=\\\"Not admitted\\\")\\n\",\n    \"\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Exam 2 score') \\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Exam 1 score') \\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Your goal is to build a logistic regression model to fit this data.\\n\",\n    \"- With this model, you can then predict if a new student will be admitted based on their scores on the two exams.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3  Sigmoid function\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the model is represented as\\n\",\n    \"\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(x) = g(\\\\mathbf{w}\\\\cdot \\\\mathbf{x} + b)$$\\n\",\n    \"where function $g$ is the sigmoid function. The sigmoid function is defined as:\\n\",\n    \"\\n\",\n    \"$$g(z) = \\\\frac{1}{1+e^{-z}}$$\\n\",\n    \"\\n\",\n    \"Let's implement the sigmoid function first, so it can be used by the rest of this assignment.\\n\",\n    \"\\n\",\n    \"<a name='ex-01'></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"Please complete  the `sigmoid` function to calculate\\n\",\n    \"\\n\",\n    \"$$g(z) = \\\\frac{1}{1+e^{-z}}$$\\n\",\n    \"\\n\",\n    \"Note that \\n\",\n    \"- `z` is not always a single number, but can also be an array of numbers. \\n\",\n    \"- If the input is an array of numbers, we'd like to apply the sigmoid function to each value in the input array.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED FUNCTION: sigmoid\\n\",\n    \"\\n\",\n    \"def sigmoid(z):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Compute the sigmoid of z\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        z (ndarray): A scalar, numpy array of any size.\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"        g (ndarray): sigmoid(z), with the same shape as z\\n\",\n    \"         \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"          \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    g = 1/(1+np.exp(-z))\\n\",\n    \"    ### END SOLUTION ###  \\n\",\n    \"    \\n\",\n    \"    return g\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"       \\n\",\n    \"`numpy` has a function called [`np.exp()`](https://numpy.org/doc/stable/reference/generated/numpy.exp.html), which offers a convinient way to calculate the exponential ( $e^{z}$) of all elements in the input array (`z`).\\n\",\n    \" \\n\",\n    \"<details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"  - You can translate $e^{-z}$ into code as `np.exp(-z)` \\n\",\n    \"    \\n\",\n    \"  - You can translate $1/e^{-z}$ into code as `1/np.exp(-z)` \\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `g` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate g</b></font></summary>\\n\",\n    \"        <code>g = 1 / (1 + np.exp(-z))</code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When you are finished, try testing a few values by calling `sigmoid(x)` in the cell below. \\n\",\n    \"- For large positive values of x, the sigmoid should be close to 1, while for large negative values, the sigmoid should be close to 0. \\n\",\n    \"- Evaluating `sigmoid(0)` should give you exactly 0.5. \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sigmoid(0) = 0.5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (\\\"sigmoid(0) = \\\" + str(sigmoid(0)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>sigmoid(0)<b></td>\\n\",\n    \"    <td> 0.5 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\\n\",\n    \"    \\n\",\n    \"- As mentioned before, your code should also work with vectors and matrices. For a matrix, your function should perform the sigmoid function on every element.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sigmoid([ -1, 0, 1, 2]) = [0.26894142 0.5        0.73105858 0.88079708]\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print (\\\"sigmoid([ -1, 0, 1, 2]) = \\\" + str(sigmoid(np.array([-1, 0, 1, 2]))))\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"from public_tests import *\\n\",\n    \"sigmoid_test(sigmoid)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td><b>sigmoid([-1, 0, 1, 2])<b></td> \\n\",\n    \"    <td>[0.26894142        0.5           0.73105858        0.88079708]</td> \\n\",\n    \"  </tr>    \\n\",\n    \"  \\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.4\\\"></a>\\n\",\n    \"### 2.4 Cost function for logistic regression\\n\",\n    \"\\n\",\n    \"In this section, you will implement the cost function for logistic regression.\\n\",\n    \"\\n\",\n    \"<a name='ex-02'></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Please complete the `compute_cost` function using the equations below.\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the cost function is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\\\left[ loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) \\\\right] \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"where\\n\",\n    \"* m is the number of training examples in the dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* $loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)})$ is the cost for a single data point, which is - \\n\",\n    \"\\n\",\n    \"    $$loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\tag{2}$$\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)})$ is the model's prediction, while $y^{(i)}$, which is the actual label\\n\",\n    \"\\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(\\\\mathbf{w} \\\\cdot \\\\mathbf{x^{(i)}} + b)$ where function $g$ is the sigmoid function.\\n\",\n    \"    * It might be helpful to first calculate an intermediate variable $z_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x^{(i)}} + b = w_0x^{(i)}_0 + ... + w_{n-1}x^{(i)}_{n-1} + b$ where $n$ is the number of features, before calculating $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(z_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}))$\\n\",\n    \"\\n\",\n    \"Note:\\n\",\n    \"* As you are doing this, remember that the variables `X_train` and `y_train` are not scalar values but matrices of shape ($m, n$) and ($𝑚$,1) respectively, where  $𝑛$ is the number of features and $𝑚$ is the number of training examples.\\n\",\n    \"* You can use the sigmoid function that you implemented above for this part.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: compute_cost\\n\",\n    \"def compute_cost(X, y, w, b, lambda_= 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"      X : (ndarray Shape (m,n)) data, m examples by n features\\n\",\n    \"      y : (array_like Shape (m,)) target value \\n\",\n    \"      w : (array_like Shape (n,)) Values of parameters of the model      \\n\",\n    \"      b : scalar Values of bias parameter of the model\\n\",\n    \"      lambda_: unused placeholder\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost: (scalar)         cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    cost = 0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z = np.dot(X[i],w) + b\\n\",\n    \"        f_wb = sigmoid(z)\\n\",\n    \"        cost += -y[i]*np.log(f_wb) - (1-y[i])*np.log(1-f_wb)\\n\",\n    \"    total_cost = cost/m\\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"\\n\",\n    \"    return total_cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * You can represent a summation operator eg: $h = \\\\sum\\\\limits_{i = 0}^{m-1} 2i$ in code as follows:\\n\",\n    \"    ```python \\n\",\n    \"        h = 0\\n\",\n    \"        for i in range(m):\\n\",\n    \"            h = h + 2*i\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"   * In this case, you can iterate over all the examples in `X` using a for loop and add the `loss` from each iteration to a variable (`loss_sum`) initialized outside the loop.\\n\",\n    \"\\n\",\n    \"   * Then, you can return the `total_cost` as `loss_sum` divided by `m`.\\n\",\n    \"     \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_cost(X, y, w, b, lambda_= 1):\\n\",\n    \"        m, n = X.shape\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        loss_sum = 0 \\n\",\n    \"        \\n\",\n    \"        # Loop over each training example\\n\",\n    \"        for i in range(m): \\n\",\n    \"            \\n\",\n    \"            # First calculate z_wb = w[0]*X[i][0]+...+w[n-1]*X[i][n-1]+b\\n\",\n    \"            z_wb = 0 \\n\",\n    \"            # Loop over each feature\\n\",\n    \"            for j in range(n): \\n\",\n    \"                # Add the corresponding term to z_wb\\n\",\n    \"                z_wb_ij = # Your code here to calculate w[j] * X[i][j]\\n\",\n    \"                z_wb += z_wb_ij # equivalent to z_wb = z_wb + z_wb_ij\\n\",\n    \"            # Add the bias term to z_wb\\n\",\n    \"            z_wb += b # equivalent to z_wb = z_wb + b\\n\",\n    \"        \\n\",\n    \"            f_wb = # Your code here to calculate prediction f_wb for a training example\\n\",\n    \"            loss =  # Your code here to calculate loss for a training example\\n\",\n    \"            \\n\",\n    \"            loss_sum += loss # equivalent to loss_sum = loss_sum + loss\\n\",\n    \"        \\n\",\n    \"        total_cost = (1 / m) * loss_sum  \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"        \\n\",\n    \"        return total_cost\\n\",\n    \"    ```\\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `z_wb_ij`, `f_wb` and `cost`.\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate z_wb_ij</b></font></summary>\\n\",\n    \"           &emsp; &emsp; <code>z_wb_ij = w[j]*X[i][j] </code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(z_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}))$ where $g$ is the sigmoid function. You can simply call the `sigmoid` function implemented above.\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute f_wb as <code>f_wb = sigmoid(z_wb) </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate loss</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can use the <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.log.html\\\">np.log</a> function to calculate the log\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate loss</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute loss as <code>loss =  -y[i] * np.log(f_wb) - (1 - y[i]) * np.log(1 - f_wb)</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cells below to check your implementation of the `compute_cost` function with two different initializations of the parameters $w$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cost at initial w (zeros): 0.693\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"m, n = X_train.shape\\n\",\n    \"\\n\",\n    \"# Compute and display cost with w initialized to zeroes\\n\",\n    \"initial_w = np.zeros(n)\\n\",\n    \"initial_b = 0.\\n\",\n    \"cost = compute_cost(X_train, y_train, initial_w, initial_b)\\n\",\n    \"print('Cost at initial w (zeros): {:.3f}'.format(cost))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Cost at initial w (zeros)<b></td>\\n\",\n    \"    <td> 0.693 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cost at test w,b: 0.218\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute and display cost with non-zero w\\n\",\n    \"test_w = np.array([0.2, 0.2])\\n\",\n    \"test_b = -24.\\n\",\n    \"cost = compute_cost(X_train, y_train, test_w, test_b)\\n\",\n    \"\\n\",\n    \"print('Cost at test w,b: {:.3f}'.format(cost))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"compute_cost_test(compute_cost)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Cost at test w,b<b></td>\\n\",\n    \"    <td> 0.218 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.5\\\"></a>\\n\",\n    \"### 2.5 Gradient for logistic regression\\n\",\n    \"\\n\",\n    \"In this section, you will implement the gradient for logistic regression.\\n\",\n    \"\\n\",\n    \"Recall that the gradient descent algorithm is:\\n\",\n    \"\\n\",\n    \"$$\\\\begin{align*}& \\\\text{repeat until convergence:} \\\\; \\\\lbrace \\\\newline \\\\; & b := b -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} \\\\newline       \\\\; & w_j := w_j -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} \\\\tag{1}  \\\\; & \\\\text{for j := 0..n-1}\\\\newline & \\\\rbrace\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"where, parameters $b$, $w_j$ are all updated simultaniously\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"<a name='ex-03'></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"\\n\",\n    \"Please complete the `compute_gradient` function to compute $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w}$, $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ from equations (2) and (3) below.\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)}) \\\\tag{2}\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)})x_{j}^{(i)} \\\\tag{3}\\n\",\n    \"$$\\n\",\n    \"* m is the number of training examples in the dataset\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"*  $f_{\\\\mathbf{w},b}(x^{(i)})$ is the model's prediction, while $y^{(i)}$ is the actual label\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- **Note**: While this gradient looks identical to the linear regression gradient, the formula is actually different because linear and logistic regression have different definitions of $f_{\\\\mathbf{w},b}(x)$.\\n\",\n    \"\\n\",\n    \"As before, you can use the sigmoid function that you implemented above and if you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED FUNCTION: compute_gradient\\n\",\n    \"def compute_gradient(X, y, w, b, lambda_=None): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for logistic regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X : (ndarray Shape (m,n)) variable such as house size \\n\",\n    \"      y : (array_like Shape (m,1)) actual value \\n\",\n    \"      w : (array_like Shape (n,1)) values of parameters of the model      \\n\",\n    \"      b : (scalar)                 value of parameter of the model \\n\",\n    \"      lambda_: unused placeholder.\\n\",\n    \"    Returns\\n\",\n    \"      dj_dw: (array_like Shape (n,1)) The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db: (scalar)                The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    dj_dw = np.zeros(w.shape)\\n\",\n    \"    dj_db = 0.\\n\",\n    \"\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb_i = sigmoid(np.dot(X[i],w) + b)          \\n\",\n    \"        err_i  = f_wb_i  - y[i]                       \\n\",\n    \"        for j in range(n):\\n\",\n    \"            dj_dw[j] = dj_dw[j] + err_i * X[i,j]      \\n\",\n    \"        dj_db = dj_db + err_i\\n\",\n    \"    dj_dw = dj_dw/m                                   \\n\",\n    \"    dj_db = dj_db/m                                   \\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ###\\n\",\n    \"\\n\",\n    \"        \\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \" <details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"       def compute_gradient(X, y, w, b, lambda_=None): \\n\",\n    \"            m, n = X.shape\\n\",\n    \"            dj_dw = np.zeros(w.shape)\\n\",\n    \"            dj_db = 0.\\n\",\n    \"        \\n\",\n    \"            ### START CODE HERE ### \\n\",\n    \"            for i in range(m):\\n\",\n    \"                # Calculate f_wb (exactly as you did in the compute_cost function above)\\n\",\n    \"                f_wb = \\n\",\n    \"        \\n\",\n    \"                # Calculate the  gradient for b from this example\\n\",\n    \"                dj_db_i = # Your code here to calculate the error\\n\",\n    \"        \\n\",\n    \"                # add that to dj_db\\n\",\n    \"                dj_db += dj_db_i\\n\",\n    \"        \\n\",\n    \"                # get dj_dw for each attribute\\n\",\n    \"                for j in range(n):\\n\",\n    \"                    # You code here to calculate the gradient from the i-th example for j-th attribute\\n\",\n    \"                    dj_dw_ij =  \\n\",\n    \"                    dj_dw[j] += dj_dw_ij\\n\",\n    \"        \\n\",\n    \"            # divide dj_db and dj_dw by total number of examples\\n\",\n    \"            dj_dw = dj_dw / m\\n\",\n    \"            dj_db = dj_db / m\\n\",\n    \"            ### END CODE HERE ###\\n\",\n    \"       \\n\",\n    \"            return dj_db, dj_dw\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `f_wb`, `dj_db_i` and `dj_dw_ij` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; Recall that you calculated f_wb in <code>compute_cost</code> above — for detailed hints on how to calculate each intermediate term, check out the hints section below that exercise\\n\",\n    \"           <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f_wb</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can calculate f_wb as\\n\",\n    \"               <pre>\\n\",\n    \"               for i in range(m):   \\n\",\n    \"                   # Calculate f_wb (exactly how you did it in the compute_cost function above)\\n\",\n    \"                   z_wb = 0\\n\",\n    \"                   # Loop over each feature\\n\",\n    \"                   for j in range(n): \\n\",\n    \"                       # Add the corresponding term to z_wb\\n\",\n    \"                       z_wb_ij = X[i, j] * w[j]\\n\",\n    \"                       z_wb += z_wb_ij\\n\",\n    \"            \\n\",\n    \"                   # Add bias term \\n\",\n    \"                   z_wb += b\\n\",\n    \"        \\n\",\n    \"                   # Calculate the prediction from the model\\n\",\n    \"                   f_wb = sigmoid(z_wb)\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_db_i</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can calculate dj_db_i as <code>dj_db_i = f_wb - y[i]</code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_dw_ij</b></font></summary>\\n\",\n    \"        &emsp; &emsp; You can calculate dj_dw_ij as <code>dj_dw_ij = (f_wb - y[i])* X[i][j]</code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cells below to check your implementation of the `compute_gradient` function with two different initializations of the parameters $w$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dj_db at initial w (zeros):-0.1\\n\",\n      \"dj_dw at initial w (zeros):[-12.00921658929115, -11.262842205513591]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute and display gradient with w initialized to zeroes\\n\",\n    \"initial_w = np.zeros(n)\\n\",\n    \"initial_b = 0.\\n\",\n    \"\\n\",\n    \"dj_db, dj_dw = compute_gradient(X_train, y_train, initial_w, initial_b)\\n\",\n    \"print(f'dj_db at initial w (zeros):{dj_db}' )\\n\",\n    \"print(f'dj_dw at initial w (zeros):{dj_dw.tolist()}' )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>dj_db at initial w (zeros)<b></td>\\n\",\n    \"    <td> -0.1 </td> \\n\",\n    \"  </tr>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>ddj_dw at initial w (zeros):<b></td>\\n\",\n    \"    <td> [-12.00921658929115, -11.262842205513591] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dj_db at test_w: -0.5999999999991071\\n\",\n      \"dj_dw at test_w: [-44.831353617873795, -44.37384124953978]\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute and display cost and gradient with non-zero w\\n\",\n    \"test_w = np.array([ 0.2, -0.5])\\n\",\n    \"test_b = -24\\n\",\n    \"dj_db, dj_dw  = compute_gradient(X_train, y_train, test_w, test_b)\\n\",\n    \"\\n\",\n    \"print('dj_db at test_w:', dj_db)\\n\",\n    \"print('dj_dw at test_w:', dj_dw.tolist())\\n\",\n    \"\\n\",\n    \"# UNIT TESTS    \\n\",\n    \"compute_gradient_test(compute_gradient)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>dj_db at initial w (zeros)<b></td>\\n\",\n    \"    <td> -0.5999999999991071 </td> \\n\",\n    \"  </tr>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>ddj_dw at initial w (zeros):<b></td>\\n\",\n    \"    <td>  [-44.8313536178737957, -44.37384124953978] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.6\\\"></a>\\n\",\n    \"### 2.6 Learning parameters using gradient descent \\n\",\n    \"\\n\",\n    \"Similar to the previous assignment, you will now find the optimal parameters of a logistic regression model by using gradient descent. \\n\",\n    \"- You don't need to implement anything for this part. Simply run the cells below. \\n\",\n    \"\\n\",\n    \"- A good way to verify that gradient descent is working correctly is to look\\n\",\n    \"at the value of $J(\\\\mathbf{w},b)$ and check that it is decreasing with each step. \\n\",\n    \"\\n\",\n    \"- Assuming you have implemented the gradient and computed the cost correctly, your value of $J(\\\\mathbf{w},b)$ should never increase, and should converge to a steady value by the end of the algorithm.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def gradient_descent(X, y, w_in, b_in, cost_function, gradient_function, alpha, num_iters, lambda_): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Performs batch gradient descent to learn theta. Updates theta by taking \\n\",\n    \"    num_iters gradient steps with learning rate alpha\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      X :    (array_like Shape (m, n)\\n\",\n    \"      y :    (array_like Shape (m,))\\n\",\n    \"      w_in : (array_like Shape (n,))  Initial values of parameters of the model\\n\",\n    \"      b_in : (scalar)                 Initial value of parameter of the model\\n\",\n    \"      cost_function:                  function to compute cost\\n\",\n    \"      alpha : (float)                 Learning rate\\n\",\n    \"      num_iters : (int)               number of iterations to run gradient descent\\n\",\n    \"      lambda_ (scalar, float)         regularization constant\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      w : (array_like Shape (n,)) Updated values of parameters of the model after\\n\",\n    \"          running gradient descent\\n\",\n    \"      b : (scalar)                Updated value of parameter of the model after\\n\",\n    \"          running gradient descent\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # number of training examples\\n\",\n    \"    m = len(X)\\n\",\n    \"    \\n\",\n    \"    # An array to store cost J and w's at each iteration primarily for graphing later\\n\",\n    \"    J_history = []\\n\",\n    \"    w_history = []\\n\",\n    \"    \\n\",\n    \"    for i in range(num_iters):\\n\",\n    \"\\n\",\n    \"        # Calculate the gradient and update the parameters\\n\",\n    \"        dj_db, dj_dw = gradient_function(X, y, w_in, b_in, lambda_)   \\n\",\n    \"\\n\",\n    \"        # Update Parameters using w, b, alpha and gradient\\n\",\n    \"        w_in = w_in - alpha * dj_dw               \\n\",\n    \"        b_in = b_in - alpha * dj_db              \\n\",\n    \"       \\n\",\n    \"        # Save cost J at each iteration\\n\",\n    \"        if i<100000:      # prevent resource exhaustion \\n\",\n    \"            cost =  cost_function(X, y, w_in, b_in, lambda_)\\n\",\n    \"            J_history.append(cost)\\n\",\n    \"\\n\",\n    \"        # Print cost every at intervals 10 times or as many iterations if < 10\\n\",\n    \"        if i% math.ceil(num_iters/10) == 0 or i == (num_iters-1):\\n\",\n    \"            w_history.append(w_in)\\n\",\n    \"            print(f\\\"Iteration {i:4}: Cost {float(J_history[-1]):8.2f}   \\\")\\n\",\n    \"        \\n\",\n    \"    return w_in, b_in, J_history, w_history #return w and J,w history for graphing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's run the gradient descent algorithm above to learn the parameters for our dataset.\\n\",\n    \"\\n\",\n    \"**Note**\\n\",\n    \"\\n\",\n    \"The code block below takes a couple of minutes to run, especially with a non-vectorized version. You can reduce the `iterations` to test your implementation and iterate faster. If you have time, try running 100,000 iterations for better results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration    0: Cost     1.01   \\n\",\n      \"Iteration 1000: Cost     0.31   \\n\",\n      \"Iteration 2000: Cost     0.30   \\n\",\n      \"Iteration 3000: Cost     0.30   \\n\",\n      \"Iteration 4000: Cost     0.30   \\n\",\n      \"Iteration 5000: Cost     0.30   \\n\",\n      \"Iteration 6000: Cost     0.30   \\n\",\n      \"Iteration 7000: Cost     0.30   \\n\",\n      \"Iteration 8000: Cost     0.30   \\n\",\n      \"Iteration 9000: Cost     0.30   \\n\",\n      \"Iteration 9999: Cost     0.30   \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"initial_w = 0.01 * (np.random.rand(2).reshape(-1,1) - 0.5)\\n\",\n    \"initial_b = -8\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Some gradient descent settings\\n\",\n    \"iterations = 10000\\n\",\n    \"alpha = 0.001\\n\",\n    \"\\n\",\n    \"w,b, J_history,_ = gradient_descent(X_train ,y_train, initial_w, initial_b, \\n\",\n    \"                                   compute_cost, compute_gradient, alpha, iterations, 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"<summary>\\n\",\n    \"    <b>Expected Output: Cost     0.30, (Click to see details):</b>\\n\",\n    \"</summary>\\n\",\n    \"\\n\",\n    \"    # With the following settings\\n\",\n    \"    np.random.seed(1)\\n\",\n    \"    intial_w = 0.01 * (np.random.rand(2).reshape(-1,1) - 0.5)\\n\",\n    \"    initial_b = -8\\n\",\n    \"    iterations = 10000\\n\",\n    \"    alpha = 0.001\\n\",\n    \"    #\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"Iteration    0: Cost     1.01   \\n\",\n    \"Iteration 1000: Cost     0.31   \\n\",\n    \"Iteration 2000: Cost     0.30   \\n\",\n    \"Iteration 3000: Cost     0.30   \\n\",\n    \"Iteration 4000: Cost     0.30   \\n\",\n    \"Iteration 5000: Cost     0.30   \\n\",\n    \"Iteration 6000: Cost     0.30   \\n\",\n    \"Iteration 7000: Cost     0.30   \\n\",\n    \"Iteration 8000: Cost     0.30   \\n\",\n    \"Iteration 9000: Cost     0.30   \\n\",\n    \"Iteration 9999: Cost     0.30   \\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.7\\\"></a>\\n\",\n    \"### 2.7 Plotting the decision boundary\\n\",\n    \"\\n\",\n    \"We will now use the final parameters from gradient descent to plot the linear fit. If you implemented the previous parts correctly, you should see the following plot:   \\n\",\n    \"<img src=\\\"images/figure 2.png\\\"  width=\\\"450\\\" height=\\\"450\\\">\\n\",\n    \"\\n\",\n    \"We will use a helper function in the `utils.py` file to create this plot.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_decision_boundary(w, b, X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.8\\\"></a>\\n\",\n    \"### 2.8 Evaluating logistic regression\\n\",\n    \"\\n\",\n    \"We can evaluate the quality of the parameters we have found by seeing how well the learned model predicts on our training set. \\n\",\n    \"\\n\",\n    \"You will implement the `predict` function below to do this.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-04'></a>\\n\",\n    \"### Exercise 4\\n\",\n    \"\\n\",\n    \"Please complete the `predict` function to produce `1` or `0` predictions given a dataset and a learned parameter vector $w$ and $b$.\\n\",\n    \"- First you need to compute the prediction from the model $f(x^{(i)}) = g(w \\\\cdot x^{(i)})$ for every example \\n\",\n    \"    - You've implemented this before in the parts above\\n\",\n    \"- We interpret the output of the model ($f(x^{(i)})$) as the probability that $y^{(i)}=1$ given $x^{(i)}$ and parameterized by $w$.\\n\",\n    \"- Therefore, to get a final prediction ($y^{(i)}=0$ or $y^{(i)}=1$) from the logistic regression model, you can use the following heuristic -\\n\",\n    \"\\n\",\n    \"  if $f(x^{(i)}) >= 0.5$, predict $y^{(i)}=1$\\n\",\n    \"  \\n\",\n    \"  if $f(x^{(i)}) < 0.5$, predict $y^{(i)}=0$\\n\",\n    \"    \\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C4\\n\",\n    \"# GRADED FUNCTION: predict\\n\",\n    \"\\n\",\n    \"def predict(X, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Predict whether the label is 0 or 1 using learned logistic\\n\",\n    \"    regression parameters w\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"    X : (ndarray Shape (m, n))\\n\",\n    \"    w : (array_like Shape (n,))      Parameters of the model\\n\",\n    \"    b : (scalar, float)              Parameter of the model\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"    p: (ndarray (m,1))\\n\",\n    \"        The predictions for X using a threshold at 0.5\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # number of training examples\\n\",\n    \"    m, n = X.shape   \\n\",\n    \"    p = np.zeros(m)\\n\",\n    \"   \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    # Loop over each example\\n\",\n    \"    for i in range(m):   \\n\",\n    \"        z_wb = np.dot(X[i],w) \\n\",\n    \"        # Loop over each feature\\n\",\n    \"        for j in range(n): \\n\",\n    \"            # Add the corresponding term to z_wb\\n\",\n    \"            z_wb += 0\\n\",\n    \"        \\n\",\n    \"        # Add bias term \\n\",\n    \"        z_wb += b\\n\",\n    \"        \\n\",\n    \"        # Calculate the prediction for this example\\n\",\n    \"        f_wb = sigmoid(z_wb)\\n\",\n    \"\\n\",\n    \"        # Apply the threshold\\n\",\n    \"        p[i] = 1 if f_wb>0.5 else 0\\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    return p\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"       def predict(X, w, b): \\n\",\n    \"            # number of training examples\\n\",\n    \"            m, n = X.shape   \\n\",\n    \"            p = np.zeros(m)\\n\",\n    \"   \\n\",\n    \"            ### START CODE HERE ### \\n\",\n    \"            # Loop over each example\\n\",\n    \"            for i in range(m):   \\n\",\n    \"                \\n\",\n    \"                # Calculate f_wb (exactly how you did it in the compute_cost function above) \\n\",\n    \"                # using a couple of lines of code\\n\",\n    \"                f_wb = \\n\",\n    \"\\n\",\n    \"                # Calculate the prediction for that training example \\n\",\n    \"                p[i] = # Your code here to calculate the prediction based on f_wb\\n\",\n    \"        \\n\",\n    \"            ### END CODE HERE ### \\n\",\n    \"            return p\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `f_wb` and `p[i]` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; Recall that you calculated f_wb in <code>compute_cost</code> above — for detailed hints on how to calculate each intermediate term, check out the hints section below that exercise\\n\",\n    \"           <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f_wb</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can calculate f_wb as\\n\",\n    \"               <pre>\\n\",\n    \"               for i in range(m):   \\n\",\n    \"                   # Calculate f_wb (exactly how you did it in the compute_cost function above)\\n\",\n    \"                   z_wb = 0\\n\",\n    \"                   # Loop over each feature\\n\",\n    \"                   for j in range(n): \\n\",\n    \"                       # Add the corresponding term to z_wb\\n\",\n    \"                       z_wb_ij = X[i, j] * w[j]\\n\",\n    \"                       z_wb += z_wb_ij\\n\",\n    \"            \\n\",\n    \"                   # Add bias term \\n\",\n    \"                   z_wb += b\\n\",\n    \"        \\n\",\n    \"                   # Calculate the prediction from the model\\n\",\n    \"                   f_wb = sigmoid(z_wb)\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate p[i]</b></font></summary>\\n\",\n    \"           &emsp; &emsp; As an example, if you'd like to say x = 1 if y is less than 3 and 0 otherwise, you can express it in code as <code>x = y < 3 </code>. Now do the same for p[i] = 1 if f_wb >= 0.5 and 0 otherwise. \\n\",\n    \"           <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate p[i]</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute p[i] as <code>p[i] = f_wb >= 0.5</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once you have completed the function `predict`, let's run the code below to report the training accuracy of your classifier by computing the percentage of examples it got correct.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Output of predict: shape (4,), value [0. 1. 1. 1.]\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Test your predict code\\n\",\n    \"np.random.seed(1)\\n\",\n    \"tmp_w = np.random.randn(2)\\n\",\n    \"tmp_b = 0.3    \\n\",\n    \"tmp_X = np.random.randn(4, 2) - 0.5\\n\",\n    \"\\n\",\n    \"tmp_p = predict(tmp_X, tmp_w, tmp_b)\\n\",\n    \"print(f'Output of predict: shape {tmp_p.shape}, value {tmp_p}')\\n\",\n    \"\\n\",\n    \"# UNIT TESTS        \\n\",\n    \"predict_test(predict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output** \\n\",\n    \"\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Output of predict: shape (4,),value [0. 1. 1. 1.]<b></td>\\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's use this to compute the accuracy on the training set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train Accuracy: 92.000000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Compute accuracy on our training set\\n\",\n    \"p = predict(X_train, w,b)\\n\",\n    \"print('Train Accuracy: %f'%(np.mean(p == y_train) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Train Accuracy (approx):<b></td>\\n\",\n    \"    <td> 92.00 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Regularized Logistic Regression\\n\",\n    \"\\n\",\n    \"In this part of the exercise, you will implement regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly. \\n\",\n    \"\\n\",\n    \"<a name=\\\"3.1\\\"></a>\\n\",\n    \"### 3.1 Problem Statement\\n\",\n    \"\\n\",\n    \"Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. \\n\",\n    \"- From these two tests, you would like to determine whether the microchips should be accepted or rejected. \\n\",\n    \"- To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model.\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.2\\\"></a>\\n\",\n    \"### 3.2 Loading and visualizing the data\\n\",\n    \"\\n\",\n    \"Similar to previous parts of this exercise, let's start by loading the dataset for this task and visualizing it. \\n\",\n    \"\\n\",\n    \"- The `load_dataset()` function shown below loads the data into variables `X_train` and `y_train`\\n\",\n    \"  - `X_train` contains the test results for the microchips from two tests\\n\",\n    \"  - `y_train` contains the results of the QA  \\n\",\n    \"      - `y_train = 1` if the microchip was accepted \\n\",\n    \"      - `y_train = 0` if the microchip was rejected \\n\",\n    \"  - Both `X_train` and `y_train` are numpy arrays.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X_train, y_train = load_data(\\\"data/ex2data2.txt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"\\n\",\n    \"The code below prints the first five values of `X_train` and `y_train` and the type of the variables.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X_train: [[ 0.051267  0.69956 ]\\n\",\n      \" [-0.092742  0.68494 ]\\n\",\n      \" [-0.21371   0.69225 ]\\n\",\n      \" [-0.375     0.50219 ]\\n\",\n      \" [-0.51325   0.46564 ]]\\n\",\n      \"Type of X_train: <class 'numpy.ndarray'>\\n\",\n      \"y_train: [1. 1. 1. 1. 1.]\\n\",\n      \"Type of y_train: <class 'numpy.ndarray'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# print X_train\\n\",\n    \"print(\\\"X_train:\\\", X_train[:5])\\n\",\n    \"print(\\\"Type of X_train:\\\",type(X_train))\\n\",\n    \"\\n\",\n    \"# print y_train\\n\",\n    \"print(\\\"y_train:\\\", y_train[:5])\\n\",\n    \"print(\\\"Type of y_train:\\\",type(y_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions. Let's print the shape of `X_train` and `y_train` and see how many training examples we have in our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of X_train is: (118, 2)\\n\",\n      \"The shape of y_train is: (118,)\\n\",\n      \"We have m = 118 training examples\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of X_train is: ' + str(X_train.shape))\\n\",\n    \"print ('The shape of y_train is: ' + str(y_train.shape))\\n\",\n    \"print ('We have m = %d training examples' % (len(y_train)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Visualize your data\\n\",\n    \"\\n\",\n    \"The helper function `plot_data` (from `utils.py`) is used to generate a figure like Figure 3, where the axes are the two test scores, and the positive (y = 1, accepted) and negative (y = 0, rejected) examples are shown with different markers.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 3.png\\\"  width=\\\"450\\\" height=\\\"450\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot examples\\n\",\n    \"plot_data(X_train, y_train[:], pos_label=\\\"Accepted\\\", neg_label=\\\"Rejected\\\")\\n\",\n    \"\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Microchip Test 2') \\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Microchip Test 1') \\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Figure 3 shows that our dataset cannot be separated into positive and negative examples by a straight-line through the plot. Therefore, a straight forward application of logistic regression will not perform well on this dataset since logistic regression will only be able to find a linear decision boundary.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.3\\\"></a>\\n\",\n    \"### 3.3 Feature mapping\\n\",\n    \"\\n\",\n    \"One way to fit the data better is to create more features from each data point. In the provided function `map_feature`, we will map the features into all polynomial terms of $x_1$ and $x_2$ up to the sixth power.\\n\",\n    \"\\n\",\n    \"$$\\\\mathrm{map\\\\_feature}(x) = \\n\",\n    \"\\\\left[\\\\begin{array}{c}\\n\",\n    \"x_1\\\\\\\\\\n\",\n    \"x_2\\\\\\\\\\n\",\n    \"x_1^2\\\\\\\\\\n\",\n    \"x_1 x_2\\\\\\\\\\n\",\n    \"x_2^2\\\\\\\\\\n\",\n    \"x_1^3\\\\\\\\\\n\",\n    \"\\\\vdots\\\\\\\\\\n\",\n    \"x_1 x_2^5\\\\\\\\\\n\",\n    \"x_2^6\\\\end{array}\\\\right]$$\\n\",\n    \"\\n\",\n    \"As a result of this mapping, our vector of two features (the scores on two QA tests) has been transformed into a 27-dimensional vector. \\n\",\n    \"\\n\",\n    \"- A logistic regression classifier trained on this higher-dimension feature vector will have a more complex decision boundary and will be nonlinear when drawn in our 2-dimensional plot. \\n\",\n    \"- We have provided the `map_feature` function for you in utils.py. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Original shape of data: (118, 2)\\n\",\n      \"Shape after feature mapping: (118, 27)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Original shape of data:\\\", X_train.shape)\\n\",\n    \"\\n\",\n    \"mapped_X =  map_feature(X_train[:, 0], X_train[:, 1])\\n\",\n    \"print(\\\"Shape after feature mapping:\\\", mapped_X.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's also print the first elements of `X_train` and `mapped_X` to see the tranformation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X_train[0]: [0.051267 0.69956 ]\\n\",\n      \"mapped X_train[0]: [5.12670000e-02 6.99560000e-01 2.62830529e-03 3.58643425e-02\\n\",\n      \" 4.89384194e-01 1.34745327e-04 1.83865725e-03 2.50892595e-02\\n\",\n      \" 3.42353606e-01 6.90798869e-06 9.42624411e-05 1.28625106e-03\\n\",\n      \" 1.75514423e-02 2.39496889e-01 3.54151856e-07 4.83255257e-06\\n\",\n      \" 6.59422333e-05 8.99809795e-04 1.22782870e-02 1.67542444e-01\\n\",\n      \" 1.81563032e-08 2.47750473e-07 3.38066048e-06 4.61305487e-05\\n\",\n      \" 6.29470940e-04 8.58939846e-03 1.17205992e-01]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"X_train[0]:\\\", X_train[0])\\n\",\n    \"print(\\\"mapped X_train[0]:\\\", mapped_X[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"While the feature mapping allows us to build a more expressive classifier, it is also more susceptible to overfitting. In the next parts of the exercise, you will implement regularized logistic regression to fit the data and also see for yourself how regularization can help combat the overfitting problem.\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.4\\\"></a>\\n\",\n    \"### 3.4 Cost function for regularized logistic regression\\n\",\n    \"\\n\",\n    \"In this part, you will implement the cost function for regularized logistic regression.\\n\",\n    \"\\n\",\n    \"Recall that for regularized logistic regression, the cost function is of the form\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} \\\\left[ -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\right] + \\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$$\\n\",\n    \"\\n\",\n    \"Compare this to the cost function without regularization (which you implemented above), which is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w}.b) = \\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\\\left[ (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)\\\\right]$$\\n\",\n    \"\\n\",\n    \"The difference is the regularization term, which is $$\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$$ \\n\",\n    \"Note that the $b$ parameter is not regularized.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-05'></a>\\n\",\n    \"### Exercise 5\\n\",\n    \"\\n\",\n    \"Please complete the `compute_cost_reg` function below to calculate the following term for each element in $w$ \\n\",\n    \"$$\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$$\\n\",\n    \"\\n\",\n    \"The starter code then adds this to the cost without regularization (which you computed above in `compute_cost`) to calculate the cost with regulatization.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C5\\n\",\n    \"def compute_cost_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"      X : (array_like Shape (m,n)) data, m examples by n features\\n\",\n    \"      y : (array_like Shape (m,)) target value \\n\",\n    \"      w : (array_like Shape (n,)) Values of parameters of the model      \\n\",\n    \"      b : (array_like Shape (n,)) Values of bias parameter of the model\\n\",\n    \"      lambda_ : (scalar, float)    Controls amount of regularization\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost: (scalar)         cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    # Calls the compute_cost function that you implemented above\\n\",\n    \"    cost_without_reg = compute_cost(X, y, w, b) \\n\",\n    \"    \\n\",\n    \"    # You need to calculate this value\\n\",\n    \"    reg_cost = 0.\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    reg_cost = sum(np.square(w))\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    # Add the regularization cost to get the total cost\\n\",\n    \"    total_cost = cost_without_reg + (lambda_/(2 * m)) * reg_cost\\n\",\n    \"\\n\",\n    \"    return total_cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"       def compute_cost_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"   \\n\",\n    \"           m, n = X.shape\\n\",\n    \"    \\n\",\n    \"            # Calls the compute_cost function that you implemented above\\n\",\n    \"            cost_without_reg = compute_cost(X, y, w, b) \\n\",\n    \"    \\n\",\n    \"            # You need to calculate this value\\n\",\n    \"            reg_cost = 0.\\n\",\n    \"    \\n\",\n    \"            ### START CODE HERE ###\\n\",\n    \"            for j in range(n):\\n\",\n    \"                reg_cost_j = # Your code here to calculate the cost from w[j]\\n\",\n    \"                reg_cost = reg_cost + reg_cost_j\\n\",\n    \"\\n\",\n    \"            ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"            # Add the regularization cost to get the total cost\\n\",\n    \"            total_cost = cost_without_reg + (lambda_/(2 * m)) * reg_cost\\n\",\n    \"\\n\",\n    \"        return total_cost\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `reg_cost_j` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate reg_cost_j</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use calculate reg_cost_j as <code>reg_cost_j = w[j]**2 </code> \\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to check your implementation of the `compute_cost_reg` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Regularized cost : 0.6618252552483948\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_mapped = map_feature(X_train[:, 0], X_train[:, 1])\\n\",\n    \"np.random.seed(1)\\n\",\n    \"initial_w = np.random.rand(X_mapped.shape[1]) - 0.5\\n\",\n    \"initial_b = 0.5\\n\",\n    \"lambda_ = 0.5\\n\",\n    \"cost = compute_cost_reg(X_mapped, y_train, initial_w, initial_b, lambda_)\\n\",\n    \"\\n\",\n    \"print(\\\"Regularized cost :\\\", cost)\\n\",\n    \"\\n\",\n    \"# UNIT TEST    \\n\",\n    \"compute_cost_reg_test(compute_cost_reg)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Regularized cost : <b></td>\\n\",\n    \"    <td> 0.6618252552483948 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.5\\\"></a>\\n\",\n    \"### 3.5 Gradient for regularized logistic regression\\n\",\n    \"\\n\",\n    \"In this section, you will implement the gradient for regularized logistic regression.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The gradient of the regularized cost function has two components. The first, $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ is a scalar, the other is a vector with the same shape as the parameters $\\\\mathbf{w}$, where the $j^\\\\mathrm{th}$ element is defined as follows:\\n\",\n    \"\\n\",\n    \"$$\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} = \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})  $$\\n\",\n    \"\\n\",\n    \"$$\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} = \\\\left( \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)}) x_j^{(i)} \\\\right) + \\\\frac{\\\\lambda}{m} w_j  \\\\quad\\\\, \\\\mbox{for $j=0...(n-1)$}$$\\n\",\n    \"\\n\",\n    \"Compare this to the gradient of the cost function without regularization (which you implemented above), which is of the form \\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)}) \\\\tag{2}\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)})x_{j}^{(i)} \\\\tag{3}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"As you can see,$\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ is the same, the difference is the following term in $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w}$, which is $$\\\\frac{\\\\lambda}{m} w_j  \\\\quad\\\\, \\\\mbox{for $j=0...(n-1)$}$$ \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-06'></a>\\n\",\n    \"### Exercise 6\\n\",\n    \"\\n\",\n    \"Please complete the `compute_gradient_reg` function below to modify the code below to calculate the following term\\n\",\n    \"\\n\",\n    \"$$\\\\frac{\\\\lambda}{m} w_j  \\\\quad\\\\, \\\\mbox{for $j=0...(n-1)$}$$\\n\",\n    \"\\n\",\n    \"The starter code will add this term to the $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w}$ returned from `compute_gradient` above to get the gradient for the regularized cost function.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C6\\n\",\n    \"def compute_gradient_reg(X, y, w, b, lambda_ = 1): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X : (ndarray Shape (m,n))   variable such as house size \\n\",\n    \"      y : (ndarray Shape (m,))    actual value \\n\",\n    \"      w : (ndarray Shape (n,))    values of parameters of the model      \\n\",\n    \"      b : (scalar)                value of parameter of the model  \\n\",\n    \"      lambda_ : (scalar,float)    regularization constant\\n\",\n    \"    Returns\\n\",\n    \"      dj_db: (scalar)             The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"      dj_dw: (ndarray Shape (n,)) The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    dj_db, dj_dw = compute_gradient(X, y, w, b)\\n\",\n    \"\\n\",\n    \"    ### START CODE HERE ###     \\n\",\n    \"    for j in range(n):\\n\",\n    \"        dj_dw[j] = dj_dw[j] + (lambda_/m) * w[j]\\n\",\n    \"    ### END CODE HERE ###         \\n\",\n    \"        \\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_gradient_reg(X, y, w, b, lambda_ = 1): \\n\",\n    \"        m, n = X.shape\\n\",\n    \"    \\n\",\n    \"        dj_db, dj_dw = compute_gradient(X, y, w, b)\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###     \\n\",\n    \"        # Loop over the elements of w\\n\",\n    \"        for j in range(n): \\n\",\n    \"            \\n\",\n    \"            dj_dw_j_reg = # Your code here to calculate the regularization term for dj_dw[j]\\n\",\n    \"            \\n\",\n    \"            # Add the regularization term  to the correspoding element of dj_dw\\n\",\n    \"            dj_dw[j] = dj_dw[j] + dj_dw_j_reg\\n\",\n    \"        \\n\",\n    \"        ### END CODE HERE ###         \\n\",\n    \"        \\n\",\n    \"        return dj_db, dj_dw\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `dj_dw_j_reg` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_dw_j_reg</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use calculate dj_dw_j_reg as <code>dj_dw_j_reg = (lambda_ / m) * w[j] </code> \\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to check your implementation of the `compute_gradient_reg` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dj_db: 0.07138288792343662\\n\",\n      \"First few elements of regularized dj_dw:\\n\",\n      \" [-0.010386028450548701, 0.011409852883280122, 0.0536273463274574, 0.0031402782673134655]\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_mapped = map_feature(X_train[:, 0], X_train[:, 1])\\n\",\n    \"np.random.seed(1) \\n\",\n    \"initial_w  = np.random.rand(X_mapped.shape[1]) - 0.5 \\n\",\n    \"initial_b = 0.5\\n\",\n    \" \\n\",\n    \"lambda_ = 0.5\\n\",\n    \"dj_db, dj_dw = compute_gradient_reg(X_mapped, y_train, initial_w, initial_b, lambda_)\\n\",\n    \"\\n\",\n    \"print(f\\\"dj_db: {dj_db}\\\", )\\n\",\n    \"print(f\\\"First few elements of regularized dj_dw:\\\\n {dj_dw[:4].tolist()}\\\", )\\n\",\n    \"\\n\",\n    \"# UNIT TESTS    \\n\",\n    \"compute_gradient_reg_test(compute_gradient_reg)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>dj_db:</b>0.07138288792343656</td> </tr>\\n\",\n    \"  <tr>\\n\",\n    \"      <td> <b> First few elements of regularized dj_dw:</b> </td> </tr>\\n\",\n    \"   <tr>\\n\",\n    \"   <td> [[-0.010386028450548701], [0.01140985288328012], [0.0536273463274574], [0.003140278267313462]] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.6\\\"></a>\\n\",\n    \"### 3.6 Learning parameters using gradient descent\\n\",\n    \"\\n\",\n    \"Similar to the previous parts, you will use your gradient descent function implemented above to learn the optimal parameters $w$,$b$. \\n\",\n    \"- If you have completed the cost and gradient for regularized logistic regression correctly, you should be able to step through the next cell to learn the parameters $w$. \\n\",\n    \"- After training our parameters, we will use it to plot the decision boundary. \\n\",\n    \"\\n\",\n    \"**Note**\\n\",\n    \"\\n\",\n    \"The code block below takes quite a while to run, especially with a non-vectorized version. You can reduce the `iterations` to test your implementation and iterate faster. If you hae time, run for 100,000 iterations to see better results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration    0: Cost     0.72   \\n\",\n      \"Iteration 1000: Cost     0.59   \\n\",\n      \"Iteration 2000: Cost     0.56   \\n\",\n      \"Iteration 3000: Cost     0.53   \\n\",\n      \"Iteration 4000: Cost     0.51   \\n\",\n      \"Iteration 5000: Cost     0.50   \\n\",\n      \"Iteration 6000: Cost     0.48   \\n\",\n      \"Iteration 7000: Cost     0.47   \\n\",\n      \"Iteration 8000: Cost     0.46   \\n\",\n      \"Iteration 9000: Cost     0.45   \\n\",\n      \"Iteration 9999: Cost     0.45   \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Initialize fitting parameters\\n\",\n    \"np.random.seed(1)\\n\",\n    \"initial_w = np.random.rand(X_mapped.shape[1])-0.5\\n\",\n    \"initial_b = 1.\\n\",\n    \"\\n\",\n    \"# Set regularization parameter lambda_ to 1 (you can try varying this)\\n\",\n    \"lambda_ = 0.01;                                          \\n\",\n    \"# Some gradient descent settings\\n\",\n    \"iterations = 10000\\n\",\n    \"alpha = 0.01\\n\",\n    \"\\n\",\n    \"w,b, J_history,_ = gradient_descent(X_mapped, y_train, initial_w, initial_b, \\n\",\n    \"                                    compute_cost_reg, compute_gradient_reg, \\n\",\n    \"                                    alpha, iterations, lambda_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"<summary>\\n\",\n    \"    <b>Expected Output: Cost < 0.5  (Click for details)</b>\\n\",\n    \"</summary>\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"# Using the following settings\\n\",\n    \"#np.random.seed(1)\\n\",\n    \"#initial_w = np.random.rand(X_mapped.shape[1])-0.5\\n\",\n    \"#initial_b = 1.\\n\",\n    \"#lambda_ = 0.01;                                          \\n\",\n    \"#iterations = 10000\\n\",\n    \"#alpha = 0.01\\n\",\n    \"Iteration    0: Cost     0.72   \\n\",\n    \"Iteration 1000: Cost     0.59   \\n\",\n    \"Iteration 2000: Cost     0.56   \\n\",\n    \"Iteration 3000: Cost     0.53   \\n\",\n    \"Iteration 4000: Cost     0.51   \\n\",\n    \"Iteration 5000: Cost     0.50   \\n\",\n    \"Iteration 6000: Cost     0.48   \\n\",\n    \"Iteration 7000: Cost     0.47   \\n\",\n    \"Iteration 8000: Cost     0.46   \\n\",\n    \"Iteration 9000: Cost     0.45   \\n\",\n    \"Iteration 9999: Cost     0.45       \\n\",\n    \"    \\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.7\\\"></a>\\n\",\n    \"### 3.7 Plotting the decision boundary\\n\",\n    \"To help you visualize the model learned by this classifier, we will use our `plot_decision_boundary` function which plots the (non-linear) decision boundary that separates the positive and negative examples. \\n\",\n    \"\\n\",\n    \"- In the function, we plotted the non-linear decision boundary by computing the classifier’s predictions on an evenly spaced grid and then drew a contour plot of where the predictions change from y = 0 to y = 1.\\n\",\n    \"\\n\",\n    \"- After learning the parameters $w$,$b$, the next step is to plot a decision boundary similar to Figure 4.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 4.png\\\"  width=\\\"450\\\" height=\\\"450\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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2XZ1gRXruk6yVs/OVGrut7Ha2K3/GoBSNX9KFNAoLJZv1uFndffDdkVP/sb7onWEbSOIGRHfQNjT576SXDivBJXEwcb137Fgkx7p93N9onlEeqhToJCCbrfHVnyIC1B6pTX/5OnAqUq+/6Gqp3oL6e+upeWWsNubnw6KOn+fxz+/t8lFLERCe5fU5G+4i6pA/BQ75O1kmbm4ZGs276uoDI/5uloddu9TFp7J7PNc8bySnvubu+AaVi6N37JWkBBBnpQzCZr5N1th7ZyrpD67hlwC2ApHucoLEx/k2lkAK5NVO/b+BIaRjP7I7iqLrA7qoJB5GA4AFfhxTO3zSfcBXOlP5TgMA+ofiioeGy6enpATmM1pc+ByddBNTtG+g/6EdW5zcnfV46m3I32V014RCGBASl1JVKqR1KqV1KqVlunk9XShUqpTa6vh42olyr+Dr9/j9b/sOYHmNIaJFgRrW8YtcNa9xdjS9fvtwRC9LNnj3b9DkeTg1yfTr0YeVtK4mNiGXk6yPZV+j7UhKymmnw8DsgKKXCgeeBsUBfYIpSyt29/77SWl/g+nrU33Kt5MtkndyiXPYU7GF08uja39n5j+O0JS+coGa+RGPBKVAnBXpSv55te7Js6jLKKsuY/O5kyivLvS5HVjMNLka0EC4Cdmmts7TWZcDbwEQD9usYvixot/ZgdWd4Wufqvp1Q/8dpKHXipJSKO05cXtsTnl4A9GrXi5cmvMTqnNU88PkDXpdjx136hHmMCAhdgP11fs5x/a6+IUqpTUqpT5RS/RramVJqulJqrVJq7ZEjRwyonv98mayz9uBawlQYAzsNBHz/x/HnxOPv1a2RJ71AGHbq9OBklhv63cCv0n7FX1b/hUU7Fnn1t7KaaXDxe9ipUup64Aqt9R2un28BLtJa31Nnm1ZAlda6SCk1Dnhaa53S1L6dNOzUW1e9eRXZBdn8+KsfAd8XDTNq2KIv+3HKkEmncPqSHI0NqW2q3qUVpQx9ZShZ+VlsunOTx/fzlvsdOI/dw05zgLrrOHcFDtbdQGt9Qmtd5Hq8GIhUSrU3oGzH2n18N3069Kn9WZYBDnxODgbgX3orOiKaBdctoLyynDs/urPJC4GafVp1lz5hDSMCwvdAilLqPKVUFDAZOKvdqZTqqFx5C6XURa5yjxlQtmMdKz5G+9gzMc+bfxwzOjI9TYcEaieq8F+Ptj2Yc/kcPtn1CW//+Haj29a0RGTto+BiyExlVxro70A48IrWeo5S6k4ArfWLSqm7gRlABVAM/J/W+pum9huoKaMqXUXkHyJ54NIHeOzyx2p/78tsZzvTNpIyCly+prcqqyoZ+spQ9uTvYdtd22jXrJ3b7eSz4Vz+pIxqm5VO/Bo8eLAORPnF+ZoM9F+/+avf+6p+i+xhZ9nCPptyN+mIRyP0Lz74xVm/nz17tqa6I+ysr9mzZ9tTUeEWsFb7eM6VmcomKCgpAPDqTlUNsXPkixNH3UjqynwDEgYwc+hMXtv4Gt/sP9OQD9QhuMJzsridCXJO5NDtb92YO34u0wZPs7s6QUVSFdY4VXaKHs/0oG+Hvnxx6xfnPC/vg3PZPcpI1BMbEQtAcUWxzTURwjfNo5rz26G/5cu9X/L9ge/Ped6JrUfhPwkIJoiNdAWEcgkIRpCRT/aYNngaraJb8edv/nzOc1Yce1kjyXoSEEwQE1F9e6pQaSGYfXKQ3LU9WkW34s7Bd/LetvfYk7/H0rJDfakXu0hAMEGYCqNNTBsOnzpsd1UsIQvnBa+7L7obrTWvbXzN0nJljSR7SEAwSffW3ckuzJarWINJ7tpa3eK6MTJ5JPM3z7e0E1nWSLKHBASTJLVOYm/B3qC9erYrry8B1npTB0xlT8Eevt7/tWVlylIv9gipgGBlJ1X3uO5kF5y76JfdjLzxi+T1Q8OkPpNoHtmc1ze9blmZskaSPUImIFjZSZWRkcHfx/6dUw+dApw1KiZYWyzCPC2iWjCpzyTe3fouFVUVlpQpayTZI2QCgjedVP62JDIyMli5dyVkVP8c7FfPktcPfhNTJ5Jfks+3Od9aVmbde0APGbJXgoEFQiYgeNpJZVRL4oKOF6BQvlbXUKF632Bxhr/v0ejk0YSrcBZnLvb6b2U+QeAImaUrPL2Rh5E3/Oj9XG9YDtvf3e5lbc0jSw6EJiPe9/TX0iksLWTDLzd4/Dc1F1h1W+dhYc0k/WMiWbrCA552Uhk53G1w58EUDS2SE7AICuNSxrExdyMHTx5semMXmU8QWEImIHjaSWXkcLdh3YZx4OQBsvKzfKmyKSTfHzqMThVeft7lAHy9z/PhpzKfILCETEAAzzqpjBzuNip5FADLspb5VmETSL4/dBg9NHhAwgBiImJYk7PG47+R+QSBJaQCgieMHO6W0jaFrq26smyP+QFBTvTCbFHhUQzuNJg1BzwPCDKfILBIQHDDqOFuSilGJY/iiz1fUKWrvPpbb0/wMr9ANMaoVOElXS9h3cF1lFWWebS9zCcILBIQTDY6eTTHi497PX5bTvDCSEa1IC/sfCGllaVsPbLV47+R+QSBQwKCycaljCMyLJL3t71v+L7lPgHCan069AFgx9EdNtdEmEECgslax7RmVPIo3t/+fpPDT709wTtlPSEJQKEjpW0KCsWOYxIQgpEEBAv8rM/PyMrPYlPepka3c8oJ3luS3gps3ny+YiNjSYxLlIAQpCQgWGBi6kTCVBjvbHnHtDJkfoHwlbcBPbV9KjuP7TSpNsJOEhAs0KF5B8b0GMP8zfOprKr06G+8PcHbkSaS/ovQ1LVlV69mK4vAIQHBIrddcBv7T+zniz1feLS900+sgZreEtX8CegJLRI4fOqw10OphfNJQLDIhNQJtIlpw6sbX7W7KkL4FdATmidQUVVBfnG+ybUUVpOAYJGYiBim9J/Cwu0LKSgpsLs6hpL+i9AS3zwegMOnDttcE8/JEtyekYBgodsH3U5JRQmvbXzN7qoYStJEgc3bgN42ti0A+SXWthB8PalbebfEQCcBwUKDOg1iaLehPPvdsx53LgthNm8DenRENAClFaUm1MY9f07qsgS35yQgWOzXQ35NVn4WC7YssLsqQvgkKjwKwOP1jIzgz0ldluD2nAQEi13T+xr6dujLnK/myCgNEZCiw10thErrWgj+nNRlCW7PSUDwgT8dVGEqjIcue4gtR7bw3tb3TKylEOaIDI8EoLyy3LIy/TmpyxLcnpOA4CUjOqhu7Hcj/Tr046EvHjL1n8qXzl7pIBZNqUkV1aSOrODPSV2W4PacBAQvGdFBFR4WzuMjHyfzeCYvb3jZ6CrW8mWNIVmXSDSluLwYqF7XyCr+ntRlCW7PSEDwkhEdVBkZGYzvNZ5LEy/lkRWPcKrslFHVE0HKSS230+XVF0SxEeYHhLrp2aysh0hOniMndRMZEhCUUlcqpXYopXYppWa5eV4ppZ5xPb9ZKTXIiHLtYEQH1SOPPIJSiidHPUluUS5/W/M3o6rn05IEsi6R8zmp5VZcUd1CaBbZrIkt/SPzB6znd0BQSoUDzwNjgb7AFKVU33qbjQVSXF/TgX/4W65djOygGtptKJN6T+LxVY+TcyLHkPr5siSBr8sYSMAITTVLVsTFxJlajswfsJ4RLYSLgF1a6yytdRnwNjCx3jYTgdd1tTVAa6VUJwPKtpyvucyGrsK7re9Gla7it5/91oLaG8tJV63ByOyWm6+j5Q4VHQKgUwtz/4Vl/oD1IgzYRxdgf52fc4CLPdimC3Co/s6UUtOpbkWQmOjMccIJCTd7nb/MyMio/UdWSp1197TWX7bm0ZWPMiNtBsOThhtWT1/WGJJ1iZyjsc+Mv2rSMTVX4DXpGKDJz3ZuUS5x0XGmdypHRye60kXn/l6Yw4gWgnLzu/qfXE+2qf6l1nO11mla67QOHTr4XblAcP+l95MUl8Q9n9xDRVWFYfs1Y9ip9DcEB3/SMYeKDtGxRUezqlZL5g9Yz4iAkAN0q/NzV6D+3TM82SZk1L8KbxbZjL9e8Vc2523mmW+fsalW1ZpKI8h9EOxhdMvNn3TMgRMH6Nyys6H1cUfmD1jPiJTR90CKUuo84AAwGbip3jaLgLuVUm9TnU4q1Fqfky4KVnl5b5CV9RClpfuIjk5kxoxzr3Am9Z7E1b2u5qEvHmJMjzH0j+9vSz19TSMIcxkdcP1Jx+w4toPr+15vaH0a4kt6VvjO7xaC1roCuBtYAmwDFmittyil7lRK3enabDGQBewC/gX8yt9yreTPUhWeDp1TSvHShJeIi45jyntTaif/WMnbNIL0NwQuX9MxR08f5XjxcXq37+1X+XJ/AmcyZB6C1nqx1rqX1rqH1nqO63cvaq1fdD3WWuu7XM+fr7Vea0S5VvB3LLQ3J9n45vG8ds1r/Hj4R2Z+NtOI6nvF2zSCpIkCl6/pmO1HtwOQ2i7V57JlfoFzyUzlJvg7Ftrbk+yVPa/k/13y/3ju++dYtGORd5X1k6wKGVp8Wc5h25FtAH61EGR+gXNJQGiCv2OhfTnJPj7ycQZ1GsTUhVPJys/yqBwjyKgO0ZT1h9bTKroVSa2TfN6HzC9wLgkITfD3qtmXk2x0RDTvXv8uYSqMaxdce1Z/gpm5VxnVIZry7YFvubDzhYQp308d0hJ1LgkITfD3qtnXk+x5bc5j/qT5bMzdyN2L7wasyb3KqpChq6k+odPlp9mct5lLul7iVznSEnUuZeTsR6OlpaXptWvt73+uP2w0OXmOZSfK33/xex776jH+dfW/6Ff6WANDBZMYMmSvJfURwaup2dCr9q3islcvY9HkRVyderVfZdn5PxXslFLrtNZpvvytEfMQgp6dY6Ez0jNYc2ANdy2+iyWXur+ZjuRez1Z3yQdhnFX7VgFwcdf6K9N4T+YXOJOkjBwuPCyct699m8S4RA6XulsBRHKv9cmie57zZimSpbuX8pOEnxDfPN7iWgqrSEAIAO2atWPJz5fwnwMtKK08OygEW+5VruybZuQx8nQpklNlp1i1bxVjeowxpR7CGSQgBIjkNslkXLWK5/fEcKwsArNGAdn9T+7r1X0oLbpnRwtoRfYKyqvKzwoI0hILPhIQDGLFVPzzE85n1hXLuOX7SO7bPoDUC9Ybnof15J/ciSdZWXTPf40tRbJk1xJiImK4NPFS08qX5SzsJwHBAFZOxR/abSgfTP6AbUe3MWb+GApKCgwvoylGXxmG0tW9r6w4Rg3tq0pX8f729xnTYwxPPPaEKfWQ5SycQQKCAayeij+mxxgW3riQzXmbueLfV1BYUujX/uw+IRt9dW/nontmHTM7W0BrctaQcyKHG/reYFo9ZDkLZ5CAYAA7puKPSxnHuze8y/pD6xn7xlhOlp70eV+e/JPbHTS8YWedAjWv3tgxW7BlAdHh0X7PPWiMLGfhDBIQGuFpTtOuqfgTUiew4LoFfHfgO0a+PpIjp47UPmd0PtaqK1RZUrtpZhyjhgJZla7ina3vMDZlLK2iW5lWD1nOwhkkIDTAm5ymnVPxJ/WZxMIbF/LD4R+45OVL2HF0h1/5WLtPyE5scTTF6taTlcfoiz1fcPDkQW7sd6Op9ZDlLJxBAkIDvMlp2r0o3NWpV7P81uWcLD3JBZMvYNvOX/ucj/Xkn9yXoBGIJ3pPBeoIJ08C2dx1c2kX245rel9jal3s/h8S1WQtowYsXx4GuDs2ivT0Kqur45Gs/Cx6tO3B519AmNtJzfbVval1coJFoL5Od/XOK8qj69+6cu9F9/KXK/5iU82Et/xZy0haCA1wUk7T0/6A5DbJAJyojHb7vORjzWd3ys1Ir218jYqqCqYNnmZ3VYRFJCA0wCk5TU/6A+o3/Z97opSSkrP3Y0fdA2lkklEC9bXVD2Snyk7xzHfP8NPuP/X7/skicEjKqBFOWKJ39eruXi15XdP0z819g43b7yJKF5JfHkn37n9gcK/7Laixe4GaSglVD3/5MH9Y+Qe+/p+vGdptqN3VEV7wJ2UkAcHhvO3LqH/iXbp7KbcsvIWTpSd5Zuwz3D7w9torditJQAgc2QXZ9H6+N5N6T+LNa9+0uzrCS9KHEMS87cuo3/Qf02MMm+7cxLDEYUz7cBpT3pvi98xmXwRTbm1tTM8AAA+GSURBVD3YzVw2E4XiyVFP2l0VYTEJCA7nbV+Guxx2xxYdWfLzJcy5fA7vbn2XQXMH8W3Ot2ZUt0GBmlsPNV9lf8WCLQuYOWwm3eK62V0dYTEJCA5n1PjsMBXGg5c9yIpfrKCiqoJhrwzj4S8fpqyyzJR6y8qVgae4vJhpH04jMS6R3w79rWnlyGfDuaQPIQQVlhRy76f38vqm1+nTvg9zr55r6LLGNSOj6k6OCwtrJhONHO7+z+7nT9/8iSU/X3LWfQ+MJJ8N80kfgvBKXEwc866Zx8c3fczp8tNc9uplTP9wOvnF+Ybsv6lZ3pI+cp7vDnzHU6uf4o6Bd5gWDEBWNXU6CQghbFzKOLb8agu/HvJrXtnwCn2e78PbP77t92igplauNGpFUAksxiitKOW2/95G55adeWrMU+aWJauaOpoEBItYlTf1tpzmUc15asxTfD/te7rFdWPKe1MYNX8UWw5v8bkOVs3ytnqpabsCkNmfnQc/f5CtR7Yyd/xc4mLiDN13fU5aAUCcSwKCBay6G5Q/5QzsNJA1t6/hubHPsf7Qega8OIA7Ft1Bzokcr+vhbmRUZWUkv/99dkDPWrbjXgdmf3be2/oef13zV+668C7Gpow1ZJ+NccoKAMI9CQgWsCpv6m854WHh3HXRXWTek8k9F93D/M3zSXk2hZmfzeR48XGP6+FuZFT//q+ybJn/K4KG2nIYZn52lu5eyk3v38QlXS/hL2OsWbxOVjV1NhllZAGrVk41upy9BXuZvXw28zfNp1V0K+4fdj/3XnwvzaOae7Wf+kuA/P732Sxb5v/nzorZzxkZGW5bBrNnz7YkCJn12VmZvZIr/30lvdr14stbv6RNbBuf9yWcRUYZOZxVeVOjy+neujvzrpnH5hmbGZ40nAe/eJCez/bk6TVPU1RW5NE+3KU8HnggMmDGntt9rwMzPjvf7P+G8W+OJ6l1EktvWSrBQNSSgGABq/KmZpXTP74/i6YsYtVtq+jVrhf3LbmPbn/rxqxlszhw4kCjf+su5REeXm5IyiMUlsMw+j1dnLmYUa+PomOLjiy7ZRnxzeONqKYIEhIQLGBV3tTscoYlDmPFL1aw+vbVjEoexZ+/+TPdn+7O1IVT2ZS7ye3fmDnM0Op+AzsCkJHv6Zs/vMnEtyfSu31vVv3PKrq06mJ8hUVA86sPQSnVFvgP0B3YC9ygtT5ndpNSai9wEqgEKjzNbwVLH0KwysrP4uk1T/Pyhpc5VX6KkeeN5H8v/l/GpYwjPCwc8H75bmGOZ799lns/vZcRSSNYNGURraJb2V0lYRI7+xBmAZ9rrVOAz10/N+SnWusLfK2ocJ7kNsk8PfZpcv4vhydHPcn2o9uZ8PYEuj/dnUeWP8L+wv0yzNBmp8pOMeOjGdz76b1c0/saPv35pxIMRIP8DQgTgXmux/MAc+/ELRypdUxrZg6byZ7/3cP7N7xP3w59yViRQdLfk/j50tc4GnsrUdHdcNIww2Acplr/NS3OXEy/F/rx4roX+c2Q3/DO9e8QExFjS91kQbvA4G/KqEBr3brOz/la63OGLCil9gD5VI+f+6fWem4j+5wOTAdITEwcnJ19brpBON+e/D28vul15m2ax56CPbSMask1va/hhn43MDp5NNER7u/7bJVAvGFPRkZGo4Gs5jUdOnmI+5bcx4ItC+jTvg//HP9PLku6zPDyPCUL2lnL1DumKaWWAR3dPPUQMM/DgNBZa31QKRUPfAbco7Ve2VTlpA8h8FXpKr7K/op5m+axcPtCCkoKaBXdiompE7m+7/WM6THGluAQiAGhqTorpfjH9/9g1rJZlFSU8Lvhv2PmsJlEhUeZUp6npB/JWqb2IWitR2mt+7v5+i+Qp5Tq5KpEJ+BwA/s46Pp+GFgIXORLZUXgCVNhjOg+glcmvkLeb/JYfNNiru1zLR/t/IgJb08g/ql4bll4C+9seYeCkgJT6xKMs5zrv6YZF86g8IFCpp2Yxu+G/87nYOCP+ukhd8EAZEE7J/K3D2ERcKvr8a3Af+tvoJRqrpRqWfMYGAP86Ge5IgBFhUcxNmVsbXD45OZPuK7PdSzOXMwN795Ahz93YPirw3l0xaOs3r+aiqoKQ8u3e5JZQ3Vq6vmGgtjp8tMkTkxk0D8HgWs38zbOo6qqimf/9KzP9fEnaLqbiAju7+EtC9o5j799CO2ABUAisA+4Xmt9XCnVGXhJaz1OKZVMdasAIAJ4U2vt0RATSRmFhoqqCr7N+ZaPMz/myOG3GNlmL/HRcLRMsa54IPHxNzEscRiDOg0y7Ip31CjFH/6QVLucRnLyHNPy2fWX7qhbljdpmZptdx7byYtrX+TVja9SUFJAvw79uPfie/ll2i8NTYP5kjJquEWgqLsEh/QhmMeflFGEPwVrrY8BI938/iAwzvU4C/iJP+WI4BYRFsGwxGH0jN7Ljh2HqXIt0RMfrbk8cgN/3rie33wGMRExXNj5QoZ1G8awxGEM6TqEds3aeV1eXt4bPPBAZO2Jq2YFUcDwE1T9DlVfy9p9fDcAl8+7nC/3fklEWATX9b2OX6X9iksTL0UpxcHZBw2tuy8aTgNpoqOtCcDCd7K4nXCMhq4uI6K6crjN06zat4qv93/N+kPra9NJSXFJDOo0iIEdBzKo0yAGdRpEp5adfCqnfienEaNsGiorNxemTDn7d3UXzCuvLOeb/d/w0c6P+CjzI7Yf3Q5fQup1qfx8wM+5Y9AddGzRsdHWh798ef3SgWw/U0cZ2UkCQmjxdGXP0+Wn+f7A96zJWcOG3A2sP7SezOOZtc8nNE9gQMIA+rTvQ2r7VHq27UnPtj1JjEskIizC43KMGGXTVFlKKaqqqsguzOaHvB/44fAPrD24li/3fklBSQGRYZGkd0/nqpSruKrXVfRs27N2D04czunEOoUa21JGQhgpOjqxgavLszsfm0U2Y0T3EYzoPqL2dydKT7ApdxPrD61nfe56thzeUrukRo3IsEgS4xJ5qm80rSNKziknIrIzp8tPExsRW9upatZrKiWO6R9Wp47inojjZNnJ2ud6tu3JpN6TGN9rPKOTR9MyuqXbfTd2rwS7Tr415ZrVahHmkhaC8JiZ6Yma/Rt5dam15uDJg+zO382u47vYdXwXWflZtK5Yy6QOWUSHn/nsl1TCUzvg8wXAinP31fu63lww+QIiwiKICIsgXIXXPlYoiiuKKa4o5nT56bO+UmP2M7XLEWLCOaesDUVtaflNS66efjXnJ5xP//j+9I/v7/HSElbdZ0MEFkkZCdNZlQowO+icXc6DlJbuR0V0pLjFTeyvTOV48XEKSgooLC3kH+P/wdh/j6WwtJDCkkLKq8qpqKqo/aqsqqSiqoIqXUVsZCzNIpvRLLIZsRFnHreMbklaq0L6R64migJUeAKtO/2WHt2mN3jl7ynJ1wt3JCAI04Xiycfps5klXy/ckTumCdOZeV8Dp3L6DXjk/sTCaNKpLDziaYdvMAmEJS0SEm6WACAMIy0E4RG5r4EQwU8CgvCIpCeECH6SMhIek/SEEMFNWghCCCEACQhChAS5haXwhKSMhAhyRq24KoKftBBEUJIr4jMaW/NIiLokIIig4+6uXTt2TDctKDg9+ITipELhGwkIIuhYeUVsdfDxRUOTB4N5UqHwjQQEEXSsvCIOhHSMTCoUnpKAIIKOlVfEgZCOkUmFwlMyykgEneTkOW5XATXjijhQ1niSSYXCE9JCEEHHyitiSceIYCItBBGUrLoilltGimAiAUEIP0k6RgQLSRkJIYQAJCAIIYRwkYAghBACkIAghBDCRQKCEEIIQAKCEEIIFwkIQgghAAkIQgghXCQgCCGEACQgCCGEcJGAIIQQApCAIIQQwsWvgKCUul4ptUUpVaWUSmtkuyuVUjuUUruUUrP8KVMIIYQ5/G0h/Aj8DFjZ0AZKqXDgeWAs0BeYopTq62e5QgghDObX8tda620ASqnGNrsI2KW1znJt+zYwEdjqT9lCCCGMZcX9ELoA++v8nANc3NDGSqnpwHTXj6VKqR9NrFsgaQ8ctbsSDiDH4Qw5FmfIsTgj1dc/bDIgKKWWAR3dPPWQ1vq/HpThrvmgG9pYaz0XmOsqe63WusG+iVAix6KaHIcz5FicIcfiDKXUWl//tsmAoLUe5evOXXKAbnV+7goc9HOfQgghDGbFsNPvgRSl1HlKqShgMrDIgnKFEEJ4wd9hp5OUUjnAEOBjpdQS1+87K6UWA2itK4C7gSXANmCB1nqLh0XM9ad+QUaORTU5DmfIsThDjsUZPh8LpXWD6XwhhBAhRGYqCyGEACQgCCGEcHFMQJBlMM5QSrVVSn2mlMp0fW/TwHZ7lVI/KKU2+jPUzImaep9VtWdcz29WSg2yo55W8OBYpCulCl2fg41KqYftqKcVlFKvKKUONzQ/KVQ+Fx4cB98+E1prR3wBfaieULEcSGtgm3BgN5AMRAGbgL52192EY/EnYJbr8SzgyQa22wu0t7u+Jrz+Jt9nYBzwCdXzXC4BvrW73jYei3TgI7vratHxGA4MAn5s4PlQ+Vw0dRx8+kw4poWgtd6mtd7RxGa1y2BorcuAmmUwgs1EYJ7r8TzgGhvrYgdP3ueJwOu62hqgtVKqk9UVtUCofOY9orVeCRxvZJOQ+Fx4cBx84piA4CF3y2B0sakuZkrQWh8CcH2Pb2A7DSxVSq1zLfkRLDx5n0Pls+Dp6xyilNqklPpEKdXPmqo5Uqh8Ljzh9WfCirWMalm9DIaTNXYsvNjNMK31QaVUPPCZUmq768oh0HnyPgfNZ6EJnrzO9UCS1rpIKTUO+ABIMb1mzhQqn4um+PSZsDQgaFkGo1Zjx0IplaeU6qS1PuRq7h5uYB8HXd8PK6UWUp1eCIaA4Mn7HDSfhSY0+Tq11ifqPF6slHpBKdVeax2Ki72FyueiUb5+JgItZRQqy2AsAm51Pb4VOKf1pJRqrpRqWfMYGEP1/SmCgSfv8yJgqmtUySVAYU2aLcg0eSyUUh2Vaw16pdRFVP9fH7O8ps4QKp+LRvn6mbC0hdAYpdQk4FmgA9XLYGzUWl+hlOoMvKS1Hqe1rlBK1SyDEQ68oj1fBiOQPAEsUErdDuwDrofqJUFwHQsgAVjoes8jgDe11p/aVF9DNfQ+K6XudD3/IrCY6hElu4DTwG121ddMHh6L64AZSqkKoBiYrF1DTYKNUuotqkfQtHctmzMbiITQ+lx4cBx8+kzI0hVCCCGAwEsZCSGEMIkEBCGEEIAEBCGEEC4SEIQQQgASEIQQQrhIQBBCCAFIQBBCCOHy/wERTPfRHx2krQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_decision_boundary(w, b, X_mapped, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.8\\\"></a>\\n\",\n    \"### 3.8 Evaluating regularized logistic regression model\\n\",\n    \"\\n\",\n    \"You will use the `predict` function that you implemented above to calculate the accuracy of the regulaized logistic regression model on the training set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train Accuracy: 82.203390\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Compute accuracy on the training set\\n\",\n    \"p = predict(X_mapped, w, b)\\n\",\n    \"\\n\",\n    \"print('Train Accuracy: %f'%(np.mean(p == y_train) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Train Accuracy:</b>~ 80%</td> </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/C1W3A1/archive/.ipynb_checkpoints/C1_W3_Logistic_Regression-Copy1-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Logistic Regression\\n\",\n    \"\\n\",\n    \"In this exercise, you will implement logistic regression and apply it to two different datasets. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Logistic Regression](#2)\\n\",\n    \"  - [ 2.1 Problem Statement](#2.1)\\n\",\n    \"  - [ 2.2 Loading and visualizing the data](#2.2)\\n\",\n    \"  - [ 2.3  Sigmoid function](#2.3)\\n\",\n    \"  - [ 2.4 Cost function for logistic regression](#2.4)\\n\",\n    \"  - [ 2.5 Gradient for logistic regression](#2.5)\\n\",\n    \"  - [ 2.6 Learning parameters using gradient descent ](#2.6)\\n\",\n    \"  - [ 2.7 Plotting the decision boundary](#2.7)\\n\",\n    \"  - [ 2.8 Evaluating logistic regression](#2.8)\\n\",\n    \"- [ 3 - Regularized Logistic Regression](#3)\\n\",\n    \"  - [ 3.1 Problem Statement](#3.1)\\n\",\n    \"  - [ 3.2 Loading and visualizing the data](#3.2)\\n\",\n    \"  - [ 3.3 Feature mapping](#3.3)\\n\",\n    \"  - [ 3.4 Cost function for regularized logistic regression](#3.4)\\n\",\n    \"  - [ 3.5 Gradient for regularized logistic regression](#3.5)\\n\",\n    \"  - [ 3.6 Learning parameters using gradient descent](#3.6)\\n\",\n    \"  - [ 3.7 Plotting the decision boundary](#3.7)\\n\",\n    \"  - [ 3.8 Evaluating regularized logistic regression model](#3.8)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\\n\",\n    \"-  ``utils.py`` contains helper functions for this assignment. You do not need to modify code in this file.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import *\\n\",\n    \"import copy\\n\",\n    \"import math\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Logistic Regression\\n\",\n    \"\\n\",\n    \"In this part of the exercise, you will build a logistic regression model to predict whether a student gets admitted into a university.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Problem Statement\\n\",\n    \"\\n\",\n    \"Suppose that you are the administrator of a university department and you want to determine each applicant’s chance of admission based on their results on two exams. \\n\",\n    \"* You have historical data from previous applicants that you can use as a training set for logistic regression. \\n\",\n    \"* For each training example, you have the applicant’s scores on two exams and the admissions decision. \\n\",\n    \"* Your task is to build a classification model that estimates an applicant’s probability of admission based on the scores from those two exams. \\n\",\n    \"\\n\",\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Loading and visualizing the data\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_dataset()` function shown below loads the data into variables `X_train` and `y_train`\\n\",\n    \"  - `X_train` contains exam scores on two exams for a student\\n\",\n    \"  - `y_train` is the admission decision \\n\",\n    \"      - `y_train = 1` if the student was admitted \\n\",\n    \"      - `y_train = 0` if the student was not admitted \\n\",\n    \"  - Both `X_train` and `y_train` are numpy arrays.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X_train, y_train = load_data(\\\"data/ex2data1.txt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to just print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints the first five values of `X_train` and the type of the variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"First five elements in X_train are:\\\\n\\\", X_train[:5])\\n\",\n    \"print(\\\"Type of X_train:\\\",type(X_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now print the first five values of `y_train`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"First five elements in y_train are:\\\\n\\\", y_train[:5])\\n\",\n    \"print(\\\"Type of y_train:\\\",type(y_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions. Let's print the shape of `X_train` and `y_train` and see how many training examples we have in our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The shape of X_train is: ' + str(X_train.shape))\\n\",\n    \"print ('The shape of y_train is: ' + str(y_train.shape))\\n\",\n    \"print ('We have m = %d training examples' % (len(y_train)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Visualize your data\\n\",\n    \"\\n\",\n    \"Before starting to implement any learning algorithm, it is always good to visualize the data if possible.\\n\",\n    \"- The code below displays the data on a 2D plot (as shown below), where the axes are the two exam scores, and the positive and negative examples are shown with different markers.\\n\",\n    \"- We use a helper function in the ``utils.py`` file to generate this plot. \\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 1.png\\\" width=\\\"450\\\" height=\\\"450\\\">\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Plot examples\\n\",\n    \"plot_data(X_train, y_train[:], pos_label=\\\"Admitted\\\", neg_label=\\\"Not admitted\\\")\\n\",\n    \"\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Exam 2 score') \\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Exam 1 score') \\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Your goal is to build a logistic regression model to fit this data.\\n\",\n    \"- With this model, you can then predict if a new student will be admitted based on their scores on the two exams.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3  Sigmoid function\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the model is represented as\\n\",\n    \"\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(x) = g(\\\\mathbf{w}\\\\cdot \\\\mathbf{x} + b)$$\\n\",\n    \"where function $g$ is the sigmoid function. The sigmoid function is defined as:\\n\",\n    \"\\n\",\n    \"$$g(z) = \\\\frac{1}{1+e^{-z}}$$\\n\",\n    \"\\n\",\n    \"Let's implement the sigmoid function first, so it can be used by the rest of this assignment.\\n\",\n    \"\\n\",\n    \"<a name='ex-01'></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"Please complete  the `sigmoid` function to calculate\\n\",\n    \"\\n\",\n    \"$$g(z) = \\\\frac{1}{1+e^{-z}}$$\\n\",\n    \"\\n\",\n    \"Note that \\n\",\n    \"- `z` is not always a single number, but can also be an array of numbers. \\n\",\n    \"- If the input is an array of numbers, we'd like to apply the sigmoid function to each value in the input array.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED FUNCTION: sigmoid\\n\",\n    \"\\n\",\n    \"def sigmoid(z):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Compute the sigmoid of z\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        z (ndarray): A scalar, numpy array of any size.\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"        g (ndarray): sigmoid(z), with the same shape as z\\n\",\n    \"         \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"          \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    ### END SOLUTION ###  \\n\",\n    \"    \\n\",\n    \"    return g\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"       \\n\",\n    \"`numpy` has a function called [`np.exp()`](https://numpy.org/doc/stable/reference/generated/numpy.exp.html), which offers a convinient way to calculate the exponential ( $e^{z}$) of all elements in the input array (`z`).\\n\",\n    \" \\n\",\n    \"<details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"  - You can translate $e^{-z}$ into code as `np.exp(-z)` \\n\",\n    \"    \\n\",\n    \"  - You can translate $1/e^{-z}$ into code as `1/np.exp(-z)` \\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `g` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate g</b></font></summary>\\n\",\n    \"        <code>g = 1 / (1 + np.exp(-z))</code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When you are finished, try testing a few values by calling `sigmoid(x)` in the cell below. \\n\",\n    \"- For large positive values of x, the sigmoid should be close to 1, while for large negative values, the sigmoid should be close to 0. \\n\",\n    \"- Evaluating `sigmoid(0)` should give you exactly 0.5. \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print (\\\"sigmoid(0) = \\\" + str(sigmoid(0)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>sigmoid(0)<b></td>\\n\",\n    \"    <td> 0.5 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\\n\",\n    \"    \\n\",\n    \"- As mentioned before, your code should also work with vectors and matrices. For a matrix, your function should perform the sigmoid function on every element.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print (\\\"sigmoid([ -1, 0, 1, 2]) = \\\" + str(sigmoid(np.array([-1, 0, 1, 2]))))\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"from public_tests import *\\n\",\n    \"sigmoid_test(sigmoid)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td><b>sigmoid([-1, 0, 1, 2])<b></td> \\n\",\n    \"    <td>[0.26894142        0.5           0.73105858        0.88079708]</td> \\n\",\n    \"  </tr>    \\n\",\n    \"  \\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.4\\\"></a>\\n\",\n    \"### 2.4 Cost function for logistic regression\\n\",\n    \"\\n\",\n    \"In this section, you will implement the cost function for logistic regression.\\n\",\n    \"\\n\",\n    \"<a name='ex-02'></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Please complete the `compute_cost` function using the equations below.\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the cost function is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\\\left[ loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) \\\\right] \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"where\\n\",\n    \"* m is the number of training examples in the dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* $loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)})$ is the cost for a single data point, which is - \\n\",\n    \"\\n\",\n    \"    $$loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\tag{2}$$\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)})$ is the model's prediction, while $y^{(i)}$, which is the actual label\\n\",\n    \"\\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(\\\\mathbf{w} \\\\cdot \\\\mathbf{x^{(i)}} + b)$ where function $g$ is the sigmoid function.\\n\",\n    \"    * It might be helpful to first calculate an intermediate variable $z_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x^{(i)}} + b = w_0x^{(i)}_0 + ... + w_{n-1}x^{(i)}_{n-1} + b$ where $n$ is the number of features, before calculating $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(z_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}))$\\n\",\n    \"\\n\",\n    \"Note:\\n\",\n    \"* As you are doing this, remember that the variables `X_train` and `y_train` are not scalar values but matrices of shape ($m, n$) and ($𝑚$,1) respectively, where  $𝑛$ is the number of features and $𝑚$ is the number of training examples.\\n\",\n    \"* You can use the sigmoid function that you implemented above for this part.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: compute_cost\\n\",\n    \"def compute_cost(X, y, w, b, lambda_= 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"      X : (ndarray Shape (m,n)) data, m examples by n features\\n\",\n    \"      y : (array_like Shape (m,)) target value \\n\",\n    \"      w : (array_like Shape (n,)) Values of parameters of the model      \\n\",\n    \"      b : scalar Values of bias parameter of the model\\n\",\n    \"      lambda_: unused placeholder\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost: (scalar)         cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"\\n\",\n    \"    return total_cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * You can represent a summation operator eg: $h = \\\\sum\\\\limits_{i = 0}^{m-1} 2i$ in code as follows:\\n\",\n    \"    ```python \\n\",\n    \"        h = 0\\n\",\n    \"        for i in range(m):\\n\",\n    \"            h = h + 2*i\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"   * In this case, you can iterate over all the examples in `X` using a for loop and add the `loss` from each iteration to a variable (`loss_sum`) initialized outside the loop.\\n\",\n    \"\\n\",\n    \"   * Then, you can return the `total_cost` as `loss_sum` divided by `m`.\\n\",\n    \"     \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_cost(X, y, w, b, lambda_= 1):\\n\",\n    \"        m, n = X.shape\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        loss_sum = 0 \\n\",\n    \"        \\n\",\n    \"        # Loop over each training example\\n\",\n    \"        for i in range(m): \\n\",\n    \"            \\n\",\n    \"            # First calculate z_wb = w[0]*X[i][0]+...+w[n-1]*X[i][n-1]+b\\n\",\n    \"            z_wb = 0 \\n\",\n    \"            # Loop over each feature\\n\",\n    \"            for j in range(n): \\n\",\n    \"                # Add the corresponding term to z_wb\\n\",\n    \"                z_wb_ij = # Your code here to calculate w[j] * X[i][j]\\n\",\n    \"                z_wb += z_wb_ij # equivalent to z_wb = z_wb + z_wb_ij\\n\",\n    \"            # Add the bias term to z_wb\\n\",\n    \"            z_wb += b # equivalent to z_wb = z_wb + b\\n\",\n    \"        \\n\",\n    \"            f_wb = # Your code here to calculate prediction f_wb for a training example\\n\",\n    \"            loss =  # Your code here to calculate loss for a training example\\n\",\n    \"            \\n\",\n    \"            loss_sum += loss # equivalent to loss_sum = loss_sum + loss\\n\",\n    \"        \\n\",\n    \"        total_cost = (1 / m) * loss_sum  \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"        \\n\",\n    \"        return total_cost\\n\",\n    \"    ```\\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `z_wb_ij`, `f_wb` and `cost`.\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate z_wb_ij</b></font></summary>\\n\",\n    \"           &emsp; &emsp; <code>z_wb_ij = w[j]*X[i][j] </code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(z_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}))$ where $g$ is the sigmoid function. You can simply call the `sigmoid` function implemented above.\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute f_wb as <code>f_wb = sigmoid(z_wb) </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate loss</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can use the <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.log.html\\\">np.log</a> function to calculate the log\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate loss</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute loss as <code>loss =  -y[i] * np.log(f_wb) - (1 - y[i]) * np.log(1 - f_wb)</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cells below to check your implementation of the `compute_cost` function with two different initializations of the parameters $w$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"m, n = X_train.shape\\n\",\n    \"\\n\",\n    \"# Compute and display cost with w initialized to zeroes\\n\",\n    \"initial_w = np.zeros(n)\\n\",\n    \"initial_b = 0.\\n\",\n    \"cost = compute_cost(X_train, y_train, initial_w, initial_b)\\n\",\n    \"print('Cost at initial w (zeros): {:.3f}'.format(cost))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Cost at initial w (zeros)<b></td>\\n\",\n    \"    <td> 0.693 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute and display cost with non-zero w\\n\",\n    \"test_w = np.array([0.2, 0.2])\\n\",\n    \"test_b = -24.\\n\",\n    \"cost = compute_cost(X_train, y_train, test_w, test_b)\\n\",\n    \"\\n\",\n    \"print('Cost at test w,b: {:.3f}'.format(cost))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"compute_cost_test(compute_cost)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Cost at test w,b<b></td>\\n\",\n    \"    <td> 0.218 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.5\\\"></a>\\n\",\n    \"### 2.5 Gradient for logistic regression\\n\",\n    \"\\n\",\n    \"In this section, you will implement the gradient for logistic regression.\\n\",\n    \"\\n\",\n    \"Recall that the gradient descent algorithm is:\\n\",\n    \"\\n\",\n    \"$$\\\\begin{align*}& \\\\text{repeat until convergence:} \\\\; \\\\lbrace \\\\newline \\\\; & b := b -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} \\\\newline       \\\\; & w_j := w_j -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} \\\\tag{1}  \\\\; & \\\\text{for j := 0..n-1}\\\\newline & \\\\rbrace\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"where, parameters $b$, $w_j$ are all updated simultaniously\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"<a name='ex-03'></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"\\n\",\n    \"Please complete the `compute_gradient` function to compute $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w}$, $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ from equations (2) and (3) below.\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)}) \\\\tag{2}\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)})x_{j}^{(i)} \\\\tag{3}\\n\",\n    \"$$\\n\",\n    \"* m is the number of training examples in the dataset\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"*  $f_{\\\\mathbf{w},b}(x^{(i)})$ is the model's prediction, while $y^{(i)}$ is the actual label\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- **Note**: While this gradient looks identical to the linear regression gradient, the formula is actually different because linear and logistic regression have different definitions of $f_{\\\\mathbf{w},b}(x)$.\\n\",\n    \"\\n\",\n    \"As before, you can use the sigmoid function that you implemented above and if you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED FUNCTION: compute_gradient\\n\",\n    \"def compute_gradient(X, y, w, b, lambda_=None): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for logistic regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X : (ndarray Shape (m,n)) variable such as house size \\n\",\n    \"      y : (array_like Shape (m,1)) actual value \\n\",\n    \"      w : (array_like Shape (n,1)) values of parameters of the model      \\n\",\n    \"      b : (scalar)                 value of parameter of the model \\n\",\n    \"      lambda_: unused placeholder.\\n\",\n    \"    Returns\\n\",\n    \"      dj_dw: (array_like Shape (n,1)) The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db: (scalar)                The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    dj_dw = np.zeros(w.shape)\\n\",\n    \"    dj_db = 0.\\n\",\n    \"\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    err  = None\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_wb = None\\n\",\n    \"        for j in range(n): \\n\",\n    \"            z_wb += None\\n\",\n    \"        z_wb += None\\n\",\n    \"        f_wb = None\\n\",\n    \"        \\n\",\n    \"        dj_db_i = None\\n\",\n    \"        dj_db += None\\n\",\n    \"        \\n\",\n    \"        for j in range(n):\\n\",\n    \"            dj_dw[j] = None\\n\",\n    \"            \\n\",\n    \"    dj_dw = None\\n\",\n    \"    dj_db = None\\n\",\n    \"    ### END CODE HERE ###\\n\",\n    \"\\n\",\n    \"        \\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \" <details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"       def compute_gradient(X, y, w, b, lambda_=None): \\n\",\n    \"            m, n = X.shape\\n\",\n    \"            dj_dw = np.zeros(w.shape)\\n\",\n    \"            dj_db = 0.\\n\",\n    \"        \\n\",\n    \"            ### START CODE HERE ### \\n\",\n    \"            err  = 0.\\n\",\n    \"            for i in range(m):\\n\",\n    \"                # Calculate f_wb (exactly as you did in the compute_cost function above)\\n\",\n    \"                f_wb = \\n\",\n    \"        \\n\",\n    \"                # Calculate the  gradient for b from this example\\n\",\n    \"                dj_db_i = # Your code here to calculate the error\\n\",\n    \"        \\n\",\n    \"                # add that to dj_db\\n\",\n    \"                dj_db += dj_db_i\\n\",\n    \"        \\n\",\n    \"                # get dj_dw for each attribute\\n\",\n    \"                for j in range(n):\\n\",\n    \"                    # You code here to calculate the gradient from the i-th example for j-th attribute\\n\",\n    \"                    dj_dw_ij =  \\n\",\n    \"                    dj_dw[j] += dj_dw_ij\\n\",\n    \"        \\n\",\n    \"            # divide dj_db and dj_dw by total number of examples\\n\",\n    \"            dj_dw = dj_dw / m\\n\",\n    \"            dj_db = dj_db / m\\n\",\n    \"            ### END CODE HERE ###\\n\",\n    \"       \\n\",\n    \"            return dj_db, dj_dw\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `f_wb`, `dj_db_i` and `dj_dw_ij` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; Recall that you calculated f_wb in <code>compute_cost</code> above — for detailed hints on how to calculate each intermediate term, check out the hints section below that exercise\\n\",\n    \"           <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f_wb</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can calculate f_wb as\\n\",\n    \"               <pre>\\n\",\n    \"               for i in range(m):   \\n\",\n    \"                   # Calculate f_wb (exactly how you did it in the compute_cost function above)\\n\",\n    \"                   z_wb = 0\\n\",\n    \"                   # Loop over each feature\\n\",\n    \"                   for j in range(n): \\n\",\n    \"                       # Add the corresponding term to z_wb\\n\",\n    \"                       z_wb_ij = X[i, j] * w[j]\\n\",\n    \"                       z_wb += z_wb_ij\\n\",\n    \"            \\n\",\n    \"                   # Add bias term \\n\",\n    \"                   z_wb += b\\n\",\n    \"        \\n\",\n    \"                   # Calculate the prediction from the model\\n\",\n    \"                   f_wb = sigmoid(z_wb)\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_db_i</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can calculate dj_db_i as <code>dj_db_i = f_wb - y[i]</code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_dw_ij</b></font></summary>\\n\",\n    \"        &emsp; &emsp; You can calculate dj_dw_ij as <code>dj_dw_ij = (f_wb - y[i])* X[i][j]</code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cells below to check your implementation of the `compute_gradient` function with two different initializations of the parameters $w$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute and display gradient with w initialized to zeroes\\n\",\n    \"initial_w = np.zeros(n)\\n\",\n    \"initial_b = 0.\\n\",\n    \"\\n\",\n    \"dj_db, dj_dw = compute_gradient(X_train, y_train, initial_w, initial_b)\\n\",\n    \"print(f'dj_db at initial w (zeros):{dj_db}' )\\n\",\n    \"print(f'dj_dw at initial w (zeros):{dj_dw.tolist()}' )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>dj_db at initial w (zeros)<b></td>\\n\",\n    \"    <td> -0.1 </td> \\n\",\n    \"  </tr>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>ddj_dw at initial w (zeros):<b></td>\\n\",\n    \"    <td> [-12.00921658929115, -11.262842205513591] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute and display cost and gradient with non-zero w\\n\",\n    \"test_w = np.array([ 0.2, -0.5])\\n\",\n    \"test_b = -24\\n\",\n    \"dj_db, dj_dw  = compute_gradient(X_train, y_train, test_w, test_b)\\n\",\n    \"\\n\",\n    \"print('dj_db at test_w:', dj_db)\\n\",\n    \"print('dj_dw at test_w:', dj_dw.tolist())\\n\",\n    \"\\n\",\n    \"# UNIT TESTS    \\n\",\n    \"compute_gradient_test(compute_gradient)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>dj_db at initial w (zeros)<b></td>\\n\",\n    \"    <td> -0.5999999999991071 </td> \\n\",\n    \"  </tr>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>ddj_dw at initial w (zeros):<b></td>\\n\",\n    \"    <td>  [-44.8313536178737957, -44.37384124953978] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.6\\\"></a>\\n\",\n    \"### 2.6 Learning parameters using gradient descent \\n\",\n    \"\\n\",\n    \"Similar to the previous assignment, you will now find the optimal parameters of a logistic regression model by using gradient descent. \\n\",\n    \"- You don't need to implement anything for this part. Simply run the cells below. \\n\",\n    \"\\n\",\n    \"- A good way to verify that gradient descent is working correctly is to look\\n\",\n    \"at the value of $J(\\\\mathbf{w},b)$ and check that it is decreasing with each step. \\n\",\n    \"\\n\",\n    \"- Assuming you have implemented the gradient and computed the cost correctly, your value of $J(\\\\mathbf{w},b)$ should never increase, and should converge to a steady value by the end of the algorithm.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def gradient_descent(X, y, w_in, b_in, cost_function, gradient_function, alpha, num_iters, lambda_): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Performs batch gradient descent to learn theta. Updates theta by taking \\n\",\n    \"    num_iters gradient steps with learning rate alpha\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      X :    (array_like Shape (m, n)\\n\",\n    \"      y :    (array_like Shape (m,))\\n\",\n    \"      w_in : (array_like Shape (n,))  Initial values of parameters of the model\\n\",\n    \"      b_in : (scalar)                 Initial value of parameter of the model\\n\",\n    \"      cost_function:                  function to compute cost\\n\",\n    \"      alpha : (float)                 Learning rate\\n\",\n    \"      num_iters : (int)               number of iterations to run gradient descent\\n\",\n    \"      lambda_ (scalar, float)         regularization constant\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      w : (array_like Shape (n,)) Updated values of parameters of the model after\\n\",\n    \"          running gradient descent\\n\",\n    \"      b : (scalar)                Updated value of parameter of the model after\\n\",\n    \"          running gradient descent\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # number of training examples\\n\",\n    \"    m = len(X)\\n\",\n    \"    \\n\",\n    \"    # An array to store cost J and w's at each iteration primarily for graphing later\\n\",\n    \"    J_history = []\\n\",\n    \"    w_history = []\\n\",\n    \"    \\n\",\n    \"    for i in range(num_iters):\\n\",\n    \"\\n\",\n    \"        # Calculate the gradient and update the parameters\\n\",\n    \"        dj_db, dj_dw = gradient_function(X, y, w_in, b_in, lambda_)   \\n\",\n    \"\\n\",\n    \"        # Update Parameters using w, b, alpha and gradient\\n\",\n    \"        w_in = w_in - alpha * dj_dw               \\n\",\n    \"        b_in = b_in - alpha * dj_db              \\n\",\n    \"       \\n\",\n    \"        # Save cost J at each iteration\\n\",\n    \"        if i<100000:      # prevent resource exhaustion \\n\",\n    \"            cost =  cost_function(X, y, w_in, b_in, lambda_)\\n\",\n    \"            J_history.append(cost)\\n\",\n    \"\\n\",\n    \"        # Print cost every at intervals 10 times or as many iterations if < 10\\n\",\n    \"        if i% math.ceil(num_iters/10) == 0 or i == (num_iters-1):\\n\",\n    \"            w_history.append(w_in)\\n\",\n    \"            print(f\\\"Iteration {i:4}: Cost {float(J_history[-1]):8.2f}   \\\")\\n\",\n    \"        \\n\",\n    \"    return w_in, b_in, J_history, w_history #return w and J,w history for graphing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's run the gradient descent algorithm above to learn the parameters for our dataset.\\n\",\n    \"\\n\",\n    \"**Note**\\n\",\n    \"\\n\",\n    \"The code block below takes a couple of minutes to run, especially with a non-vectorized version. You can reduce the `iterations` to test your implementation and iterate faster. If you have time, try running 100,000 iterations for better results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"intial_w = 0.01 * (np.random.rand(2).reshape(-1,1) - 0.5)\\n\",\n    \"initial_b = -8\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Some gradient descent settings\\n\",\n    \"iterations = 10000\\n\",\n    \"alpha = 0.001\\n\",\n    \"\\n\",\n    \"w,b, J_history,_ = gradient_descent(X_train ,y_train, initial_w, initial_b, \\n\",\n    \"                                   compute_cost, compute_gradient, alpha, iterations, 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"<summary>\\n\",\n    \"    <b>Expected Output: Cost     0.30, (Click to see details):</b>\\n\",\n    \"</summary>\\n\",\n    \"\\n\",\n    \"    # With the following settings\\n\",\n    \"    np.random.seed(1)\\n\",\n    \"    intial_w = 0.01 * (np.random.rand(2).reshape(-1,1) - 0.5)\\n\",\n    \"    initial_b = -8\\n\",\n    \"    iterations = 10000\\n\",\n    \"    alpha = 0.001\\n\",\n    \"    #\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"Iteration    0: Cost     1.01   \\n\",\n    \"Iteration 1000: Cost     0.31   \\n\",\n    \"Iteration 2000: Cost     0.30   \\n\",\n    \"Iteration 3000: Cost     0.30   \\n\",\n    \"Iteration 4000: Cost     0.30   \\n\",\n    \"Iteration 5000: Cost     0.30   \\n\",\n    \"Iteration 6000: Cost     0.30   \\n\",\n    \"Iteration 7000: Cost     0.30   \\n\",\n    \"Iteration 8000: Cost     0.30   \\n\",\n    \"Iteration 9000: Cost     0.30   \\n\",\n    \"Iteration 9999: Cost     0.30   \\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.7\\\"></a>\\n\",\n    \"### 2.7 Plotting the decision boundary\\n\",\n    \"\\n\",\n    \"We will now use the final parameters from gradient descent to plot the linear fit. If you implemented the previous parts correctly, you should see the following plot:   \\n\",\n    \"<img src=\\\"images/figure 2.png\\\"  width=\\\"450\\\" height=\\\"450\\\">\\n\",\n    \"\\n\",\n    \"We will use a helper function in the `utils.py` file to create this plot.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plot_decision_boundary(w, b, X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.8\\\"></a>\\n\",\n    \"### 2.8 Evaluating logistic regression\\n\",\n    \"\\n\",\n    \"We can evaluate the quality of the parameters we have found by seeing how well the learned model predicts on our training set. \\n\",\n    \"\\n\",\n    \"You will implement the `predict` function below to do this.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-04'></a>\\n\",\n    \"### Exercise 4\\n\",\n    \"\\n\",\n    \"Please complete the `predict` function to produce `1` or `0` predictions given a dataset and a learned parameter vector $w$ and $b$.\\n\",\n    \"- First you need to compute the prediction from the model $f(x^{(i)}) = g(w \\\\cdot x^{(i)})$ for every example \\n\",\n    \"    - You've implemented this before in the parts above\\n\",\n    \"- We interpret the output of the model ($f(x^{(i)})$) as the probability that $y^{(i)}=1$ given $x^{(i)}$ and parameterized by $w$.\\n\",\n    \"- Therefore, to get a final prediction ($y^{(i)}=0$ or $y^{(i)}=1$) from the logistic regression model, you can use the following heuristic -\\n\",\n    \"\\n\",\n    \"  if $f(x^{(i)}) >= 0.5$, predict $y^{(i)}=1$\\n\",\n    \"  \\n\",\n    \"  if $f(x^{(i)}) < 0.5$, predict $y^{(i)}=0$\\n\",\n    \"    \\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C4\\n\",\n    \"# GRADED FUNCTION: predict\\n\",\n    \"\\n\",\n    \"def predict(X, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Predict whether the label is 0 or 1 using learned logistic\\n\",\n    \"    regression parameters w\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"    X : (ndarray Shape (m, n))\\n\",\n    \"    w : (array_like Shape (n,))      Parameters of the model\\n\",\n    \"    b : (scalar, float)              Parameter of the model\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"    p: (ndarray (m,1))\\n\",\n    \"        The predictions for X using a threshold at 0.5\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # number of training examples\\n\",\n    \"    m, n = X.shape   \\n\",\n    \"    p = np.zeros(m)\\n\",\n    \"   \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    # Loop over each example\\n\",\n    \"    for i in range(m):   \\n\",\n    \"        z_wb = None\\n\",\n    \"        # Loop over each feature\\n\",\n    \"        for j in range(n): \\n\",\n    \"            # Add the corresponding term to z_wb\\n\",\n    \"            z_wb += None\\n\",\n    \"        \\n\",\n    \"        # Add bias term \\n\",\n    \"        z_wb += None\\n\",\n    \"        \\n\",\n    \"        # Calculate the prediction for this example\\n\",\n    \"        f_wb = None\\n\",\n    \"\\n\",\n    \"        # Apply the threshold\\n\",\n    \"        p[i] = None\\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    return p\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"       def predict(X, w, b): \\n\",\n    \"            # number of training examples\\n\",\n    \"            m, n = X.shape   \\n\",\n    \"            p = np.zeros(m)\\n\",\n    \"   \\n\",\n    \"            ### START CODE HERE ### \\n\",\n    \"            # Loop over each example\\n\",\n    \"            for i in range(m):   \\n\",\n    \"                \\n\",\n    \"                # Calculate f_wb (exactly how you did it in the compute_cost function above) \\n\",\n    \"                # using a couple of lines of code\\n\",\n    \"                f_wb = \\n\",\n    \"\\n\",\n    \"                # Calculate the prediction for that training example \\n\",\n    \"                p[i] = # Your code here to calculate the prediction based on f_wb\\n\",\n    \"        \\n\",\n    \"            ### END CODE HERE ### \\n\",\n    \"            return p\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `f_wb` and `p[i]` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; Recall that you calculated f_wb in <code>compute_cost</code> above — for detailed hints on how to calculate each intermediate term, check out the hints section below that exercise\\n\",\n    \"           <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f_wb</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can calculate f_wb as\\n\",\n    \"               <pre>\\n\",\n    \"               for i in range(m):   \\n\",\n    \"                   # Calculate f_wb (exactly how you did it in the compute_cost function above)\\n\",\n    \"                   z_wb = 0\\n\",\n    \"                   # Loop over each feature\\n\",\n    \"                   for j in range(n): \\n\",\n    \"                       # Add the corresponding term to z_wb\\n\",\n    \"                       z_wb_ij = X[i, j] * w[j]\\n\",\n    \"                       z_wb += z_wb_ij\\n\",\n    \"            \\n\",\n    \"                   # Add bias term \\n\",\n    \"                   z_wb += b\\n\",\n    \"        \\n\",\n    \"                   # Calculate the prediction from the model\\n\",\n    \"                   f_wb = sigmoid(z_wb)\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate p[i]</b></font></summary>\\n\",\n    \"           &emsp; &emsp; As an example, if you'd like to say x = 1 if y is less than 3 and 0 otherwise, you can express it in code as <code>x = y < 3 </code>. Now do the same for p[i] = 1 if f_wb >= 0.5 and 0 otherwise. \\n\",\n    \"           <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate p[i]</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute p[i] as <code>p[i] = f_wb >= 0.5</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once you have completed the function `predict`, let's run the code below to report the training accuracy of your classifier by computing the percentage of examples it got correct.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Test your predict code\\n\",\n    \"np.random.seed(1)\\n\",\n    \"tmp_w = np.random.randn(2)\\n\",\n    \"tmp_b = 0.3    \\n\",\n    \"tmp_X = np.random.randn(4, 2) - 0.5\\n\",\n    \"\\n\",\n    \"tmp_p = predict(tmp_X, tmp_w, tmp_b)\\n\",\n    \"print(f'Output of predict: shape {tmp_p.shape}, value {tmp_p}')\\n\",\n    \"\\n\",\n    \"# UNIT TESTS        \\n\",\n    \"predict_test(predict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output** \\n\",\n    \"\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Output of predict: shape (4,),value [0. 1. 1. 1.]<b></td>\\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's use this to compute the accuracy on the training set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#Compute accuracy on our training set\\n\",\n    \"p = predict(X_train, w,b)\\n\",\n    \"print('Train Accuracy: %f'%(np.mean(p == y_train) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Train Accuracy (approx):<b></td>\\n\",\n    \"    <td> 92.00 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Regularized Logistic Regression\\n\",\n    \"\\n\",\n    \"In this part of the exercise, you will implement regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly. \\n\",\n    \"\\n\",\n    \"<a name=\\\"3.1\\\"></a>\\n\",\n    \"### 3.1 Problem Statement\\n\",\n    \"\\n\",\n    \"Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. \\n\",\n    \"- From these two tests, you would like to determine whether the microchips should be accepted or rejected. \\n\",\n    \"- To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model.\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.2\\\"></a>\\n\",\n    \"### 3.2 Loading and visualizing the data\\n\",\n    \"\\n\",\n    \"Similar to previous parts of this exercise, let's start by loading the dataset for this task and visualizing it. \\n\",\n    \"\\n\",\n    \"- The `load_dataset()` function shown below loads the data into variables `X_train` and `y_train`\\n\",\n    \"  - `X_train` contains the test results for the microchips from two tests\\n\",\n    \"  - `y_train` contains the results of the QA  \\n\",\n    \"      - `y_train = 1` if the microchip was accepted \\n\",\n    \"      - `y_train = 0` if the microchip was rejected \\n\",\n    \"  - Both `X_train` and `y_train` are numpy arrays.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X_train, y_train = load_data(\\\"data/ex2data2.txt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"\\n\",\n    \"The code below prints the first five values of `X_train` and `y_train` and the type of the variables.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# print X_train\\n\",\n    \"print(\\\"X_train:\\\", X_train[:5])\\n\",\n    \"print(\\\"Type of X_train:\\\",type(X_train))\\n\",\n    \"\\n\",\n    \"# print y_train\\n\",\n    \"print(\\\"y_train:\\\", y_train[:5])\\n\",\n    \"print(\\\"Type of y_train:\\\",type(y_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions. Let's print the shape of `X_train` and `y_train` and see how many training examples we have in our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The shape of X_train is: ' + str(X_train.shape))\\n\",\n    \"print ('The shape of y_train is: ' + str(y_train.shape))\\n\",\n    \"print ('We have m = %d training examples' % (len(y_train)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Visualize your data\\n\",\n    \"\\n\",\n    \"The helper function `plot_data` (from `utils.py`) is used to generate a figure like Figure 3, where the axes are the two test scores, and the positive (y = 1, accepted) and negative (y = 0, rejected) examples are shown with different markers.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 3.png\\\"  width=\\\"450\\\" height=\\\"450\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Plot examples\\n\",\n    \"plot_data(X_train, y_train[:], pos_label=\\\"Accepted\\\", neg_label=\\\"Rejected\\\")\\n\",\n    \"\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Microchip Test 2') \\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Microchip Test 1') \\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Figure 3 shows that our dataset cannot be separated into positive and negative examples by a straight-line through the plot. Therefore, a straight forward application of logistic regression will not perform well on this dataset since logistic regression will only be able to find a linear decision boundary.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.3\\\"></a>\\n\",\n    \"### 3.3 Feature mapping\\n\",\n    \"\\n\",\n    \"One way to fit the data better is to create more features from each data point. In the provided function `map_feature`, we will map the features into all polynomial terms of $x_1$ and $x_2$ up to the sixth power.\\n\",\n    \"\\n\",\n    \"$$\\\\mathrm{map\\\\_feature}(x) = \\n\",\n    \"\\\\left[\\\\begin{array}{c}\\n\",\n    \"x_1\\\\\\\\\\n\",\n    \"x_2\\\\\\\\\\n\",\n    \"x_1^2\\\\\\\\\\n\",\n    \"x_1 x_2\\\\\\\\\\n\",\n    \"x_2^2\\\\\\\\\\n\",\n    \"x_1^3\\\\\\\\\\n\",\n    \"\\\\vdots\\\\\\\\\\n\",\n    \"x_1 x_2^5\\\\\\\\\\n\",\n    \"x_2^6\\\\end{array}\\\\right]$$\\n\",\n    \"\\n\",\n    \"As a result of this mapping, our vector of two features (the scores on two QA tests) has been transformed into a 27-dimensional vector. \\n\",\n    \"\\n\",\n    \"- A logistic regression classifier trained on this higher-dimension feature vector will have a more complex decision boundary and will be nonlinear when drawn in our 2-dimensional plot. \\n\",\n    \"- We have provided the `map_feature` function for you in utils.py. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Original shape of data:\\\", X_train.shape)\\n\",\n    \"\\n\",\n    \"mapped_X =  map_feature(X_train[:, 0], X_train[:, 1])\\n\",\n    \"print(\\\"Shape after feature mapping:\\\", mapped_X.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's also print the first elements of `X_train` and `mapped_X` to see the tranformation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"X_train[0]:\\\", X_train[0])\\n\",\n    \"print(\\\"mapped X_train[0]:\\\", mapped_X[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"While the feature mapping allows us to build a more expressive classifier, it is also more susceptible to overfitting. In the next parts of the exercise, you will implement regularized logistic regression to fit the data and also see for yourself how regularization can help combat the overfitting problem.\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.4\\\"></a>\\n\",\n    \"### 3.4 Cost function for regularized logistic regression\\n\",\n    \"\\n\",\n    \"In this part, you will implement the cost function for regularized logistic regression.\\n\",\n    \"\\n\",\n    \"Recall that for regularized logistic regression, the cost function is of the form\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} \\\\left[ -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\right] + \\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$$\\n\",\n    \"\\n\",\n    \"Compare this to the cost function without regularization (which you implemented above), which is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w}.b) = \\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\\\left[ (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)\\\\right]$$\\n\",\n    \"\\n\",\n    \"The difference is the regularization term, which is $$\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$$ \\n\",\n    \"Note that the $b$ parameter is not regularized.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-05'></a>\\n\",\n    \"### Exercise 5\\n\",\n    \"\\n\",\n    \"Please complete the `compute_cost_reg` function below to calculate the following term for each element in $w$ \\n\",\n    \"$$\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$$\\n\",\n    \"\\n\",\n    \"The starter code then adds this to the cost without regularization (which you computed above in `compute_cost`) to calculate the cost with regulatization.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C5\\n\",\n    \"def compute_cost_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"      X : (array_like Shape (m,n)) data, m examples by n features\\n\",\n    \"      y : (array_like Shape (m,)) target value \\n\",\n    \"      w : (array_like Shape (n,)) Values of parameters of the model      \\n\",\n    \"      b : (array_like Shape (n,)) Values of bias parameter of the model\\n\",\n    \"      lambda_ : (scalar, float)    Controls amount of regularization\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost: (scalar)         cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    # Calls the compute_cost function that you implemented above\\n\",\n    \"    cost_without_reg = compute_cost(X, y, w, b) \\n\",\n    \"    \\n\",\n    \"    # You need to calculate this value\\n\",\n    \"    reg_cost = 0.\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    \\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    # Add the regularization cost to get the total cost\\n\",\n    \"    total_cost = cost_without_reg + (lambda_/(2 * m)) * reg_cost\\n\",\n    \"\\n\",\n    \"    return total_cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"       def compute_cost_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"   \\n\",\n    \"           m, n = X.shape\\n\",\n    \"    \\n\",\n    \"            # Calls the compute_cost function that you implemented above\\n\",\n    \"            cost_without_reg = compute_cost(X, y, w, b) \\n\",\n    \"    \\n\",\n    \"            # You need to calculate this value\\n\",\n    \"            reg_cost = 0.\\n\",\n    \"    \\n\",\n    \"            ### START CODE HERE ###\\n\",\n    \"            for j in range(n):\\n\",\n    \"                reg_cost_j = # Your code here to calculate the cost from w[j]\\n\",\n    \"                reg_cost = reg_cost + reg_cost_j\\n\",\n    \"\\n\",\n    \"            ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"            # Add the regularization cost to get the total cost\\n\",\n    \"            total_cost = cost_without_reg + (lambda_/(2 * m)) * reg_cost\\n\",\n    \"\\n\",\n    \"        return total_cost\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `reg_cost_j` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate reg_cost_j</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use calculate reg_cost_j as <code>reg_cost_j = w[j]**2 </code> \\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to check your implementation of the `compute_cost_reg` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_mapped = map_feature(X_train[:, 0], X_train[:, 1])\\n\",\n    \"np.random.seed(1)\\n\",\n    \"initial_w = np.random.rand(X_mapped.shape[1]) - 0.5\\n\",\n    \"initial_b = 0.5\\n\",\n    \"lambda_ = 0.5\\n\",\n    \"cost = compute_cost_reg(X_mapped, y_train, initial_w, initial_b, lambda_)\\n\",\n    \"\\n\",\n    \"print(\\\"Regularized cost :\\\", cost)\\n\",\n    \"\\n\",\n    \"# UNIT TEST    \\n\",\n    \"compute_cost_reg_test(compute_cost_reg)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Regularized cost : <b></td>\\n\",\n    \"    <td> 0.6618252552483948 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.5\\\"></a>\\n\",\n    \"### 3.5 Gradient for regularized logistic regression\\n\",\n    \"\\n\",\n    \"In this section, you will implement the gradient for regularized logistic regression.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The gradient of the regularized cost function has two components. The first, $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ is a scalar, the other is a vector with the same shape as the parameters $\\\\mathbf{w}$, where the $j^\\\\mathrm{th}$ element is defined as follows:\\n\",\n    \"\\n\",\n    \"$$\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} = \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})  $$\\n\",\n    \"\\n\",\n    \"$$\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} = \\\\left( \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)}) x_j^{(i)} \\\\right) + \\\\frac{\\\\lambda}{m} w_j  \\\\quad\\\\, \\\\mbox{for $j=0...(n-1)$}$$\\n\",\n    \"\\n\",\n    \"Compare this to the gradient of the cost function without regularization (which you implemented above), which is of the form \\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)}) \\\\tag{2}\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)})x_{j}^{(i)} \\\\tag{3}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"As you can see,$\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ is the same, the difference is the following term in $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w}$, which is $$\\\\frac{\\\\lambda}{m} w_j  \\\\quad\\\\, \\\\mbox{for $j=0...(n-1)$}$$ \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-06'></a>\\n\",\n    \"### Exercise 6\\n\",\n    \"\\n\",\n    \"Please complete the `compute_gradient_reg` function below to modify the code below to calculate the following term\\n\",\n    \"\\n\",\n    \"$$\\\\frac{\\\\lambda}{m} w_j  \\\\quad\\\\, \\\\mbox{for $j=0...(n-1)$}$$\\n\",\n    \"\\n\",\n    \"The starter code will add this term to the $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w}$ returned from `compute_gradient` above to get the gradient for the regularized cost function.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C6\\n\",\n    \"def compute_gradient_reg(X, y, w, b, lambda_ = 1): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X : (ndarray Shape (m,n))   variable such as house size \\n\",\n    \"      y : (ndarray Shape (m,))    actual value \\n\",\n    \"      w : (ndarray Shape (n,))    values of parameters of the model      \\n\",\n    \"      b : (scalar)                value of parameter of the model  \\n\",\n    \"      lambda_ : (scalar,float)    regularization constant\\n\",\n    \"    Returns\\n\",\n    \"      dj_db: (scalar)             The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"      dj_dw: (ndarray Shape (n,)) The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    dj_db, dj_dw = compute_gradient(X, y, w, b)\\n\",\n    \"\\n\",\n    \"    ### START CODE HERE ###     \\n\",\n    \"    \\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ###         \\n\",\n    \"        \\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_gradient_reg(X, y, w, b, lambda_ = 1): \\n\",\n    \"        m, n = X.shape\\n\",\n    \"    \\n\",\n    \"        dj_db, dj_dw = compute_gradient(X, y, w, b)\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###     \\n\",\n    \"        # Loop over the elements of w\\n\",\n    \"        for j in range(n): \\n\",\n    \"            \\n\",\n    \"            dj_dw_j_reg = # Your code here to calculate the regularization term for dj_dw[j]\\n\",\n    \"            \\n\",\n    \"            # Add the regularization term  to the correspoding element of dj_dw\\n\",\n    \"            dj_dw[j] = dj_dw[j] + dj_dw_j_reg\\n\",\n    \"        \\n\",\n    \"        ### END CODE HERE ###         \\n\",\n    \"        \\n\",\n    \"        return dj_db, dj_dw\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `dj_dw_j_reg` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_dw_j_reg</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use calculate dj_dw_j_reg as <code>dj_dw_j_reg = (lambda_ / m) * w[j] </code> \\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to check your implementation of the `compute_gradient_reg` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_mapped = map_feature(X_train[:, 0], X_train[:, 1])\\n\",\n    \"np.random.seed(1) \\n\",\n    \"initial_w  = np.random.rand(X_mapped.shape[1]) - 0.5 \\n\",\n    \"initial_b = 0.5\\n\",\n    \" \\n\",\n    \"lambda_ = 0.5\\n\",\n    \"dj_db, dj_dw = compute_gradient_reg(X_mapped, y_train, initial_w, initial_b, lambda_)\\n\",\n    \"\\n\",\n    \"print(f\\\"dj_db: {dj_db}\\\", )\\n\",\n    \"print(f\\\"First few elements of regularized dj_dw:\\\\n {dj_dw[:4].tolist()}\\\", )\\n\",\n    \"\\n\",\n    \"# UNIT TESTS    \\n\",\n    \"compute_gradient_reg_test(compute_gradient_reg)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>dj_db:</b>0.07138288792343656</td> </tr>\\n\",\n    \"  <tr>\\n\",\n    \"      <td> <b> First few elements of regularized dj_dw:</b> </td> </tr>\\n\",\n    \"   <tr>\\n\",\n    \"   <td> [[-0.010386028450548701], [0.01140985288328012], [0.0536273463274574], [0.003140278267313462]] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.6\\\"></a>\\n\",\n    \"### 3.6 Learning parameters using gradient descent\\n\",\n    \"\\n\",\n    \"Similar to the previous parts, you will use your gradient descent function implemented above to learn the optimal parameters $w$,$b$. \\n\",\n    \"- If you have completed the cost and gradient for regularized logistic regression correctly, you should be able to step through the next cell to learn the parameters $w$. \\n\",\n    \"- After training our parameters, we will use it to plot the decision boundary. \\n\",\n    \"\\n\",\n    \"**Note**\\n\",\n    \"\\n\",\n    \"The code block below takes quite a while to run, especially with a non-vectorized version. You can reduce the `iterations` to test your implementation and iterate faster. If you hae time, run for 100,000 iterations to see better results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Initialize fitting parameters\\n\",\n    \"np.random.seed(1)\\n\",\n    \"initial_w = np.random.rand(X_mapped.shape[1])-0.5\\n\",\n    \"initial_b = 1.\\n\",\n    \"\\n\",\n    \"# Set regularization parameter lambda_ to 1 (you can try varying this)\\n\",\n    \"lambda_ = 0.01;                                          \\n\",\n    \"# Some gradient descent settings\\n\",\n    \"iterations = 10000\\n\",\n    \"alpha = 0.01\\n\",\n    \"\\n\",\n    \"w,b, J_history,_ = gradient_descent(X_mapped, y_train, initial_w, initial_b, \\n\",\n    \"                                    compute_cost_reg, compute_gradient_reg, \\n\",\n    \"                                    alpha, iterations, lambda_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"<summary>\\n\",\n    \"    <b>Expected Output: Cost < 0.5  (Click for details)</b>\\n\",\n    \"</summary>\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"# Using the following settings\\n\",\n    \"#np.random.seed(1)\\n\",\n    \"#initial_w = np.random.rand(X_mapped.shape[1])-0.5\\n\",\n    \"#initial_b = 1.\\n\",\n    \"#lambda_ = 0.01;                                          \\n\",\n    \"#iterations = 10000\\n\",\n    \"#alpha = 0.01\\n\",\n    \"Iteration    0: Cost     0.72   \\n\",\n    \"Iteration 1000: Cost     0.59   \\n\",\n    \"Iteration 2000: Cost     0.56   \\n\",\n    \"Iteration 3000: Cost     0.53   \\n\",\n    \"Iteration 4000: Cost     0.51   \\n\",\n    \"Iteration 5000: Cost     0.50   \\n\",\n    \"Iteration 6000: Cost     0.48   \\n\",\n    \"Iteration 7000: Cost     0.47   \\n\",\n    \"Iteration 8000: Cost     0.46   \\n\",\n    \"Iteration 9000: Cost     0.45   \\n\",\n    \"Iteration 9999: Cost     0.45       \\n\",\n    \"    \\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.7\\\"></a>\\n\",\n    \"### 3.7 Plotting the decision boundary\\n\",\n    \"To help you visualize the model learned by this classifier, we will use our `plot_decision_boundary` function which plots the (non-linear) decision boundary that separates the positive and negative examples. \\n\",\n    \"\\n\",\n    \"- In the function, we plotted the non-linear decision boundary by computing the classifier’s predictions on an evenly spaced grid and then drew a contour plot of where the predictions change from y = 0 to y = 1.\\n\",\n    \"\\n\",\n    \"- After learning the parameters $w$,$b$, the next step is to plot a decision boundary similar to Figure 4.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 4.png\\\"  width=\\\"450\\\" height=\\\"450\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plot_decision_boundary(w, b, X_mapped, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.8\\\"></a>\\n\",\n    \"### 3.8 Evaluating regularized logistic regression model\\n\",\n    \"\\n\",\n    \"You will use the `predict` function that you implemented above to calculate the accuracy of the regulaized logistic regression model on the training set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#Compute accuracy on the training set\\n\",\n    \"p = predict(X_mapped, w, b)\\n\",\n    \"\\n\",\n    \"print('Train Accuracy: %f'%(np.mean(p == y_train) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Train Accuracy:</b>~ 80%</td> </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/C1W3A1/archive/C1_W3_Logistic_Regression-Copy1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Logistic Regression\\n\",\n    \"\\n\",\n    \"In this exercise, you will implement logistic regression and apply it to two different datasets. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Logistic Regression](#2)\\n\",\n    \"  - [ 2.1 Problem Statement](#2.1)\\n\",\n    \"  - [ 2.2 Loading and visualizing the data](#2.2)\\n\",\n    \"  - [ 2.3  Sigmoid function](#2.3)\\n\",\n    \"  - [ 2.4 Cost function for logistic regression](#2.4)\\n\",\n    \"  - [ 2.5 Gradient for logistic regression](#2.5)\\n\",\n    \"  - [ 2.6 Learning parameters using gradient descent ](#2.6)\\n\",\n    \"  - [ 2.7 Plotting the decision boundary](#2.7)\\n\",\n    \"  - [ 2.8 Evaluating logistic regression](#2.8)\\n\",\n    \"- [ 3 - Regularized Logistic Regression](#3)\\n\",\n    \"  - [ 3.1 Problem Statement](#3.1)\\n\",\n    \"  - [ 3.2 Loading and visualizing the data](#3.2)\\n\",\n    \"  - [ 3.3 Feature mapping](#3.3)\\n\",\n    \"  - [ 3.4 Cost function for regularized logistic regression](#3.4)\\n\",\n    \"  - [ 3.5 Gradient for regularized logistic regression](#3.5)\\n\",\n    \"  - [ 3.6 Learning parameters using gradient descent](#3.6)\\n\",\n    \"  - [ 3.7 Plotting the decision boundary](#3.7)\\n\",\n    \"  - [ 3.8 Evaluating regularized logistic regression model](#3.8)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\\n\",\n    \"-  ``utils.py`` contains helper functions for this assignment. You do not need to modify code in this file.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import *\\n\",\n    \"import copy\\n\",\n    \"import math\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Logistic Regression\\n\",\n    \"\\n\",\n    \"In this part of the exercise, you will build a logistic regression model to predict whether a student gets admitted into a university.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Problem Statement\\n\",\n    \"\\n\",\n    \"Suppose that you are the administrator of a university department and you want to determine each applicant’s chance of admission based on their results on two exams. \\n\",\n    \"* You have historical data from previous applicants that you can use as a training set for logistic regression. \\n\",\n    \"* For each training example, you have the applicant’s scores on two exams and the admissions decision. \\n\",\n    \"* Your task is to build a classification model that estimates an applicant’s probability of admission based on the scores from those two exams. \\n\",\n    \"\\n\",\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Loading and visualizing the data\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_dataset()` function shown below loads the data into variables `X_train` and `y_train`\\n\",\n    \"  - `X_train` contains exam scores on two exams for a student\\n\",\n    \"  - `y_train` is the admission decision \\n\",\n    \"      - `y_train = 1` if the student was admitted \\n\",\n    \"      - `y_train = 0` if the student was not admitted \\n\",\n    \"  - Both `X_train` and `y_train` are numpy arrays.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X_train, y_train = load_data(\\\"data/ex2data1.txt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to just print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints the first five values of `X_train` and the type of the variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"First five elements in X_train are:\\\\n\\\", X_train[:5])\\n\",\n    \"print(\\\"Type of X_train:\\\",type(X_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now print the first five values of `y_train`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"First five elements in y_train are:\\\\n\\\", y_train[:5])\\n\",\n    \"print(\\\"Type of y_train:\\\",type(y_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions. Let's print the shape of `X_train` and `y_train` and see how many training examples we have in our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The shape of X_train is: ' + str(X_train.shape))\\n\",\n    \"print ('The shape of y_train is: ' + str(y_train.shape))\\n\",\n    \"print ('We have m = %d training examples' % (len(y_train)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Visualize your data\\n\",\n    \"\\n\",\n    \"Before starting to implement any learning algorithm, it is always good to visualize the data if possible.\\n\",\n    \"- The code below displays the data on a 2D plot (as shown below), where the axes are the two exam scores, and the positive and negative examples are shown with different markers.\\n\",\n    \"- We use a helper function in the ``utils.py`` file to generate this plot. \\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 1.png\\\" width=\\\"450\\\" height=\\\"450\\\">\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Plot examples\\n\",\n    \"plot_data(X_train, y_train[:], pos_label=\\\"Admitted\\\", neg_label=\\\"Not admitted\\\")\\n\",\n    \"\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Exam 2 score') \\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Exam 1 score') \\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Your goal is to build a logistic regression model to fit this data.\\n\",\n    \"- With this model, you can then predict if a new student will be admitted based on their scores on the two exams.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3  Sigmoid function\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the model is represented as\\n\",\n    \"\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(x) = g(\\\\mathbf{w}\\\\cdot \\\\mathbf{x} + b)$$\\n\",\n    \"where function $g$ is the sigmoid function. The sigmoid function is defined as:\\n\",\n    \"\\n\",\n    \"$$g(z) = \\\\frac{1}{1+e^{-z}}$$\\n\",\n    \"\\n\",\n    \"Let's implement the sigmoid function first, so it can be used by the rest of this assignment.\\n\",\n    \"\\n\",\n    \"<a name='ex-01'></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"Please complete  the `sigmoid` function to calculate\\n\",\n    \"\\n\",\n    \"$$g(z) = \\\\frac{1}{1+e^{-z}}$$\\n\",\n    \"\\n\",\n    \"Note that \\n\",\n    \"- `z` is not always a single number, but can also be an array of numbers. \\n\",\n    \"- If the input is an array of numbers, we'd like to apply the sigmoid function to each value in the input array.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED FUNCTION: sigmoid\\n\",\n    \"\\n\",\n    \"def sigmoid(z):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Compute the sigmoid of z\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        z (ndarray): A scalar, numpy array of any size.\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"        g (ndarray): sigmoid(z), with the same shape as z\\n\",\n    \"         \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"          \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    ### END SOLUTION ###  \\n\",\n    \"    \\n\",\n    \"    return g\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"       \\n\",\n    \"`numpy` has a function called [`np.exp()`](https://numpy.org/doc/stable/reference/generated/numpy.exp.html), which offers a convinient way to calculate the exponential ( $e^{z}$) of all elements in the input array (`z`).\\n\",\n    \" \\n\",\n    \"<details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"  - You can translate $e^{-z}$ into code as `np.exp(-z)` \\n\",\n    \"    \\n\",\n    \"  - You can translate $1/e^{-z}$ into code as `1/np.exp(-z)` \\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `g` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate g</b></font></summary>\\n\",\n    \"        <code>g = 1 / (1 + np.exp(-z))</code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When you are finished, try testing a few values by calling `sigmoid(x)` in the cell below. \\n\",\n    \"- For large positive values of x, the sigmoid should be close to 1, while for large negative values, the sigmoid should be close to 0. \\n\",\n    \"- Evaluating `sigmoid(0)` should give you exactly 0.5. \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print (\\\"sigmoid(0) = \\\" + str(sigmoid(0)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>sigmoid(0)<b></td>\\n\",\n    \"    <td> 0.5 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\\n\",\n    \"    \\n\",\n    \"- As mentioned before, your code should also work with vectors and matrices. For a matrix, your function should perform the sigmoid function on every element.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print (\\\"sigmoid([ -1, 0, 1, 2]) = \\\" + str(sigmoid(np.array([-1, 0, 1, 2]))))\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"from public_tests import *\\n\",\n    \"sigmoid_test(sigmoid)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td><b>sigmoid([-1, 0, 1, 2])<b></td> \\n\",\n    \"    <td>[0.26894142        0.5           0.73105858        0.88079708]</td> \\n\",\n    \"  </tr>    \\n\",\n    \"  \\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.4\\\"></a>\\n\",\n    \"### 2.4 Cost function for logistic regression\\n\",\n    \"\\n\",\n    \"In this section, you will implement the cost function for logistic regression.\\n\",\n    \"\\n\",\n    \"<a name='ex-02'></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Please complete the `compute_cost` function using the equations below.\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the cost function is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\\\left[ loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) \\\\right] \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"where\\n\",\n    \"* m is the number of training examples in the dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* $loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)})$ is the cost for a single data point, which is - \\n\",\n    \"\\n\",\n    \"    $$loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\tag{2}$$\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)})$ is the model's prediction, while $y^{(i)}$, which is the actual label\\n\",\n    \"\\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(\\\\mathbf{w} \\\\cdot \\\\mathbf{x^{(i)}} + b)$ where function $g$ is the sigmoid function.\\n\",\n    \"    * It might be helpful to first calculate an intermediate variable $z_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x^{(i)}} + b = w_0x^{(i)}_0 + ... + w_{n-1}x^{(i)}_{n-1} + b$ where $n$ is the number of features, before calculating $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(z_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}))$\\n\",\n    \"\\n\",\n    \"Note:\\n\",\n    \"* As you are doing this, remember that the variables `X_train` and `y_train` are not scalar values but matrices of shape ($m, n$) and ($𝑚$,1) respectively, where  $𝑛$ is the number of features and $𝑚$ is the number of training examples.\\n\",\n    \"* You can use the sigmoid function that you implemented above for this part.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: compute_cost\\n\",\n    \"def compute_cost(X, y, w, b, lambda_= 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"      X : (ndarray Shape (m,n)) data, m examples by n features\\n\",\n    \"      y : (array_like Shape (m,)) target value \\n\",\n    \"      w : (array_like Shape (n,)) Values of parameters of the model      \\n\",\n    \"      b : scalar Values of bias parameter of the model\\n\",\n    \"      lambda_: unused placeholder\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost: (scalar)         cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"\\n\",\n    \"    return total_cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * You can represent a summation operator eg: $h = \\\\sum\\\\limits_{i = 0}^{m-1} 2i$ in code as follows:\\n\",\n    \"    ```python \\n\",\n    \"        h = 0\\n\",\n    \"        for i in range(m):\\n\",\n    \"            h = h + 2*i\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"   * In this case, you can iterate over all the examples in `X` using a for loop and add the `loss` from each iteration to a variable (`loss_sum`) initialized outside the loop.\\n\",\n    \"\\n\",\n    \"   * Then, you can return the `total_cost` as `loss_sum` divided by `m`.\\n\",\n    \"     \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_cost(X, y, w, b, lambda_= 1):\\n\",\n    \"        m, n = X.shape\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        loss_sum = 0 \\n\",\n    \"        \\n\",\n    \"        # Loop over each training example\\n\",\n    \"        for i in range(m): \\n\",\n    \"            \\n\",\n    \"            # First calculate z_wb = w[0]*X[i][0]+...+w[n-1]*X[i][n-1]+b\\n\",\n    \"            z_wb = 0 \\n\",\n    \"            # Loop over each feature\\n\",\n    \"            for j in range(n): \\n\",\n    \"                # Add the corresponding term to z_wb\\n\",\n    \"                z_wb_ij = # Your code here to calculate w[j] * X[i][j]\\n\",\n    \"                z_wb += z_wb_ij # equivalent to z_wb = z_wb + z_wb_ij\\n\",\n    \"            # Add the bias term to z_wb\\n\",\n    \"            z_wb += b # equivalent to z_wb = z_wb + b\\n\",\n    \"        \\n\",\n    \"            f_wb = # Your code here to calculate prediction f_wb for a training example\\n\",\n    \"            loss =  # Your code here to calculate loss for a training example\\n\",\n    \"            \\n\",\n    \"            loss_sum += loss # equivalent to loss_sum = loss_sum + loss\\n\",\n    \"        \\n\",\n    \"        total_cost = (1 / m) * loss_sum  \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"        \\n\",\n    \"        return total_cost\\n\",\n    \"    ```\\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `z_wb_ij`, `f_wb` and `cost`.\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate z_wb_ij</b></font></summary>\\n\",\n    \"           &emsp; &emsp; <code>z_wb_ij = w[j]*X[i][j] </code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(z_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}))$ where $g$ is the sigmoid function. You can simply call the `sigmoid` function implemented above.\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute f_wb as <code>f_wb = sigmoid(z_wb) </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate loss</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can use the <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.log.html\\\">np.log</a> function to calculate the log\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate loss</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute loss as <code>loss =  -y[i] * np.log(f_wb) - (1 - y[i]) * np.log(1 - f_wb)</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cells below to check your implementation of the `compute_cost` function with two different initializations of the parameters $w$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"m, n = X_train.shape\\n\",\n    \"\\n\",\n    \"# Compute and display cost with w initialized to zeroes\\n\",\n    \"initial_w = np.zeros(n)\\n\",\n    \"initial_b = 0.\\n\",\n    \"cost = compute_cost(X_train, y_train, initial_w, initial_b)\\n\",\n    \"print('Cost at initial w (zeros): {:.3f}'.format(cost))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Cost at initial w (zeros)<b></td>\\n\",\n    \"    <td> 0.693 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute and display cost with non-zero w\\n\",\n    \"test_w = np.array([0.2, 0.2])\\n\",\n    \"test_b = -24.\\n\",\n    \"cost = compute_cost(X_train, y_train, test_w, test_b)\\n\",\n    \"\\n\",\n    \"print('Cost at test w,b: {:.3f}'.format(cost))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"compute_cost_test(compute_cost)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Cost at test w,b<b></td>\\n\",\n    \"    <td> 0.218 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.5\\\"></a>\\n\",\n    \"### 2.5 Gradient for logistic regression\\n\",\n    \"\\n\",\n    \"In this section, you will implement the gradient for logistic regression.\\n\",\n    \"\\n\",\n    \"Recall that the gradient descent algorithm is:\\n\",\n    \"\\n\",\n    \"$$\\\\begin{align*}& \\\\text{repeat until convergence:} \\\\; \\\\lbrace \\\\newline \\\\; & b := b -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} \\\\newline       \\\\; & w_j := w_j -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} \\\\tag{1}  \\\\; & \\\\text{for j := 0..n-1}\\\\newline & \\\\rbrace\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"where, parameters $b$, $w_j$ are all updated simultaniously\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"<a name='ex-03'></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"\\n\",\n    \"Please complete the `compute_gradient` function to compute $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w}$, $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ from equations (2) and (3) below.\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)}) \\\\tag{2}\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)})x_{j}^{(i)} \\\\tag{3}\\n\",\n    \"$$\\n\",\n    \"* m is the number of training examples in the dataset\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"*  $f_{\\\\mathbf{w},b}(x^{(i)})$ is the model's prediction, while $y^{(i)}$ is the actual label\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- **Note**: While this gradient looks identical to the linear regression gradient, the formula is actually different because linear and logistic regression have different definitions of $f_{\\\\mathbf{w},b}(x)$.\\n\",\n    \"\\n\",\n    \"As before, you can use the sigmoid function that you implemented above and if you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED FUNCTION: compute_gradient\\n\",\n    \"def compute_gradient(X, y, w, b, lambda_=None): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for logistic regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X : (ndarray Shape (m,n)) variable such as house size \\n\",\n    \"      y : (array_like Shape (m,1)) actual value \\n\",\n    \"      w : (array_like Shape (n,1)) values of parameters of the model      \\n\",\n    \"      b : (scalar)                 value of parameter of the model \\n\",\n    \"      lambda_: unused placeholder.\\n\",\n    \"    Returns\\n\",\n    \"      dj_dw: (array_like Shape (n,1)) The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db: (scalar)                The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    dj_dw = np.zeros(w.shape)\\n\",\n    \"    dj_db = 0.\\n\",\n    \"\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    err  = None\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_wb = None\\n\",\n    \"        for j in range(n): \\n\",\n    \"            z_wb += None\\n\",\n    \"        z_wb += None\\n\",\n    \"        f_wb = None\\n\",\n    \"        \\n\",\n    \"        dj_db_i = None\\n\",\n    \"        dj_db += None\\n\",\n    \"        \\n\",\n    \"        for j in range(n):\\n\",\n    \"            dj_dw[j] = None\\n\",\n    \"            \\n\",\n    \"    dj_dw = None\\n\",\n    \"    dj_db = None\\n\",\n    \"    ### END CODE HERE ###\\n\",\n    \"\\n\",\n    \"        \\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \" <details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"       def compute_gradient(X, y, w, b, lambda_=None): \\n\",\n    \"            m, n = X.shape\\n\",\n    \"            dj_dw = np.zeros(w.shape)\\n\",\n    \"            dj_db = 0.\\n\",\n    \"        \\n\",\n    \"            ### START CODE HERE ### \\n\",\n    \"            err  = 0.\\n\",\n    \"            for i in range(m):\\n\",\n    \"                # Calculate f_wb (exactly as you did in the compute_cost function above)\\n\",\n    \"                f_wb = \\n\",\n    \"        \\n\",\n    \"                # Calculate the  gradient for b from this example\\n\",\n    \"                dj_db_i = # Your code here to calculate the error\\n\",\n    \"        \\n\",\n    \"                # add that to dj_db\\n\",\n    \"                dj_db += dj_db_i\\n\",\n    \"        \\n\",\n    \"                # get dj_dw for each attribute\\n\",\n    \"                for j in range(n):\\n\",\n    \"                    # You code here to calculate the gradient from the i-th example for j-th attribute\\n\",\n    \"                    dj_dw_ij =  \\n\",\n    \"                    dj_dw[j] += dj_dw_ij\\n\",\n    \"        \\n\",\n    \"            # divide dj_db and dj_dw by total number of examples\\n\",\n    \"            dj_dw = dj_dw / m\\n\",\n    \"            dj_db = dj_db / m\\n\",\n    \"            ### END CODE HERE ###\\n\",\n    \"       \\n\",\n    \"            return dj_db, dj_dw\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `f_wb`, `dj_db_i` and `dj_dw_ij` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; Recall that you calculated f_wb in <code>compute_cost</code> above — for detailed hints on how to calculate each intermediate term, check out the hints section below that exercise\\n\",\n    \"           <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f_wb</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can calculate f_wb as\\n\",\n    \"               <pre>\\n\",\n    \"               for i in range(m):   \\n\",\n    \"                   # Calculate f_wb (exactly how you did it in the compute_cost function above)\\n\",\n    \"                   z_wb = 0\\n\",\n    \"                   # Loop over each feature\\n\",\n    \"                   for j in range(n): \\n\",\n    \"                       # Add the corresponding term to z_wb\\n\",\n    \"                       z_wb_ij = X[i, j] * w[j]\\n\",\n    \"                       z_wb += z_wb_ij\\n\",\n    \"            \\n\",\n    \"                   # Add bias term \\n\",\n    \"                   z_wb += b\\n\",\n    \"        \\n\",\n    \"                   # Calculate the prediction from the model\\n\",\n    \"                   f_wb = sigmoid(z_wb)\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_db_i</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can calculate dj_db_i as <code>dj_db_i = f_wb - y[i]</code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_dw_ij</b></font></summary>\\n\",\n    \"        &emsp; &emsp; You can calculate dj_dw_ij as <code>dj_dw_ij = (f_wb - y[i])* X[i][j]</code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cells below to check your implementation of the `compute_gradient` function with two different initializations of the parameters $w$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute and display gradient with w initialized to zeroes\\n\",\n    \"initial_w = np.zeros(n)\\n\",\n    \"initial_b = 0.\\n\",\n    \"\\n\",\n    \"dj_db, dj_dw = compute_gradient(X_train, y_train, initial_w, initial_b)\\n\",\n    \"print(f'dj_db at initial w (zeros):{dj_db}' )\\n\",\n    \"print(f'dj_dw at initial w (zeros):{dj_dw.tolist()}' )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>dj_db at initial w (zeros)<b></td>\\n\",\n    \"    <td> -0.1 </td> \\n\",\n    \"  </tr>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>ddj_dw at initial w (zeros):<b></td>\\n\",\n    \"    <td> [-12.00921658929115, -11.262842205513591] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute and display cost and gradient with non-zero w\\n\",\n    \"test_w = np.array([ 0.2, -0.5])\\n\",\n    \"test_b = -24\\n\",\n    \"dj_db, dj_dw  = compute_gradient(X_train, y_train, test_w, test_b)\\n\",\n    \"\\n\",\n    \"print('dj_db at test_w:', dj_db)\\n\",\n    \"print('dj_dw at test_w:', dj_dw.tolist())\\n\",\n    \"\\n\",\n    \"# UNIT TESTS    \\n\",\n    \"compute_gradient_test(compute_gradient)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>dj_db at initial w (zeros)<b></td>\\n\",\n    \"    <td> -0.5999999999991071 </td> \\n\",\n    \"  </tr>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>ddj_dw at initial w (zeros):<b></td>\\n\",\n    \"    <td>  [-44.8313536178737957, -44.37384124953978] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.6\\\"></a>\\n\",\n    \"### 2.6 Learning parameters using gradient descent \\n\",\n    \"\\n\",\n    \"Similar to the previous assignment, you will now find the optimal parameters of a logistic regression model by using gradient descent. \\n\",\n    \"- You don't need to implement anything for this part. Simply run the cells below. \\n\",\n    \"\\n\",\n    \"- A good way to verify that gradient descent is working correctly is to look\\n\",\n    \"at the value of $J(\\\\mathbf{w},b)$ and check that it is decreasing with each step. \\n\",\n    \"\\n\",\n    \"- Assuming you have implemented the gradient and computed the cost correctly, your value of $J(\\\\mathbf{w},b)$ should never increase, and should converge to a steady value by the end of the algorithm.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def gradient_descent(X, y, w_in, b_in, cost_function, gradient_function, alpha, num_iters, lambda_): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Performs batch gradient descent to learn theta. Updates theta by taking \\n\",\n    \"    num_iters gradient steps with learning rate alpha\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      X :    (array_like Shape (m, n)\\n\",\n    \"      y :    (array_like Shape (m,))\\n\",\n    \"      w_in : (array_like Shape (n,))  Initial values of parameters of the model\\n\",\n    \"      b_in : (scalar)                 Initial value of parameter of the model\\n\",\n    \"      cost_function:                  function to compute cost\\n\",\n    \"      alpha : (float)                 Learning rate\\n\",\n    \"      num_iters : (int)               number of iterations to run gradient descent\\n\",\n    \"      lambda_ (scalar, float)         regularization constant\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      w : (array_like Shape (n,)) Updated values of parameters of the model after\\n\",\n    \"          running gradient descent\\n\",\n    \"      b : (scalar)                Updated value of parameter of the model after\\n\",\n    \"          running gradient descent\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # number of training examples\\n\",\n    \"    m = len(X)\\n\",\n    \"    \\n\",\n    \"    # An array to store cost J and w's at each iteration primarily for graphing later\\n\",\n    \"    J_history = []\\n\",\n    \"    w_history = []\\n\",\n    \"    \\n\",\n    \"    for i in range(num_iters):\\n\",\n    \"\\n\",\n    \"        # Calculate the gradient and update the parameters\\n\",\n    \"        dj_db, dj_dw = gradient_function(X, y, w_in, b_in, lambda_)   \\n\",\n    \"\\n\",\n    \"        # Update Parameters using w, b, alpha and gradient\\n\",\n    \"        w_in = w_in - alpha * dj_dw               \\n\",\n    \"        b_in = b_in - alpha * dj_db              \\n\",\n    \"       \\n\",\n    \"        # Save cost J at each iteration\\n\",\n    \"        if i<100000:      # prevent resource exhaustion \\n\",\n    \"            cost =  cost_function(X, y, w_in, b_in, lambda_)\\n\",\n    \"            J_history.append(cost)\\n\",\n    \"\\n\",\n    \"        # Print cost every at intervals 10 times or as many iterations if < 10\\n\",\n    \"        if i% math.ceil(num_iters/10) == 0 or i == (num_iters-1):\\n\",\n    \"            w_history.append(w_in)\\n\",\n    \"            print(f\\\"Iteration {i:4}: Cost {float(J_history[-1]):8.2f}   \\\")\\n\",\n    \"        \\n\",\n    \"    return w_in, b_in, J_history, w_history #return w and J,w history for graphing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's run the gradient descent algorithm above to learn the parameters for our dataset.\\n\",\n    \"\\n\",\n    \"**Note**\\n\",\n    \"\\n\",\n    \"The code block below takes a couple of minutes to run, especially with a non-vectorized version. You can reduce the `iterations` to test your implementation and iterate faster. If you have time, try running 100,000 iterations for better results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"intial_w = 0.01 * (np.random.rand(2).reshape(-1,1) - 0.5)\\n\",\n    \"initial_b = -8\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Some gradient descent settings\\n\",\n    \"iterations = 10000\\n\",\n    \"alpha = 0.001\\n\",\n    \"\\n\",\n    \"w,b, J_history,_ = gradient_descent(X_train ,y_train, initial_w, initial_b, \\n\",\n    \"                                   compute_cost, compute_gradient, alpha, iterations, 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"<summary>\\n\",\n    \"    <b>Expected Output: Cost     0.30, (Click to see details):</b>\\n\",\n    \"</summary>\\n\",\n    \"\\n\",\n    \"    # With the following settings\\n\",\n    \"    np.random.seed(1)\\n\",\n    \"    intial_w = 0.01 * (np.random.rand(2).reshape(-1,1) - 0.5)\\n\",\n    \"    initial_b = -8\\n\",\n    \"    iterations = 10000\\n\",\n    \"    alpha = 0.001\\n\",\n    \"    #\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"Iteration    0: Cost     1.01   \\n\",\n    \"Iteration 1000: Cost     0.31   \\n\",\n    \"Iteration 2000: Cost     0.30   \\n\",\n    \"Iteration 3000: Cost     0.30   \\n\",\n    \"Iteration 4000: Cost     0.30   \\n\",\n    \"Iteration 5000: Cost     0.30   \\n\",\n    \"Iteration 6000: Cost     0.30   \\n\",\n    \"Iteration 7000: Cost     0.30   \\n\",\n    \"Iteration 8000: Cost     0.30   \\n\",\n    \"Iteration 9000: Cost     0.30   \\n\",\n    \"Iteration 9999: Cost     0.30   \\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.7\\\"></a>\\n\",\n    \"### 2.7 Plotting the decision boundary\\n\",\n    \"\\n\",\n    \"We will now use the final parameters from gradient descent to plot the linear fit. If you implemented the previous parts correctly, you should see the following plot:   \\n\",\n    \"<img src=\\\"images/figure 2.png\\\"  width=\\\"450\\\" height=\\\"450\\\">\\n\",\n    \"\\n\",\n    \"We will use a helper function in the `utils.py` file to create this plot.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plot_decision_boundary(w, b, X_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.8\\\"></a>\\n\",\n    \"### 2.8 Evaluating logistic regression\\n\",\n    \"\\n\",\n    \"We can evaluate the quality of the parameters we have found by seeing how well the learned model predicts on our training set. \\n\",\n    \"\\n\",\n    \"You will implement the `predict` function below to do this.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-04'></a>\\n\",\n    \"### Exercise 4\\n\",\n    \"\\n\",\n    \"Please complete the `predict` function to produce `1` or `0` predictions given a dataset and a learned parameter vector $w$ and $b$.\\n\",\n    \"- First you need to compute the prediction from the model $f(x^{(i)}) = g(w \\\\cdot x^{(i)})$ for every example \\n\",\n    \"    - You've implemented this before in the parts above\\n\",\n    \"- We interpret the output of the model ($f(x^{(i)})$) as the probability that $y^{(i)}=1$ given $x^{(i)}$ and parameterized by $w$.\\n\",\n    \"- Therefore, to get a final prediction ($y^{(i)}=0$ or $y^{(i)}=1$) from the logistic regression model, you can use the following heuristic -\\n\",\n    \"\\n\",\n    \"  if $f(x^{(i)}) >= 0.5$, predict $y^{(i)}=1$\\n\",\n    \"  \\n\",\n    \"  if $f(x^{(i)}) < 0.5$, predict $y^{(i)}=0$\\n\",\n    \"    \\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C4\\n\",\n    \"# GRADED FUNCTION: predict\\n\",\n    \"\\n\",\n    \"def predict(X, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Predict whether the label is 0 or 1 using learned logistic\\n\",\n    \"    regression parameters w\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"    X : (ndarray Shape (m, n))\\n\",\n    \"    w : (array_like Shape (n,))      Parameters of the model\\n\",\n    \"    b : (scalar, float)              Parameter of the model\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"    p: (ndarray (m,1))\\n\",\n    \"        The predictions for X using a threshold at 0.5\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # number of training examples\\n\",\n    \"    m, n = X.shape   \\n\",\n    \"    p = np.zeros(m)\\n\",\n    \"   \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    # Loop over each example\\n\",\n    \"    for i in range(m):   \\n\",\n    \"        z_wb = None\\n\",\n    \"        # Loop over each feature\\n\",\n    \"        for j in range(n): \\n\",\n    \"            # Add the corresponding term to z_wb\\n\",\n    \"            z_wb += None\\n\",\n    \"        \\n\",\n    \"        # Add bias term \\n\",\n    \"        z_wb += None\\n\",\n    \"        \\n\",\n    \"        # Calculate the prediction for this example\\n\",\n    \"        f_wb = None\\n\",\n    \"\\n\",\n    \"        # Apply the threshold\\n\",\n    \"        p[i] = None\\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    return p\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"       def predict(X, w, b): \\n\",\n    \"            # number of training examples\\n\",\n    \"            m, n = X.shape   \\n\",\n    \"            p = np.zeros(m)\\n\",\n    \"   \\n\",\n    \"            ### START CODE HERE ### \\n\",\n    \"            # Loop over each example\\n\",\n    \"            for i in range(m):   \\n\",\n    \"                \\n\",\n    \"                # Calculate f_wb (exactly how you did it in the compute_cost function above) \\n\",\n    \"                # using a couple of lines of code\\n\",\n    \"                f_wb = \\n\",\n    \"\\n\",\n    \"                # Calculate the prediction for that training example \\n\",\n    \"                p[i] = # Your code here to calculate the prediction based on f_wb\\n\",\n    \"        \\n\",\n    \"            ### END CODE HERE ### \\n\",\n    \"            return p\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `f_wb` and `p[i]` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate f_wb</b></font></summary>\\n\",\n    \"           &emsp; &emsp; Recall that you calculated f_wb in <code>compute_cost</code> above — for detailed hints on how to calculate each intermediate term, check out the hints section below that exercise\\n\",\n    \"           <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate f_wb</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can calculate f_wb as\\n\",\n    \"               <pre>\\n\",\n    \"               for i in range(m):   \\n\",\n    \"                   # Calculate f_wb (exactly how you did it in the compute_cost function above)\\n\",\n    \"                   z_wb = 0\\n\",\n    \"                   # Loop over each feature\\n\",\n    \"                   for j in range(n): \\n\",\n    \"                       # Add the corresponding term to z_wb\\n\",\n    \"                       z_wb_ij = X[i, j] * w[j]\\n\",\n    \"                       z_wb += z_wb_ij\\n\",\n    \"            \\n\",\n    \"                   # Add bias term \\n\",\n    \"                   z_wb += b\\n\",\n    \"        \\n\",\n    \"                   # Calculate the prediction from the model\\n\",\n    \"                   f_wb = sigmoid(z_wb)\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate p[i]</b></font></summary>\\n\",\n    \"           &emsp; &emsp; As an example, if you'd like to say x = 1 if y is less than 3 and 0 otherwise, you can express it in code as <code>x = y < 3 </code>. Now do the same for p[i] = 1 if f_wb >= 0.5 and 0 otherwise. \\n\",\n    \"           <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate p[i]</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute p[i] as <code>p[i] = f_wb >= 0.5</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once you have completed the function `predict`, let's run the code below to report the training accuracy of your classifier by computing the percentage of examples it got correct.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Test your predict code\\n\",\n    \"np.random.seed(1)\\n\",\n    \"tmp_w = np.random.randn(2)\\n\",\n    \"tmp_b = 0.3    \\n\",\n    \"tmp_X = np.random.randn(4, 2) - 0.5\\n\",\n    \"\\n\",\n    \"tmp_p = predict(tmp_X, tmp_w, tmp_b)\\n\",\n    \"print(f'Output of predict: shape {tmp_p.shape}, value {tmp_p}')\\n\",\n    \"\\n\",\n    \"# UNIT TESTS        \\n\",\n    \"predict_test(predict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output** \\n\",\n    \"\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Output of predict: shape (4,),value [0. 1. 1. 1.]<b></td>\\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's use this to compute the accuracy on the training set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#Compute accuracy on our training set\\n\",\n    \"p = predict(X_train, w,b)\\n\",\n    \"print('Train Accuracy: %f'%(np.mean(p == y_train) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Train Accuracy (approx):<b></td>\\n\",\n    \"    <td> 92.00 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Regularized Logistic Regression\\n\",\n    \"\\n\",\n    \"In this part of the exercise, you will implement regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly. \\n\",\n    \"\\n\",\n    \"<a name=\\\"3.1\\\"></a>\\n\",\n    \"### 3.1 Problem Statement\\n\",\n    \"\\n\",\n    \"Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. \\n\",\n    \"- From these two tests, you would like to determine whether the microchips should be accepted or rejected. \\n\",\n    \"- To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model.\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.2\\\"></a>\\n\",\n    \"### 3.2 Loading and visualizing the data\\n\",\n    \"\\n\",\n    \"Similar to previous parts of this exercise, let's start by loading the dataset for this task and visualizing it. \\n\",\n    \"\\n\",\n    \"- The `load_dataset()` function shown below loads the data into variables `X_train` and `y_train`\\n\",\n    \"  - `X_train` contains the test results for the microchips from two tests\\n\",\n    \"  - `y_train` contains the results of the QA  \\n\",\n    \"      - `y_train = 1` if the microchip was accepted \\n\",\n    \"      - `y_train = 0` if the microchip was rejected \\n\",\n    \"  - Both `X_train` and `y_train` are numpy arrays.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X_train, y_train = load_data(\\\"data/ex2data2.txt\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"\\n\",\n    \"The code below prints the first five values of `X_train` and `y_train` and the type of the variables.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# print X_train\\n\",\n    \"print(\\\"X_train:\\\", X_train[:5])\\n\",\n    \"print(\\\"Type of X_train:\\\",type(X_train))\\n\",\n    \"\\n\",\n    \"# print y_train\\n\",\n    \"print(\\\"y_train:\\\", y_train[:5])\\n\",\n    \"print(\\\"Type of y_train:\\\",type(y_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions. Let's print the shape of `X_train` and `y_train` and see how many training examples we have in our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The shape of X_train is: ' + str(X_train.shape))\\n\",\n    \"print ('The shape of y_train is: ' + str(y_train.shape))\\n\",\n    \"print ('We have m = %d training examples' % (len(y_train)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Visualize your data\\n\",\n    \"\\n\",\n    \"The helper function `plot_data` (from `utils.py`) is used to generate a figure like Figure 3, where the axes are the two test scores, and the positive (y = 1, accepted) and negative (y = 0, rejected) examples are shown with different markers.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 3.png\\\"  width=\\\"450\\\" height=\\\"450\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Plot examples\\n\",\n    \"plot_data(X_train, y_train[:], pos_label=\\\"Accepted\\\", neg_label=\\\"Rejected\\\")\\n\",\n    \"\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Microchip Test 2') \\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Microchip Test 1') \\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Figure 3 shows that our dataset cannot be separated into positive and negative examples by a straight-line through the plot. Therefore, a straight forward application of logistic regression will not perform well on this dataset since logistic regression will only be able to find a linear decision boundary.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.3\\\"></a>\\n\",\n    \"### 3.3 Feature mapping\\n\",\n    \"\\n\",\n    \"One way to fit the data better is to create more features from each data point. In the provided function `map_feature`, we will map the features into all polynomial terms of $x_1$ and $x_2$ up to the sixth power.\\n\",\n    \"\\n\",\n    \"$$\\\\mathrm{map\\\\_feature}(x) = \\n\",\n    \"\\\\left[\\\\begin{array}{c}\\n\",\n    \"x_1\\\\\\\\\\n\",\n    \"x_2\\\\\\\\\\n\",\n    \"x_1^2\\\\\\\\\\n\",\n    \"x_1 x_2\\\\\\\\\\n\",\n    \"x_2^2\\\\\\\\\\n\",\n    \"x_1^3\\\\\\\\\\n\",\n    \"\\\\vdots\\\\\\\\\\n\",\n    \"x_1 x_2^5\\\\\\\\\\n\",\n    \"x_2^6\\\\end{array}\\\\right]$$\\n\",\n    \"\\n\",\n    \"As a result of this mapping, our vector of two features (the scores on two QA tests) has been transformed into a 27-dimensional vector. \\n\",\n    \"\\n\",\n    \"- A logistic regression classifier trained on this higher-dimension feature vector will have a more complex decision boundary and will be nonlinear when drawn in our 2-dimensional plot. \\n\",\n    \"- We have provided the `map_feature` function for you in utils.py. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Original shape of data:\\\", X_train.shape)\\n\",\n    \"\\n\",\n    \"mapped_X =  map_feature(X_train[:, 0], X_train[:, 1])\\n\",\n    \"print(\\\"Shape after feature mapping:\\\", mapped_X.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's also print the first elements of `X_train` and `mapped_X` to see the tranformation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"X_train[0]:\\\", X_train[0])\\n\",\n    \"print(\\\"mapped X_train[0]:\\\", mapped_X[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"While the feature mapping allows us to build a more expressive classifier, it is also more susceptible to overfitting. In the next parts of the exercise, you will implement regularized logistic regression to fit the data and also see for yourself how regularization can help combat the overfitting problem.\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.4\\\"></a>\\n\",\n    \"### 3.4 Cost function for regularized logistic regression\\n\",\n    \"\\n\",\n    \"In this part, you will implement the cost function for regularized logistic regression.\\n\",\n    \"\\n\",\n    \"Recall that for regularized logistic regression, the cost function is of the form\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} \\\\left[ -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\right] + \\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$$\\n\",\n    \"\\n\",\n    \"Compare this to the cost function without regularization (which you implemented above), which is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w}.b) = \\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\\\left[ (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)\\\\right]$$\\n\",\n    \"\\n\",\n    \"The difference is the regularization term, which is $$\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$$ \\n\",\n    \"Note that the $b$ parameter is not regularized.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-05'></a>\\n\",\n    \"### Exercise 5\\n\",\n    \"\\n\",\n    \"Please complete the `compute_cost_reg` function below to calculate the following term for each element in $w$ \\n\",\n    \"$$\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$$\\n\",\n    \"\\n\",\n    \"The starter code then adds this to the cost without regularization (which you computed above in `compute_cost`) to calculate the cost with regulatization.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C5\\n\",\n    \"def compute_cost_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"      X : (array_like Shape (m,n)) data, m examples by n features\\n\",\n    \"      y : (array_like Shape (m,)) target value \\n\",\n    \"      w : (array_like Shape (n,)) Values of parameters of the model      \\n\",\n    \"      b : (array_like Shape (n,)) Values of bias parameter of the model\\n\",\n    \"      lambda_ : (scalar, float)    Controls amount of regularization\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost: (scalar)         cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    # Calls the compute_cost function that you implemented above\\n\",\n    \"    cost_without_reg = compute_cost(X, y, w, b) \\n\",\n    \"    \\n\",\n    \"    # You need to calculate this value\\n\",\n    \"    reg_cost = 0.\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    \\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    # Add the regularization cost to get the total cost\\n\",\n    \"    total_cost = cost_without_reg + (lambda_/(2 * m)) * reg_cost\\n\",\n    \"\\n\",\n    \"    return total_cost\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"       def compute_cost_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"   \\n\",\n    \"           m, n = X.shape\\n\",\n    \"    \\n\",\n    \"            # Calls the compute_cost function that you implemented above\\n\",\n    \"            cost_without_reg = compute_cost(X, y, w, b) \\n\",\n    \"    \\n\",\n    \"            # You need to calculate this value\\n\",\n    \"            reg_cost = 0.\\n\",\n    \"    \\n\",\n    \"            ### START CODE HERE ###\\n\",\n    \"            for j in range(n):\\n\",\n    \"                reg_cost_j = # Your code here to calculate the cost from w[j]\\n\",\n    \"                reg_cost = reg_cost + reg_cost_j\\n\",\n    \"\\n\",\n    \"            ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"            # Add the regularization cost to get the total cost\\n\",\n    \"            total_cost = cost_without_reg + (lambda_/(2 * m)) * reg_cost\\n\",\n    \"\\n\",\n    \"        return total_cost\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `reg_cost_j` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate reg_cost_j</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use calculate reg_cost_j as <code>reg_cost_j = w[j]**2 </code> \\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to check your implementation of the `compute_cost_reg` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_mapped = map_feature(X_train[:, 0], X_train[:, 1])\\n\",\n    \"np.random.seed(1)\\n\",\n    \"initial_w = np.random.rand(X_mapped.shape[1]) - 0.5\\n\",\n    \"initial_b = 0.5\\n\",\n    \"lambda_ = 0.5\\n\",\n    \"cost = compute_cost_reg(X_mapped, y_train, initial_w, initial_b, lambda_)\\n\",\n    \"\\n\",\n    \"print(\\\"Regularized cost :\\\", cost)\\n\",\n    \"\\n\",\n    \"# UNIT TEST    \\n\",\n    \"compute_cost_reg_test(compute_cost_reg)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Regularized cost : <b></td>\\n\",\n    \"    <td> 0.6618252552483948 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.5\\\"></a>\\n\",\n    \"### 3.5 Gradient for regularized logistic regression\\n\",\n    \"\\n\",\n    \"In this section, you will implement the gradient for regularized logistic regression.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The gradient of the regularized cost function has two components. The first, $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ is a scalar, the other is a vector with the same shape as the parameters $\\\\mathbf{w}$, where the $j^\\\\mathrm{th}$ element is defined as follows:\\n\",\n    \"\\n\",\n    \"$$\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} = \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})  $$\\n\",\n    \"\\n\",\n    \"$$\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} = \\\\left( \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)}) x_j^{(i)} \\\\right) + \\\\frac{\\\\lambda}{m} w_j  \\\\quad\\\\, \\\\mbox{for $j=0...(n-1)$}$$\\n\",\n    \"\\n\",\n    \"Compare this to the gradient of the cost function without regularization (which you implemented above), which is of the form \\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)}) \\\\tag{2}\\n\",\n    \"$$\\n\",\n    \"$$\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  = \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - \\\\mathbf{y}^{(i)})x_{j}^{(i)} \\\\tag{3}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"As you can see,$\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}$ is the same, the difference is the following term in $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w}$, which is $$\\\\frac{\\\\lambda}{m} w_j  \\\\quad\\\\, \\\\mbox{for $j=0...(n-1)$}$$ \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-06'></a>\\n\",\n    \"### Exercise 6\\n\",\n    \"\\n\",\n    \"Please complete the `compute_gradient_reg` function below to modify the code below to calculate the following term\\n\",\n    \"\\n\",\n    \"$$\\\\frac{\\\\lambda}{m} w_j  \\\\quad\\\\, \\\\mbox{for $j=0...(n-1)$}$$\\n\",\n    \"\\n\",\n    \"The starter code will add this term to the $\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w}$ returned from `compute_gradient` above to get the gradient for the regularized cost function.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C6\\n\",\n    \"def compute_gradient_reg(X, y, w, b, lambda_ = 1): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X : (ndarray Shape (m,n))   variable such as house size \\n\",\n    \"      y : (ndarray Shape (m,))    actual value \\n\",\n    \"      w : (ndarray Shape (n,))    values of parameters of the model      \\n\",\n    \"      b : (scalar)                value of parameter of the model  \\n\",\n    \"      lambda_ : (scalar,float)    regularization constant\\n\",\n    \"    Returns\\n\",\n    \"      dj_db: (scalar)             The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"      dj_dw: (ndarray Shape (n,)) The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    dj_db, dj_dw = compute_gradient(X, y, w, b)\\n\",\n    \"\\n\",\n    \"    ### START CODE HERE ###     \\n\",\n    \"    \\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ###         \\n\",\n    \"        \\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_gradient_reg(X, y, w, b, lambda_ = 1): \\n\",\n    \"        m, n = X.shape\\n\",\n    \"    \\n\",\n    \"        dj_db, dj_dw = compute_gradient(X, y, w, b)\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###     \\n\",\n    \"        # Loop over the elements of w\\n\",\n    \"        for j in range(n): \\n\",\n    \"            \\n\",\n    \"            dj_dw_j_reg = # Your code here to calculate the regularization term for dj_dw[j]\\n\",\n    \"            \\n\",\n    \"            # Add the regularization term  to the correspoding element of dj_dw\\n\",\n    \"            dj_dw[j] = dj_dw[j] + dj_dw_j_reg\\n\",\n    \"        \\n\",\n    \"        ### END CODE HERE ###         \\n\",\n    \"        \\n\",\n    \"        return dj_db, dj_dw\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `dj_dw_j_reg` \\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate dj_dw_j_reg</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use calculate dj_dw_j_reg as <code>dj_dw_j_reg = (lambda_ / m) * w[j] </code> \\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to check your implementation of the `compute_gradient_reg` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_mapped = map_feature(X_train[:, 0], X_train[:, 1])\\n\",\n    \"np.random.seed(1) \\n\",\n    \"initial_w  = np.random.rand(X_mapped.shape[1]) - 0.5 \\n\",\n    \"initial_b = 0.5\\n\",\n    \" \\n\",\n    \"lambda_ = 0.5\\n\",\n    \"dj_db, dj_dw = compute_gradient_reg(X_mapped, y_train, initial_w, initial_b, lambda_)\\n\",\n    \"\\n\",\n    \"print(f\\\"dj_db: {dj_db}\\\", )\\n\",\n    \"print(f\\\"First few elements of regularized dj_dw:\\\\n {dj_dw[:4].tolist()}\\\", )\\n\",\n    \"\\n\",\n    \"# UNIT TESTS    \\n\",\n    \"compute_gradient_reg_test(compute_gradient_reg)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>dj_db:</b>0.07138288792343656</td> </tr>\\n\",\n    \"  <tr>\\n\",\n    \"      <td> <b> First few elements of regularized dj_dw:</b> </td> </tr>\\n\",\n    \"   <tr>\\n\",\n    \"   <td> [[-0.010386028450548701], [0.01140985288328012], [0.0536273463274574], [0.003140278267313462]] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.6\\\"></a>\\n\",\n    \"### 3.6 Learning parameters using gradient descent\\n\",\n    \"\\n\",\n    \"Similar to the previous parts, you will use your gradient descent function implemented above to learn the optimal parameters $w$,$b$. \\n\",\n    \"- If you have completed the cost and gradient for regularized logistic regression correctly, you should be able to step through the next cell to learn the parameters $w$. \\n\",\n    \"- After training our parameters, we will use it to plot the decision boundary. \\n\",\n    \"\\n\",\n    \"**Note**\\n\",\n    \"\\n\",\n    \"The code block below takes quite a while to run, especially with a non-vectorized version. You can reduce the `iterations` to test your implementation and iterate faster. If you hae time, run for 100,000 iterations to see better results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Initialize fitting parameters\\n\",\n    \"np.random.seed(1)\\n\",\n    \"initial_w = np.random.rand(X_mapped.shape[1])-0.5\\n\",\n    \"initial_b = 1.\\n\",\n    \"\\n\",\n    \"# Set regularization parameter lambda_ to 1 (you can try varying this)\\n\",\n    \"lambda_ = 0.01;                                          \\n\",\n    \"# Some gradient descent settings\\n\",\n    \"iterations = 10000\\n\",\n    \"alpha = 0.01\\n\",\n    \"\\n\",\n    \"w,b, J_history,_ = gradient_descent(X_mapped, y_train, initial_w, initial_b, \\n\",\n    \"                                    compute_cost_reg, compute_gradient_reg, \\n\",\n    \"                                    alpha, iterations, lambda_)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"<summary>\\n\",\n    \"    <b>Expected Output: Cost < 0.5  (Click for details)</b>\\n\",\n    \"</summary>\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"# Using the following settings\\n\",\n    \"#np.random.seed(1)\\n\",\n    \"#initial_w = np.random.rand(X_mapped.shape[1])-0.5\\n\",\n    \"#initial_b = 1.\\n\",\n    \"#lambda_ = 0.01;                                          \\n\",\n    \"#iterations = 10000\\n\",\n    \"#alpha = 0.01\\n\",\n    \"Iteration    0: Cost     0.72   \\n\",\n    \"Iteration 1000: Cost     0.59   \\n\",\n    \"Iteration 2000: Cost     0.56   \\n\",\n    \"Iteration 3000: Cost     0.53   \\n\",\n    \"Iteration 4000: Cost     0.51   \\n\",\n    \"Iteration 5000: Cost     0.50   \\n\",\n    \"Iteration 6000: Cost     0.48   \\n\",\n    \"Iteration 7000: Cost     0.47   \\n\",\n    \"Iteration 8000: Cost     0.46   \\n\",\n    \"Iteration 9000: Cost     0.45   \\n\",\n    \"Iteration 9999: Cost     0.45       \\n\",\n    \"    \\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.7\\\"></a>\\n\",\n    \"### 3.7 Plotting the decision boundary\\n\",\n    \"To help you visualize the model learned by this classifier, we will use our `plot_decision_boundary` function which plots the (non-linear) decision boundary that separates the positive and negative examples. \\n\",\n    \"\\n\",\n    \"- In the function, we plotted the non-linear decision boundary by computing the classifier’s predictions on an evenly spaced grid and then drew a contour plot of where the predictions change from y = 0 to y = 1.\\n\",\n    \"\\n\",\n    \"- After learning the parameters $w$,$b$, the next step is to plot a decision boundary similar to Figure 4.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 4.png\\\"  width=\\\"450\\\" height=\\\"450\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plot_decision_boundary(w, b, X_mapped, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.8\\\"></a>\\n\",\n    \"### 3.8 Evaluating regularized logistic regression model\\n\",\n    \"\\n\",\n    \"You will use the `predict` function that you implemented above to calculate the accuracy of the regulaized logistic regression model on the training set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#Compute accuracy on the training set\\n\",\n    \"p = predict(X_mapped, w, b)\\n\",\n    \"\\n\",\n    \"print('Train Accuracy: %f'%(np.mean(p == y_train) * 100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Train Accuracy:</b>~ 80%</td> </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/C1W3A1/data/ex2data1.txt",
    "content": "34.62365962451697,78.0246928153624,0\n30.28671076822607,43.89499752400101,0\n35.84740876993872,72.90219802708364,0\n60.18259938620976,86.30855209546826,1\n79.0327360507101,75.3443764369103,1\n45.08327747668339,56.3163717815305,0\n61.10666453684766,96.51142588489624,1\n75.02474556738889,46.55401354116538,1\n76.09878670226257,87.42056971926803,1\n84.43281996120035,43.53339331072109,1\n95.86155507093572,38.22527805795094,0\n75.01365838958247,30.60326323428011,0\n82.30705337399482,76.48196330235604,1\n69.36458875970939,97.71869196188608,1\n39.53833914367223,76.03681085115882,0\n53.9710521485623,89.20735013750205,1\n69.07014406283025,52.74046973016765,1\n67.94685547711617,46.67857410673128,0\n70.66150955499435,92.92713789364831,1\n76.97878372747498,47.57596364975532,1\n67.37202754570876,42.83843832029179,0\n89.67677575072079,65.79936592745237,1\n50.534788289883,48.85581152764205,0\n34.21206097786789,44.20952859866288,0\n77.9240914545704,68.9723599933059,1\n62.27101367004632,69.95445795447587,1\n80.1901807509566,44.82162893218353,1\n93.114388797442,38.80067033713209,0\n61.83020602312595,50.25610789244621,0\n38.78580379679423,64.99568095539578,0\n61.379289447425,72.80788731317097,1\n85.40451939411645,57.05198397627122,1\n52.10797973193984,63.12762376881715,0\n52.04540476831827,69.43286012045222,1\n40.23689373545111,71.16774802184875,0\n54.63510555424817,52.21388588061123,0\n33.91550010906887,98.86943574220611,0\n64.17698887494485,80.90806058670817,1\n74.78925295941542,41.57341522824434,0\n34.1836400264419,75.2377203360134,0\n83.90239366249155,56.30804621605327,1\n51.54772026906181,46.85629026349976,0\n94.44336776917852,65.56892160559052,1\n82.36875375713919,40.61825515970618,0\n51.04775177128865,45.82270145776001,0\n62.22267576120188,52.06099194836679,0\n77.19303492601364,70.45820000180959,1\n97.77159928000232,86.7278223300282,1\n62.07306379667647,96.76882412413983,1\n91.56497449807442,88.69629254546599,1\n79.94481794066932,74.16311935043758,1\n99.2725269292572,60.99903099844988,1\n90.54671411399852,43.39060180650027,1\n34.52451385320009,60.39634245837173,0\n50.2864961189907,49.80453881323059,0\n49.58667721632031,59.80895099453265,0\n97.64563396007767,68.86157272420604,1\n32.57720016809309,95.59854761387875,0\n74.24869136721598,69.82457122657193,1\n71.79646205863379,78.45356224515052,1\n75.3956114656803,85.75993667331619,1\n35.28611281526193,47.02051394723416,0\n56.25381749711624,39.26147251058019,0\n30.05882244669796,49.59297386723685,0\n44.66826172480893,66.45008614558913,0\n66.56089447242954,41.09209807936973,0\n40.45755098375164,97.53518548909936,1\n49.07256321908844,51.88321182073966,0\n80.27957401466998,92.11606081344084,1\n66.74671856944039,60.99139402740988,1\n32.72283304060323,43.30717306430063,0\n64.0393204150601,78.03168802018232,1\n72.34649422579923,96.22759296761404,1\n60.45788573918959,73.09499809758037,1\n58.84095621726802,75.85844831279042,1\n99.82785779692128,72.36925193383885,1\n47.26426910848174,88.47586499559782,1\n50.45815980285988,75.80985952982456,1\n60.45555629271532,42.50840943572217,0\n82.22666157785568,42.71987853716458,0\n88.9138964166533,69.80378889835472,1\n94.83450672430196,45.69430680250754,1\n67.31925746917527,66.58935317747915,1\n57.23870631569862,59.51428198012956,1\n80.36675600171273,90.96014789746954,1\n68.46852178591112,85.59430710452014,1\n42.0754545384731,78.84478600148043,0\n75.47770200533905,90.42453899753964,1\n78.63542434898018,96.64742716885644,1\n52.34800398794107,60.76950525602592,0\n94.09433112516793,77.15910509073893,1\n90.44855097096364,87.50879176484702,1\n55.48216114069585,35.57070347228866,0\n74.49269241843041,84.84513684930135,1\n89.84580670720979,45.35828361091658,1\n83.48916274498238,48.38028579728175,1\n42.2617008099817,87.10385094025457,1\n99.31500880510394,68.77540947206617,1\n55.34001756003703,64.9319380069486,1\n74.77589300092767,89.52981289513276,1\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/C1W3A1/data/ex2data2.txt",
    "content": "0.051267,0.69956,1\n-0.092742,0.68494,1\n-0.21371,0.69225,1\n-0.375,0.50219,1\n-0.51325,0.46564,1\n-0.52477,0.2098,1\n-0.39804,0.034357,1\n-0.30588,-0.19225,1\n0.016705,-0.40424,1\n0.13191,-0.51389,1\n0.38537,-0.56506,1\n0.52938,-0.5212,1\n0.63882,-0.24342,1\n0.73675,-0.18494,1\n0.54666,0.48757,1\n0.322,0.5826,1\n0.16647,0.53874,1\n-0.046659,0.81652,1\n-0.17339,0.69956,1\n-0.47869,0.63377,1\n-0.60541,0.59722,1\n-0.62846,0.33406,1\n-0.59389,0.005117,1\n-0.42108,-0.27266,1\n-0.11578,-0.39693,1\n0.20104,-0.60161,1\n0.46601,-0.53582,1\n0.67339,-0.53582,1\n-0.13882,0.54605,1\n-0.29435,0.77997,1\n-0.26555,0.96272,1\n-0.16187,0.8019,1\n-0.17339,0.64839,1\n-0.28283,0.47295,1\n-0.36348,0.31213,1\n-0.30012,0.027047,1\n-0.23675,-0.21418,1\n-0.06394,-0.18494,1\n0.062788,-0.16301,1\n0.22984,-0.41155,1\n0.2932,-0.2288,1\n0.48329,-0.18494,1\n0.64459,-0.14108,1\n0.46025,0.012427,1\n0.6273,0.15863,1\n0.57546,0.26827,1\n0.72523,0.44371,1\n0.22408,0.52412,1\n0.44297,0.67032,1\n0.322,0.69225,1\n0.13767,0.57529,1\n-0.0063364,0.39985,1\n-0.092742,0.55336,1\n-0.20795,0.35599,1\n-0.20795,0.17325,1\n-0.43836,0.21711,1\n-0.21947,-0.016813,1\n-0.13882,-0.27266,1\n0.18376,0.93348,0\n0.22408,0.77997,0\n0.29896,0.61915,0\n0.50634,0.75804,0\n0.61578,0.7288,0\n0.60426,0.59722,0\n0.76555,0.50219,0\n0.92684,0.3633,0\n0.82316,0.27558,0\n0.96141,0.085526,0\n0.93836,0.012427,0\n0.86348,-0.082602,0\n0.89804,-0.20687,0\n0.85196,-0.36769,0\n0.82892,-0.5212,0\n0.79435,-0.55775,0\n0.59274,-0.7405,0\n0.51786,-0.5943,0\n0.46601,-0.41886,0\n0.35081,-0.57968,0\n0.28744,-0.76974,0\n0.085829,-0.75512,0\n0.14919,-0.57968,0\n-0.13306,-0.4481,0\n-0.40956,-0.41155,0\n-0.39228,-0.25804,0\n-0.74366,-0.25804,0\n-0.69758,0.041667,0\n-0.75518,0.2902,0\n-0.69758,0.68494,0\n-0.4038,0.70687,0\n-0.38076,0.91886,0\n-0.50749,0.90424,0\n-0.54781,0.70687,0\n0.10311,0.77997,0\n0.057028,0.91886,0\n-0.10426,0.99196,0\n-0.081221,1.1089,0\n0.28744,1.087,0\n0.39689,0.82383,0\n0.63882,0.88962,0\n0.82316,0.66301,0\n0.67339,0.64108,0\n1.0709,0.10015,0\n-0.046659,-0.57968,0\n-0.23675,-0.63816,0\n-0.15035,-0.36769,0\n-0.49021,-0.3019,0\n-0.46717,-0.13377,0\n-0.28859,-0.060673,0\n-0.61118,-0.067982,0\n-0.66302,-0.21418,0\n-0.59965,-0.41886,0\n-0.72638,-0.082602,0\n-0.83007,0.31213,0\n-0.72062,0.53874,0\n-0.59389,0.49488,0\n-0.48445,0.99927,0\n-0.0063364,0.99927,0\n0.63265,-0.030612,0\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/C1W3A1/public_tests.py",
    "content": "import numpy as np\nimport math\n\ndef sigmoid_test(target):\n    assert np.isclose(target(3.0), 0.9525741268224334), \"Failed for scalar input\"\n    assert np.allclose(target(np.array([2.5, 0])), [0.92414182, 0.5]), \"Failed for 1D array\"\n    assert np.allclose(target(np.array([[2.5, -2.5], [0, 1]])), \n                       [[0.92414182, 0.07585818], [0.5, 0.73105858]]), \"Failed for 2D array\"\n    print('\\033[92mAll tests passed!')\n    \ndef compute_cost_test(target):\n    X = np.array([[0, 0, 0, 0]]).T\n    y = np.array([0, 0, 0, 0])\n    w = np.array([0])\n    b = 1\n    result = target(X, y, w, b)\n    if math.isinf(result):\n        raise ValueError(\"Did you get the sigmoid of z_wb?\")\n    \n    np.random.seed(17)  \n    X = np.random.randn(5, 2)\n    y = np.array([1, 0, 0, 1, 1])\n    w = np.random.randn(2)\n    b = 0\n    result = target(X, y, w, b)\n    assert np.isclose(result, 2.15510667), f\"Wrong output. Expected: {2.15510667} got: {result}\"\n    \n    X = np.random.randn(4, 3)\n    y = np.array([1, 1, 0, 0])\n    w = np.random.randn(3)\n    b = 0\n    \n    result = target(X, y, w, b)\n    assert np.isclose(result, 0.80709376), f\"Wrong output. Expected: {0.80709376} got: {result}\"\n\n    X = np.random.randn(4, 3)\n    y = np.array([1, 0,1, 0])\n    w = np.random.randn(3)\n    b = 3\n    result = target(X, y, w, b)\n    assert np.isclose(result, 0.4529660647), f\"Wrong output. Expected: {0.4529660647} got: {result}. Did you inizialized z_wb = b?\"\n    \n    print('\\033[92mAll tests passed!')\n    \ndef compute_gradient_test(target):\n    np.random.seed(1)\n    X = np.random.randn(7, 3)\n    y = np.array([1, 0, 1, 0, 1, 1, 0])\n    test_w = np.array([1, 0.5, -0.35])\n    test_b = 1.7\n    dj_db, dj_dw  = target(X, y, test_w, test_b)\n    \n    assert np.isclose(dj_db, 0.28936094), f\"Wrong value for dj_db. Expected: {0.28936094} got: {dj_db}\" \n    assert dj_dw.shape == test_w.shape, f\"Wrong shape for dj_dw. Expected: {test_w.shape} got: {dj_dw.shape}\" \n    assert np.allclose(dj_dw, [-0.11999166, 0.41498775, -0.71968405]), f\"Wrong values for dj_dw. Got: {dj_dw}\"\n\n    print('\\033[92mAll tests passed!') \n    \ndef predict_test(target):\n    np.random.seed(5)\n    b = 0.5    \n    w = np.random.randn(3)\n    X = np.random.randn(8, 3)\n    \n    result = target(X, w, b)\n    wrong_1 = [1., 1., 0., 0., 1., 0., 0., 1.]\n    expected_1 = [1., 1., 1., 0., 1., 0., 0., 1.]\n    if np.allclose(result, wrong_1):\n        raise ValueError(\"Did you apply the sigmoid before applying the threshold?\")\n    assert result.shape == (len(X),), f\"Wrong length. Expected : {(len(X),)} got: {result.shape}\"\n    assert np.allclose(result, expected_1), f\"Wrong output: Expected : {expected_1} got: {result}\"\n    \n    b = -1.7    \n    w = np.random.randn(4) + 0.6\n    X = np.random.randn(6, 4)\n    \n    result = target(X, w, b)\n    expected_2 = [0., 0., 0., 1., 1., 0.]\n    assert result.shape == (len(X),), f\"Wrong length. Expected : {(len(X),)} got: {result.shape}\"\n    assert np.allclose(result,expected_2), f\"Wrong output: Expected : {expected_2} got: {result}\"\n\n    print('\\033[92mAll tests passed!')\n    \ndef compute_cost_reg_test(target):\n    np.random.seed(1)\n    w = np.random.randn(3)\n    b = 0.4\n    X = np.random.randn(6, 3)\n    y = np.array([0, 1, 1, 0, 1, 1])\n    lambda_ = 0.1\n    expected_output = target(X, y, w, b, lambda_)\n    \n    assert np.isclose(expected_output, 0.5469746792761936), f\"Wrong output. Expected: {0.5469746792761936} got:{expected_output}\"\n    \n    w = np.random.randn(5)\n    b = -0.6\n    X = np.random.randn(8, 5)\n    y = np.array([1, 0, 1, 0, 0, 1, 0, 1])\n    lambda_ = 0.01\n    output = target(X, y, w, b, lambda_)\n    assert np.isclose(output, 1.2608591964119995), f\"Wrong output. Expected: {1.2608591964119995} got:{output}\"\n    \n    w = np.array([2, 2, 2, 2, 2])\n    b = 0\n    X = np.zeros((8, 5))\n    y = np.array([0.5] * 8)\n    lambda_ = 3\n    output = target(X, y, w, b, lambda_)\n    expected = -np.log(0.5) + 3. / (2. * 8.) * 20.\n    assert np.isclose(output, expected), f\"Wrong output. Expected: {expected} got:{output}\"\n    \n    print('\\033[92mAll tests passed!') \n    \ndef compute_gradient_reg_test(target):\n    np.random.seed(1)\n    w = np.random.randn(5)\n    b = 0.2\n    X = np.random.randn(7, 5)\n    y = np.array([0, 1, 1, 0, 1, 1, 0])\n    lambda_ = 0.1\n    expected1 = (-0.1506447567869257, np.array([ 0.19530838, -0.00632206,  0.19687367,  0.15741161,  0.02791437]))\n    dj_db, dj_dw = target(X, y, w, b, lambda_)\n    \n    assert np.isclose(dj_db, expected1[0]), f\"Wrong dj_db. Expected: {expected1[0]} got: {dj_db}\"\n    assert np.allclose(dj_dw, expected1[1]), f\"Wrong dj_dw. Expected: {expected1[1]} got: {dj_dw}\"\n\n    \n    w = np.random.randn(7)\n    b = 0\n    X = np.random.randn(7, 7)\n    y = np.array([1, 0, 0, 0, 1, 1, 0])\n    lambda_ = 0\n    expected2 = (0.02660329857573818, np.array([ 0.23567643, -0.06921029, -0.19705212, -0.0002884 ,  0.06490588,\n        0.26948175,  0.10777992]))\n    dj_db, dj_dw = target(X, y, w, b, lambda_)\n    assert np.isclose(dj_db, expected2[0]), f\"Wrong dj_db. Expected: {expected2[0]} got: {dj_db}\"\n    assert np.allclose(dj_dw, expected2[1]), f\"Wrong dj_dw. Expected: {expected2[1]} got: {dj_dw}\"\n    \n    print('\\033[92mAll tests passed!') \n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/C1W3A1/test_utils.py",
    "content": "import numpy as np\nfrom copy import deepcopy\n\n\ndef datatype_check(expected_output, target_output, error):\n    success = 0\n    if isinstance(target_output, dict):\n        for key in target_output.keys():\n            try:\n                success += datatype_check(expected_output[key],\n                                          target_output[key], error)\n            except:\n                print(\"Error: {} in variable {}. Got {} but expected type {}\".format(error,\n                                                                                     key,\n                                                                                     type(\n                                                                                         target_output[key]),\n                                                                                     type(expected_output[key])))\n        if success == len(target_output.keys()):\n            return 1\n        else:\n            return 0\n    elif isinstance(target_output, tuple) or isinstance(target_output, list):\n        for i in range(len(target_output)):\n            try:\n                success += datatype_check(expected_output[i],\n                                          target_output[i], error)\n            except:\n                print(\"Error: {} in variable {}, expected type: {}  but expected type {}\".format(error,\n                                                                                                 i,\n                                                                                                 type(\n                                                                                                     target_output[i]),\n                                                                                                 type(expected_output[i]\n                                                                                                      )))\n        if success == len(target_output):\n            return 1\n        else:\n            return 0\n\n    else:\n        assert isinstance(target_output, type(expected_output))\n        return 1\n\n\ndef equation_output_check(expected_output, target_output, error):\n    success = 0\n    if isinstance(target_output, dict):\n        for key in target_output.keys():\n            try:\n                success += equation_output_check(expected_output[key],\n                                                 target_output[key], error)\n            except:\n                print(\"Error: {} for variable {}.\".format(error,\n                                                          key))\n        if success == len(target_output.keys()):\n            return 1\n        else:\n            return 0\n    elif isinstance(target_output, tuple) or isinstance(target_output, list):\n        for i in range(len(target_output)):\n            try:\n                success += equation_output_check(expected_output[i],\n                                                 target_output[i], error)\n            except:\n                print(\"Error: {} for variable in position {}.\".format(error, i))\n        if success == len(target_output):\n            return 1\n        else:\n            return 0\n\n    else:\n        if hasattr(target_output, 'shape'):\n            np.testing.assert_array_almost_equal(\n                target_output, expected_output)\n        else:\n            assert target_output == expected_output\n        return 1\n\n\ndef shape_check(expected_output, target_output, error):\n    success = 0\n    if isinstance(target_output, dict):\n        for key in target_output.keys():\n            try:\n                success += shape_check(expected_output[key],\n                                       target_output[key], error)\n            except:\n                print(\"Error: {} for variable {}.\".format(error, key))\n        if success == len(target_output.keys()):\n            return 1\n        else:\n            return 0\n    elif isinstance(target_output, tuple) or isinstance(target_output, list):\n        for i in range(len(target_output)):\n            try:\n                success += shape_check(expected_output[i],\n                                       target_output[i], error)\n            except:\n                print(\"Error: {} for variable {}.\".format(error, i))\n        if success == len(target_output):\n            return 1\n        else:\n            return 0\n\n    else:\n        if hasattr(target_output, 'shape'):\n            assert target_output.shape == expected_output.shape\n        return 1\n\n\ndef single_test(test_cases, target):\n    success = 0\n    for test_case in test_cases:\n        try:\n            if test_case['name'] == \"datatype_check\":\n                assert isinstance(target(*test_case['input']),\n                                  type(test_case[\"expected\"]))\n                success += 1\n            if test_case['name'] == \"equation_output_check\":\n                assert np.allclose(test_case[\"expected\"],\n                                   target(*test_case['input']))\n                success += 1\n            if test_case['name'] == \"shape_check\":\n                assert test_case['expected'].shape == target(\n                    *test_case['input']).shape\n                success += 1\n        except:\n            print(\"Error: \" + test_case['error'])\n\n    if success == len(test_cases):\n        print(\"\\033[92m All tests passed.\")\n    else:\n        print('\\033[92m', success, \" Tests passed\")\n        print('\\033[91m', len(test_cases) - success, \" Tests failed\")\n        raise AssertionError(\n            \"Not all tests were passed for {}. Check your equations and avoid using global variables inside the function.\".format(target.__name__))\n\n\ndef multiple_test(test_cases, target):\n    success = 0\n    for test_case in test_cases:\n        try:\n            test_input = deepcopy(test_case['input'])\n            target_answer = target(*test_input)\n            if test_case['name'] == \"datatype_check\":\n                success += datatype_check(test_case['expected'],\n                                          target_answer, test_case['error'])\n            if test_case['name'] == \"equation_output_check\":\n                success += equation_output_check(\n                    test_case['expected'], target_answer, test_case['error'])\n            if test_case['name'] == \"shape_check\":\n                success += shape_check(test_case['expected'],\n                                       target_answer, test_case['error'])\n        except:\n            print('\\33[30m', \"Error: \" + test_case['error'])\n\n    if success == len(test_cases):\n        print(\"\\033[92m All tests passed.\")\n    else:\n        print('\\033[92m', success, \" Tests passed\")\n        print('\\033[91m', len(test_cases) - success, \" Tests failed\")\n        raise AssertionError(\n            \"Not all tests were passed for {}. Check your equations and avoid using global variables inside the function.\".format(target.__name__))\n\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/C1W3A1/utils.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef load_data(filename):\n    data = np.loadtxt(filename, delimiter=',')\n    X = data[:,:2]\n    y = data[:,2]\n    return X, y\n\ndef sig(z):\n \n    return 1/(1+np.exp(-z))\n\ndef map_feature(X1, X2):\n    \"\"\"\n    Feature mapping function to polynomial features    \n    \"\"\"\n    X1 = np.atleast_1d(X1)\n    X2 = np.atleast_1d(X2)\n    degree = 6\n    out = []\n    for i in range(1, degree+1):\n        for j in range(i + 1):\n            out.append((X1**(i-j) * (X2**j)))\n    return np.stack(out, axis=1)\n\n\ndef plot_data(X, y, pos_label=\"y=1\", neg_label=\"y=0\"):\n    positive = y == 1\n    negative = y == 0\n    \n    # Plot examples\n    plt.plot(X[positive, 0], X[positive, 1], 'k+', label=pos_label)\n    plt.plot(X[negative, 0], X[negative, 1], 'yo', label=neg_label)\n    \n    \ndef plot_decision_boundary(w, b, X, y):\n    # Credit to dibgerge on Github for this plotting code\n     \n    plot_data(X[:, 0:2], y)\n    \n    if X.shape[1] <= 2:\n        plot_x = np.array([min(X[:, 0]), max(X[:, 0])])\n        plot_y = (-1. / w[1]) * (w[0] * plot_x + b)\n        \n        plt.plot(plot_x, plot_y, c=\"b\")\n        \n    else:\n        u = np.linspace(-1, 1.5, 50)\n        v = np.linspace(-1, 1.5, 50)\n        \n        z = np.zeros((len(u), len(v)))\n\n        # Evaluate z = theta*x over the grid\n        for i in range(len(u)):\n            for j in range(len(v)):\n                z[i,j] = sig(np.dot(map_feature(u[i], v[j]), w) + b)\n        \n        # important to transpose z before calling contour       \n        z = z.T\n        \n        # Plot z = 0\n        plt.contour(u,v,z, levels = [0.5], colors=\"g\")\n\n        "
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/C1_W3_Lab01_Classification_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Classification\\n\",\n    \"\\n\",\n    \"In this lab, you will contrast regression and classification.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_common import dlc, plot_data\\n\",\n    \"from plt_one_addpt_onclick import plt_one_addpt_onclick\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Classification Problems\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_Classification.png\\\"     style=\\\" width:380px; padding: 10px; \\\" > Examples of classification problems are things like: identifying email as Spam or Not Spam or determining if a tumor is malignant or benign. In particular, these are examples of *binary* classification where there are two possible outcomes.  Outcomes can be  described in pairs of 'positive'/'negative' such as 'yes'/'no, 'true'/'false' or '1'/'0'. \\n\",\n    \"\\n\",\n    \"Plots of classification data sets often use symbols to indicate the outcome of an example. In the plots below, 'X' is used to represent the positive values while 'O' represents negative outcomes. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_train = np.array([0., 1, 2, 3, 4, 5])\\n\",\n    \"y_train = np.array([0,  0, 0, 1, 1, 1])\\n\",\n    \"X_train2 = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])\\n\",\n    \"y_train2 = np.array([0, 0, 0, 1, 1, 1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=800.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"0c778f117b684846964cee159a64a057\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pos = y_train == 1\\n\",\n    \"neg = y_train == 0\\n\",\n    \"\\n\",\n    \"fig,ax = plt.subplots(1,2,figsize=(8,3))\\n\",\n    \"#plot 1, single variable\\n\",\n    \"ax[0].scatter(x_train[pos], y_train[pos], marker='x', s=80, c = 'red', label=\\\"y=1\\\")\\n\",\n    \"ax[0].scatter(x_train[neg], y_train[neg], marker='o', s=100, label=\\\"y=0\\\", facecolors='none', \\n\",\n    \"              edgecolors=dlc[\\\"dlblue\\\"],lw=3)\\n\",\n    \"\\n\",\n    \"ax[0].set_ylim(-0.08,1.1)\\n\",\n    \"ax[0].set_ylabel('y', fontsize=12)\\n\",\n    \"ax[0].set_xlabel('x', fontsize=12)\\n\",\n    \"ax[0].set_title('one variable plot')\\n\",\n    \"ax[0].legend()\\n\",\n    \"\\n\",\n    \"#plot 2, two variables\\n\",\n    \"plot_data(X_train2, y_train2, ax[1])\\n\",\n    \"ax[1].axis([0, 4, 0, 4])\\n\",\n    \"ax[1].set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax[1].set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"ax[1].set_title('two variable plot')\\n\",\n    \"ax[1].legend()\\n\",\n    \"plt.tight_layout()\\n\",\n    \"plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Note in the plots above:\\n\",\n    \"- In the single variable plot, positive results are shown both a red 'X's and as y=1. Negative results are blue 'O's and are located at y=0.\\n\",\n    \"   - Recall in the case of linear regression, y would not have been limited to two values but could have been any value.\\n\",\n    \"- In the two-variable plot, the y axis is not available.  Positive results are shown as red 'X's, while negative results use the blue 'O' symbol.\\n\",\n    \"    - Recall in the case of linear regression with multiple variables, y would not have been limited to two values and a similar plot would have been three-dimensional.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Linear Regression approach\\n\",\n    \"In the previous week, you applied linear regression to build a prediction model. Let's try that approach here using the simple example that was described in the lecture. The model will predict if a tumor is benign or malignant based on tumor size.  Try the following:\\n\",\n    \"- Click on 'Run Linear Regression' to find the best linear regression model for the given data.\\n\",\n    \"    - Note the resulting linear model does **not** match the data well. \\n\",\n    \"One option to improve the results is to apply a *threshold*. \\n\",\n    \"- Tick the box on the 'Toggle 0.5 threshold' to show the predictions if a threshold is applied.\\n\",\n    \"    - These predictions look good, the predictions match the data\\n\",\n    \"- *Important*: Now, add further 'malignant' data points on the far right, in the large tumor size range (near 10), and re-run linear regression.\\n\",\n    \"    - Now, the model predicts the larger tumor, but data point at x=3 is being incorrectly predicted!\\n\",\n    \"- to clear/renew the plot, rerun the cell containing the plot command.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=800.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"808d426b720d4c2eadf0c89872f7fe84\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"w_in = np.zeros((1))\\n\",\n    \"b_in = 0\\n\",\n    \"plt.close('all') \\n\",\n    \"addpt = plt_one_addpt_onclick( x_train,y_train, w_in, b_in, logistic=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The example above demonstrates that the linear model is insufficient to model categorical data. The model can be extended as described in the following lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you:\\n\",\n    \"- explored categorical data sets and plotting\\n\",\n    \"- determined that linear regression was insufficient for a classification problem.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/C1_W3_Lab02_Sigmoid_function_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Logistic Regression\\n\",\n    \"\\n\",\n    \"In this ungraded lab, you will \\n\",\n    \"- explore the sigmoid function (also known as the logistic function)\\n\",\n    \"- explore logistic regression; which uses the sigmoid function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from plt_one_addpt_onclick import plt_one_addpt_onclick\\n\",\n    \"from lab_utils_common import draw_vthresh\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Sigmoid or Logistic Function\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_LogisticRegression_left.png\\\"     style=\\\" width:300px; padding: 10px; \\\" >As discussed in the lecture videos, for a classification task, we can start by using our linear regression model, $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot  \\\\mathbf{x}^{(i)} + b$, to predict $y$ given $x$. \\n\",\n    \"- However, we would like the predictions of our classification model to be between 0 and 1 since our output variable $y$ is either 0 or 1. \\n\",\n    \"- This can be accomplished by using a \\\"sigmoid function\\\" which maps all input values to values between 0 and 1. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Let's implement the sigmoid function and see this for ourselves.\\n\",\n    \"\\n\",\n    \"## Formula for Sigmoid function\\n\",\n    \"\\n\",\n    \"The formula for a sigmoid function is as follows -  \\n\",\n    \"\\n\",\n    \"$g(z) = \\\\frac{1}{1+e^{-z}}\\\\tag{1}$\\n\",\n    \"\\n\",\n    \"In the case of logistic regression, z (the input to the sigmoid function), is the output of a linear regression model. \\n\",\n    \"- In the case of a single example, $z$ is scalar.\\n\",\n    \"- in the case of multiple examples, $z$ may be a vector consisting of $m$ values, one for each example. \\n\",\n    \"- The implementation of the sigmoid function should cover both of these potential input formats.\\n\",\n    \"Let's implement this in Python.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"NumPy has a function called [`exp()`](https://numpy.org/doc/stable/reference/generated/numpy.exp.html), which offers a convenient way to calculate the exponential ( $e^{z}$) of all elements in the input array (`z`).\\n\",\n    \" \\n\",\n    \"It also works with a single number as an input, as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Input to exp: [1 2 3]\\n\",\n      \"Output of exp: [ 2.72  7.39 20.09]\\n\",\n      \"Input to exp: 1\\n\",\n      \"Output of exp: 2.718281828459045\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Input is an array. \\n\",\n    \"input_array = np.array([1,2,3])\\n\",\n    \"exp_array = np.exp(input_array)\\n\",\n    \"\\n\",\n    \"print(\\\"Input to exp:\\\", input_array)\\n\",\n    \"print(\\\"Output of exp:\\\", exp_array)\\n\",\n    \"\\n\",\n    \"# Input is a single number\\n\",\n    \"input_val = 1  \\n\",\n    \"exp_val = np.exp(input_val)\\n\",\n    \"\\n\",\n    \"print(\\\"Input to exp:\\\", input_val)\\n\",\n    \"print(\\\"Output of exp:\\\", exp_val)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The `sigmoid` function is implemented in python as shown in the cell below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def sigmoid(z):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Compute the sigmoid of z\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        z (ndarray): A scalar, numpy array of any size.\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"        g (ndarray): sigmoid(z), with the same shape as z\\n\",\n    \"         \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    g = 1/(1+np.exp(-z))\\n\",\n    \"   \\n\",\n    \"    return g\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's see what the output of this function is for various value of `z`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Input (z), Output (sigmoid(z))\\n\",\n      \"[[-1.000e+01  4.540e-05]\\n\",\n      \" [-9.000e+00  1.234e-04]\\n\",\n      \" [-8.000e+00  3.354e-04]\\n\",\n      \" [-7.000e+00  9.111e-04]\\n\",\n      \" [-6.000e+00  2.473e-03]\\n\",\n      \" [-5.000e+00  6.693e-03]\\n\",\n      \" [-4.000e+00  1.799e-02]\\n\",\n      \" [-3.000e+00  4.743e-02]\\n\",\n      \" [-2.000e+00  1.192e-01]\\n\",\n      \" [-1.000e+00  2.689e-01]\\n\",\n      \" [ 0.000e+00  5.000e-01]\\n\",\n      \" [ 1.000e+00  7.311e-01]\\n\",\n      \" [ 2.000e+00  8.808e-01]\\n\",\n      \" [ 3.000e+00  9.526e-01]\\n\",\n      \" [ 4.000e+00  9.820e-01]\\n\",\n      \" [ 5.000e+00  9.933e-01]\\n\",\n      \" [ 6.000e+00  9.975e-01]\\n\",\n      \" [ 7.000e+00  9.991e-01]\\n\",\n      \" [ 8.000e+00  9.997e-01]\\n\",\n      \" [ 9.000e+00  9.999e-01]\\n\",\n      \" [ 1.000e+01  1.000e+00]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Generate an array of evenly spaced values between -10 and 10\\n\",\n    \"z_tmp = np.arange(-10,11)\\n\",\n    \"\\n\",\n    \"# Use the function implemented above to get the sigmoid values\\n\",\n    \"y = sigmoid(z_tmp)\\n\",\n    \"\\n\",\n    \"# Code for pretty printing the two arrays next to each other\\n\",\n    \"np.set_printoptions(precision=3) \\n\",\n    \"print(\\\"Input (z), Output (sigmoid(z))\\\")\\n\",\n    \"print(np.c_[z_tmp, y])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The values in the left column are `z`, and the values in the right column are `sigmoid(z)`. As you can see, the input values to the sigmoid range from -10 to 10, and the output values range from 0 to 1. \\n\",\n    \"\\n\",\n    \"Now, let's try to plot this function using the `matplotlib` library.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\",\n      \"image/png\": 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\",\n 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' width=500.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"5044dd9e80614568a1b86dcd2c71d14e\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot z vs sigmoid(z)\\n\",\n    \"fig,ax = plt.subplots(1,1,figsize=(5,3))\\n\",\n    \"ax.plot(z_tmp, y, c=\\\"b\\\")\\n\",\n    \"\\n\",\n    \"ax.set_title(\\\"Sigmoid function\\\")\\n\",\n    \"ax.set_ylabel('sigmoid(z)')\\n\",\n    \"ax.set_xlabel('z')\\n\",\n    \"draw_vthresh(ax,0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"As you can see, the sigmoid function approaches  `0` as `z` goes to large negative values and approaches `1` as `z` goes to large positive values.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Logistic Regression\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_LogisticRegression_right.png\\\"     style=\\\" width:300px; padding: 10px; \\\" > A logistic regression model applies the sigmoid to the familiar linear regression model as shown below:\\n\",\n    \"\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(\\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b ) \\\\tag{2} $$ \\n\",\n    \"\\n\",\n    \"  where\\n\",\n    \"\\n\",\n    \"  $g(z) = \\\\frac{1}{1+e^{-z}}\\\\tag{3}$\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"  \\n\",\n    \"Let's apply logistic regression to the categorical data example of tumor classification.  \\n\",\n    \"First, load the examples and initial values for the parameters.\\n\",\n    \"  \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_train = np.array([0., 1, 2, 3, 4, 5])\\n\",\n    \"y_train = np.array([0,  0, 0, 1, 1, 1])\\n\",\n    \"\\n\",\n    \"w_in = np.zeros((1))\\n\",\n    \"b_in = 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Try the following steps:\\n\",\n    \"- Click on 'Run Logistic Regression' to find the best logistic regression model for the given training data\\n\",\n    \"    - Note the resulting model fits the data quite well.\\n\",\n    \"    - Note, the orange line is '$z$' or $\\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b$  above. It does not match the line in a linear regression model.\\n\",\n    \"Further improve these results by applying a *threshold*. \\n\",\n    \"- Tick the box on the 'Toggle 0.5 threshold' to show the predictions if a threshold is applied.\\n\",\n    \"    - These predictions look good. The predictions match the data\\n\",\n    \"    - Now, add further data points in the large tumor size range (near 10), and re-run logistic regression.\\n\",\n    \"    - unlike the linear regression model, this model continues to make correct predictions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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' width=800.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"7fb097bd9f7b4aadb77f7c1ea69c2864\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close('all') \\n\",\n    \"addpt = plt_one_addpt_onclick( x_train,y_train, w_in, b_in, logistic=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have explored the use of the sigmoid function in logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/C1_W3_Lab03_Decision_Boundary_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f1cc65d6\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Logistic Regression, Decision Boundary\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"86fe6af0\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- Plot the decision boundary for a logistic regression model. This will give you a better sense of what the model is predicting.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"6ee320a5\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_common import plot_data, sigmoid, draw_vthresh\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7867bf8d\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Dataset\\n\",\n    \"\\n\",\n    \"Let's suppose you have following training dataset\\n\",\n    \"- The input variable `X` is a numpy array which has 6 training examples, each with two features\\n\",\n    \"- The output variable `y` is also a numpy array with 6 examples, and `y` is either `0` or `1`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"9baca38b\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])\\n\",\n    \"y = np.array([0, 0, 0, 1, 1, 1]).reshape(-1,1) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"182b8eb5\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Plot data \\n\",\n    \"\\n\",\n    \"Let's use a helper function to plot this data. The data points with label $y=1$ are shown as red crosses, while the data points with label $y=0$ are shown as blue circles. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"89a44af6\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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CAEQIEAGCEAAE6UlcnFRd3XFNc7K4D+hgCBPCnrk5KT5eWLJGKinzXFBW5l6enEyLocwgQwJeW8Dh82D1dUOAdIkVF7vmSu44QQR9DgADttQ+PFm1DpG14tCBE0McEPECampq0aNEiTZo0SVOnTlV2draOHTvms/b111/XzTffrIkTJyo/P1/Nzc0B7hZ9UlmZd3i0KCiQxo71Do8Whw+71wf6AEv2QJYsWaJ9+/apoqJCs2bNUoGPX8aqqiqtXLlSZWVl2r9/v+rq6vTGG28Evln0Pbm5UmGh/+VffOF/WWGhe32gDwh4gPTr108zZ86Uw+GQJN1yyy2qqqryqistLdWcOXMUGxsrh8OhpUuXqqSkJMDdos/Kz+84RHwpLHSvB/QRlo+BvPTSS8rIyPCaX11drYSEhNbpxMREnThxIpCtoa/rTogQHuiDLA2Q1atXq7KyUv/6r//qc3nLXookuVyuQLUFfC8/XxozpuOaMWMID/RJlgXIc889py1btmjTpk0aMGCA1/KEhAQdP368dbq6ulrx8fGBbBFwn23V0ZiH5F7u7zoRoBezJEDWrl2rkpISbd68WdHR0T5r5s6dq61bt+r06dNyuVxav369srOzA9so+jZfp+r64+s6EaCXc9TX1wf02FBNTY1uuOEGJSUladCgQZKkiIgI7dy5U3l5ecrMzNTs2bMlScXFxSosLJTT6VRaWprWrFmj8PDwDl8/MjJSISGWD+3A7roTHm0xFoIr5HQ61djYaHUbXRLwAOlpBAiuWHGx+/Yk/owZ0/Fhrdde41ReGLNTgPBNC7SXkSGlpPheVlgoVVb6PzsrJcW9PtAHECBAeyNGSLt2eYdI28NTvk7xTUlxrzdiRCC6BCxHgAC+tA8RX2MbbUOE8EAfxBgI0JG6Ove9rToa0ygudh+2IjxwFdhpDIQAAYAgYqcA4ZsWAGCEAAEAGCFAAABGCBAAgBECBABghAABABghQAAARq5KgGRlZV2NlwEA2EhYd4r/7d/+zWuey+XSF509cAcA0Ot0K0A2bNigJ5980uvxsr6eKAgA6N26FSA//OEPdfvtt2vkyJEe83fv3n01ewIA2EC37oXlcrnkcDh6sp8rxr2wANhZr7oX1uOPP956yCrYwwMAEDidBkh5ebnuuusunT9/3mN+U1OTnn/++R5rDAAQ3DoNkO3bt8vlcikjI0MnT57UxYsX9cILL2jChAl68cUXA9EjACAIdWkMxOVyacWKFdq6datcLpciIiL00EMPKScnR+Hh4YHos8sYAwFgZ3YaA+n0LKyLFy+quLhY27ZtU3Nzs86dO6e3335bt912WyD6AwAEqU7/VE9NTdWzzz6rRx55RIcOHdIzzzyjnJwcbdy4MRD9AQCCVKd7II8++qhyc3N1zTXXSJJycnI0evRo5ebm6rPPPtOvfvWrHm8SABB8jJ+JXllZqTvvvFN/+ctfrnZPV4QxEAB2ZqcxEOMAkaT6+npFR0dfxXauHAECwM7sFCBX9E0bbOEBAAgc/lQHABghQAAARggQAICRbt3OHV3X1Cx9fFY62yRFhEpjB0uJkVZ3BQBXDwFylVU2SC98Iq0/Iv3toueytDjp/hul7LFSGPt+AGzuik7jDUZWnsb7widSwfvSJWfHdX83XCrNlOIGBqYvAPbRZ07jxfeeOSA9UN55eEjSvjPSbZul09/0eFsA0GMIkKvg/ZPSI3t9LxvR3z0G0t7nDdI9f+zZvgCgJ1kSICtWrND48eMVHR2tQ4cO+awpLy9XXFycpk6d2vpz4cKFAHfaNU/vl9ofB1yWIn16l3RqidTwT9KbP5YSB3nWvFctfXgmUF0CwNVlSYDMmzdPZWVlSkhI6LAuOTlZFRUVrT/9+/cPUIddd6xR2nrMc94TN0n/MU36YbR7+ppQ6a4fSv/9j1JMhGftC58EoksAuPosCZApU6Zo1KhRVrz1Vbf1mOfeR2S49C+TfNfGDZQeHO85792qnuoMAHpWUI+BHD16VGlpaUpPT9e6deusbsenk56Pilf6KGlQBw9pnJvkOf1lk3Tp8lVvCwB6XNBeBzJhwgQdPHhQUVFRqqmp0cKFCzV06FBlZWVZ3RoAQEG8BzJ48GBFRUVJkkaNGqUFCxZo714/pzpZqP21HLtqpK8v+a8vrfKcHtZPCvdxlhYABLugDZBTp07J6XRfVNHY2Kjt27crNTXV4q68zRktOdpMN16SVn7gu7b2vPTsx57z5iX1VGcA0LMsCZBHH31U48aNU21trebPn6+bbrpJkpSXl6dt27ZJkkpLSzV58mRNmTJFM2bM0LRp03T33Xdb0W6HRke6Q6Stp/ZLy3ZJn9W7p7+9LL35qfQ//0s62+72JvffGJA2AeCq41YmV8H7J91XlvvakLH9pXPfShd9DJTPTJC2z+np7gDYCbcy6WOmxEmrJ/tedvqC7/C4drD0xvSe7QsAehIBcpU8NEF6/jYpvAtb9JZYqXy+FDugx9sCgB7DIayrrLJBevG727m3H++4faR0/w3SP3I7dwB+2OkQFgHSQy5elj7+SvqKB0oB6AY7BUjQXkhodxGh0t/FWt0FAPQc6/9UBwDYEgECADBCgAAAjBAgAAAjBAgAwAgBAgAwQoAAAIwQIAAAIwQIAMAIAQIAMEKAAACMECAAACMECADACAECADBCgAAAjBAgAAAjBAgAwAgBAgAwQoAAAIwQIAAAIwQIAMAIAQIAMEKAAACMECAAACMECADASJjVDSDw/nZROnhW+vqSNChcuiFGGhJhdVcA7IYA6UMqTkrPfyK9XSldcn4/PzxEyh4rPXCjNDXOuv4A2Iujvr7eZXUTV1NkZKRCQjgy19a3l6Wf7ZE2HOm89t7rpZfSpGtCe74vAN6cTqcaGxutbqNL2APp5S47pTt3SJu/6Fr9hiNS/UVp00wplBwG0AG+Inq5VR/5Dg+HpJED3f+2984X7vUAoCOWBMiKFSs0fvx4RUdH69ChQ37rXn/9dd18882aOHGi8vPz1dzcHMAu7a+pWVpzwHNe/zDpqVulL++Vaha7/33qVvf8ttYckC5eDlyvAOzHkgCZN2+eysrKlJCQ4LemqqpKK1euVFlZmfbv36+6ujq98cYbAezS/koqpS+bPOdtzpAev1mK6eeejunnnt6c4Vn3ZZO06fPA9AnAniwJkClTpmjUqFEd1pSWlmrOnDmKjY2Vw+HQ0qVLVVJSEqAOe4d32x26Sh8pzfST2TMTpGkjO14fANoK2jGQ6upqjz2UxMREnThxwsKO7OfkN57TP0nquH5uu+Xt1weAtoI2QCTJ4fh+iNfl6lVnGwOA7QVtgCQkJOj48eOt09XV1YqPj7ewI/uJG+A5vaWq4/rSdsvbrw8AbQVtgMydO1dbt27V6dOn5XK5tH79emVnZ1vdlq3MG+M5vatWeq/ad+171dLu2o7XB4C2LAmQRx99VOPGjVNtba3mz5+vm266SZKUl5enbdu2SZKSkpL0xBNPaNasWZo4caKGDx+ue+65x4p2bWvBWGlYP89588ukpz6Uzn53dtZXTe7p+WWedcP6SQuvDUyfAOyJW5n0ck99KP3Ln7znOyTFDZROnpd8fQBW3io9cXNPdwegPTvdyoRv2l5uxURpvo9DUS5JtX7CI2uMez0A6AgB0suFhkgbZ7hvktgV914vvTWD+2AB6ByHsPqQ97+7nXuJj9u5L/judu5TuJ07YCk7HcIiQPqgv12UDp2VGi9JkeHSOB4oBQQNOwUIt3Pvg4ZEsKcB4MrxpzoAwAgBAgAwQoAAAIwQIAAAIwQIAMAIAQIAMEKAAACMECAAACMECADACAECADBCgAAAjBAgAAAjBAgAwAgBAgAwQoAAAIwQIAAAIwQIAMAIAQIAMEKAAACMECAAACMECADACAECADBCgAAAjBAgAAAjBAgAwEiY1Q0AbR1vlCobpIuXpZh+0vgYqR+fUiAo8asJyzU7pbcrpec/kcpPei4bEiEtvV66/0Zp7GBr+oOBujqprEzKzfVfU1wsZWRII0YEri9cVRzCgqVqz0s/+i/ppzu8w0OS/nZRWn1Auv4/pRc/CXx/MFBXJ6WnS0uWSEVFvmuKitzL09Pd9bAlAgSWOf2NlLZZ2nem89pLTun+cumZAz3eFq5ES3gcPuyeLijwDpGiIvd8yV1HiNgWAQLL3PNH6fMG7/kRodKI/r7XeWSv9L6PPRUEgfbh0aJtiLQNjxaEiG1ZEiCff/65Zs6cqUmTJmn69Ok6cuSIV015ebni4uI0derU1p8LFy5Y0C16wgdnpPeqPeclDpL+88dSwz9Jp5ZIn94lLUvxrHFJWvVRgJpE95SVeYdHi4ICaexY7/Bocfiwe33YiiWD6AUFBcrNzVVOTo7effdd5eXlaceOHV51ycnJ2r17d+AbRI9rP54REyH99z9KcQO/n/fDaOk/pknD+0lP7f9+/tZj0rFGaXRkIDpFl+XmSvX1/kPiiy/8r1tY2PGAO4JSwPdAzpw5owMHDujOO++UJM2dO1fHjh3TsWPHAt0KLPRuled0fqpneLT1xM1SZPj3006XO0QQhPLz3WHQHYWF7vVgOwEPkJqaGsXFxSkszL3z43A4FB8frxMnTnjVHj16VGlpaUpPT9e6desC3Sp6yKXL0pdNnvN+Mtp/feQ1Uvooz3knz1/9vnCVdCdECA9bs+QQlsPh8Jh2uVxeNRMmTNDBgwcVFRWlmpoaLVy4UEOHDlVWVlag2gRgKj/fPWDe0WGrMWMID5sL+B7IqFGjVFtbq+bmZknu8KipqVF8fLxH3eDBgxUVFdW6zoIFC7R3795At4seEB4qDevnOW9LB4ekGr+VdtV4zvN3uAtBorPwkNzL/V0nAlsIeIAMHz5c48eP18aNGyVJpaWlSkxM1OjRnscwTp06JafTKUlqbGzU9u3blZqaGuh20UPmJXlOF/3VfVGhL099KDVe+n46xCHN6eCQFyzm61Rdf3xdJwLbsOQ03sLCQr322muaNGmSnnnmGT333HOSpLy8PG3btk2SO1gmT56sKVOmaMaMGZo2bZruvvtuK9pFD7j/Rs/psxfdV6T/52fSt5fd8z6rl5bt8jwDS3KHB2dgBanuhEcLQsS2HPX19d4DEDYWGRmpkBCuj7SDWVu9rwWR3BcSRl0jnfZx2Y9DUvl8aUpcT3eHbisudt+exJ8xYzo+rPXaa5zKK8npdKqxsdHqNrqEb1pY5o3p0rU+bpB48bLv8JCk1ZMJj6CVkSGlpPheVlgoVVb6PzsrJcW9PmyFAIFlYge49yZuie28NjxEeuE26aEJPd4WTI0YIe3a5R0ibU/V9XWKb0qKez3uyms7HMKC5Zqd0n99dzv3Pe3ucxXz3e3cf87t3O2j7T2x/F3n0TJWQnh4sdMhLAIEQaXtA6WG9pPGD3WPicBmeB6IMQLEQgQIADuzU4DwTQsAMEKAAACMECAAACMECADACAECADBCgAAAjBAgAAAjBAgAwAgBAgAwQoAAAIwQIAAAIwQIAMAIAQIAMEKAAACMECAAACMECADACAECADBCgAAAjBAgAAAjBAgAwAgBAgAwQoAAAIwQIAAAIwQIAMAIAQIAMEKAAACMECAAACMECADACAECADBCgAAAjFgSIJ9//rlmzpypSZMmafr06Tpy5IjPutdff10333yzJk6cqPz8fDU3Nwe4UwCAP476+npXoN/0Jz/5iX76058qJydH7777rtauXasdO3Z41FRVVSkjI0N79uzR8OHDddddd2nWrFm69957O3ztgQMHKiSEHSsA9uR0OnX+/Hmr2+iSgAfImTNnNGnSJFVWViosLEwul0vJycnasWOHRo8e3Vr37LPP6vjx4/rd734nSXrvvfdUVFSkP/zhD4FsFwDgR8D/VK+pqVFcXJzCwsIkSQ6HQ/Hx8Tpx4oRHXXV1tRISElqnExMTvWoAANax5FiPw+HwmHa5fO8Eta3zVwMAsEbAA2TUqFGqra1tHRB3uVyqqalRfHy8R11CQoKOHz/eOl1dXe1VAwCwTsADZPjw4Ro/frw2btwoSSotLVViYqLH+IckzZ07V1u3btXp06flcrm0fv16ZWdnB7pdAIAflpyF9dlnn+n+++/X2bNnFRkZqRdffFEpKSnKy8tTZmamZs+eLUkqLi5WYWGhnE6n0tLStGbNGoWHhwe6XQCAD5YECADA/mx1wUQwX4DYld7Ky8sVFxenqVOntv5cuHChR/tasWKFxo8fr+joaB06dMhvnRXbrCu9WbHNmpqatGjRIk2aNElTp05Vdna2jh075rM20Nutq71Zsd2ysrI0efJkTZ06VZmZmfrrX//qs86Kz1pXerNim7X17//+7x3+LgTjhdW2CpCCggLl5ubqgw8+UH5+vvLy8rxqqqqqtHLlSpWVlWn//v2qq6vTG2+8ERS9SVJycrIqKipaf/r379+jfc2bN09lZWUep0S3Z9U260pvUuC3mSQtWbJE+/btU0VFhWbNmqWCggKvGqu2W1d6kwK/3TZs2KC9e/eqoqJCDzzwgH7xi1941Vi1zbrSm2TNZ02SPvroI+3bt8/viUJWbbfO2CZAzpw5owMHDujOO++U5B5kP3bsmNdfX6WlpZozZ45iY2PlcDi0dOlSlZSUBEVvVpgyZYpGjRrVYY0V26yrvVmhX79+mjlzZutp5Lfccouqqqq86qzYbl3tzQrR0dGt/93Q0ODzjhBWfda60ptVLl68qMcee0y/+93vvC5xaGHVdutMmNUNdFVHFyC2PYPLigsQu9qbJB09elRpaWkKDQ1VTk6Oli1b1qO9dUWwX7Rp9TZ76aWXlJGR4TU/GLabv94ka7bb8uXLVVFRIUk+v+Cs3Gad9SZZs81WrlypO+64Q0lJSX5rguGz5ottAkQK7gsQu9LbhAkTdPDgQUVFRammpkYLFy7U0KFDlZWVFZAeOxKsF21avc1Wr16tyspKPfPMMz6XW7ndOurNqu328ssvS5LefPNN/frXv9amTZu8aqzaZp31ZsU2+/Of/6wPP/xQv/nNbzqtDcbf0eDZj+tEMF+A2NXeBg8erKioqNZ1FixYoL179/Zob10RzBdtWrnNnnvuOW3ZskWbNm3SgAEDvJZbud06683qz9qiRYtUXl6us2fPeswPhs+av96s2Gbvv/++PvvsM6Wmpmr8+PGqra1Vdna2181lg2G7+WKbAAnmCxC72tupU6fkdDolSY2Njdq+fbtSU1N7tLeuCOaLNq3aZmvXrlVJSYk2b97scfy8Lau2W1d6C/R2a2ho0MmTJ1unt2zZopiYGA0ZMsSjzopt1tXerPisPfTQQzpy5Ig+/vhjffzxxxo5cqTefvttzZgxw6MuWH9HbXUdSDBfgNiV3l555RWtX79eoaGhunz5subNm6fHH3/c78DZ1fDoo49q27Ztqqur09ChQzVw4EDt378/KLZZV3qzYpvV1NTohhtuUFJSkgYNGiRJioiI0M6dOy3fbl3tLdDb7cSJE1q8eLGamprkcDg0bNgwPfnkk0pNTbV8m3W1Nys+a+21/CE6btw4y7dbV9gqQAAAwcM2h7AAAMGFAAEAGCFAAABGCBAAgBECBABghAABABghQAAARggQAIARAgTw45VXXtF1113X+lChxsZGTZ48WStWrLC4MyA4ECCAH7m5uQoPD1dxcbGcTqeWLVum+Ph4PfXUU1a3BgQFW93OHQikiIgIPfzww1q9erU+//xznThxQmVlZQoNDbW6NSAocC8soAPffvutxo0bp9DQUO3cudPjFtobN27UunXrJEm/+tWvdPvtt1vVJmAJ9kCADmzatEnnzp3TkCFDNHTo0Nb5586dU2Fhof74xz/qwoUL+od/+AdVVFSwd4I+hTEQwI+Kigr98z//s0pKStSvXz+9+uqrrcs++OAD3Xrrrerfv79iYmIUHx+vo0ePWtgtEHgECOBDZWWlFi9erFWrVun222/XQw89pGeffVbffPONJOmrr77yeJhTdHS0vvrqK4u6BaxBgADt1NfX64477lBubq4WLVokScrJyVF4eHjrXkhMTIzq6+tb1zl37pxiYmKsaBewDIPogIH6+nplZmZq165dunDhgmbPnq3y8nKFhTGsiL6DTztgIDo6Wg8++KDmzJkjh8OhlStXEh7oc9gDAQAYYQwEAGCEAAEAGCFAAABGCBAAgBECBABghAABABghQAAARggQAIARAgQAYIQAAQAYIUAAAEb+P2Wz/rU+Z9N0AAAAAElFTkSuQmCC\",\n 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' width=400.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"3437ff673e5f43ac88942427c0311ba8\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig,ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"plot_data(X, y, ax)\\n\",\n    \"\\n\",\n    \"ax.axis([0, 4, 0, 3.5])\\n\",\n    \"ax.set_ylabel('$x_1$')\\n\",\n    \"ax.set_xlabel('$x_0$')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bdfd265a\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Logistic regression model\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* Suppose you'd like to train a logistic regression model on this data which has the form   \\n\",\n    \"\\n\",\n    \"  $f(x) = g(w_0x_0+w_1x_1 + b)$\\n\",\n    \"  \\n\",\n    \"  where $g(z) = \\\\frac{1}{1+e^{-z}}$, which is the sigmoid function\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* Let's say that you trained the model and get the parameters as $b = -3, w_0 = 1, w_1 = 1$. That is,\\n\",\n    \"\\n\",\n    \"  $f(x) = g(x_0+x_1-3)$\\n\",\n    \"\\n\",\n    \"  (You'll learn how to fit these parameters to the data further in the course)\\n\",\n    \"  \\n\",\n    \"  \\n\",\n    \"Let's try to understand what this trained model is predicting by plotting its decision boundary\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3251639a\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Refresher on logistic regression and decision boundary\\n\",\n    \"\\n\",\n    \"* Recall that for logistic regression, the model is represented as \\n\",\n    \"\\n\",\n    \"  $$f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(\\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b) \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"  where $g(z)$ is known as the sigmoid function and it maps all input values to values between 0 and 1:\\n\",\n    \"\\n\",\n    \"  $g(z) = \\\\frac{1}{1+e^{-z}}\\\\tag{2}$\\n\",\n    \"  and $\\\\mathbf{w} \\\\cdot \\\\mathbf{x}$ is the vector dot product:\\n\",\n    \"  \\n\",\n    \"  $$\\\\mathbf{w} \\\\cdot \\\\mathbf{x} = w_0 x_0 + w_1 x_1$$\\n\",\n    \"  \\n\",\n    \"  \\n\",\n    \" * We interpret the output of the model ($f_{\\\\mathbf{w},b}(x)$) as the probability that $y=1$ given $\\\\mathbf{x}$ and parameterized by $\\\\mathbf{w}$ and $b$.\\n\",\n    \"* Therefore, to get a final prediction ($y=0$ or $y=1$) from the logistic regression model, we can use the following heuristic -\\n\",\n    \"\\n\",\n    \"  if $f_{\\\\mathbf{w},b}(x) >= 0.5$, predict $y=1$\\n\",\n    \"  \\n\",\n    \"  if $f_{\\\\mathbf{w},b}(x) < 0.5$, predict $y=0$\\n\",\n    \"  \\n\",\n    \"  \\n\",\n    \"* Let's plot the sigmoid function to see where $g(z) >= 0.5$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"a73f3496\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\",\n      \"image/png\": 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\",\n 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' width=500.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"5f47f35b1ff241eeb51b1b0e37c9363c\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot sigmoid(z) over a range of values from -10 to 10\\n\",\n    \"z = np.arange(-10,11)\\n\",\n    \"\\n\",\n    \"fig,ax = plt.subplots(1,1,figsize=(5,3))\\n\",\n    \"# Plot z vs sigmoid(z)\\n\",\n    \"ax.plot(z, sigmoid(z), c=\\\"b\\\")\\n\",\n    \"\\n\",\n    \"ax.set_title(\\\"Sigmoid function\\\")\\n\",\n    \"ax.set_ylabel('sigmoid(z)')\\n\",\n    \"ax.set_xlabel('z')\\n\",\n    \"draw_vthresh(ax,0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7b5bea62\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"* As you can see, $g(z) >= 0.5$ for $z >=0$\\n\",\n    \"\\n\",\n    \"* For a logistic regression model, $z = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$. Therefore,\\n\",\n    \"\\n\",\n    \"  if $\\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b >= 0$, the model predicts $y=1$\\n\",\n    \"  \\n\",\n    \"  if $\\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b < 0$, the model predicts $y=0$\\n\",\n    \"  \\n\",\n    \"  \\n\",\n    \"  \\n\",\n    \"### Plotting decision boundary\\n\",\n    \"\\n\",\n    \"Now, let's go back to our example to understand how the logistic regression model is making predictions.\\n\",\n    \"\\n\",\n    \"* Our logistic regression model has the form\\n\",\n    \"\\n\",\n    \"  $f(\\\\mathbf{x}) = g(-3 + x_0+x_1)$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* From what you've learnt above, you can see that this model predicts $y=1$ if $-3 + x_0+x_1 >= 0$\\n\",\n    \"\\n\",\n    \"Let's see what this looks like graphically. We'll start by plotting $-3 + x_0+x_1 = 0$, which is equivalent to $x_1 = 3 - x_0$.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"f3c4a990\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=500.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"0ee7dd8594764f6691466cd9a114ecc9\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Choose values between 0 and 6\\n\",\n    \"x0 = np.arange(0,6)\\n\",\n    \"\\n\",\n    \"x1 = 3 - x0\\n\",\n    \"fig,ax = plt.subplots(1,1,figsize=(5,4))\\n\",\n    \"# Plot the decision boundary\\n\",\n    \"ax.plot(x0,x1, c=\\\"b\\\")\\n\",\n    \"ax.axis([0, 4, 0, 3.5])\\n\",\n    \"\\n\",\n    \"# Fill the region below the line\\n\",\n    \"ax.fill_between(x0,x1, alpha=0.2)\\n\",\n    \"\\n\",\n    \"# Plot the original data\\n\",\n    \"plot_data(X,y,ax)\\n\",\n    \"ax.set_ylabel(r'$x_1$')\\n\",\n    \"ax.set_xlabel(r'$x_0$')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dbb5b861\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"* In the plot above, the blue line represents the line $x_0 + x_1 - 3 = 0$ and it should intersect the x1 axis at 3 (if we set $x_1$ = 3, $x_0$ = 0) and the x0 axis at 3 (if we set $x_1$ = 0, $x_0$ = 3). \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* The shaded region represents $-3 + x_0+x_1 < 0$. The region above the line is $-3 + x_0+x_1 > 0$.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* Any point in the shaded region (under the line) is classified as $y=0$.  Any point on or above the line is classified as $y=1$. This line is known as the \\\"decision boundary\\\".\\n\",\n    \"\\n\",\n    \"As we've seen in the lectures, by using higher order polynomial terms (eg: $f(x) = g( x_0^2 + x_1 -1)$, we can come up with more complex non-linear boundaries.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cfde5655\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have explored the decision boundary in the context of logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"40e0932c\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"a2ef86f2\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/C1_W3_Lab04_LogisticLoss_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Logistic Regression, Logistic Loss\\n\",\n    \"\\n\",\n    \"In this ungraded lab, you will:\\n\",\n    \"- explore the reason the squared error loss is not appropriate for logistic regression\\n\",\n    \"- explore the logistic loss function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from plt_logistic_loss import  plt_logistic_cost, plt_two_logistic_loss_curves, plt_simple_example\\n\",\n    \"from plt_logistic_loss import soup_bowl, plt_logistic_squared_error\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Squared error for logistic regression?\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_SqErrorVsLogistic.png\\\"     style=\\\" width:400px; padding: 10px; \\\" > Recall for **Linear** Regression we have used the **squared error cost function**:\\n\",\n    \"The equation for the squared error cost with one variable is:\\n\",\n    \"  $$J(w,b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})^2 \\\\tag{1}$$ \\n\",\n    \" \\n\",\n    \"where \\n\",\n    \"  $$f_{w,b}(x^{(i)}) = wx^{(i)} + b \\\\tag{2}$$\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Recall, the squared error cost had the nice property that following the derivative of the cost leads to the minimum.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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R284Q1v4PLLL5/TSmchCILAD37wAz75yU/y8Y9/nKmpKVauXMl73vMe3v72twOta2DLli27dV5OvwYW89qF2SPwVFXdr+GqOx9TVdUZ0VoTExMMDg7u9Xz8/Oc/54ILLuC1r30t73vf++Lr9t3vfves45/rtdfmm9/8Jv39/UxOTnL99dfzta99jRNPPJE3vvGN8xrn2NgYRx555C5/3929u7tnxLnnnjvr9m2fxXOf+1xuueUWbrzxRt70pjcBrRXB1VdfzTHHHEM+n+fHP/4xn/nMZ3j/+99PvV7nyCOP5LLLLtvtvud6b7XZ23e7EA6YgOzMkiVLeMtb3sJll13G5s2bOfHEE+nt7WV8fHyXbXd+rb203Dlao31RtPnxj3+MJEl8+9vfRlXV+PVmszlrpMbONs+2PfWOO+6Y1Wnf/kJ6e3upVCp4njdDRGb7LPvKniI+dv5be3zj4+MzbhLf95mamtrFjj7XaJLnP//53HLLLdx99927vbinj6FcLuO67ozvoP1QbY8hk8nwsY99jI997GNs376dH/7wh3z84x9HVVU+/vGPz2lc09F1Hdd1d3l955nfqlWruPHGG4miiIcffpivfvWr/Mu//AsrVqzgjDPOoFgs0t3dPcP0MJ22P2+u1+5TnWKxyMqVK2cNIgBYsWIFALfffjurV6/mq1/96oy/73z+28w3kumoo46Ko7BOP/10TjvtNK644gpe8YpXkE6n5zzO3t7eWASms7vvbXfPiK985SuzClEmk4n/f+6553LuuefSaDS47777uPLKK3nNa17DY489hiiKsa/P933+/Oc/87nPfY63vvWt3HfffRx11FG77Huu99b+5ICYsHa3HNu4cSPwN7V/1rOexd13302z2Zzx3j/84Q8z3rd8+XKglYvQxvM8/vd//3fGdqZpIknSDLHYsGHDHh1T03nhC1+IKIoMDg7yzGc+c5eftm37Wc96FkEQ8KMf/WjG+2+//fY5HWd/cfLJJ6NpGt///vdnvH777bfj+368GpwvL3/5y1m7di1XXnklk5OTu/zddd04QfS0004jDEN+8IMfzNjmu9/97m5Dl1esWMG73/1ujjrqKB5//HGAeKIw28pxNpYvXx6/t829995LvV6fdXtBEDjuuONi52r7vS9+8YvZsGEDy5Ytm/UayGazwNyv3ac6L37xixkeHiadTs96Pto2d9M0dwkB/+Uvfzkn08x80TSNT3ziE0xMTHDTTTfNa5wnn3wyDzzwwAxTjmVZ3HXXXXM69rOe9Syy2SxbtmyZ9TjToznbZDIZzjzzTP7pn/6J0dHRXSa+sixz8skn86//+q+xs302FnJvLTYHZAVy6qmn8tznPpdzzjmHlStXUq/Xufvuu/n617/Oq171qlgQ3v/+9/ODH/yA8847j3e/+914nscnP/nJXZaTJ5xwAqtXr+Y973kPH/7whwnDkK997Wu7zDjPOOMMvvKVr3DRRRfx5je/mcHBQa699tr4eHtj9erVvPe97+WDH/wgmzZt4rTTTkPXdYaGhvjVr37FW9/6Vk499VRe+MIX8pznPIf3ve99lEqlOApr5wfYnnBdd5fwYmjZY3eOFpsrHR0dXHLJJXzuc58jlUrx0pe+lPXr13P11VfznOc8Zxeb6FxRFIVvfOMbvOpVr+L000/nne98Z5xI+Ne//pWbbrqJU089lZe85CWcccYZPOc5z+HSSy9lcnKSI488krvuuotvfOMbXHrppfGNfMYZZ3DWWWdx1FFHkU6n+e1vf8sjjzwSmyR6enooFovcfvvtHH300aTTaVauXLnbWdZ5553HzTffzAc+8AHOOeccNm3axA033EAul4u3eeSRR7jssss477zzOOSQQwiCgNtuuw1Zljn99NMBeNe73sUdd9zBWWedxbve9S7Wrl2LaZps3LiR3//+93Eo8Vyv3QPJL37xi13Ml/l8nhe84AUL3ufrXvc6vvWtb3HuuedyySWXcOyxx+K6Llu3buVnP/sZ//Vf/xWHcf/kJz/hgx/8IGeffTbr1q3juuuuY+nSpfv4qWbnZS97WRwEc+GFF855nJdcckkc/PKhD30IVVW5/vrr0TRtTvkkuVyOT3ziE7z//e+nVCrxkpe8hFwux44dO7jvvvt4wQtewHnnncfVV1/NxMQEz3ve8+jr62NkZIQbb7yRY489lq6uLn7+859z8803c/bZZ7Ny5UpM0+TGG28km83uVgjmem/tTw6IgFx55ZXcddddXHPNNUxMTCBJEmvWrOHKK6+cEat/+OGH893vfpfLL7+ct73tbSxZsoT3vve9/PGPf2T79u1/G7Qsc9ttt/H+97+fCy+8kI6ODt75zndyyimncNttt8XbvfCFL+S6667jS1/6Ej/5yU9Ys2YNV111FXfdddeM/e2JK664gsMOO4yvfe1rfO1rX0MQBPr7+3n+858/I4z01ltv5UMf+hCf+MQnEEWRs846i09/+tN7jKKYzuTk5KxhsUceeST333//nPYxG5dffjmdnZ3853/+JzfddBPFYpE3vOENXHHFFfuUcHXUUUdx33338aUvfYlbb72Va665BkEQWLNmDa9//et55zvfCbTyfL7zne9w1VVX8f/+3/9jamqKFStWcPXVV8+ILjv11FO54447+MIXvoDv+6xatYprrrlmxn6++MUvctVVV/HKV74S3/dnRJ/szOmnn87nP//5eHzHHXccX/3qV3nzm98cb9Pb28uyZcu4/vrrGRkZQdM0jjrqKL7zne/E2e75fJ677rqLa6+9li984Qvs2LGDfD7PoYceyitf+cp4X3O9dg8kH/zgB3d57ZhjjuG+++5b8D4VReH222/n85//PLfccgsDAwOkUikOOeQQXvrSl8Ym3Le85S2MjIzwzW9+k2984xscddRRfPWrX+XTn/70go+9Nz760Y9y3nnn8fWvf51LLrlkTuPs7Ozkhz/8IZdddhnvfOc7KRaLvPWtb6VUKsUO8L3x1re+lf7+fr74xS/yve99D8/zWLJkCaeeemo8+TvppJO48cYb+chHPkK5XKa7u5sXvvCF/Ou//ivQCpgwDIPPfOYzjI2NkclkOOGEE7jjjjtmOPinM9d7a38iVCqVaO+bPblcfPHFbN++nTvvvPPJHkpCQsLTnCAIOP3003nmM585IzQ3YVeeNCd6QkJCwsHAv/3bv3HIIYewfPlypqamuPXWW9mwYQP//u///mQP7aAnEZCEhIS/e6699lpGR0eRJInjjz+e22+/PU5eTdg9TwkTVkJCQkLCwUfSUCohISEhYUEkApKQkJCQsCASAUlISEhIWBCJgCQkJCQkLIinVRRWPp9/soeQkJCQsCgsdkO6/UGyAklISEhIWBCJgCQkJCQkLIhEQBISEhISFkQiIAkJCQkJCyIRkISEhISEBZEISEJCQkLCgkgEJCEhISFhQSQCkpCQkJCwIBIBSUhISEhYEImAJCQkJCQsiERAEhISEhIWRCIgCQkJCQkLIhGQhISEhIQFkQhIQkJCQsKCSAQkISEhIWFBJAKSkJCQkLAgEgFJSEhISFgQiYAkJCQkJCyIREASEhISEhZEIiAJCQkJCQsiEZCEhISEhAWRCEhCQkJCwoJIBCQhISEhYUEkApKQkJCQsCASAUlISEhIWBCJgCQkJCQkLIhEQBISEhISFkQiIAkJCQkJCyIRkISEhISEBZEISEJCQkLCgkgEJCEhISFhQSQCkpCQkJCwIBIBSUhISEhYEImAJCQkJCQsiERAEhISEhIWRCIgCQkJCQkLIhGQhISEhIQFkQhIQkJCQsKCSAQkISEhIWFBJAKSkJCQkLAgEgFJSEhISFgQiYAkJCQkJCyIREASEhISEhZEIiAJCQkJCQsiEZCEhISEhAWRCEhCQkJCwoJIBCQhISEhYUEkApKQkJCQsCASAUlISEhIWBCJgCQkHMT4vo/v+0/2MBISZiURkISEg5itW7eybdu2PW4TBAGmaWLb9i5/8zyPRqOxn0aX8PeO/GQPICHh6UgURURRhCAICIKwz/up1+u4rjvrTxAE8faGYdDR0UGxWETXdSqVCgMDA5x00kmL8bESEmaQCEhCwn6gVquxceNGjj32WDRN2+12QRDMEAPHcXYRiPb+ACRJQlVVVFUlk8mgaVr8u+/7TE1NMTo6ysjICIZh7PHYCQn7SiIgCQn7Ec/z8DxvF1FoC8X01QMQi0FbICqVCpIksWrVKlRVJQgCJElCkqRZj1coFAjDkGq1ytTUFJVKBYDHHnuMYrFIR0dHIioJi4ZQqVSiJ3sQi0U+n3+yh5DwFCcIAh566CFWr15NR0fHXredzaTkOA62be/i/J6+etj5R9M0FEXZxdy1YcMGRFFk7dq1RFHE+Pg4YRii6zr3nn8Nz/vWR/A8j56enlnHODo6ytDQEIVCgWq1ShRFpNPpWExUVY23HR8fJ4oient7F3j2EhaTarX6ZA9hryQrkISEnQjDkDAM8TxvVpNS+2dngVAUJTYpKYpCuVxm5cqVpNNpVFVFlvftdjNNk2azSbPZ5N5LPo+AwPj4OOl0Gs/zUBRll/eIooggCKxdu5YgCKhUKpTLZYaGhhgcHCSTycRiUqvVEgFJmBeJgCQ8LQiCgMcff5xly5ZRKBT2un0YhrP6HRzHAVrRT9MRRTFeLaTT6Xj23v5RFAVR/FtQY7VapVwuk8vl9tlkJAgCURTF0VStfyMiWs51RVGwbXtWAWk78qG1Aurs7KSzsxPf92MxGRwcZPv27UiShKIouxWjhISdSQQk4WmDbdsEQUAURfi+v0fH9Gyrh7YQAHR2ds4QCUmS9imaal8xTZMgCBAEgZ+88v2Iy3rxh0c53nHQdR3Hcchms3PenyzLdHV10dXVhe/7lMtlhoeHsW2bhx56iFwuR0dHBx0dHfu8ckp4+pJcGQlPKqZpMjw8zMqVK2fY4/fE9NXDziuHoaEhtm3bRhT9zbU3ffWQSqUoFAq7+B7aq4cgCPjzn/9MLpeb00rmQNAO452amuI3/7/P4BISDA4D8PuPfJmu/7iSdDpNGIYzVkFzRZZluru7KZfLAHR0dDA1NcXAwADbt28nm81SLBYpFAqxmGzcuBFFUVi1atWifc6Epx6JgCQ8qfi+T7VaJQxDgF1WD7P9eJ43Yx+KosQrB8MwyOfzMwTiyV497AtRFGGaJqlUiiiKsH2P4qHLGf/reiRDR0CgVqthGAaO42AYxi7vnw+SJNHd3U13dzee51Eul5mammLbtm0IgkAul6NYLOL7/m4jwRL+fkgEJGFBTE5OAtDV1TWv9+3snG7b9bdt2xYLR1tMoGX/b4vBzuLQ/hFFkTAMefDBB2Mb/8HCvgpXGIaYpgnA/33giwDIooimQvHwVUDroS/LMrZt7yIg+zIGRVHo6emhp6cH13VjMWn7hzzPY2pqinw+n4jJ3ymJgCQsiEqlQhRFMwQkiqK9JsbtvHpoP3jas9vpiXHtyKX5PADnO+PemYNtpWKaZrwqi4V1xw5UWUEWWufu4Y/cSN/XPxab8RbKns6dqqr09vbS29uL4zisX7+eIAjYsmULoiiSz+cpFovk8/ldzGjtSYGu6/s0voSDj0RA/o5wHIdGo0GxWFzQg7K9epheQmPbtm0zBGJ3qwdd12cVCNM0WbduHStWrJh19jxXDrYH/2LQrnElyzL3XvxJogj8MMSLQvKZHPWh7RRWrCYkJIqi2Dc03Zc0X0Gdy3ls+41yuRxLlixhamqKqakpNm/ejCiKFAoFisUiuVwOURTZsWMHjUaDo48+et7nIOHgJhGQvyOazSZbt26lUCjManLYne+hvYrYefUgCAKmaaKqKrlcbpfEuPmuHhJmUq/XcRyHWq3G+JbHCaMIx/MBgewRy6g8thEv9Gls34JlWaRSKWzb3iUYYa7fwXzEpr2tpmksWbKEJUuWYNs2U1NTlMtlNm3ahCRJFAqFXa6bhKcPiYAc5EyfVS4kwqZN2wwCMDU1NatYTC+rIQhCnBjXXj1MF4iRkRF83+fwww/f58/YHl/C3/B9H9M0cRyHgS/eSmrNMhqbBpEFkfSaZXiBjypKCGMjuMB977+aZ13zAWRZJpfLLfi48xH8nbfVdZ2lS5eydOlSLMuKxaRdJXjbtm10dHSQy+WSicXThERADnKazSbr16/n6KOP3qOJp+172FPmdJuBgYG4rIamaWSz2V0c07OV1ZjOYj0AFvtB8nQRonbxxCiK8MMACZnAdDD0FGEUEiKQllQi2yRzzFq8wI8nCb7vx+G2++t87G2/hmHQ39/P0qVL2bx5M81mk3q9zuTkJLIsxzkm2Wx2xjXQjsrL5XJJMuNTgERADnLaN1d7hbA7kdhbUT5N03Bdlx07dnDMMcfss0OznR19sPB0mtG6rhvP2ss3/DcCrc+WyUjkDj2EMbOGJkgUiiq1SitCK4yieIJh2zaZTCbe33xMWPuyAtndNpIkoWkahx9+OKZpxtFcExMTsZgUi0UymQyu67J161aOOOKIRECeAiQCsh8ol8sMDAxw3HHHzcnstKeifO3Imo0bN8bb71zSey5F+YC4Muu+mMKmsxgC0h7nwSRGTzbtInqWZRFGQAT+6BC6JOHtGETK5YnCECGMIKVhWjaaKLF+/XqWLl2KIAgzBGR/sBB/iSAIpNNp0uk0/f39NJvNGWKiKMq8sukTnnwSAdkLURTN6PQWRRG6ru/xIdw2JbSbAc1Wznu6SOxp9aBpGqVSiWXLlsV+iIWWlljMWfrTacY/G0+GoEVRRK1Wo1wuU6/Xeez/uwzL86gbWSTHJb9mNbWBIaQgh1QZxZcVvAianoNhZCh/5TsIl7yBkZERqtUq3d3du1xbezv+fNiX1Upb5DKZDMuWLaPRaFAulymVSgBs3ryZYrFIsVgklUo97a+3pyqJgMyB6f4DYEZxvCAIdqna2k6Oe/TRR/E8b7dlNWYryqeq6oybxbIsSqUSmUyGVCq1T59jsWf7i/mQPRjHtNi0JxPta2W2f0ulEvV6nWq1it+dJxyZIFOrIK5YgidGSJJMIZfHbFTQ1i4j5TiEW4eQnCYmAscvX47v+1iWFSf8iaJIpVKJw2r3xP6M2NrTMbPZLNlslo6ODtavX08mk6FUKjE2NoamabGZyzCMGWNsNBrYtj3vhNaExeGgF5B77rmHT3ziE4RhiO/7vPvd7+Yf//EfmZiY4J3vfCdbt25F0zQ+97nPceaZZwKt5Ku3v/3tPPDAA4iiyKc+9SnOO++8BY+hXbF0/fr1dHV1cfTRR8eritmK8rVDZPP5PIZhzBCHdlmNudqb94eJZzFNTwfLfhZ7XwshiqJ4EtF2gg8ODs6IeJt+7mVZjleZqVQK13VJp9OtniT/9hlCI03THUFTNVTDwAtC0rKCPLwVVZap2ja6LKM9sSLVVi2jWq1SLBbp6ekhnU6zbds2Go0GmzZt2sXfsPP5OpArkL1tt3TpUnRdj2uATUxMMDo6iqZp8crEMAwqlQpTU1OJgDxJHNQCEkUR//zP/8yPf/xjjjnmGAYGBnjWs57Fy1/+cq688kpOOukkvv/97/Pggw/ylre8ha1btyLLMp/97GfRNI1NmzaxdetWnvOc5/DCF75wrw2CZuOcc87hpz/9KYVCgd7eXl796ldz5JFHxkX5pifGtYvyVatVNm7cyJIlS3aJyfd9P16mz6fvwsH00G9zsIna/mbnMiw7/7vzShVaDm1d10mlUvG10v53ei5OEASMj49jGAbrr/wU7dOhaQHGyhXUI48wiug44hD8zY8grzqEcqVCXtMRRBOH1up0+5e/yvJrP4HjOHR0dJBOp7Ftm0MPPZSpqSlKpRITExOoqkqxWKSzs3NGdN/+WoHMd7/tXvK5XI5cLseKFStiMRkfH2fHjh3ouo4kSU+Ja+fpykEtIG3aTsV6vU6xWETTNH7wgx/w0EMPAXDCCSfQ09PDfffdxwte8AK+853vcPPNNwOwevVqTj/9dH74wx/yT//0T/M+9le/+lWCIJgRW59Op/foh9h51RAEAY7j8KNXfYi6Z3PiZy9GUZRYgPbEYq5A9se+Dpb9LAZhGMZBC+0kvp0j3qbTLgGvadou/ckty2LLli0ceuihc6oy3G7m5Ps+tu8jCiLWyBbUtIFc2YGQ70aVBMLRVukQyw+QRbFlEpVlIqX1nUpC67XpvjtRFEmlUqRSKfr7+2k0GrGQjI6OYhgGnZ2d+20FslCH+3Ta5VLy+TxhGFKr1eI8kyiKePTRR+PGWEnJlAPHQS0ggiBw88038+Y3v5lUKkW1WuXWW2+l0WgQhuGMZeuKFSvYvn07ANu3b2flypXx31atWhX/bb4sXbo0vrnnM274280wOTnJ2NgYh33yrfzx0uuBvxWi6+vrm1O+xcEmIIu5n8VkT2NqJ2XubgUxPWO6VCrN6DA4PVem/dqe/AnzqUvlui6WZeG6Luuv/TcqA1tp5nsxfY9lq1fRHNpBSlHwwwgvipB0DTcIyKga/ug20uk0U6aJ6rnkdT0uqmhZ1i7Hmu5vWLFiRdw7fXh4OK5lZhjGXvuAPJli0y6XUigUGBgYoFqtYhgGO3bsYHh4mFQqFZvqdp6g1Wo1fN+nWCzOa/wJs3NQC4jv+3zuc5/jtttu49nPfjYPPvgg559/Pr/97W/3ar+d/vfFftDNxSk4fbtUKjWjRtTv3/cljvjYm2NH+p4yh/fHDP1gWoG0WazvyPM8qtXqrCKxs79qtjpdiqKwceNGVq5cSXd396KMaW9Uq1WazSalUomKZeNHIVJpGEWUcAKfgJC8qmMOb8QTBFjRj7htkHT/WmphiLRqGcEj6wgiqA1uZPN1n+Koj7SKK06/7nZGFMU4oS8IAh5++GEEQYj7gLQLJBYKhVnFcj6iMN/Q8flcX7Isc8ghhxAEQdwJsi0m7UCVYrGIqqqUSiVs204EZJE4qAXk4YcfZnR0lGc/+9lAy1TV19fHI488ArRm9u1VyODgICtWrABaq5Ft27bFD4CBgQFe9rKXHbBx7ywghmGQzWapVCrUt27BDQP++K9f5dRrL44d7btLmjoYzU7tfR3oMe2cSLnzv1EUMTo6yujo6C6lWHbuEbJzC9o2uzOh7C+azSae56GqKkM3fiV+XRYlOlYso1xv0iHJSOMDqKKI3ddNFATosowyMYgoCFRNC1WSUKd2EEkCFctiYmKCjo4OPM+b02eRJCmuXdXb2xuH1G7ZsiV+vbOzM84cX8worNm2XciKRZKk2ME+vf/78PAwQ0NDpNPpg3LV/FTmoBaQ/v5+RkZG2LhxI4ceeihbtmxh69atrF27lnPPPZevfvWrfPjDH+bBBx9kfHyc5z73uQC89rWv5frrr+fmm29m69at/PrXv+aGG25Y8Djme8Ps/NBvR9s0Gg20tUtxNgwCxIXyJEliyZIls940B7MJazGZ3khq5x7ls+XLTK/02w5o2LFjB93d3fT19e21FMvBQBiG1Ot1AEa/eB3wxPUSNkhpCqYAXhiQXrkMYWAbmcMOY6pcIaMp6E+In7FqGWXLomvtasyN60EyiKKWWUxRFOr1+ryjpaaXbrdtm1KpFDvgFUWhWCweFP6S9vaz7Xvn/u9tU13bn7pu3brYZ5JkvC+cg1pAenp6+PznP88FF1wQP8Svu+46li5dysc//nEuuugiTjjhBFRV5YYbbohtth/4wAd429vextq1axFFkeuvv35Rl6zzNWEB8YPuiA9dwP1v+wTO9u38z/mX86Kbr6Ber5PJZGY1ZR3MYbzznVm2gwmmC0Pb0bt58+ZZS8G3Q1zb+TJt/8NsAjE6OhqLylOBWq1GGIaEYUjTcREQsEe3ks5mMFSFWhihihJKZRA53SrjHkYRkiihyq0SJvWgkyAIyWgqvgH097f2fctNCB+9Mo4CWyi6rsc1rZrNZiwk7agxgGKxuMdjLOR6W8wQYVmWYzHZuHEjjuMgiiLbt2+f0bJ3Nr+PbdtYlrWgCM6/Bw5qAQF4zWtew2te85pdXu/p6eGOO+6Y9T3pdJrvfOc7+3tou2W2h34ul2s5O6emEFd142wZIYgiHr/uFo677J8plUqkUqndOi4PNhPWzsxlBTFdINoJle3ZX0dHR2xmWmgp+IN9xTGddsKp4zhs/fI1lLdvxs4vwfR9eteuxRrajiZJ+LKE64WoRgpzxyZSuVapEill4PgBggCGqhCMb0MRBMo7tmD0rkISxPiamS28eDb2dI1Nzxxfvnw5f/7zn1FVldHRUUZGRkin07H5aLYZ/ULCeOe6/XzFRtM0Dj30UDzPi/NIBgYGGBgYiFv2tvu/VyoVRkZGEgHZDQe9gBwMLPRBNv2G1DSNXC5HR0cHoiCSPWQplUe3EIQBYRjG2eY7O24PFhNWWyCmt6L1fZ8NGzbM2kxKFMV4tZDJZOjs7JyxgmgLhOu6/PWvf6Wjo4NCobDPn3Gx2J9mviiKqFQqeJ7H5OQkNdMiJEKeGkaXJPwwwvIDcpqKWBrABYIlSwm2bKHDMGiObkY8dC3mpk0ICOQ0HbcRoKw5FHfjBuQgpDK8jmazCcwvImw+ya3FYpHu7m6q1SqlUomhoSEGBwfJ5XJ0dnbGfWf2l79kodu3x68oyl77vy9W3binK4mALICFmLDaD9RisUhKVml4Nl4QMPjwOvTP3szRl/0zjUYDwzBmraS6vwVkes2u2RLkHMeZ8b62CUsUxbhG1/REuXbG/VzHdLCw2OOZbX9t8VVVlbFbvhy/LgkiHatXULUscitWoU4M4YsizWInDcdBk0R0RcEWBGq2QxBBujaCJIqYvUsIAh9VFNFqI3iCwMb/928o574JRVHmNFNfiF9juuPa9/24O+HWrVvjcNudo9/muu+5MN8VyO4iwnbX/70twps2bYpb9ib93/9GIiD7gd09qNv2/JM+8S7u/uf3ox2+DHPrEA3HYd2nv07HJ/+FMAx3seMvxkOtXWYDWrb3ZrO5i0jsHNHSFoTprWjb/05MTDA2NsbatWv3eWyLycEYIDAd3/fjWmljX7mG9jerGhEpz8QRBCzPoy+fBc9ElRRKfoDghRRlBWFyG8aKFZSaJvlly9EnxwEBy/ORRZGcqhK6JsqqtbhBgELr+mnnhuyNfXlwy7IcP4Qdx4nFpH2NiaK42zIq0/c7n3HMd9u5CM70IILt27czOTmJ53kz+r+3Ta6ziYllWSiKsuCip08lnv6fcBFYDBMWtARkbGwMABkRYWqCIAopD2+jGakMDg7SaDTizm7t/czFYT29DtNsK4jpAjE2NrZLHabpWdTtFcTeOBgd+wc7lUolNgdWh9fha71UR7fQYSgYuTyNIMBQVIIwwkhFONlevFqDtCiQKgiEzTpW1IPr+RhZBVWsES49lqhSIYygt1OiXG7N+KMoiv0RpmnuVUAW8/uc3ur24Ycfjkv87KmMykLGsJAVyHwQRRFZljnyyCNjUSyXy7GYFAqFWEzaFQAeffTRA5pH9GSSCMh+YHcCoqoqvu+jaRpda1dhr9+Ep0qYgU/kewx+9Tb0d7+NcrlMNpuNo7IEQYjLbMy1DpMsy7EopNPp2O+wZcsWVq9eTWdn56J8xn3lqfDQXyxM08SyLGq1GkM3fwrL9vCVVmSV0b8Sa3QI2ZBIiyJBaQuCbhDVhtCkLmRJAB+EVAYvCFFkCbGyDUQJc2wDSF0EQoAsCQRaCtN2yac0pB99g/DcC+IghsWsxjvXbUVRJJvNsnz58lnLqLTFZCGr7vkmKS5EcNrb767/+9TUVJwr0/bj/b1c14mALICF+EDar3d3d+M4Doe+923Yn72R2sOPYTkSihhRsy0G//0bWP/02hld5YIgYHh4mOHh4Xhfe6rDtHOhvjZtW/RiXdwH2wrkYKadJS2KIrVaDeeJ70KujZBWJPwwxPNDcs1h3DDCCUPC3n7sTRvIphSoDGJrEm4Q0XCayEKE6bkEvcuwR4ZQlAjVHMVSZfwgwJsawFEOoV4ZRH2iRItlWaTT6X3+LAt1dO+ujMrIyAjDw8Nxc7T57nuxTVhz2X7n/u9tIWkXSp2cnIwbZD2dHfGJgMyBxTBhteswtX8qlQoKoGs6vmUSyBqTj29BO+IQrMFBoiiiu7s7TjDM5XL09PTMqQ7TYn2OA7Gvg7UkymLTNl1FUUTj9q9ABELvUuTSVvS+1YxV6nR09SI1JpEIsTIdWJNTGKKAIou4RDRz3QSToxTFMrooUkcEWUGUJLoKBXy/TLPQjZBykMZHkKuDNIo9eGNjLF++PF6N7o4DWc595zIq7Qfw1NQU8Den9e7KqCx0zPMd98DAQGxC3t37DMPAMAyWLFlCo9Fg/fr1OI7Dxo0bkWWZQqFAsVjcpf/704FEQBaJnctsAIyPjzMxMTGjDpPv+5TLZTzPQ0SgY8VS6us24HflSE1V8CqT6Ld8j/ynP0Y6nSaTydBoNEilUuTz+X0a42JHdB2sD+uDgfa5aTabNBoNJiYmWn6oH/47ptmkbDqkwzqd2QxV18F2XZSsjpIKwJSRFBkvDEkrMro9hKlqVCIBY+kKsuUJhChE6Oul7roYooheWo8raUx6HrlUCkPXCJ4YR+q332dzRwfNZhNd1/fY7nYuD7jF9lNIkkRXVxddXV0MDw8zOjqK67qxn6FdyyqXy81aA29/rUCq1SrVahXbtrnpxe/g5d/+N7q6unbrHxQEIU6oXLFiBZqmxaI4OTkZ92Pp7+9/2jjYD/pPUalUOOecc+LfLcti27ZtbNq0Cd/3ZzSV+o//+I+4nMliN5WC1gU1PDzM8uXLY6d1WzCmhyq2L9AgCMhkMjMS5FRVpVwuY1kWP/3Wj1EaJTRNJqiXaYgiOBaOELD1hhvx//nt6Lq+SwjtvnIwPvifik70dmb9dH/Uzhn269evp1wu4/s+nuehjA9gSx1EdgnF0NAVmaao0VVQkFUdIZBQOnVCW0CRFITepYj2CEpuBc1ShbxhIOsOoa3i+xFuENBzyGrE0mbILSecqiGJInpGoIGIJLcedsVikVKpxJ/+9Cd6e3vp7OzcJfP6YChPIkkSoihy1FFHzVpGpaOjg87OzhltbveXgIyNjcXb+2HI8PBwHHm4u+6g7c+6cwl90zSZmpqiVqs9rUxaB72AFAoF7rvvvvj3L33pS9x33310dHRwySWXzGgqdf7557N58+ZFbSr1i1/8gssuu4zt27czMTFBT08PP/3pT3epwzQ9zFVRFP785z/HdZl2RlEUxsfHWXnx+az/2CfJ5w1KlSauFCDYOorsY7utnurtEMKFxNLvzFO9mOKBZnr599lEYudJg6ZpsU8qlUpRKpXo6emhr6+PUqnE6Pe/iA8YKQ23IaMtWY05NYISTKBml+I1RpDWHEl9YDOyLCILEoo/BXoKJwhQRBFRFBBkiTAvgR8hCgKyOQaCiOV6aIqMJAro6TQ7yg2cwKEzbZD95a0sf8tHaDQaiKIYV9wtFAp0dXXNCNjYG/vTbDT9AT9bGZV2QylN0+js7CQIgv0iIKVSifHxccIwZODT3yNltARDFMU9RrPNFoYsCALpdHpR/E8HGwe9gOzMt771LT760Y8C7NJUqre3d9GbSq1Zs4b3ve99LFmyhKVLl9LV1RXPLPbEnh6w7Qspn89jrOjH3DKAYRhIoofjRkSqxvj6TeRuvoWef7ogLrkw3+X6bGOCxXd+L1aeymIw36zndofB6cIAMDQ0xMDAwIztp4c9tycN7Z+dS69UKhWGhoYQRRFFUWjeeQNhGCF2ryAc3UhHSsf1ffx8Hx3NcTxvAieKsF0f0/Pp6DBoVoZwJIEAcKOAjoyB1xiBVIpm00HVZWQpxAtCAkkjMMfIZ5bgV0cIVqxBKD8OAmBOMOrkMBwnzhIXBCE2rWzcuBFFUWLBnCv7YwUy27Y7l1FpN5MaHR2NW13rur7bMio7738uCZXbtm2j2Wyy4TPfwBua4Ll3fBaArq6uPb7/QFdzfrJ5SgnIH//4R6ampjjzzDOZmprapanU9MZRi9VUavXq1fT19c3rxoI9C0i7rIllWaRVhaooYAgBoSgS4hB09iFPjlF3HORvfJPwZWeSzWYplUr73Pv5YFs5HMj6XLOFQU8/F+3INiDuI9EWCFVV5zVWz/PiLObG96/DDyKCIARnnHRWRe1ay1CpSmc2hSQKCJKAlFtGuWGhSiKGquBKIs10J4IkodYmSHcsp9IMcQtL8OpbSCkKUXMHThjhd/YjjA+S9SapiQJl08boW4U6vh1EAdsNmJqaolgsYpomHR0d9PX10dvbi2ma8Yx7bGyMer1OZ2fnbh/IT2ZxREEQ4s6EK1as4JFHHkEQhBllVNqFEWfzVcxFQKrVKlu2bEFVVZpbRxGAcrlMOp3ea25HIiAHMd/85jd5wxveENttZ3OoTWf63/flobmQi2FPD+p2VJVpmix5+wU4/3ETzYcfJ/JEpAi8qR3IskZpZAuBaKD98L8YfuUb6enpIZ1Ozymj+ECxWCuQhTK9wm8YhlSrVSzLisVien0uSZLiVcTOZsfpoc9/+tOfKBQKC04Ei6KIcrlMGIZYlsWOofU0hCIV0yYVBfR0FLCfGJckCojpEByZMALTdelesRbGNqLKEuO2Q0rT6dBlFGcMuWcZ5YaNLAik/Al8WWJCK6JFISlVRhQExN4VWA2bQkojSEeYWh8yYP7sP1j69laF3nZOyHTzyvj4OF1dXfi+z9DQEENDQ+TzeTo7O+NEuensj5yR+Wzb7mGSz+dZsmRJ3MNk27ZtcUOszs7OGTWt5pKQ+9hjj+F5Huv+323Umw3WXHMRURTR09Oz1wTbREAOUprNJnfccQf33HMPQFyefXpTqYGBgYOqqdSeLtb2KkTXdbTyKNLypUSbtyKmFULbpya7KJaPlPYJBkO0J1rgFgqFfSqTcLCtQNrsbkyztaHdnR8CWoUDZVmOCzhOF4kDFfnSbptaqVQYuuNz2K6HwBii66CmNCLAqg1TyPTimjsQsilELcKzAiRRRBQEhLSEnj0Ea+sw2bSApkBoQRS1Sp30L1+DML4Jdclh2KNTyJJIOgtCFDyRU+KT0jJ4qkrNHMNXl7f8J098/7vLCUmlUvT09MTFBScnJ9m8eXNc86qrq2ve/TP2dzFFQRCQZTkujDi9jMqmTZtm1OsKw3CP126tVmNoaAiAyqNbiGiVdK/X6yxbtmyv42lPWBIBOcj4wQ9+wNFHH81hhx0Wv7ZzU6nR0dH91lRqOnO5yPf2oNZ1PXa8NjwPw2tS1wREBfAFZBV8X8GXJWzPRPifH9Lou4h6vY4kSXR3dy/6ymghLNa+bNtmcnJyF0f1zqbDthgYhrHLKuLxxx+ns7NzTjf6/sK27dh01TvwC2w/QuxcRjixmVxKQ+laRk0QSPljoCo07Yig2I89OoAmS6iyhNscQTcyWJ6HIorIkoigpxC0CN8MiCJQgwnEnEbD8VBlsSU8agrbDYmCVol37FG84jLsga34lovhl2g0GmQyGUzTnFVApleqbde1siyLUqkUZ5C3+4xPb/C1Nw5kbavpGePt6KepqSkmJiaAlkgUCoVZy6hs2LAB3/fZ/JXvArD0o29u/bt06V79nu19zPczPJV5ygjIN7/5Td70pjfNeG3nplK33nrrAWsqtTf29qCWJAnHcZAkCaO/H3dwO7pmYOMiKDKKCGFoExZX4Y8O03Q96v/9DSbffgmqqjI5OblPJpZ9ZT4mienhrrNFMwF/qxH2RAmW6Rn27Z/dtaGd75j2F+02qtCaiXq+jx+ICEKInlIwutYwUW2iSCIdqkQYTtAETMfDdjzynQaN6iCOKCJJYJsj5DMZBLdEZKQwnQBZEskYGo5no0gaVmOElNaNYI8TyhpNxwUZOrIGdqOCI4fIkkhKlfGCkME7rqPvnPeQz+dbYcVPrCb2dE0YhsGyZcvo7++nVqvFuU3btm1jampqRun22ZjvCmQxa1tND6VtJ/k1m00effTRuIxKsVhE0zRGR0dZt24dgiDQ3LiN6InVhCzLHHHEEfMaz5N9LR4onjIC8rOf/WyX13ZuKjU90W4xm0rtr5l+Z2cntVqNZW+9EPemfyc9sB2/KeGFNr6nI4sibugjawr1ye2otTT+f34e6w3vpLe3d5fS7/vrs+yJtkDszlG9Nz+EqraKSPb19bFkyZJ9KpX9ZN60bb9HWyynfngFdVPB8kENK3T3FEAUsV2PTC4NQcv/ofQsZ6pm0aHIqN4YqizhZHppigKKM4Kq6NhWDSfTh13fhpaWEd1xnCAiyC+D5gC5lE7TFWikegnrA+hRiXQgsSPdQ+C7pFQZzRtB7F6D80Q+iiiKNJvNXXqwzMWBbRgGlUqFnp4eTNOMS7e3a1rNVm13f0Rstbefy77bZVQEQaC/vx9VVWeUUUmlUjz00ENs27aN8Zu+hy6GdBy/Jq4IMdfJ5/Q8kL8HnjICcjCxGCYsaM2Ostks9XqdtKZRFURSUkhVUhACFzWl4I0PURcVBMtH7hTxB4Ywvv81lDdcHPdzbpsU5sJCTFjTy7C0RaFdkvyRRx7ZJR9ieo2u9uyu/TNbn5ChoaHd1u96qlCr1eI+EqXf3EBV7kAVRvCDAE2VkbuWUxnaQj5dxI9CxIIOokBgRni+R6Z/FZS2oskqFcdDFAW6dQVBqOFLIuWGiVpcStorEcoSdbkDyfZQZQnNH8eXRSYbFunOflKNIUBuFeD0Arr7luPVBnA9H1WR4XffgFe+H8uyFtQ0qX39tCOhHMeJTVyTk5Ooqhq3kNV1fb850dvMZ9/t4os7l1HZsGED//d//4dlWfi2Q90O6Dr7OTiOw5o1a+YtgMkKJGGfmMuDWlGUOL5dk2Syy/qprttA1NWBMDVB1NGDOjFCoICPjB2EyEG91bHOsqhUKnEZiPk4iHce1/R8iNlWErP5IdoPnXZW8PQkyoXcPAdjdvxcsSwr9nsEf7qFptUqXigTkjEUpOIyak0bUYCsodNojOCqKjICnh+QNjQ8P0TP6ij6MpyREhlDQ0FE0AMiuY9602FFNoVku0iyhuP6yKJEUZMR0hFRqh+nVKMzb7RMW/JS1IZF0wrJKXUmtRR1y0X1A+z6IMAMZ/pCHnzTq9QuXbo0rgVVKpUYGxtjx44dZDIZoiiasQqd637nwr4KjiRJdHR00Gg0yOVyOL/5PwhCVENiYGCAZcuWkcvl5nycREASdmF/OqvbGbVD570e4UtXEYohyuQEriIhVceQFBVZDBBEBzqWYI8PY7ou8g9vZuqN70LTNDzPo7+/f7czyel+iCiKaDQaDAwMzBCM6WOdXgp+eqXf6YUcK5UKmzZtoq+vb95RObOdq8XiQAtRO8kTWj6Qpu3ieB6+H5BPK6SLhzBVN6k6HssMlVBoVWv1Mn3Y5SEy6ZYYuPY4esag0diBpqRajvOMge/5uEGAJAitKKqMhiMvRSzVkCRQCmkix8RxAxRZRPZKoKWw6iNIUicFtYnra0TZJVAfQJVkphoy6ugoHU/Ux5pvhvSewtPb1XaXL19OpVKJq9MODw9jmmYcVru773yxfSCzbbvz/icmJli/fj2yLOMOtXxxS97xeoIg4LDDDqPRaPDQQw/FJrrpZVTmeoynK4mALIDFMmFBKxpraGgIWZaRBIF8XqdSMREiF68pEGlghAaRIBARIaR1JsYGEPpWUf3cB7Eu/jhdXV2oqko2m93F1NQWiekRM+2Wqu1Q4ukmprmakhY7q/1gDC3eG2EYMjU1Fde+2nHnFZi2T9NJgTdFtltHjso0LZFsWkNAQMJF6eynblqoQUhRrCBIAbYX4qZ6satb6cimsKxRPF/Ddn0ENLJpHc+aQNN1mvURDK2DiIgoCGn4Eq7vkTV03MCEVD9BdQBFkUilNCbCLIYkoqsSmixDYQnl334N7UWXoGlaHPo8X/ZWILGdjNjOq2lHRCmKEpu4DmRDqdke7p7n8de//pXx8XFqd90LQQhSywy7atUqVq9eHdcM27mMSrFYjIsn7ukYT2cSAdlPzGcF0i75PL50KantgzQNgzByEWUJpAA7jEhpKvbwdsK+ZbgTwwhsISPITP3gBrYc91L6+/tJp9Px7G66H6L9f03T2LJlS2y7Xgyeig/+uTCXhLO209yyLMbuvRFL7QJ7FCWsouoKDa0XxZ4im245b8O0jhi2ulHWTZdlvavAbDnOLb2bcs0ipchkDA3bFrD1HszaACmlgSqLNG0P3+jE85pISkAUlBn3O7EDBSOqkFcUSnWPZrWBQkSGUUJRpt5w0FM6xaKGadkIYYjrEoelNpvNOABlsWthTfeXHHLIIXHW++TkJKOjo6RSqV2y3venD2Tn7Tdv3sz999/fMgkPDkEUsfaSf0LTNI477jiiKEJVVZYtW7ZLGZWRkZFdxp8ISMIu7E8TliC0mkyZpkn+3Aswv/s1Ulu24PkRgm8T5IqotSm8TC96NElgTSLIMpYXoIk+YRTR8/j/oq+5kGXLlsXL7N2N+alQguSpQLVaxXEcAIKHv4nptP1EHrmUim90Y1oumSgilzGo18dwfIWUnsKzfDRFgigCAxR9Oc0dUwRhSJchgTuELMDw6CQprUBBNQFoiHmiWpO8BMW0jenKTPgR6VSaXOCgiuAZXVhNk1ymiJw2mWimkSWLKIrIZrNMmiFe3SKb1jD/9xNoZ12NbdtzynHYmfn6S6ZnvS9btoxqtUqpVJqR9T6foqFtp/hCI7yazSZ/+ctfGB8fp3znXRD5CJJAo9Ggs7OTww47LA7rbX+G6WVU2g2xppdRWagIPlVJBGQBzFUYdnYc+r4/q6O6VqvFfRDkIECQZQSnSTMMyDWmiCQInSqSriNIIGk+YrEPe2oI3yqhpFfg3f0Nmm+8lDAMMQxjt3btA5VB/lTez97OUaPRwDSfeKj/5WZKE9upNVXCIKA7Y5DtWMXoRIVKw2JJl0okyRBFWEon2CVcV0ISIqzmGEZGwjXHaDZddFWBggGBS0AW36qiqhJaXgU7Qo00/DAk370GoT6IXFyBs6NEPiOg6QJhCGqo4gYR3ZmAIADTcpFFAcEt4as6kiDgOmUEVWF4Qkaq1Uin03EQwP5agezM9Ego74kqC6VSCdM0EQSB7du379Xf0GYhK5Aoiti+fTuPPfZYS4hcF9+H7te2qlUcc8wxaJq2W4Gant3e7vHTjkSD1spm5zIqT0cSAVlEpvctbzeR2rRpUywY0/0QoijGPofu7u7YYT3+v1UmDRGpKSB09BJZJZAUBMvG0WQygoYZgjg+SpDRGd4xSRiF4IZI4+NkMhnS6fRuy04fjKVMnkqronZP8/a/E8OTBLZHFMlEfhW181AEd4qm5aIpMk0pR8bfgacVaNYaNASNvpRDzdHxgwgn3Y85Pkh3ZweiKBCFDsgGgiyRMnRkSQJBxE8pRHbre1PEOmQMLNtDkSUkUUTQ0phNB1mS0FUFQfCw0svR69sIlQyy6LDD0SGto0YTyKJEkOmldP9XkZ77ToB5RUrB4mWXK4pCb28vvb29bNiwAdu2KZfLjI+Po+t67C/Zud3tQs1FgiDEq4/R0VHqd/0vRH/7W19fHyeeeGJ8jL3tf3oZlaGhIcbGxrBte5cyKrPlxzzVeUoIiOM4fPSjH+Wee+5BVVWOO+44/uM//oOJiYkD0lBqusO4LQyzOaq9J3pPT39fEASkUqm4suv0ukzTL6bx8XE8z2P8Ve8md9vnsFQRI6ph+SG6ISCEKr4kYvkh6XSaeqNBKr+MxvggFdMjLwnYv/kmnP4marUasizHIrUzB2Mpk4M9jDcMQxqNBjt27MBxnFa3uof/C9f1UcIAKayh6TKNhokfOKR0Fd8PkOUmgmKQ07OMTtToKWTRhDJ52aMaLcW0fCIEujvSTFVGcT0ZzwsIPZNivgPPLRGJaRpNB03NEoURru8QhiK+O0UuVSBwpgiUlsNdlAWKuk3DCvCi1vVYTDl4vozvgaqI5IsatSkHQbaRRQHDMAiCIG6CtTf2Z22r9sRn7dq11Go1SqVSnOzXLkXf0dGBKIoLcri3/92+fTsPPvgg5fv/gGdbCL7Ikte9nEwmwwknnBCv4OcbFdZuiHX00UfvUkZFVdW4P8zThaeEgFx55ZUIgsCf/vQnBEFgdHQ0fn1/N5QKw5B3vetdbNq0iW3btjE4OMgXvvAFTjnllDiJT9f1GdFMqqoyPDyM4zgcfvjhczqOYRiIotiq0iuL6HkDv9xE0URcP0CWIhRRx/MdKC4lFbm41gQpRcZLd2A1xlvb/fbbTL74raRSKUZHR1myZMmMmdvBONvfl30tptOybWKMoiheZbQnCKZpzujJkpu4h4ZpEwQRRlaiK2NgCkUQBERBoquQo1Ibww9VUAwiS0CURCRZBE1EsSNCJ6TedOlPa4jhJIosYkqdCAKk9BKKbFGyIiphjsAfI5vVcexJbAd8vQdB2EFHzqBcLlONCoCFITfQZZlJM4MRRWiqjKZEWGofYbVBPptBE0JGK1M4tQYKdUp/uAF/1atis9xc2R95Ee3zO93fMN1EtHXrVrZv305HR0ecRT9fE9bExAQPPvhgXBuLCOR0S0BXrFjBSSedtMt45ko7URF2LaPSbkHxdOKgF5Bms8m3vvUtHnvssfiLbCv4gWgoJYoiS5cu5eijj2b58uX09vbGPUL2ZNuc7wyp3f88n89T7ltOZvtWXF3CQ0DyIlxCBCdAMUTM2gRpVSWSRCI5RJIkXBGmhjcgLDuc+rc/jvvqD8f1iXp6emIROViLKc71WLVarVXB+Inw02q1iqqq8cNkT+OJomiXleP0n+kmxlqthmEY8QRBkiSy2Wwr12P996jUJ0DQicIyup4jmzGwqwIN06E7J4PQQBQFXKULz6vhOyUKmTyuVwZdQkhruE0fURRQO5ZBMIamdzO5o0w2q5PSWwmZrlygWTXpVEUMuYGgyky4abQgJKOrSFEFIdtHrWGTyy3FCEcJQoEgiPCCgI7OLK7n4rsBhiojh1XcMEJVFCTFQJRsdkzUaLINXdfZsmULa9asIZ/P7zXXYT7sSyb6dBNRu81tO5IL/tarY28VGdoJs5s2beLee++l/Kt7ED0fQRRJveAMdF3n6KOPnuE/XIiAzFbGpZ0f83TjoBeQrVu3UiwW+exnP8uvfvUrDMPgsssu49hjjz0gDaUALr/88rh0R5u9Ocbm+6BuOxUbjQbFl7+Nxvf+HbWxiQnXJytGNNMd6HYVCssIK6M4GR3NC5EUnag0hNC9nNHRAYTUEFoI1h+/Dc96I/l8nlKpNMOGfLD5QGBuY3Jdl2azSa1W49cXXgPAc2/4EL7vx1FEURRhmuasArFzA6npHQanN49at24dy5Yto7e3lyAImJycRFEUbNtm5IH/pGG2oq8UsYGsycjZpUTh1BNZ5Squ0YEmlwidIgQhlShLId3ACRUsOyRIrcKpDCGLGpoi4vpTaKJIGET4YYCqSAi6CLRMoK4Xkl22CsJxZL0fa3AcRZYwckprm1qI4/mkDBVBTNM0s+hqa4ySJFJtRCBAPpvCskzCVA+G5hB4FUTBIBJak4xKpUK9Xmfjxo1xOZKurq5dch3aLLbDfS77nd7mtlKpsHnzZiqVCuVymUwmQ1dX1x6bSU1MTLBu3TomJibwPI/QA1VpTRy6urp43vOet8vY93dm/FOZg15AfN9n27ZtHH744Vx55ZU8/PDDvPKVr+T+++/f5Yva+WKd/vfFninv7UJZyEw/lUpRLBYZHx9HskvYUUhKCrFCgbRg4UsCmBUMTScIBZwgoLBkGeXBTaRkCUXXqZsuiiHgeSHy//0348YFGIYR901ZbBPWgRQjy7Li0uJNp9UUad26daRSqdgPZllWPDNt+4CmN5Cabmbc3SSgPZ4gCCiVSvHKJBy4nWrdwnV9tCgim9YItS5czydwXArZIk3bxfOn0BQRVZGpNB0KGQNFESFqYtnQNG1cy6Wzo5NaYxzXlVF0BcscJ5tOE0YRKEbLFxIFKLKMEFVBEDEtF0WVUWQJQTawHQ8BAU1pbeMGEq47haYWEIIKDQscsYAWVcnIGmNaF6EXoBf7iawRynYWw/eRh36GXHgefX19dHZ2YpomExMTjI6Okslk6O7ujle0B3oFsrv9tVcKhxxyCGEYzmgmVSgUdsl6tyyLkZER/vKXv9B49K+EXoAkCWinPp90Os1znvOcGcUlEwHZOwe9gCxfvhxRFHnd614HwLHHHsvKlStZv3498NRtKDUbuq7HjnhZltALaag1saOAoGmDoSMKEW7okepdSXPHdrwwxMhnse0SGVXGz/VQr4+iTm1F7l7D2I+uITj7Q2QyGYIgwPO8RW2stD+c6DtHszmOg23bDA8PY1kWGz9xa7z9xk98g+M/9U4kScL3fTo6Oujv7581UGE++L5PqVSKTV61jd9jYsdWmpaC5wWkOgyKnRlKVYFqzaQnLaEpDUzHwfUEolQKPwwQBAFRlCAtoSAiBnmmyg16UiqqVEdRJDy5gwYSgmDTkU/TaI4TKKuoVwfRVAVZkXE8H1mWcbwSmVSOKKgQhSmalgtodBYy2E6ZUOsCRknpKpEvM1KVMHRIaQohrZWI7boUMy6mJ+G7Fep+DkNs4BhO/F2sXLmS5cuXx02ltm7dGkcUtVd7+2MFshAHfXv13tnZieu6sYlretZ7sVhkx44dPPbYY0xNTRFUa0BEEIWEYUihUOC0006bdf+JgOyegz5AubOzk+c///lxJ8Lt27czMDDAoYceGjeUAnbbUAqIG0q94hWvWNAY9mci4XREUYzt+cbLLiFvqIiahKZq+IKMIEoITtgK/9wxgqGI1Ma3IYoCkm2BKiNXxqDYz/aJBpXKMKbjU/u/b1Eul+MCjHONttnb51sobVt0u/BeEARUq1XWrVvHQw89xIMPPsijjz7Kpk2bGBoaiivdZjIZ+vv7KWQypNMpMtk0Hbk8pc/cHleFbReoXGhRRyCu0Or7Pr7vM/HofzFeqhKGIYosIStNNP1v4iRJImK6F0GJUBSZUOvCCnNIkklHriUkESqCpBEEPkEUYXQsB6GVUGjZLk3TJpvW0NUmgiBQqZsEQUBnR0TGcLEdj2aURxCgM59BEETKboYwjDA0i7Rm0oiyeJ6PJIno8hSkerBcFwQwcjJNWUESRSRJRJYgMpYiSQK63CSIQtTSr6lUKnieh2maiKJIZ2cnhx9+OMccc0xs5hoYGACgVCrNOfFvsUJ+97a9qqosWbKEo48+miOOOIJCocDExAS///3veeCBB9i4cSPBlk0Qhq3zcvKppNNpTjzxRHK53Iz9JgKydw76FQjA5z//eS655BKuvPJKRFHkC1/4An19fU/phlK7o6uri2q12orG6l2G3lyPU7WJZAHL99A0EV9Q8PHQOlciVUdwiFBkjZQiUPcCCppMQ1GoNG2KRqviq/zI9xnT/jEuwbGQInqzsbvPOL0V7Ww/syVZGoZBNpvdpYmUIAhMTU3FwpfWDJpmDcf1yWl/y6COooh6vb5Pn6fdijZutLTjx0xVatiO3+o7rtfR0PGkIp7XQBYVNFXGdT3UrIomdeDUTSYrDXqzOhEKDauEKy1DdEuEkUQ2peMEZQzDQNE6MMvj5DMGiiyBICClepiqNOjpXIohTuL7ImU3jWq75HQVkSpqto+JqRqFXA9peeqJsYeEQUShewUwjl31UBUZQ7cJxRSO24qy6sz6lCoOUjpLSlcRIg9XKuLbbvwArNfrGIYRPwyn+x5GR0fjxNfR0VEKhQJdXV2zFklcaBTWXLfd3b4FQYgrXff19fGrX/2K++67j8Zf/4joRciIiFKELMv09PTw0pe+dF77X4zxPx14SgjIqlWruPPOO3d5/UA1lJqN/eEDgdaNms/nW5EluoqqagSyi+n5pAWwPAG9uxeaE1jWJGldxxfAjTxShWVIwXZ8r0JO13BTXdStcZSJrch9a5m45zNYh74ewzAYHx+nq6trwZEh7c9u23ZcCn5nh/X0bafX5mr3Km//PPbYYxQKBZYvXz7rsdomLYCfPvefsUIf+ZB+6p5F2VbJKBo/f947WPP1D2DbNo7jzKtHShvP8+IVkWVZjK/7DuM7ttC0WgKhZDSK3TrlZhrP87FDj1xGpFSRcIMqgpRDFWQaDRtdl1sVdRUbLLAdj8C0KRZ6qDdtXNfB0A2azclWvxBZQkinQQC/GRAGIYauIEgGgZvBK1eRRIFUMQ+hjV8L8Dy/lTSo65h2HlUxcT0fTW3SbMiERKQMFfBohjnARJFNUprMpKWRjiIKPQWmpsqt/BXfY/XY92DlvxAEwaxtbwVBiE1YRxxxBPV6ncnJyV0c7+3zfyBMWHt7YI+Pj1OpVNi2bRtuEEIQIYsh4coj0TyPo446Ko6anM1vOh9B2FvP9acbTwkBebI5UCYsaJmxMplMa+l9yqvxBz+NrgrUFRVcD0cy0BpjhJKG2LSwDIlC7yHUhrfhOB6qZhCYDZRUGqE5CYWlbJ/czpLUKILroY38kmrXaZimSb1ex/d9CoXCrJ9xb2GvQGzOkCQpFoR2SOV0h/XexHZP2LaN7/tUq1Uyz1hO88GtyKKALik0PRtr0yCCIFAul+no6Ii75c3ne3Mch6mpqfg788bvoe54sdkqCBqkjAyiYiDLIg3TJ9fZhyhUUFQbz4/wxQKOOYqR0iCKEHQFUZZQsn1MVWr0dC1Bk+o4novj+phBDs+v09XRiemU8TyDIIoAiVRKxw1qSLKA7VVRFaklMoKAGygEfoihqwhSg0jQsZwpFDmNLDexHQFLyIMwQWeHSLMZ4toustFNiglMK0RTdFwvoJiXkWQNQWhiKBHjpTrKo98id9jrqNfreywloigKfX199Pb20mw2mZycjPuA5HK5GUKyP0xYc7m/bNtmaGiIe++9l+bmdS3TFRCIkMtm6enp4YgjjuDxxx/HMIzYX6KqarICmQOJgOwn9iXfQtM0wrDl3MunVCxLJdNwsEQBQ5NwXRdFFxHRCRCoDw+RXbqK2uR2Cl0r8RsTqJKIK4ZkdZWmpFFrOOQNAWo7yEv3M/q7v5J6yXswTbP1YM5k4kS6uYS9ZrNZxsbGWLFiBcVicdZOg4tFuz7SdHt7ecM2cmtX0vQdnChg5YmHUr7h++Q/9DaCIKBer+9i097T/ts9PQCW6X9g/UgDP4jQtTwpo4aq6vhSB2HoEISth7fr+RhZFdUN8aQC9YaFErXCZRtmCdtTSakafhAQRaAoMhCgaTJWmGWq0qAzraMpKpYrYJHHbY6h6zK6lsFx69hRDt8tkc9lcb0KjqvTNF0QQoodHdhOGV/qJKKBoatIksaUZaDIEaoqoasSU6aO73gUdRVZS1MLPJRQIgwrWLaKmu1DtrajKQKB0kG52kBqNlEUhUajscsqdbZox7a5aLrjfcuWLXE4rW3bc2q/vFgmrPbfx8bGeOyxx3j88ceJzCatmiUC+pHPJJ/P86pXvYrnPOc5cWHH4eFhhoaGyOVy8fWTCMjuSQRkgexNHPZFQFKpVNxzwO5ehtpw0NwANwhxGyZGRsfzAlQVFEnD82yCIERXJCx7EgUQMt0I1gBRUCNjKNhKgYZbAdUH28auj1P58bXUsqdQKBRIpVIUCoW4gdTewl4dx2FsbCyOdtpXdneu2v3WRVFk6NqvA9D7zFVs/dNGnC3DhKGPG/qxT6X6pe/Al6+g0Wi0fAx7aHbVTkxsNptEUUS1WqU29HNK41N4vkjgA5pJR1eBakPH830aDZtCRmeqIuL5NSIM1FwflckqtuOxvFvHD2QaJlRsHUULCUOBTNrA9WpoqRSqWqBRGkNTZQxdAaGGkumhVK6Ty3SSMSz8wKVs5WjWTLKqQC5jUG3UqdoGgpTCkBukUw6j1TxmrYkmQCrVIPBF6g2LQj5NOmfgegGCCKEfYug2Taf1cBNFkYyhU2rqaDp0dGUoV5uYtSaBH7K8eg8UXk2j0SCVSs2aV7G7IoNdXV10dXVhWRajo6NxeG3bbFosFnd7zSymCatSqTA4OMivf/1ryhsfgyBEEUHUWuVbli1bxktf+lIEQaBQKFAoFOKs98nJSYaGhgAYGxuLRXJv4jA9E/3vgb+fT3qA2RcBEQSB3t5estks6We9llxXH6oqEgkSkSjgeD6iH+J6IX6qA0VXaJglkDQ0WcYKPJqWjaAZeFYdJa0hNcdxpRxDZRvPqeMHIWEk0mX/ha6uLo444giWLVvGypUrWbt2LcuXL6enp4d8Po+u67vcFAeqlMn08hqaqOCFPlNWk75nrAJAFWX6nrGapuciSzKS8LdxtkuPzEa7GdT0KrSVgZ8yUargez6G5qFoHoahIso6iirhuj6i1oUoCsiaSxSBJ3YhRk1cx0dTW33IZaUBShHX9qjVLDpyIYos47o+iCqmVUaSWnkikZYBRYMIHNtF11VEXUVVZWzHw7Y80mkNxCqi1snYZBXHcTEMCIKQarVOpVYlUgoImkHJUpEkIAJJNWjYrRyWQi6NZfs4UWs1YWgmfhjiP3F+Uuk0YSQRBCGuW2ZkxzZs245Fdjpzva4Nw6CnpwdoheMrisL27dt56KGH2LJlC7VabdZ97UsUVhvHcXj88ce57777WHfPj6HZICLEE0KkQ44hk8lwzjnn7OIra2e9H3nkkaxZswZoVcNYv349jzzyCCMjI7H5djb+3lYgiYDMgYX6QGDheRLpdJpisUg2m0WhQRDRKjkdCTi+gKwENKUsNMfwIhHBtnACF992SesyXnUHol7A9iV0VUbWFXK5DIamY9oOoixCYxRFkak99DUmJibiCqjzqdmzv4sptgVk05WfZfSxxxERsQOfMGxt79fqpGUV03epOxaO7/HAuZcAxKHCO+O6LhMTE3FhzOboXQw8/B+Mjk9Rq5lULIV8R55c1iCUCnieD2ETVZFxvQA0HU2RQS1i2y4N06HY0Woa5Yo6yDqKIlFvmqhGJ4osoekeYRjR9FKYpkNXZw5BdHBcjxC15aRXFFyvVVzRiWRM0yIiQk3nQNGoNZr4nockt6rvOqJGOp1CUXS6ujRcL8RxfIQIoqiOaTkEfoSi+nTkAqpWqzy5aHSjpjXsKI+mSIg0cN2AlK4gijaaJlN300xu/D6maWJZ1oygiDbzyQPJZrMceuihHHfccSxdupRms8mGDRt45JFH2LFjR7z/xTBhRVHEyMgIQ0ND3HfffVheiEfY6uiJiCzL9Pf384IXvGCP+29Xbli7di2HHXYYmUyG0dFRHn74YdavX8/k5OSM8jfzHf/TgcSEtUDmYsJqbzeXC2q2sNfR0VG2bduGr+cQ0k3SYUjVdJEVgboPhubi+SGSKqGLKp4k4EQBenEVbmkQUbAQBAFP60SxTQRMdE3GCwIsL8K0Lbxqk8ixiB7+Doau0nvsG9F1nfHxcQqFwj6VsZgru9tX+wFfLpepOTZBFBFsG0LrK+IEPtm8QCCreANDSL1FLN+h7joIns0jH/0ka/71vUArsq3dLa7RaMwI9Z3cdieVagO72UCWQkAkk5VIZ7NEkoxtuzTdFIWMi+2GBNiEgYGSzhKVA8rVBsu6VcJIwfHquK6CrqfwfBdRlBAEsSU4kUPdNhifqNCTV5FEiMKQalNBpk5IgK4ruG6dhtuD3ZggpWv4kY8fhPiRgKJo9HQXSRkRiAq21UDXdAxDw7ZcrKBAKjWJoXXgOBVGpiJkIUV3ysHzA0SptQrqKoY0bR9QUZSW78oJs/iiTK6jSaNsIgUWsiyx9Invv1qtxkm5+5KJ3s7T6Ovro9FoMDk5yY4dOxgeHiafzxMEwZz3vzsBaTQajI6O8vvf/56BP/yGMPARgUAQCHpXks/nef3rX7/X1s3TExUzmQy5XI4VK1ZQqVSYnJyckfXejmhMBCRhUZhtBdIuA98ONd1d2Cu0HOlte/K2Zc8nW/4eUqTREBQE38FHJrBtZF0jCgVcXNIdK2iOb8dzPBRNJwgiIjHC8wMixSB0LDLFDKWxCnqhj8mxYVS/giJHeOUR5K7ljD50G+rJ/4QgCNi2TaFQmLUpzmKWMtkdbed5m67jVzP6581kNR3L8zBDn9XPPZrxv2xBl2QC0aO5fZCMouMGAc1mE8MwqFQqFAqFOEkOWr6Vxuj3GR1rUKk1URSDrN4kI4kEkUHgByiyTsMzUZXWbaLqIY4DLh3ochPHsVDllmNcUZ3WOQvSSKaNLLtkMzq21yBDCkFNUZqaICJC600jSg6KLVGqW+i6QXdHRBQJ1GoWvg9EAn19nVRrE9iOT0AOSZyio1ikWi9RN1OEUQNdV1EUh6maiqKGSJJIOuXh0Y1g1pBlkVQhy8iI2/In+SGSbOAKnSBMYagithPh+D6CAGlDp9EI8VyfWm2Mic130Ln6XKD1YJ7uCN+XarzTCwxOd7z7vh9Xye3q6pq1p83O+56O7/uMj4/z+OOP86effA8v8CGMCAAtm0HVdY499lhOOeWUBY293eu9s7MzjtybnJyMs96BRa30cLDz9/NJ95G5+DSmh722Z7lbt27FdV1c150RRTSXsFff91m/fn2rAulgF7Y7RjpqUglBESQCItwgQJc8vFDAHBsmnc9Tr+4gbRhIgY8kCXjVEYzO5TSrY6RzEumsRhhBWldxogyi0KDuWPhNG8+axP7jzeRXnkVXVxeqqmLbNvl8fo83876y87kNwzBOHKx++T8RAMf38aIQadswqdX9lKIQ23PxohBxaJR0Xxe1MCR3dCufZPKLX6fz4x+gXC5TqVTinIbyyF1Uq01qlUk83yOKBILIoqO7k6aVolKp0HR1UloTRZXxfJ9I0VBVASfIYDs2gWOj6wqO42GFGhlFxguhUa9Rl0RW9Bk4tksYhphOBsI6KcNA11RQdUQpQMt0EFTGSBkGmuIQRSKhWKBabVDMKmhq6xjjZchlAzIpDVXzEOwOKrUamXQH6bRJFIg0TZuMmCJXyBDJAkEQEoWQywrYroiqgeuIFDMR4xN1QhF0ESRJIJQ6CF2LfE7A9yUMQ8VuNlFkjclSjSC4ne41r4qTCxe7H8j0art/+ctf0DSNUqnE2NgY6XQ6drzvbsUw/QE/Pj7OwMAA999/PxNNE4hAfGKb4hJ0Xed1r3vdnBzde3PSa5oWr6aazSalUomJiQkqlQqPP/54HBL8dBaUp8wnO/bYY+My3gCXXnop55133oymUoZhcMMNN+yXplJt2kltnuftNewVWnb4doSTruuoqhqXB9/bDE6W5bi66NauU9DKPyUQIsIIvGYTLW9gB5ASIUx3E1oTuGIGXXNxIiCISHctpzG5FT1qBTBWXA1NMtE0hyjdid2cRCgupVoeRHTKCFGEX9tBc+h/8Mdl9BMuiB/m7XLz08uE7K9iipZlxft2t2zB7emh5thkjlhCsHEC//FNqKqC6XlkjujHXb+DSJDQRekJh3rrYfPXf72G3ve8HWjlLQT1/2Fs1GS8VEYUZTJGhKJ6ZDIpBFFvObajCNt2yKdlwsgDwcPxRCRVoznZwLY9lvfrSCLIWoTrBoSRTiotUm1YdHYUkCSXfIeC6aTxgwjPdljS20ndKuO4HpKhY9sOqqIgyxKoBggqrlvF830yHUtBNHEDEdOqks2kSeVzQITn2ziOR293B6IG9ZqCqtUIwghNT1Ort6LKshmdEI+qqSCLNtlsGk11mKwK6FqIls9iuS2/kaooeJ5JJBXRtTIBITVLwwrq6JodmwCnhzvvr/Ik+Xyevr6+2FQ0MDDA4OAgxWKRrq4u0un0jAlde9+VSoWNGzdyzz338Mc7v4tnOkhPHFZdfiiGYXDcccdx3HHHzWkcc80DmR7GXK/X4wng9u3bGRwcnFHY8ekWofWUERCAW265haOOOmrGa9ObSm3YsIHXvOY1i95U6r//+7/5wQ9+wNatW9m8eTOnnHIKH/vYx/ZY7dWyLDZv3syaNWt2acU5H7LZLB0dHQiCQKgp6Bkd02/ihSKmE5DVBZq2i643ERUFx60iSwq6JtNoNEmFIbKWIQpryKKI74e4YQ4aDTq7iwzWIwQRVEXHD1rRQg23lTVdmZrE+sN/klv20rgc/MTEBIZhzCifvhjsvB/TNImiiG2f/iRlx4HB7YRdnYBA8bhVlH73KMtPWMuU2cQPAkRBINwygK5peIHP1tIES3IFGBqiF2iUfs/YgIUggudMEQQCvh+Q6UujqD6mq1BpiGQMizACx3HxAwUjJWBaEfWGQmdRRBJl0ikFUS5Q7LaZLAWEQoYoCglCm0Ku1TVQNHQMLaJphZRKVZb2GuhaQNMRcb00UdgkwqNYzOIGFpGQbbWklUUk2cAPLRRRwHZcNE1BkWUQFSzLQRIFZElCVFw8L8T2ahiaCqKN40o4jgcidOY0RsdrICvIegeZVJ2alSadkpBFCUUWsJoivuORz0Oj4SOqPrqmkxIEHNfBc6FS9ZjY+iPy/WcC7NJ5cz7f61y2bxWgFONWsI7jxP0/Jicn0XWdrq6ueEXSXq3v2LGDjRs38qc7/4ua1WoMFgCqKsdZ8meccca8xz7fPBDDMFi+fHlc2aBUKrFp0yZkWeaQQw6Zc37SU4FFEZDBwUGWLVs2azTE0NDQbktULAbTm0qdfPLJ+6WpVBiGrF69mhe84AUsWbKEQw45hNWrV8+oFbQz7VC/fX3Atk0GsiyT7erDcX1U1QEJPFEmEAQiUUWzm7iqjuw6mLJIodCH4TnUrDJpScSzPbSUjtUoIWe7aVghWdenoytFJIjIsoTnuKTyyymNbkZQqxBEeI1RGiN3407IiEe1KiLbth335ZhrMb09sfM5bLcHLpfLmJ5H9ujl1B8dRJVVgjDA9ByyHTLB4CBydzdu5JM7ejmT9z+GnPGxPR878KnZJqHr8PBnriB8wVqmKi6GZrBimQGCSzarI8gafqQQBK0VpSK3Mr5FQSJU9Fbipp9CFEV81yefz7RWHEEIsopudGNZNlMVkXzOwBIFXN/C9VKIooTjNpAkCVXrArGGpilUGw6iqNBZkEEQcasRpq1jWnXy+Qy218R2PDxPRFahoKYJsPF9kWbDRhRlOrtUbNsjjLKAQzpjEAYRUxURRSmgSk0kWcTydYTQp6MY4UsGfhCiaQpgY9kCktZFVi4jSQG+kMFrmui6gi92oKuTOJaJ5ylMTFaJop/TvfrlcQWD/bUC2XlbTdNYunQpS5YsiUunDA8Px/dWtVrF9302bdrEn//8Z7aNTrT+JkIkCEjdK9F1ndNPP31Gj6C9sa+Z6O1M/b6+PkzTpFQq7Vcz8JPBogjI8ccfz/r16+MojTblcpnjjz+eqampxTgMF154IVEUceKJJ8YrgAPRVOoNb3gDZ5999ozQ1r2VCl8sE48gCHEvBrXnTNTJ/ySb1ig1PSLPxRJ1UpqIFwnIgogqS0SSSG1yO/meVTi1HbjpNKLrIxgdCK6FKLYiUqp+CiVoYhgNVK2P8bEhPDdAT+UREIhklZrpogkmvlPFdL9DJqPTueIsVFXFcRzGx8eRZZl0Or3biK350s7NmN6XOohC5JFh5P5+Gtu2kTEUZEBobcD29eswuqSW89y2kKOIseoorlklmvCQv/8A+vOPxY5slFSRnlQKz9MJgxTFoke1KrfM5SKoqoIgg+9CJqcjiA6m5ZDSBdJpEdcFxzfJCHlUVWJiskIum0JVZcIoxK2D5WgQ1tE0FVEQcUKLlG6gBlmaozsoFjKoigCiBFKeyYkKuaxIKiOCqdIwZSRJQpZtOgo65arJVFVEICKdlVBlmdGJEFX1kCWJVMqn2YhaIcUFhUw+jWk5qKqEZfkYukKl4oIUIqMjShJNW0dVQM+omFYIeGSzEr4X4nkOmibjRwoNS8PxK6RSfvx9tHM45pJY176O58Ke9ikIQpwh7vs+g4ODlEqlOBT4gQce4E93/5Cm5SJGEYIoIEutOmwrVqzgzDPP3C+1tuYy/nZ726cbi2KQ291JK5VKi1LxFeCnP/0pv/3tb/n1r39NsVjk4osvBmaPAZ/ObMXRDgSL6SPo6Oggn8+TSqXI9yxDEEUC28P1I/zQxzVNIjHC9TyCKEA0OkCSMO0pVMNotVYlAAQCUUVRfXRNwauP48vdVKoWmQzkMm0BiHAdl1S+F8t28bwmERG+OUaz6bD1rzczsumHrcZOzWYcjTI2NkatVpuXiaNN+zxNd57X/v3LiAhUTBt/ZSeyICBHrfBXqy/LlNVkastmrKaF63u4S3J4YUgwMUogOkwMjzNm27iBjypL5Dt0OnszOA5ohoEky/hBiK61pCiKIlzPp9DZOg9OkGoPDkWWCMkgyxKSGhCSxfcCXL+Oqj5RuVdWUFMKgpyjVjPxPJ+uzgIoAY7jESFiOXVUTUGSZdB0BDVN4Ps4nk82twRBVdA0hXrDwnF9stkUgigiyQVqNRNF68QwJERRxHM9HNsl29FJpOg4fhpJEFo+FVHFsqMnJiAa1ZpNJLSSCFOpgCDK4bk+oiQQRX+r+2RoCqGQJZVKI+kpND2F7dp4fp1yuc7I5p9gmmZcgn+u3+t8r4O9IcsyuVwO13VRVZWBgQG23vcTxiZLrQmHAAig9Kyis7OTs846i+7u7nmLASSlTPbEPq1AzjnnHARBQBAE3vSmN80oGxEEAZs2bdqlSctCaZvBFEXh4osv5qSTTorLsx+IplILzY5dDAFRFIVCodDKnu5/HowMohsOnhPghgKaIGBaLoWsgeV46HqdlKbjBiGCF5Dp6cV3BzGdKoosYjUtCh05Gs0msizRaERUbA09rSJpoGq9TIwNoVke2XweSVXwXYHJmonhNyCwEEa2kBLSOOUxrO5ufL/V8CgIAhqNltmmHTSgadpenYdhGFKv12ObsW3bWOsepen61FMZ7CAk7/kEmzbgiSKm40BvDmO0jlSrECkKfmTT7IbxrSbCgIkhiXhdGn4YkdIUpF+tQ3n56TiOguP4iKLyRKkUAVWRcRwXJ1RIpRWCqogURDSbDul0ikaj2eoQmJNR9U4sy6FSF0gbEtmMihOYeJ6KqukIgkWjaZJKdyAqHpoq43g6tbpEEIZ0deaxPZPAV/FDFxBIpTQCwUZBwPVTCJhIkoBspBCkCL/sEvhBK1NdFajVW74RPwhRNZlGvRXAkc4aIDo0rJadXRIl8rkUQyMu2SyohkEgiYRhhKoqSKKF46UBi1xWIIwiJEkkCqNWU7NUN52dEbVqjR2THnVrkGOP8phiDaZpxv1X9sb+MHeFYRhHPa1bt44Ng2O4fogQRoiKiKSIiKLIihUreMUrXkG9Xk8EZJHZJwF59rOfDcB9993HCSecMGOJpigKb3rTmzj33HP3bYS0TBqe58XtJr/3ve9x7LHHAsRNpT784Q/zwAMPzNpU6uabb46bSt1www37PB7YuzC0H5iLFaWkqiq+76PrOildwUlruLJPo9rEk3RkPYUfAVoXkVPDQiWtatj4mKUR0l2tMN5UJo1rmnhCFlFoougtu7/ZtFG1DmS7RqGjA6epQxThOAFSYFPsWYk3vh0EG2SVesPG8cFslrGc2zEMjXwug7rsjLh2VfBELsb08+H7PkEQ4Pt+XAa+veJoZ8M7joP40/8m6unAHhwlECWCMMJeUSRat51Cn0ZT16g7Dum1OSb+MkRZhEbZwg0D0kvTWAMVBF2nYKjUXQ/bDxFLU9T/4w763vkqBEHBtl0URcbzfJSUgB34uEEOIwJZDnE9l1Q6h647NJoCTtgkooCmqZTLVSRZpquoQSTgmOD6OpLcav6UyaQIgoBQElH1LqrNKWzbYUlvGgQRuwymo+LaFdLpFL4fYNsOstqD6ewgk0khqQFBENE03SfyJtL4kYUbFHHsHWi6hio6NJsOluWCDMWsTrlSJ3BtFDlHV6+FZbkoiojteBQ60lSrDUDEMHwcx0OUI0Ihh244WE0JSRSJQpOILAKgGSpGYOD7Br5vMj5ZwbIsHn/8cbq6ulizZs1u2wIs1Ik+F6rVKoODgwwODrL5dz9hqmERhFGr1IwkIOb7yefzHHvssQwODs47nHahAvJ0i7TaE/skIB/96EeBVk/iV7/61QvqwTAXJiYmePOb3xyXDVi5cmUsBNObSum6ftA0lVrsMNd0Oo0oijQaDZqdfbieT9OsIqsqZggF36GJSkoxCQWVKBKxXQ8jY2A2TYzAR9VUQgGQRMIowhdUPMen0FmgXBpDVZdRrrmksz5G1kBSdTSth/GJIeS6iZFKoaW68N0yU1MmWkrD8w0atR2YViejI4N0lKqoqkI2Y+AKR9FsNmNH+/RzoShKHNLcaDTikOX2KmbkiZswIEIqj6N19eGGPka3ShgGBEGNqabJWDVEl2xsSQAhRSQIeCGogkA+LyAYBrYX0XQD9Cgi6s0wesfdyK89HUlIIQhghREZVQUx80SYdoAoiKiKhOf5RKkISQsIgyyOq0JUQxBEFFkmCEIkwwc7g207eK5FVzHPVLlKJDiAiiyLmE2LfC6LrEgggKTmmZysUChkyOZFLCugYWpUKjUEUaCrK0+5PsVEyUGWPVRVprNgUK+buH6rPEsmbRCKAqUpH13Poil2qyMiGRzbIV00kDWYnLJa/ozAxHFlIiEHUhVNU3E8Hdv1SKXB8yJEJYBARBQFgihCFAREUSWXUajUGjiWz44xl47CJgThREZHRxkbG2PlypX09/fvtuDgfJIO54Jt22zYsIH169cz+tCvGSmZ+GFE+zCCINLV1cWzn/1szj//fGq1WhyCvHnz5t02wJptPMkKZPcsihP91FNPZXR0NHZY//73v+f73/8+hx56KBdeeOE+n9BVq1Zx7733zvq36U2lpjeUgsVtKvVkmrDaZsKenh4sy6J52FlUxm9CUiRCL8BzXBxFRJVDfNtETuuIcidhUMMXMhiZiHqzhiEDUg5JcrFdD0XNU6tV6OrKk0obSJKIgMBkVSab6cZzy2TzeQp2CkEUsB0V2x6m2LOCDkEkCMGXfKoNl4gyquxRLg3j+Bqy6JDJT6HrGh3ZiHS+j3pzBdlslkKhQBC0+oXLsszmzZvjhj6SJCFJEoooMWWahD2dMDxKFDUpD40w7AWIgFeawstmscMASREQQggrNTp6O4gQkDsVgkhAKJXJZHMEYUgYtpx+fhDi+wF62qNm1hGUDNlcBkURcByPWi2iq0uiOhViuSVSUQrNKOI1LGq1Jrms0IqWsl1Mz8UwdGRZpFSq0NvbiZaK0JwAywrw/BSN+hj5fBZJFHHCAEkUCYIIz3PRdY2AOpqmMT7ZQBQFeroMZN1DdxVGdlTo6iqSKypIksT4pIzg2mTTMnomwrSyNM0xNE3HyKo0bRtZVlvVmVMRE6UQRQ0RBYlsKk25KqBrIOvSE+1v9VaZE1GkUldIGz6CaBJFrdWHHzaRRQmUDJpuEwUSjudTKo1xXP8I6eKp1Ot1duzYQaVSIZPJ0N3dTWdnJ7Is7xented5rF+/nscff5zBwUEGBoeoOR4QISAgywLp3tV0d3fz2te+lp6eHnp6eti2bRuVSmv11G6A1dXVFTc4m2088xWPREAWwIUXXshFF13EypUrGR4e5jWveQ2nnnoqP/3pT9mxYwcf+9jHFuMwBxXzqYW1r7T3lcvl6OnpYWhoCFFTUHUJ2YxwRQk7CBECEUOTaTghXVoN1w8QzEn0lIEieNi+gNaYRNDyBHYFoyOPbYbYQQY3sMimwPdULGsCU+rGdW3SuSxqygCvClqOasVndHSCtAGarqBonShKjUiU8VyJRtNENdJEeJiNEqaZoVRqki+IRNEo1bJNdaqHMApRVJeU0UkYVJEln/FxhWbDonzDrdRcj3HVwPVcKNtQqbVKt3cYyJKINGWjNpp4no8jCfSuKlAZqpOSNYJSmYYfkl6ZxR2pkq7VcMKIchhQII0AmD/7A5k3Po+gmaLREMkVAmRZx3H81qpCEkGugQ+uK6DIJrbtIAoimpbCR6FhT+C4BXQjwnU9NE1GFAWcqIaqKpSrAZOTFdJpkWw6x1R1BMcxkCSJMIRUysDxplA1DSdwCIKWX0bSwAsdgiiF70+iqhJIHkEAURTiugE9fWncyMW0InRNRZQAQaJp+oioFDo1Gg0T1wVdhXQGXLf1XkNXUdQUTff/z96fxtqWneX96G+MMfu52t2cpupUlbtyhxtwwAkK5C9wbBwCJARjK0SxsUPgQxyUREmIlEZCkYBIQJxGUTopXwOCBAS5IQRxo2C4F2MMriqDXY2rPc3uVjf70d4P85x9q+yqcnUu3D3S0dl7r7XmXGvvMeY73+Z5ngYJTKaSuu6AFKJRGRgzTrgpBERTgvfEaY6KYvRpT6c1Dz9yjcvD/+HClXdweHhI27ZYa3niiSd44okn2NvbO++LvlQ9kBACf/iHf8jv//7vc99991E99P/heNWM4poCpBRk+3ewt7fH29/+9qdIlgghiOOYN77xjecGWDdu3ODatWvnBliLxeIp5ecXI6T61YCXJIB85jOf4W1vexsA/+2//Te+8Ru/kZ//+Z/nd37nd/jhH/7hr8gA8oXwxQggt4iLRVFQvvq7aD/138kLi2k11oIZOoY4JS4P6fqaqDhAiIbeQJHEJFlMU7dMMjFKwweQsaJp23Ha6fSUvcMDdH98k1lvuXY0MJtNkGrHdJ4iKVHpEm0MZ2cnzPcnZHkxEtsWl6i3VxFSYWzG0NTEWQAKzk4fR0VzEB2bTYV3BUJ2KFVRNw3WNgxDRNN0mETQrwbsNGHQDrnMUEfbMXAoQWssF28riFcDeSSw+znaQYTArzYUSYQ5KBiso7gyw1xrkMKT3r6k0wZEYHCK9r//LtE7/xyDNuy2nsWiR3ZgrWPw43SVtTl6iAl+IElipFRok0J8ihDjJNZ2O5AVEikzqu6IZZ6johltu0aguHxphg8NUgiaRiJES5rFpOmEflhRFHvsdkfnvhvajKXaMSjFCNUSKNjsKrIsR8pRjbjvLMY1FOUc6yp21Wxcb6pjsZjz6KMVQgSCmBBijW5ystSA6hhu+oJ4pVEyAlFi3YCUCm8jiCzeaFQ8QwrB4GqSJEKg2MVbrC+o65aHH3uY6eL3UPM/c84Qv3LlyrlQ4i0+1MnJyXlW8kz4QhmIc45HH32Uhx56iPvvv58bf/R/ePS4otMOIUAqRRKN5d7Lly/zvve97/OmMG9l889mgHXLlveFZCDP9v6/EvGSd3v+z//5P/yFv/AXALhy5QpnZ2cv9Sn+RPAnXcK6dayLFy9y4cIFlssl+fyQPE1Isoy203ghaQcQfkfvJC70eCICgXbQeEqSPMU4TRCBbTt6UigqPKN3xK6NySYlFy7PmZQFuBpnoTdqvPsuCpTqWM6mYw3ee5pWsrspm54WU4rpAcv9S8z355RTSZoHoiTCyw5CjnMjnyb4HGcrrPUMg0aInv53fxcfAogA2y1SSSIJ8VQxWURcnCQUqcIjSGxAakcSxQzWoi5OEQhibVjkGdtuoLeBWILvB5TwWG8ZTKDuHaePXqP/tV/DWsdmM2qXSSlRcYMZHDIZf++7qiGKFdPpOG3UdEcIEZMkMXXdEnzOpMxBVTjnsXbCYNaEwKiUazssLXE6Z7upgILJJCOKx9eenW0hBA4Olviwpe8UVdXhwpb9/SVaG6pKoK1BCEleWjabHau1xlpPOfWE4Ll+/YRBx0wmGUNviONRTXg6DzT1GJTSbJw8MzYFOSVKPNrYm9pso3MicvycTo5/J+89SkqEmOF8yWw5Z7pI0KambTo+85lHuPbYr+C9J4RAVVVMp1Pe9KY3nfuBPP7449xzzz08/PDD1HX9tPvi2fZK3/c88MADfOITn+BTn/oUN+75NY6O16wai2csXsWRYHLxVSyXS77927+du++++xn30i3cEix9/etfz9d93ddxcHDAarXij/7ojzg+PiaE8JzJsrd4Yl8LIM8Tb3/72/npn/5pfv7nf56PfvSjvPvdo+TBww8/zKVLl16KU3zZ4YsVQLIs4/bbbx9Vcu/8c8gsxgwDUaqoW4tzhrYzOCS2bdDOILwlEOi7NXGcApogYpStkOkBNkj2DgoEYpTx0BJtHAcXD4kjBQi6fmBTg0r3MQ6IHUkSkSYJy8UEEeTNkklM3403DSrJSLIl5fSA6WLB3t6M+b5iMs/Jp5586klLQZxq4iyiKBWzNAIC2WFBkUgOrKboOqSQSCER25oiinFOEKaBZBERVxWpUkRCkBYeHTmU9Egp6LUlKSxRBn61RUmBFAJzdsbgPNZ72rbDOc/x6TFBjtyGpo2JI8WgDVGk8D4QRQ3ar9F6LMsYaxBSoJTE0pOkMZDTVC1t27O/vxxJhINBEGOMxYdAmsYYX5OmMf0wUFfdyCSXZ8RZwtHxiq4TZHnCYm+s+V+7dkTwgXI2kKUJx0c9Uiom05wkUXhKvBsnkLy0rHab0bQqNVirsTbDsiUrxiAxDANSghAKw3jhS5Lk/LNZAkqNEjzaVKhojlKSwVQItaAsM5wIpNmS9eaUhx++xvr0t885QLvdjtVqdS7j89a3vvWcSf7pT3/6/AL9dBfnJ1+Abxl/PfbYYzzwwAPcf//9PP7gR3nsxoqjTT8qAgiII0EWx5Rlyate9Sre8573fN5xv1BGcUuC5C1veQuvfvWrz4nKtwJfVVXPup+/loG8QPz0T/80p6enfOQjH+Enf/Inz5vp/+t//S/+/J//8y/FKb7k8HL1QEII55tyu93yxBNPnG/Oxx9/HCEjymlOGseYQaMHj/UBZx0qjem0GKVK0gNC8AwGpFCjOZLzgMB7ydAbZsspThtkPOHsZIX1nrTMmc0KyklGuzlmt2sZjAc5IS5KkKPkepbFTCYFeZ5iBkPXDXifovUtspmAIBByhlIJabogTRckSY5zMUoWuN+9BzZbEAoLRIczlBjvLNXFKf5gFCYs2xYlx2mr+StGjk+sEjyOnTb4NBpLWakg4Gi1p3j1hXFKRyrmhaI3nnAwG0c+P/a7WL+iG3rarqMfRj2pthuYz0uUVDTdDitrIjXBh8BmUxMlLWWRU/c3sG5AxR5rPGerHbNpQVEkWNYMOqFtB4xbM5uVNP0RAUHT1zRNS697nOgxvqdrA7tdjbOWJB/lQrpOAZLp9BChHNZlpGmGlIqsUFTtBiUVRZFTzgNNoyGMfZnlsuT6tR4fPFEUoV2NkJNxoEB1DMMorRHF5uZaHZV8ublunWhurkPw1EQ3/UN67Tg4nBOldiSb2px7732QRz/7y3h7HzAGqZOTE9q2JYoiLl++zJvf/Gbuvvtu0jTlscce45577uGRRx6hruun3MGHEGiahhs3bnBycsLHP/5xfu/3fo+H7v1/ce2BB7lxUtH0hkBAKkESK8oLr+Dg4IDv+Z7vYX9//2n30nO5uEspWS6XLJdL4jjm8uXL1HV97kr4ZAOszz3+rff/1YKXpAfyile8gp//+Z//vJ//xE/8xEtx+C8JfDFLWE+WgX+yV8itr28d4/j4+JyYd/HiReq65ihNyc1vMvSGIVU02hCblDgy1E1gMp3QG0+iWsosH++6jaGYXyYyA70ejYO6tiPNUqK4I46XNI2jqiUQY4Vhub/P2p+NPYhKs1pvUSpFEHCyR6SKXp+SJ4ekWUKWp4QAbTcysqXMMbYmTsa75ECNYELbDShlWa81chj5DllbUycpBhAEMuMQscC7QHr7gmzVorqWnfHsuo7WWsrthkLCygXMQY4/rkl2LfFiTl81KK8x3pNudqRJRBxJGq2JVcSsTAj/74/jvuMdhNRSNy2TUBCpAqIW3WyQWlKSkSYxp6cN5SRjsYhYmw3BBZyeIqKWQY+2tManRMWGyEg26y0Cwf6FBGsbutayWQesbcjzUWdr/B1FSBGxWM5uCu5pNtWGLEkZMCB3WOvY1dfJ8ymICmenNK0jFjDfC7R1C6JEKMfeYcBoQd+3KAXZxZimcSQK8kLTto5IJcAEL9d0TUFZKozbEqmRiNjrI9JkZOY3zcBieZG6bnDOMZleJIgTijJCKtjsTnGPQT88yB13HDFfvoMQAm3bcuPGDYqiIM9z5vM58/kcrfW5QOItkURr7fmkVN/3nJ6e8tBDD/HIp3+do4c/y+P3P8ITJzWrWmMDxEKQKkVWTs7LZrcqIE+3z14Ip+PZDLAODg6Yz+fnU4RP3vtfDXjJ1Hjruubnfu7neOCBBwB47Wtfy3vf+97nxFL9SsTnBpAnOw5+bpD4XBn4W4q+s9mMNE2JooiHH36YV73qVec8lsuXL5+PvF5d52yrDi8ULni6XpMlCh8VBO/GPojtaPuUsojpB0G9vsZkNmfQLVE+wfQtRTSjqs+4/Y6Yvs8w1iJFwupsw6XL+6gsYTotiaKYXXWK9Slx7MgnS6JkStseY9khYo8dNkTpgihSxDcNmQISEAg5QYRRHsXZlDgxZPd9Eicj3KUlyY0VZZYhgyNkgTyTJFnC9U2H8QZhLW7QZLft01uLOpwTVjWJC+S3z+i1JbtQkp916NMzgrrJ17gwJaw6wmBZXDngtN6xai1pLOlPd6S/+X+J3vVNeAveB4xJSfIKhIBQYo2jN6dEUYJAYkNHkkQ0rWS93oLqSJMSPRj64ZgySTA2ZlftONhfUOQJxkqGPqHvLEmquP32i5yuH0VriTUpxq85PLhE3d4g1hdp2xVJXBLnHV0XCKEAeuaLKV3fsVoHhCxAbZlOp6zXjji25PkUETVsjsfpPRW3GC3w1tK7Y6aTyZjZBEeUtlgHKmkJuLGR7j2eCiVulrH8BqnkSBJVA1meEAJ0nWV/f4+qahFCIqTgwQcfYLO9jStXfonDw9sRIjvPKJqmQcrRVlYpRVmW5HnOer3m+vXr5w1tKSVCCNbXf4snrp3yxOOPcf2Ra5xte1a7Dm0tUiriRJHmMcXyNi5cuMB73vOeZ+SjvZAAcuv5T2eAdXJycq78fUuu/cl7/6sBL0kA+cQnPsH3f//3k+c5X//1Xw/Ar/7qr/ITP/ET/MIv/ML5z76S8HSZxS2vkCezq2+NCT455RVCPKMMfJIkn8dk/VzfZRhl3u+66y5OTk64uvgzRKvfII8Nw+DQA9R9wiJuaLqYtEgJCKyHvtOobEmvtyQuQQqHVHK0+wwBKSJWG8d0OWXoB4Scst5qNjsQIka7iqyMcC7DkTMMFSdH15nN9/FeABOUgs4ck3rwwuCcRKlyHA9lLIcE72naHms9zntks+MspHjvkcZRVDvaPMMODp8qys2WWI13wi7xxEoStRW7OAYhUJdL7NUt00xxfesRWrB/5wL/wAnZHXv0w4B1gfi2BebxM5J6hZIJ1gtcuCml0mnCr/1/mfz5t9N2PevdExxmJbEaM6WjG569g4i28tTDDZIhB6Ww2rKuel7xyilpKjk969BGkFlwVMxnM/Iix4Qa1Gja1DQ9B/sXseKINEtYnTiS1DCZpqRFS90JTo/XqAjy2YAUGSfHhiy1qEiR5i29Fuyqhvl8QpJ7+m5k1nfdwHJPUNcDMnbEYgEqQusUIRLyXDMMBmcDfa+Zp+BMAaIlAFKU+DCWQZN45FY13Q2Wsyt4D9WuY77Ypx/WxFGEUlM8Ww4uTKl3BqSk6zR/+Mkb7O1VXL5cMgyOKHrzzRFmf74fvPdPEUV95JFHRq8c+0cc31hzenKdzXrH8eNXOVo3rKqOrr8ZPGJFEivS2W0sFgu++Zu/+VmdBl9MAHkynmyA1bYtp6en5wZYMPZ/yrL8gpa5Xwl4SQLIj/3Yj/Hd3/3d/MzP/Mz5L805x9/7e3+Pf/gP/yG//uu//lKcBoCf+qmf4qd+6qf4nd/5Hd74xje+bIZStxaS957dbkeapmy322e0pb0VBIQQLJfLczOsJzsOPt9zPzloCSHOCVDT6ZRuUtIPhjyDVjuiAfrUkqqUaGjwyRQhSjw1ph+Yz+f0ukUFTypKlOowriOKFX27Io4OMF5zsCcZhpS+1yilGIaew8OSIB1pasiyQ/p+g3Wepulwtqac5CAiIEepnFafkQhNEIaga2QUgYsZeovzju5jH6fTDuVqbJLh9maEXUNe7aiQ6OUctdkytQ1VmjI4T3rbPhxvR+FAAv2qpswkaduTqphIBuyqQuQCt9kQ8pQ0UfRnp0yWEVJIHIJJpqiP1igZUFIQgCyLabsNqxUURYyIDJtNz2xSkCYx+aymayVmmJCXA0JGLJczkmgBakOaJXSt4sb1ntkiojxYUDXXSGyJcwHvPVmWoMMZsQ9Eao9+OCZKIuLMYX1L8AVtVzGZ5qhEI33JMLQQYO+iYHA7rC0I3pBEMVJJNrstcbzAuS3DIHBmAmzJJ5ZhgKbuKcucIN0oMJnMiPOWpuoBgYo12mrSaIIL25ELAwxmRZQkDG6D6T15HtO1HVLBZHJw06MkIssTmnbDdJbQ1GsAVqsdJyfXuXp1y2L+Kfb2LpNnGuSfIo5j+r5HCEHbtmzOPkYcjml2iq4+4fq1I1brmus3zji+UbHbttStwQmIIzn+S6LzrOCv/tW/+qz76PnKjDyXgFMUBXfeeSdXrlzh+PiYJ554ghs3bnB8fPx5BlhfiXhJAsg999zDv/t3/+4pEVcpxYc//GG+9Vu/9aU4BQB/+Id/yMc//nGuXLly/rOXw1Dq/vvv55//83/OQw89xIMPPsh6vea3fuu3npI53JIzv/V9HMf8wR/8AQcHB1y8ePFFfe5n6qcsl0vuuusu1us1TfNnabpfp201VnsaV5OkE2TkKPKMqurYW0psiIiVRmtJkaX0xtHujinmFxiGDfPFjOF0C0i61lLXLfO9gqHVoCZsNms224JIRXhniJIGlGE6LSnylF11QlMHtGmBjCSNkSgEOULkmLBBAnW7Q7uWqjF4o+FgRnaypcehBAzLkvLMklxaMBhLMy84aAd8r+m8QDpPcIG87WExofIN7vYD2FbkusWXE6rWkt15keHqKWHXMrsw5WztYT5FbirSvsZFM3Il0PszTD9yIuRvfwzxxq8nBMH6DKYLz+Hh/k0+REWUeKJhitYWbToW8320tuz660wiRZ7tc3p0RBCe2XSCNhsA+i5hGDbMpgcY6+iHLSrK2Oyuk6UZSawIOKwdm9hCCLKyJ4SY9XZNlhXnTOum1gQEZZmhwxm+mRFCh4oUe/tTblwfmE4hihSWmhCWo46ZrBiGW1NXKV4O6H40xALwXtKbY0SISOKxtq+NpSj2CKGj1zsm+SWs36JQlEXGrj6hyPfpOoOKYmZlgTYrYrXHdnuKUpLN5ozVWeDhh6+RZTFK3T/K5gePNg7vHF0nsXqDGQbWm5rVruOJa2t2R8fUnWbXDHTGEglJJCVRopD5yHp/97vffS6i+kx4IQMtz/XCL6U81wO7++67z4mKTzbA2t/ff4rg7FcCXpIAcnh4yD333PN5c9ef/OQnn+LV8WIwDAP/4B/8A/7Tf/pPfPd3f/f5z18OQ6ksy7jrrrv41m/9Vu644w7uuOMOrly5wnw+f9YF9mTbzZcCTydV/5rXvIajoyNWqxVCxERpQmI8fWfoOk0UdfS9Iiln9DaMUzQyoC2owVAuLrLdXCf3nhAExjqifFR5jZOIajfADJwwTGcOY3KG3mCUQpuavb0SpQT9cIyKI9I0Js+mGJvRtCusKTBuoMAQ3bTCdSZDa4sPkukDj3CsQZuByHjKoBmSDF01JJliajVdiBBBYrwj8p740j4+OIblhFnXkzc1WkqqXmNnBdmqwUaKIUBnBvSiIN/WFE1HE0V0g4V5TuY9YrtDJRFloni0qkjTnPXVNbH/A5q730rlu7GslMRU3XXUkJEVGZ0QbLY1Fy5GpHlH3W9hEIRJRt3dwIdAkkboUEMEUk45Pdmy3JOkkx5TtTid07ix+bp/MGdTXyPROU09EEcFy8UUPawJboq1HWWZ0ZszqjrCDBY4Y7YsWZ31KDWy0eN8hzFj/0AbQzmFtvEkatTO6oaWJFogBRhxDeEOyAtouhMCCu8yhNoSgsAPKZ6aLL/VRK/J8wQvdgy9Ybk84Oj0CbI0Zjot2TzxWWaTQ+puTZplJJHGOIl1Ats7hEghDFS7QBx1nPYdcWQxNibYhoCl6zRdp9lVLZttg252DNbTdppmsAgEKlJEiSSPY4rJhFe+8pXPqbLwUpWwvtD+TJKE+Xz+eQZYN27c4K1vfetXVDbykgSQH/7hH+ZHf/RHue+++/hTf+pPAfDxj3+c//yf/zM/9mM/9lKcgp/4iZ/gve99L694xSvOf7ZarV4WQ6k777yTf/JP/sk5qxa+sKEUvHQB5BZ79umONZvNODw8HBnqh2+n6T5KGku8j9hUPUVR0AyBOO0xRAQZkASyIqI3mtQaynJC3ddEgLUpQTg8W7I8YbfpsG5B3TSkuaacKewAkLLdadabiulkgvMNeRxhfcvgLGl0SJLEZFmODyldv8H7EuMMXXODEAqM1YT1itBpQpIyzKdkTYOLFA7o9vdIuoZUjUZNdm9O0TSkrmerFMJDM5vC2Yr0tn26pmfXaKYK/GpNFkmM8LS9Y3rpAHe8YhJHmEhSd5a9Ow5pH7yOzSMi4cniCGNg8AFhLO3v/R6TP/N2tDb05trN4FfgvWboPXmWokSJZxSQNDqj2kqcD+wdzGn6I6yZEUUSPVgIgaI4YAinpFnCdtOjlGCxnxFlNaqTrI4DceaJigqlFGePe/KsJU4FadlDH3N66phNJ6SZJo4VhAl9N7C3vySwQvcL8tzR6VMSk409B1ZMJzltV6KDQUYNwgWCHzOFWCnaVjKfpXTDKBTprUdrS/A5aeIJgIr3aOrrROnIp+k7TRLN2O6OWCwmKBljKsuk3Kdpz8YhEGcJKVij0YMjEQFjHHEcGHpDFFuGACpS9MZirKCqevqzM4beUNU9dTvgPERxhIoVSaSQxQXm8znf+I3fyKOPPsrh4SHL5fIZew8vVwB5cuP9yQZYXdd9RQUPeIl4ID/6oz/KRz7yET760Y/y4Q9/mA9/+MP89m//Nv/6X/9r/vbf/tsv+vgf+9jH+MQnPsEP/dAPfd5jL5eh1AvVxPliZiC3zvGa17yGS5cuMZvNKCcFSZZijMcax6bucGagGyzWCYQzGBew/YBKEnbVKR5II4EXgr5ZkSZTmsYQRxFJPkp7SCFYndZ4D9o1ZJOeveUUH6DvNZtVjdERURyjjUb7U2xoMH6FvFlySLOEJJrhQ8A4S3/vH9NNc1IZgQ8IYRm8Z9rWxFJAgLa3THyHkqCrijrA4D3Oj+SxVmtMpihcj5AehMccToCAmKZEUqBiyWAdLlPE8xgl1M3SjEPujxYE/apmf5riqppVPRBLRZ4m5H/8Keph5GgMbUw/aNbrhsnCkCSKTXuE9p78pkdIte2YzQvifORPmCGnrnqiWFJOc3bdMSAQYobRBoGE2NK7DsGUtu1J4j1E4ohjRdeN8vDT6SFWdgRf4owlBIhyw6bakaQRzgZUWtHUgcGdEkURRZlydmoJvkBGA8a4kcSoLUoJCGOD3PvdOPoLdMMxkRqnqQZ3TFGkCAlVc4qSE4aup+89SZKyrh8hjhRCCna7BhFKfKhYLqcQwsgdcTneghQz4jghzRaU84S0SJgtciaLCcV0xmJvhg8xTWW4cXTK7viYzbah2tV0g6HTHiHk6BgZS4rFFRaLBV//9V/P937v96KU4pFHHuGee+7hscceo+u6Z9yTz2e/PZ/nPxsTPYqiZ5S8/3LGiwog165d45/+03/Kbrfj+7//+/nf//t/88gjj/DII4/wi7/4i/zBH/wBN27ceNFv8rd/+7d54IEHeMtb3sKb3/xmrl27xvd93/fx+7//+8BoKHULT2co9XSPvVg8l8DwUgaQZzvWcrnkta997dhUn7+VEARpIsdx201N2xms9RjrQQqqBoKE4BOSJKZpOrS25MUS7Qy93gBQNyMPI05rimIPYyxNI6kbPeo0FZJpIZnPJ0SxYrdt6dqIoXNIMUWpCG0GjF9hfENvT2jbMYuLlCK4iK4b/cszaxAI+klBaz35IsU6S5Vn4CHXA9aB3ZvQa0eqB/JEQtfQBQhYkmEgjWL6TQ3LkSBnty2LIkLvKjqliKXA1jsmhWLY7rA+YA8WaAt5PxBJiV2UbJsBgqe3A+n//QRSpmhtOT1aU2YH5HnKELY453FWUvUnaG3J8nSUQlcBq1O265ph0BSzAaIaPRhwS87W1yinOUka4Z3CGjmSPyOFERtAcLapSeJRisarCj04jLHjDQJrrBkJoEIIJnuWzbrB33RRTIqW4KHvxsejOKXpxqCWlT1tPf4djDcMbpRxuSXyGEVzBOVNZz9DpJKbOmwxnoo0jei7sUemMk/T3hil7b1ntaog5MioZ29/hop7ggiE0OJtQIoeoz1pktD1njRNMFqyXtccH51SNy39+oyhbekGS9UYto0hBFDRaPyV5QllWXLbbbedkwZf+9rX8uY3v5nDw0NWqxWf+tSn+OM//mNOT0/PL+wvdwby1YAXFUD+1b/6V/R9fz7//GTMZjOGYeAjH/nIizkFAH/37/5dPv3pT3Pvvfdy7733ctttt/GLv/iLvPOd7zw3lAKe0VAKODeU+p7v+Z4X/X6eK17qAPJseO1rX8tsNiPLstG3I0uJE4kNgU1V07YDzg1Uu47JRNAOBm0bnBNkWUrdNRhriNMU5yxg0LoBZmzWDeXUjrP/ziNE4OR0R99LtKvwcsNib0pRBuI4pml6VusdzhQYHXAuRwhJtW1p9DGDrXEf/y1Ms8UEw1CmSCFIhgFlBnSeoNRNT/A4opsVCG/JYokQgaqIUQIyPV7wh8WMdjIhjiPyNMb5gJ7nuOWMgGReJCgBTVnS7E2IpSDpetI4wu5N6HqD2x9FAydpRJkmnFU9Dku/qTltBvTH7kNrTZTEVNuayvQkaYRiTrON0dpycLhAStjUA4SYALTtQJEfgvKkWYwIE05PdggJBwcLer9B9xn1rkOzZW9vhh4sbpijtSErEpJC07aatnb0bs3e3ozgA/X2ppWu2jKd5TQ7hXOeEAlaU0MoSWKJF1v6fpQMGdwpSkqknGKduylnEmPZIFQ7qiMHT9OdkGdjaXhdPUxR7COQGOMoJwcYX6MSRd9Jmq5BZVC3ZyQ3x4h3u5a+VSipSJIJZZmSZDPSLCGEAu8DzqdUu4bN5oy67vAuYKszQvBUraZte6pBY4xHKkEcSZI0Jp/ezsHBAf/P//P/nDuVwsifunLlCm95y1t41atehZSSRx55hE9+8pM89thj5xYCzxXe+68FkC+AFxVAfvM3f5P3ve99z/j4+973Pn7jN37jxZziC+LHf/zH+djHPsbb3vY2fvAHf/DzDKW6ruM1r3kN3/Ed3/GiDKX+pEtYX+hYRVHwdV/3dSOh6eCtZHkCfkz7d5Whblu08WgvMHoUU/TO0fUdwY0S35v19ZFR6wVJkqFtg1IS42C9sqjEEmct0/IAYyxV1dF0hqGPAUNvdigJy+WUWPVUdcVm03B6tqbeSeq2x/Qx9jOPUicJsRLEvQE8bZmSxAII2EVJbwyZ7RDSjeJ/WUS2zAkEgojo5zNwnslNP/PTbUcZC5KhJ4siBLBthtGZrm2ZpAoXDCebluzKHgiB91CmEeu6J1YS7yxxsNjgiSKF86CEoC1yto1lcfV+pBBc3xxT73pEFI9lq6phNi8oipTabLFmJBNaY5lMRmfH3kKeHYx6Yb1mOi/owzFxrDg72SKYUU4yVNFhrWO9GrOC6RLyMmV7BiLMUJEiLhrwE9qmJ1JzRCrpm4QsT+j7gaJIGG5WcIpylKjHTVBiho8MzpUkaUw3nI6KzGKK9T2G8UXBeQgCKSWD7kcJFH9K1ZwQJzF6GEe659MDrKuRKqKtAsOwBWXohy1pPKPr13Rdj9YNu22HHnbUdUcUGYbBkGce7xOGwdK3Ldvjq/R9z2q1o+0NVW/pOodQAhVJ4iQmTRMWiwWvf/3recc73vG0+1JKyd7eHq973et405vedJ6VDMPAer3m7OzsPCt5NnwtA/nCeFFN9Mcff5zbbrvtGR8/PDzk6tWrL+YUT4t77733/OuXy1Dqc/GlVMK6hTe96U189rOfHRWQRUGcdKR2lC7ZbDrSPGcZKQYjkB7SWYL3grbvKMsDsqymrmuSNCVVU0Ko0X5NFCn63iBNSicrLhxmlF2C1gFP4Pj4GmUxRyhw+RYhwbmBvb2LGCvAl2x2NX1rSNOcXkPda+IkJ3cBc1OPSxtLLiWNkNRZwkwbJs7TGYsWEPmAMpYyidHW0yeK6SyhMwJrFP0sR51sbzKdFYMeMMsJdleTSEEap5ztevan4J3DOgd+ynySYqzHTDLySOE3LUlWkMaK9WpAZhnOBdI4wj98H9Udd9LXBUY3hJAwmeTIMOXMnpFES7qmw9Ey3U9JQsZZveIwv5Oj9Q2UjEb2uhFIBcLPMGaDEAIjBYmS2CHDOc3FxUVMvEX2JcOwJUkM6TRi3fQIxpKaizuCcTTNGZN0jvanNLsEEWZ0bs1kHlNtxyZ7XGpw0PkVudojS1OsyVAJOCvOm8/dsCIvxhutoTfMZxfRdstgd8ymr2G1vUakJFmWUVWCvDxgtz1CRjFDb3B+IFgDHpzLUdLgSZFS431OCBapRjJl13V0XcV6W7NZ71itW+pqoGoGmk6PIpaJIlGKOImZLK5w4cIFfuAHfoA8z7/gnsiyjCtXrnDbbbdxzz33IITg4Ycf5rHHHuPg4ICDgwPyPH/a134tgHxhvKgMZLlc8vjjjz/j45/97GfPfcy/GvFyB5A4jnnzm99MWZZMlneT5ylxHKGSmO2uZbve0XaGYbDIIOi6AakyXPDs6lN8GEURm6amrhqkUAy9Jk5GRVbvLE2lOT4ZiAuYLCyTcg98AEbP7a6zJNGcEDyOLUJ52n5FEkeUk5To+oNI3WGNQWtHayzSaKyz9HFCmIyGRsFL+iJFKYGSApYTmjQjUpJpnhAPHZ0D50AMPWkao43FFBlylkHXkxejGm9XpsR7BXJomeQxjXbYaYpalJhdw2ISI6Rn2w7o/XEwINYDw7pib5LQ9ZZd23FadYSqpXzkGrtNTVN3Y5NZSI53p0iRIiU0TUcSLUizGK16IrlgvdqhB8P+4ZxedOhBY4ZRuDDLYjpfI0XK6U4Tx/FoTCUarA30nSZNY1pfo0RCW1ukkkznxSiv0pQEAnGsmM5yquqmdIyCTdcTRaP3iwCEHwOD9sdAQElJ05+eu1G2w3Xkza+b7pQsG6VMmrqhLEra4TpWO5SccHTyBHmZkaYJCEGRHWCsJYQpve6xfsCLNb2rcaxp2h1RvGPbrBn0jvV2Q93tqOqO3ckxVTPQD5qmH+i0pR88KpbEShIninJ6icViwZ/7c3+Ot7/97c/rAi+lPM9M3vSmN3FwcMDZ2Rmf+tSn+PSnP/20WckLDSBfTZ7oL+qTftu3fduz9jg+8pGP8G3f9m0v5hRfMviTLmHBc8t6XvOa1/DGN76RxWJBNn0dIlII71FScHK6o+s1TdvjcdRNwPkabQKxgqbeYYxnOsuo2zNCyDGDx/sMGRmSYjQ4woPucpquI5toJsuIosjI032265rtpsE6QfATvPPs6jW9W2MfeYymMzgXmAiPioDpjExJMm+JpMA4z4SxRBKAYdDk8bhM+8FhfSBxBikFZlqyjVOEgAmG0GuMd3RlTq8d+7OSSBvq3mLmU4bBkaeKwgwE59DzGb12zLxj6h0gWG16wt7YQE5wpJeWCBUwzkPVYb3jZF2j//g+8jzhrDvjxuYYbyc449DakOcpQgoaf5ML0muqTUNepAxRjYoUQ5sytCldqDk4mI9DDl3G0BmyPEHklq4b2JwIGlczm2dIKdicBJy1VHpLVGiOrw3UVU3wHp/saJsI7xzOO6IowQzjmkmnmt1uLFEp5nQuoORsLNU5hwglWsfUw4BT4qYWmCPP9sZeiZLE0ZKqqYhyg7EObz2CCWdnV1ksJyDHz5YkMUpGWFvgEbStuimNkqJdwHvB9aMdddNx41pDdeM6dVUzdIamM3TG0/b2XCpfRYqsyJlOp7zqVa/ir/21v/ac98Pn7p9ROTo7l21/5StfCYw90nvuuYfHH3/8XIYInv/U1lcbXlQA+Uf/6B/xsY99jHe961388i//Mvfddx/33Xcfv/RLv8R3fMd3cM8997xkPJAvNXwplrBuPe+bv/mbKcsSrTVprEiTmDiO8AEee+wY5wK7qkNGmrbRWGdwQaJUTN02iDAZhf/8brxzFeImydAwmcfYm72R3XZgvfLEsWTwR+ztTSnKFKTAm4yjo8dYnwWEUBAKxGDYNR19MkpvyH6cBLLWk0iPn88wRT4GEaEJHmyWwiSnCA6Epy9y9GCZJRJEYFUNMC8RIpBGirCYUrcOoSS5HSgShSlLTrctZR6T2YFIKdThkvWuwe1N8AhSGUgOD1jtepRU5HsFIUswVUMeKyZ5xrYe2EQZUaSYFgnpgw9jkCimrFdbqhWQOcpJSTVUWC3YdgFjHSpRlNkBrYUy26euGqxxlNMcHQ3IMOX0ZA0EfDJmHGfHBqstiEDINMEHzs7WWFNQzsa7/ixLAYnKSzaNwxqLkJ56OKNvRlHBLjQIIcjTffpB0/kdhJjKbOnMiiyPMdZhfUMSj+WcbXONIovxPlDVx8wmFzHG4IMilkva4QYi0ZjBjs3vIadtBuazQ6JoIE4USaJxNlAUgaG3RJlls6qptjX11nHj+or11UdZbTv0YGkHg/Wjppe1AaUkkVJkeUQxucTFixd5z3vec877erFTVVJK9vf3z82k9vb2OD095b777uMzn/nMuY3C8z3+V1MJ60X1QO68805+7dd+jb//9/8+H/zgB5/y2Ld+67fya7/2a08h8n214Ys1hXXLJe2Wqu/n/u+9Z29vDyklvT0gTm8gjSX4wG7Xcna24cKFJbmNcdIgXcwwdKTpgkg1rLdHFGVOlszohzVNfcykvEDTrMaSWGqI4lHMTvc7lFhi3IY8PyVOHXmSUpY520dOMAz0WuFv3EuqDcL4kTSYTonsDuMdPk1waUziNHWAkOXMnSHWFi9gq2IKaymKmEEHaiFYznImg6MLEmtHd8JEQIgjtrsOVU6wTpMpgU4TdnVHOp8Q1Q3BWvJsNKXyTmAnEQiHqbeUWUISSbw26L0Fer1lEis6kdBqR9IbpJDUQ0dy1nF5coOT+avZrkfi3/xCTt+3ANTrgIw7ZvMF3nmOdqdkRUrb9VjjAE/jAOvY7jq6tudyeUCHR5oMo8dR3LLMqW0gEnPipGE+m2BoqLeaclLStgNSwNADypEXiqYRbDdbkjRDpgZvJ8SxoKpOSEREGi+xbkNlLAfFIU4NtL1nUu7hnGewFSqV9O0xKlKjHEm9YlYc0nU9zkGaZGy2j7Io76TtTglA3fQYPRDHc1RUI/oCIToiOWW3OaNrB+p6QA8doa6JYnCtpW4GBu1oGkOnA0pCpCRJGpEVl9jb2+MbvuEbeOc73/mM++L57qMnI8/zc02rW0q7WmvOzs6IooiDgwOyLHvWY79QD/UvZ7xoJvrdd9/NL//yL7NarXj44YeBUTbkhU47faniT6KE9eQgccsnoaoqhmF4ikLvLY+QyWTC/v4+WZbxute9Du89f/AHf8DRkUKKzyKVIooDV6+eUZYZSaaIXUJepPSdRUU7lIiIY0nTdIQQmJR7bOsb9G5F8B5rU/quJopPmS4yqu2AAJpWE28ERbnPtr9GICGOFWlWkH/2YR7vNJUThF4jgiCJRx2k1AxYJJ1MmAaLCqMe1NBb8lSwTQu8kwzas8gEWzPgAuySFNHVlGWGD5YuUuRZTGQ1SQLBBfo4ZjaL0cPATghsAJUlJN4R6pZJGWOs53TTsv+KC8jHTsiCIUlyhm7A6oBOJ1wIA7LZMi8TOhGhnaaIU9qhpzutMTc+QX7b63HO8cTxhnJW4E3Ger3j8EqOyC3bdY3TCUO/wzhPXqTsbMvclnSNpixyYpVgYkfkoRoaprMJZhiQMhu97KOMssiobEXiF4RwMlrRzqBaQV7McWFHkimCO8B0NSI1DIOg92tKWZAXMabPIIbeQBKPF8VOb4nUFOcD2+aEaXkbLmzZNB1X9u9kU99AEJhMc9a7G6TJAj1EQMTgz2htwzy/g75foeQUa3dobVGRpGk1QljWK4uUgWG9hWHA2oGmGagaTe8E/WBo2gEEqJsju2mWMZvNuPPOO/ngBz/4FJb5F4PXcSsr2d/f55577kEpxcnJCTdu3GA6nXLhwoVz/48XcvyvNLxkfiB7e3tfcUHj2fBSlbCcc+fZw+dmEp9r93nLk+Bz1X2fSbrhO7/zO3nooYdG1eA4wrrRpa7XmkcfPSLNIiYTya7qSLOYQRuU8yiZkecpdb0jTWZEMib4gJcWM1iyZMlus2KxV5LmgbzI6ExM06zwfslm11KUE5yq0dcfwFuPtJakKIjJabTGOYVWOVFb4WYLhDPsBk2Zp3QBTJoQZRFFCKydRRRTrO0pE8lKTTCtJrGeaWTotCEATZwjm4rLZcrJAE03IPcP6K+fURZjM9hYh13sEVZrpgl0cYKzA8PgMJMZWVcT655ymvBgpbHWMbkwYSkclYjpjGZWpsiuZ3Cedj1w8TBn2l/jAZakpOihwdcOpSQxE7bWkKQx250mtinTfUGURJi1ZagiXOiQqUOl49STMHNCGFjMJxztetoNuODYiYrZIufstMUnGQjQSUOwAq0tceKRZcHRpmKZ5EymOdWwJouXOFkzyAZhR37D2e6YNE1QYs6uP0JJSRCSZjhDKTn2W3aaNI/Z6Q261SwXlzhZXSPNYtI4pqpPmZSH1M0ZUsRU/VWCd4igCcGixD7OVaN22mABzebGCU3d0rSGpmrZVsM4Xj5odu2Avem/HicxcRRRTi5ycHDAd33Xd31eNeOF9kCeK4QQ55pWn+v/cXBwcC4hdAvPlzfylYCXLIB8DZ+PWwHklk/I0wWJJ9dZoygiTVOyLGM+n58HiSzLuP/++8nz/ClaYF8Ir371q/mWb/kW/sf/+B903R00/cPjVJOSbHcNjzx6zOtfewUXLHEicW6ULGm6hlLuUZQpq+01JpNidANMUgZ9yqK4wq72VLvxwiaiM6aLCc22wTtH2wyYYcfEtmxOGnykRhfCvsdOMqKNJZIeGQu8KogTqG2MzSKsM0htMUJgfEEZeqQAHzxbL9hTgiSVNA3IsgSnKeKIJi/oW4uISvZjz9QYquDYVj2RC0xwKAKdgNW2RaiMuwtIuoE+TThZtyTxWHP32qAXh4TdlgDk1qD25piTCjUYsr0CP3iudpJUGAaTUjY1FwbDib+LoDoKWRDFksp2ZCIhVXMIp8TpqMVlnCcWM+pdzWJviSoHhIfNDcuQDERZRJ90KCXYrncs9maookMqScSUvuuJ5imd8+SmoCgM237NNC2RQbEdKsoiYjhTxHOPVRHGeqZqBsrgrccOMRZDrce/beIDVltms4s463HOs5jextnu6kg4lDD0mjxZ0usNZTlOoHkCWbzPoM9QYoFni9VAfMr16xVpAtta47YNbdujtWO9rmi7AWehGyxtY+gHh5IRUTSqKORlyWKx4K1vfevTEoBfDma5EON4862R37ZtOTk5Oc9KbmnRzefzr2UgX8Mz4wstjFsmOU8OEnVd45zjE5/4xPnzlFLnQWE6nT4lSNwiQD7T+V9IOey7v/u7ueeee1itVqRxhLERzgWMthzfWDMrU2674xA9DBgZkc4D3km6bkVRjNyQ4aYXSJovMO1Aa9aoFKx2RKpgu9qhDhd0biAE2F9e4Gx9TLXrOTrdMZnNyYoJtqnBBZgU+LpGyIQ+z4mcJReBQSmcLJhF/c3pKknXeA4nCZtBoYKlTzKmKtAKgZLQEzFxhjhRrHY9SSSwVlJEgn55QN0OZNOSYAaM1qj9fcJJjVCKpixJjaEuCnzVgZC4JMYOAbkdL8AKxapq8Ys5fQmx2WI2DWkcsZxnbKp2VK4NUDeOcPppxN2vIJ4rVuuafGuYJHucNMdMJhMq3zJ1Jd4r8KMnfZzENE4zY4r3p+Mk16Kg0RrTxQRvkEJgZcLxqmeSZTSVJYpj+qaj9zv28gWxjzBtRBxneLnGOUWap6Pg4UwSyX0at0MpR1EU6Daw69dEKobgaW1NluUIIVhVRxRFhnMea2AxL7m6usEkHgNG32my5BLdsLrptLgh+IBjgwsB6x3b046+G9htDX43lqu6ZmC1aXDG4YJEW4PuB1ptEGG0kYniiDyPEbKkKAre9a53PeP+ezkCyJNRFAV33XXXU3olDz30EHEckyTJcz72Vwq+bALI937v93J0dHSuu/8v/sW/4C1vecvLZih1C1pr7r//fqbTKfP5/CmGUrcw6galCCGI45jbbrvtKUHihd6lvJAAkqYp733ve3niiScYhoG2fxghxgbloA2PP36GjBSXLi+YTBKabiCOI2IkdXdKnqRMpnust9dx21PSWGJ6TRInGNESqyV9F9iuYFMNZKlFFKDWW6yIybOYpq4QYjrKejjGBnaaEZKEWAqqADPpwQeCGRiUoogE695CkjMYzVRaKhuoVEzaDezlip0HnKNNMtJgydIIvGTrPIdlRuEMK6uJk5RexUyymFg44khhg6Pf1kzKDFUPiCBJIsV23RAt9yh0zTIRNEGSBcV222Gdx2YTUlMxm5asbSBNI1xdoycJbevwwfPqvuIRXY4jvI3kan+DyWHCcm9GddzCkNHqNfN4wmw+4aRecTC/xNXVNaZ5zlbXpC5Fdw7ByESvbEPURXg/TnRFM8X6dMu82Kdni080dIK+HYjnEToktJ0nlRIbWbpGMy1LWmuAwGG+JJ3B6aYhThcMvaZzHSFW2PZ0nERLlpxtb5BlKSJMMabDFxn9TckSax3aWKTYw7s1ebqHcRvarWDQHc0uYtCW6qima3qadkAPA84GrFSEYBkGS3dT40spRRyNWldFeZHLly/zrne9i9lsxr333stiseDChQtMJpPzG6rnupdeCMnv2Y7/dFnJ6ekpIQQeeOCB86zkKz0j+bIJIP/lv/yXc1Lir/7qr/LhD3+Y//t//+/LYigF8P73v5/f/d3f5dFHHwXgJ3/yJ3n3u99NlmUj5+JJmUR80/fi4YcfZhiGl8QT5cUsxDe96U284x3v4Od+7udQ0R5KHSFlhBSe7a7m6DghSLgSSyIXEUUCbyVJLumGBikF02lJ23domSEtZPk+6+oJoj3NUDn6tiVPZmw2Z6itYVsNpIml2J+jtrvRPa4saXY1znuGJGVuLCoSCCGx1lNGkt5Dl04owkAuHE5lNETsyx6RTQjaUcmYUgkWwtIHSSNSbNNwaZpyvPP0g+GoLFF9zUEe4SOF1YYqnpN2LfuFYOtimr5H7S+wmzOS4ECk7C9yjrWm9TGviARLa+iiCO/HCaPFbEo8XVCrQG4tZBnB9jzeeoIIxCpid3rG3a6nijIe6WckaYp3iod3p8ySBevTNZNZgY4t3ntsZ9mstwAs9uZ0mwG9E0RRQRttmc1LurMeaTJQmlo2JFECOsHGlpBmnNYd83gGycBWV6ib62VQPWmU4FyOsx4ZIqQSrIcdwhkWswt45+ldSyqX+NBzWvUclHs0bYf3geXsIjdOniBJY4Kb0Aw7KBNW2xMuTA4Z9Gr0PNen1LuOptF45/HCY6sBxM0LcQhjP0xJ9KDpOkPbj/4fQowS8mmakGVj6eobvuEb+JEf+RGSJOHs7Izj42M+85nPkOc5Fy5ceF49h5c6gDwZt7KSEAKbzQZjDA8++CBxHJ/3Sr5Ss5MvmwDyZEb7brc7n4J4OQylYCwFfd/3fR+vfvWruXTpEmmavqyGUi/2WH/5L/9l7rvvPj72sY/RdQopLVIKIhVx4/p6nHqJJLO9kigap7WiKCdOUuq2YqEukUQeESnqqiHLDN7CtUdaiqnCq5o9WXByY8WRDxDFtNuGC0VBFCmM7piWE6wzCCkQqaI2nrg39C4wqISZ05CmeB/YhZhlDrURKG/okMzSwFFniRPFlpg96anzlK7R7KxkQmCZWOog0NbiVMK+8kQq0HnJZhiovOLOiSQ2hspEnJw2WCIuZJLSG/Iy58Zaj3yJwwPm/ZaVk+zqgcODKWoYsFZQy5xCenJvqAdLWU7ptUYqcD5wvRVos+NCMtDPX8W2dcgBpK3xzhHHCTsGpokCndCbnmyRsPEVUiraXUM+KwlZilSSVEzpu4H9CwdUoiEeEvJ8LEVGZYRuBCtqslQwVJpluY8QsHE7cl8S5YKT9Ql5mZKpOZv+mOAFMynZNJvR7lUJuiGQJAm9NOhdRZnO2KxHeZrF5JAbp1dJ0py2M2MvTVcY43AGhO4Z+gRrW2ztqdcVgzUYbeh7jXMeLSRDp+m7nkFb6rYfeThpTJxEJGnCZHLAlStXeP/7309RjOrCFy9e5MKFC+x2O05OTs5v5Ha7HV3XPaMcyS28kL3zQpruSZLwxje+kaZpODk54ejoiOvXrzOfz7l06dJXnKT7l00AAfiRH/kRPvrRjwLwC7/wCy+boRTAe9/7Xqqqel6v+VIKIGVZ8gM/8ANcu3aNk5MTlBrZ3CAILnB6tEVJiXeB4B2TosD5AZlMmE9S1s0agWc2vYCn4fEbj5LLGT5USHGRsLrGddeh8ox2U5GlCUoIqvUOoojIa4IZCGmMihMKKdBKkQc16mF5ixMpIGi60Sd70w2UaUwzWOo8I9/1ZDLQm4DwhiaVLPOItrVkScQuJBzkgqHM6aseRyAoECGg9vYIJw3aOLpsStSfYcopotXEcYSd5Lh2h8sSktTjPXTbCpmneNcziT1ZmlCmgoe3llBrfJkx6RtmmaS2niSJiSJJu2vYNjvSLGJ/kTDdPsS15etphpqYFOc916o1k72Cs60mjRQ+OGyU0veGWSgYVHUzwCecrHqWSc6gBWvT4Kyls5q9bMKm30KVUMQzel8RxRHzfEnbdLgiEFxg4xr21JQkT1E2hxiCi0iziBDGScBJvmDoBnxw7C0ucbq7gVeCZZZytj0mj2esdsdjCdbneLtjUhxS9yu6xtE2LQKHsx2itrRNRz8Y2mGgrcdMptMeM4zBZBg8u6rDWk+kFJFSJEmClBMuXrzId37nd/KGN7zh8/bAfD4/Lx1/6lOfOv//1ojtYrF42ov+FzMDebrnl2VJWZbccccdrFYrTk5O6LruKy6AfFmJtvyH//Af+NSnPsU//sf/mH/2z/4Z8PIZSj0dvtDxvpQCCMCb3/xm3vnOd96cY5/fZM2Ox27qgbPjHbtdy+lxTd12rFYDXV1x/WiL1QNNrbl6/VEiphAirNIUQqFv3OD6Wc21G2NAT9OIaaZIpiVdZzAWOivGGnhc0qsET0D7QBclRAoSKaCckESKRS7JswSKKZESTJZzoiiikRGTImWZRySpoo0LtLHsFYokjnHDgFOSTHosjiSJ2ISUkMSUMqAiBQHOVg3ZrGSaBFSsKIsY2zXoLKNpDZMokKUxdTNQi5QwmxMpSWQH4lhR5BnrbYPRgfmlfWRZ4voWpSTzKGCtoekG8jQiFYZOa/wj93GpOqZRlj4ONJsafezQg6aY5PTSMbQ9ujYcdxWTxYSNbtEbh7MOqRTMIup1TRpKAHRskFLStR2BgFWCTe2Iooja99jeIvQ4ZrrqVkzTBTJSHK9PUJEil3MeWR2RZNFIjjQNaR7jXcAMgbKccK1ao1GkeUbfDQgXY3xFlEj6vqfeNQSd0neOoU7x65660pjOjLLw1YA1nk4HhsGie4c2gX4Y6AdDdFMkMYojkmTOZDLhLW95C+95z3uedS2naUoURVy4cIFXvvKVeO956KGHuPfee7l27drnscifbwAJIbwkTXelFIeHh7zhDW/g8PDwOR/rywVfVgHkFn7gB36A3/qt3zr//uUwlPqT1sJ6qY71V/7KX+Huu+9muVySpntjOelmENluW46PKvrGcnRjQ1Nr6mqgrjWbTYtUE3ptODo7QfYOWRmuPnHGjZMNEZIAmH4sHVX1MDKzJykpFiUELk7p6/bc+yF2DmMtLk5wSYZxnpoEHwKZcsTC0RITxQIlIpSQnHlFFgsiJbA+cK3xpJlikoy/m5OQMAyGZapI44hEGNaNYVd1FMIwm+ckWHQQOOuIgyZJE7IkZuNiTvU4zlwox+FeicexqztMOUE4Q3CertVMylHYcbCOJs0RCGI9oLsBOV8ym+SjC6RxVCZms23oW8s3puuR4+BTNqsNXT1wYiriNMbuIBMlUSQxhcA7x25bkakJG9GTxBGRz9CDxqmYTW/I1ZQojli1O3CSW0skn2QIExOcZ7CC3o9bvfUNQkCupqy7DSJIep9QDzuSSDFJlhxvjkjzmETMcWa8m97Up8RpTJJk9J1ht7IcHV8Fm9IMa0oHrlmzPquoqpqT05rTVYULgd55nHFYY3AI9GCoG42Uo85VFEfkRXxuEvWBD3zgKfyKZ0II4Zz494Y3vIE3vOENzGYzrl+/zj333MNnP/tZ6rp+yr55qZjrz/R+nm1a7Cuxof5lEUB2ux3Xr18///5XfuVX2NvbY7lcflUZSr0Ux8qyjO/6ru/i8uXLzOdz0jRCSgUInA+cnW45O93S7SxDZdF1QA0SXXnWN05ITUxY9xxdW/PYY8dIBJ1xICAqciQSM2iGwdBWLUZE5JMJSTlB5Tl5FqGsY6thGCyu7m5auwoib1BKMEQ5Soye2zrN6AdHJhxRrJBB0jpFsZgjpEAiWfuUMpWkicLYwEkPCEi8RQKdzFmZiPm8YJFCHEuOjOLYSpIkIvWWxTSj6zUnpzXM5uxP4pvijhCnESEIaplyozaUiWc+K5imHmctTWeoVUaDIk9jZmkgMGYxVavZtB1JmmCFZ7cbmK+usuyu45zDq4Rdo3G1oO96tNaYNGXdanJZYo0ljmOMkJxte7I8xRhLFEf43nPa1ehoDFSJzynjGY93W4ILpEVG1w+AYJ4ecNY3eOs4mI+N864d2JtehOA5awayZIo1DkFgUR5wujkhSRNUmNCagA4x106foB80va7p2gHRVsidpmladKORkaRvBgKj/D1Ckahxegwp6dqb/uZuzKqiSJGlEWVxkcPDQ77lW76F173udc9pLX/uBbssS17xilfw1re+ldtvv52mafj0pz/NH/3RH53fZH4pNN2/kvBl0QPZ7Xa8//3vp+/H2vjBwQH/9b/+V4QQ/PiP/zg/8iM/wtve9jayLPs8Q6kPfehDvOY1r0FK+aIMpZ4OL2cJ67mc77lACMGdd97Ju9/9bn75l3/5JuP9Cbx3WAvGOU5Pa0IYRQ7brmM+LxGMftTVlnFaJlO0nR5909Xoi62sQ0cK6SV9cEyEoK57rMqJhcV5yOcLYIOPYvIio9luaVyCNYHY9KS5w8qU1hrKPB6b30ayjAPTVKIHi/YCpS258PhIMbSauhAQp/Q7jQ+BlpQLiWM5TWi3nqbWbJcz9qMBlSV0WuJsoC8L4r5C+mSURXGBrrewN8HsOqJGky8mUDes1j3JYo+9SJNEAm+hUQVaVxhtSScp1WwPtiv2C4XpO3QkSZJ4NK/Cst3B0U5TpAmvW24QkePTuuBGv6IMMdfXZyzTPZyxRHFJkiSs+h3zbEnPQJwm1PQ0qx3LyT6N2ZHmGUnI6ZqeIR25Ja2BvUziEkdqx/1Q9z2REiyAja5QclyfVaVJs5TKG8J2y8HyIsOgCcGzKA65fnqV4AWn2y19N5ClGW1v2I9TurZnu25xwdI3GiFBSIWKYqw2WKsx2qGtp+0Gqqan6wbUkxwGi+KQ/f193vzmN/Pt3/7tL3o/RFHEpUuj+OJut+P4+Pjcl+jk5OR8WvK5HPtrAeTZ8WURQK5cucJv/uZvPu1jf1KGUs8FX4oZyC28613v4oknnqBpGrTWOHcDHwJDH+j7ns1G4LzHuoyhN2R5SlFk+OAJwRN8zGIyYTCGNFFUnSZTEW3dczDPkAKSIkU3mqbtWOQRfduTxAl5ntI0A2oaMdlfoo0nKIXWkkh5ZAgYEVG5CMFA11mCS5hbDSGwCynr3rLIPGWsMNajgyKPI/YKzyBHb+/aR8g4ZZoOtINktxtY7BdY5clszxAptpuOdJ6RT3LKZsBOE6TV1L2iVROsOyZuO6ZlxhCVbDYN8YUpt80T2pMzdmdrpEq4/bZ9+mpLta6Ik5zCtwTnsElMHMeUWYLe1lw/0+RFRFZm+OC48dgZt00b0jLlEZkRWkN/1NKLgSEbKPZy2m1NEcZppA3t6NBnYpy1mEhwXDXsywmBgHOOWbqgNTvO+i1xEhNM4KQ6Q8mYSClutFuE9+wtLtI3PQEooiXrdoUUgrO+xrY102zJ1RuPMfSaabHHbqhIxJS0b1GdZ2c0uu1QCpqdRgSP0QGZJDRVSzCGoTej+ddgqar25siuOCfepWnEbDbjla98Je973/uI4/h574tne+xW032323H//fdTVRX33Xcfs9nsXNfqpWq6e++/qrxA4MukhPWlgC+FHshLeRylFB/4wAe46667KIqCJFMksSJJFFIquk4z9BZnPdY6nDW0zYBE4G5mKn2nkUISfMBqjxAQIzBOYIOisZK9/TkqgIuSsc9ytmGwY33fhRSjMoJ3pCKQpDFCBiaJgmKCjzOSRHFpmZPnCSLLmRzsUeQZSgpsXDCZpGSJYhAZx61DSMGyiCiUZzCWG7VASFjOp+QiUDcdpw1UTc9sknN5L0Nbz9UKolhymMdM85QzHXFyuqYOOXkiWZQxMpEQgekcxjqOwxSrPRenMYczyaSI2W56NpsWVRSky+XofSID0ySAEgQfkCgWqUAExXrdcHS0YSIdr88tb8oDfddTqJLae9pdja8D69UKg6QxDtkrnLdc357hDWAD1mqG2NFsG5wxdC7QWk/k0pts9IFZNqdMZmyaDqPSkbfQb1kUS6wecNqwLC5wfLrmdNPS9wN11aNcyun6mNvSmDka0w7Udc+uamjqHmM8wTtQClREteuw2tJ2hiACbdfRdT1db/B+HG5QsSROBYv5FS5fvsx73vMebr/99i/aHf+twPTqV7+aV7ziFVhrefDBB7n33nu5fv36i266P9/385WCL4sM5EsVXw5TWCEEjDHnEiu3zHKuXbuGtZa3ve1tPPLII9T1Pi45wTiP8g5jPG3bESeS2bRAyhgXPN4HpBz9spvBkIUARU6sNMYFVJLQmUCRpWy3NWmeUkwSpA9M9/dYHx2jtaVLJrSrDflkggsxsQvISBFI8VFELiSbRuOShL1E4LXGe0HTavJE0AygnccOnmkZs6tG61shUwohWJQpT+yg33XMFoJMWNoQOOkV3hkOJhmTBLIs5+xIUG9r0gsLLmaGpjb0nQWpSLOYkCQ0MqZUHUOWkyvHjeMKogI5nSMVRAT6qISwwd8UStZJzq7xxMbgepBRQp4LVBIRCDx81hFFgiSJEUiuPnGK144rMdxWej7apax3A9Og2LUtUjqCd7RRxKVyRjc4EpuQZhN2bkeMYJpN2azW+FlCJCJ2XhMPmrIsGLqBrWlIk1Gu5Or66OYIMVw9uk6cxOxWO/pqIEtjHj2+RrCeC6VDDYHVWcWu6XCDwUtGQUulqOqOICLa1uCGgWEwIAPWe4ZhNJ9q2g49mFEaPpKkccx8ejt7e3t8y7d8C+985zuf0ud8rmv7+fY0pJTnDPKmaTg+PubatWtcu3aNvb09Dg8PKcvyawHkOeJrAeSLiJczgDjnnhIknqzJdcuq8xbRCUb/g729Pe6++26stfz6r/86V68mOP/YqLzrLf2gkZVCqbEktFhO6AdDIQV9H5jPU7pWo4YelaQYRuE5bxwWSVGkDLsWmSXouuVSnrJ/8YB+8EyyFG0NdhiwQlHpgSy2eKEwqiCLDCCIZIQ2PXms2LiU4AKJ7bk4L+h6w0CEdTHz3KNdDAG6QZJmftSnaltuxBPunAuKRQ46Zle1rFxM6S2JjCiLgrYxtK1hJxQmxEzTAPMp/eComp6KhAmCVFiMDajZlK4a0MYSoimDl7i2JpWOyWJKniUcrXq89axCQlrmJKlh4T1NCLSNJhBG4lwiWK0q2sYCgv1Jzv1PbLkSx+RlygkTTC6RLWTFhNo2rHVDPCi2uw2TxZRd0KgguVzM2A4NvuqYZiW1GwjBc9f0AGst9abi0v4ldGM4Xe8oygmb9TUgII3guDtBSsEFIWi9IIsjztY7hLEM2ozBwXqKIiIkEmc9KIEzHukcKElWJrRVP5ZEtaZrO9pGI9XoK5LEijSPmc1mvPGNb+SDH/zgeenni3XBfjq72bIseeUrX8mVK1c4PT3l5OSEs7MziqI4V6v4WgB5dnwtgDwPPN+A8FIHEO/9MwaJJ6fgcRyTpillWZ77g2RZdpOoJfnEJz7BfD4/n0v/wAc+wOOPP07btgxmhfMVwXuCV7Rdj4pubopNoJykDHb00t5WjnKSMxhPQCOkPC+B4R0hSRjqhixOUAJWqy3JfA8pemIZMJOSKZ7gBdpJhFIM1tFVFYMMJLGgahwijQlAIh2b3qAjRWYdSsJaS8LgSGzPIo9pe8POGJzIWE4kQswRCLRIiIQkCZokT7HOUQ2KKIuIxeig13cDYZJj0gmh2bKIBRdnOScnlutHW7ap4lKZkESwcpAWCaIXbHcdZ02OJuXgQJHHARtGS9ZhMMwyh1IFZzZlGjsm/UDbBOIoIy9SiggeulqTRIpyljKfFzx6ckoTenLhefVCctpYvAxM4sCnXMLxruNKMUdoy/XNKctswc5WfLapSVVErBWD62mHllhJHmqvIYxhWSzZbSo2/Y5lsUfVVTR9x2EyYSZ6SuHJlWTXtmRCcHqypTWWYAwiUqRSkExSqrrDB4klIAg4PxCnMZu2QbpArw19N6AHTbXrRlVbCWmqSPOEJNojz3Pe8Y53nI/svpCA8FzxbM+P45jLly9z6dIlttvt0zbdn89Y8VcTvhZAXgSeawnr+W6MW0ZST/53S9n3vvvuA/7/go1PVvW9pcf1bKq+T35ftzCdTvnBH/xBTk9P6boOax8es5AQCNpT1x2RihAiIGTAeU+epYCl7waQiiyOaXtNrGOESmi7nrLISNOIBIfPEgYTkH1LZwPe95BGtCimyoOMIJsw84ao7xFRRspA4hwe2LmIzGpSEbAeGu2ZJBIpFJ0eIM45KCJmk5yjFmrrmWE5nOc03YCxlhMXUTjHpWWJ0ZbttuH+a45l5rk4z4kjifGWza5FiJglgjJSPK5y+vaMNCmZ7++jm5qoH8iLhC7KaU+3bAbD/sUZrpwihy2rtWPQhvlywjwVPHLakWYJa5lRZim6rtnPE9JM0XcGEQJRHHF5UXB6Vo3rwFgms4LHzxr6TjNLJL0fuF0kZHnMQdZRJfDxJ3psZCnIObE7DrMpNhiurU+Ii5RMZJx1G1Qco7Y7tkNNFEW84mDgWtPzmllGP7Q0nWVRJBydbKgaM5YyBaRSItKUOJK0TcdZ0yJvMti9DTeFEAVV1YBzbLc9Lni6TrPeNngfiCJFnEYkacxschtXrlzhXe96F7fffjv33XffeTP7uQaGF0IM/ELPF0KwWCxYLBZst1seeOABttst6/X6/Ibr2eSLvpaBfA0vKW4tpqdbWJ9rJPXkbOJz3QZvBYZhGHj1q1/9FMHGF/O+now3vOEN/KW/9JeoqgprLcY+QvAeAmjjaOqOKJZExkGwRDLCRQIRNHE6lrLms4K66ZmUMLhAZh0+KAbnSPMUjyV4CIzByVbDKKY4n+KMZRh6dBShhMDgKNIM6QZCUuBtoOk8l/cztq0licDKiMNFxLVTTZpEdE5yIZeE3lPXAzZPuC2HZZnyRBswg2HnFVmnKWJFR4q1hj7KOSjj8eJncoSsCQZaI3CVJheOvb0paRZTdwNrm9AaB51mXmZky5JhY6mqntsPpjQmZ1edUhYJr748pekdoaqody2XFzlFnrPqHX0vuCtyqOBZFAmdCATvOe0CeZrQR4rVakffehCB2azk6rrFdhVuMsrgbzvDaxJJok8YUBykEftxzVHb8NppzCQK7PSWRSI5mMV0TUelIpZFwuOPHuHiCK0Vj1/fIERge+bQLlAkMcYEZnmM6UeXwLru0cESrMCFUQgyhEDvHLbt6drRgMsLT9N07Haj77tSI98miRXT8jL7+/v86T/9p/mhH/oh4jhmtVpxdHRE142ZysnJCXt7e89olPaF1vLT4fkGnFtN97vvvpthGDg+PubBBx8kSRIuXLjAwcHB592ofS2AfA3Pihdaktput5/nFaK1Pn/8lpFUnufnboO3gsatlPjq1aucnZ0xm82+aJ/jL/7Fv8hDDz1E27YYY1j7J3A+EAWPsZam6kiSm7Ihzo3SIEoigsd6aNuBNFUE70lEoNejNHylA0EInAtM9ydEg6G3jjJVtK3meNeSKYkeDHI6o8xSpB7dGDckTJyj7SwqHqfD9sqEpjesLeRDy+WDCcfbgdQFzjrJhVlK3w5EUjF4hVCCeWx5ZNWTqIjeZuzvT7iSOB5+Yo0xHiNiIiXJjGVIYjo7sFpVpMt9loVigSHPYyIp2VU13a5DLqfcXqaQZtDsmBUZm3XDae0YZE6al8SRglhhe4uKFPNFyXE7TnExeIb5nFNt2V86olbTVA3OBmQS8aoLE65dW+MZP0Mg4AZLP1guLMFZRz84FjlYG1hXLdNJQWQ1m23HvMjprOP4ZMvBwZK2bnnitCbLYjZmYNX0KCTV6RaLZJbFaO8pJhmRtaSTDGMszTDgQozpe5yPkEGP7pVS0rQd1lq0tqP6rjWjNHwzriEpBEkSEcWStEhYLBa89rWv5W/8jb9xXhY6ODhgf3+fhx56iN1ux6OPPsoTTzzB4eHh57n+3cILaXI/n+ffOv4t2fb9/f3zpvvVq1e5evUqe3t7XLhwgbIsz1/ztQDyJYq+7/nQhz50Lud88eJFfvZnf5a77rrr3BPk0UcfJU3TL7onyC3cWmRPV3K6FSgAHnroIYQQ5yWnvb298yDxhYykbuHlICUqpfjQhz7EtWvX0FqjzRpnLMErXAi0/YDYjAzxcRJrHJEVAtIope17SpnihCfLEnbdwDJWRN4RyQxrNNfPdiwnOakITBZziJuRKxDUKAHeN6xMQmx7CA4vE9pBM08TBhRBBSwRSZbgq5Y2REw83L5fsN11nNaerK44mKWcbgYqN3ASEqZx4FW371H3HoLjs2vLfuK5sMzZDA7rPNcrj2xr0jzh0m1Lqm3Lqm7RJFyZZsjgOOoDwYOKFMsiYlA5TvcssgjhDFEcI5TEBo8NgcqlrNcrJqmkLFJUrHDSk0QxMgzsdi0iyzk1gmmqqHYVizyhnBREPtAYCNLxmtv26dqBJE0IQDdoqiGM54lTNr0FGxBmoLYCAnij2Q0GpGR1tmMbHMZDFhyrfiCJU/JI0PWWopjg9EBZZiipaPqBnTXouiFWEcZrokhQxJK2AZkpTG8ICCKlMNKDsWjt6AZL3WgIEMdj8EuyhMPlq7jjjjt4//vfz8WLFz9vfd/q3b3mNa/h+Pj43PVvuVw+xQfkyev3i5WBfO7zhRBMJhMmkwnGmKc03cuy5PDw8GuWtl/q+MEf/EHe+c53IoTgP/7H/8jf+Tt/h//+3//7uSfIb/zGb/B7v/d7XzRPkL7vefDBB/nkJz+JUoo/+2f/LFrrp3iXx3F83pcoy5KzszNe//rXU5bli1pcLxenZD6f84EPfICf/dmfHZvz7gFCGCd5Bh/omoE6HvWigiiwzoDMMN4xKbNRLUBKpIAsktTGkUeSVdezyFOapsOHmL7XPHZ0ymw+wQVNnCrKyZKhqpnmEmtSWm2Qsue00+x5kEJQecfZTjFNFb4bcCrhiVPLpYlEe0FTNbSxoigEd16c0GmH7j3r1rBYRFyZxtw43tJUHTqNuG2SUqSj9e162xOhuPtCwcGiZGUkfb3B1Rq/KGk2FbbpuDjPiC4f0lc7Hri6JclTSgIKj5cRcSHYy2JiM/D44zdoRMJssc9e7unqjrj39BL292cctxbfdngb2NufcOxjIqvIULRGs5zEWO9x/cBpawkhcOXClM2mhhCYZBlZmpA6QSQVWvd0XpDFCc5ZauMo0wxhNdsusFjOYehGnkqW4JuG+WKKM45Ga0SScXR8glAK7UY7ZiJJrAIIRd13+ODwBqwxxLGiacchjqYauR7bdUu42feIIkVexOzN7+DixYt813d9F29/+9ufdW2macodd9zBbbfd9hQfkKIouHDhAnt7ey+4if5SBJyna7rf0tyrqophGJ5T0/0rAV82IwNZlj3F2vKbvumbzv9ov/RLv8Tf/Jt/8/zntzxBAH7u536Ov/W3/hbwVE+Q54uf/Mmf5OLFi7zrXe/i3/7bf8sf/uEfntdDX/WqV/HGN76Rb/iGb+Ctb30rr3vd67jrrrvOg1SSJC/6zuTlHAl+05vexHve8x4uXbrE/uIVpEVMFMckscKHwG7bo7Whqvqb/ZEBM1h2VTfO5ARP0/dYa1DOoqREGo2IIE1i+q4nzmN03dI3A5EQ2KbD4imLDEnAJBE+jYjTmAuHe7hYMSkTpmWKSAQ77djbn7I3TdifJliVsFhMWM5SyjLDiZvyKnlCZxxeCq7t7DjptBh7Ll3Tc3Vn6HtDVuZMZuNFtbHQWckyjSjylEhKVic7zgyYKEXFEZcXKbNZztBpjq+fUkc56XKBsZZlAkmWoKRi1XqC8/hY4ZKCz571WGO4OM+5eDDFaocdLJf3CozVBAXNrsU5y7Xa0KcpnYrZNQOlCCyLiEgIVBQReUcRC5yz2ODxAibTDCkEIgIpI/Iiw0mBVJKLeyWYFkJgf5IR6YFIKXrnOFuvMcJw9XRD12uMEyRCMC0zUgVDN9C68b0663HWgJDsqoa2GejbnsEYqqrDWjM2zRNFksdk8ViK+qZv+qZnzf4/twSklOLChQt83dd9HXfffTdRFPHII49wzz33cOPGjfO1/FzwxWy6v/a1r+VNb3rTqGrdNNx7773nDfiXWgH8Sw1fVhnIk/Hv//2/593vfvfL5gnywQ9+kPe85z0cHh6eL6pbfYpnwuem2y8GL7eu1rvf/W4effRR/uf//J8YY7D2UXwIJEJgektVDcylwhpJQJAmEc44tDIk2RhsBiexWpPEo4RGV3fYWIK2ZHlGXmTYvoM8x3Y9qoIQR/jgSJKYuvcs5ikeh4wlTgiIFLmzVA7Oesudi5y6M5wNmhtNwx3LGavOIQR8dt1xeVpwcZZQ9YECy9XTmiAhDhZLwqKQiCgBHzicZGy0x2jD/TfWLBNFbA2zaUqSZ+w2LW3bEeUpt+MZjMc5z6wsuVDEOAdHjWVPew6WEUMWUxRArNhLJWdnW3Rn2fqYKwdTusGRS4914+ddmYgkCXjj2W5qtJdo77h9XrDZVGy1YxJFCKOpXSDLE6zWBKWYKMjimIBjkUd0TUe2KIg89H1HkWd4xp5FmijOKk9XVcg8p151RMESyZxMOtLlBERgu+nQRtI0DXGa0Ncd1ljw0DvN0BucHvs53aDZbUcP9ChW48BFEpHEY8n29a9//bm74PNdk0+WJOn7nqOjI05OTgA4OjoapWJu9iG+0LG/WAEnyzKEEFy6dIk4jjk+PuaBBx4gTVMODw+ftun+lYAvy0/0Mz/zM3z2s5/lX/7Lf3kusPhkfDE8QS5dusRkMnnKhNQXwpdqAHkum0JKyV//63+dGzdu3AwgllV4AucCIhJobWmaDhVJJnGEtoY8y+itR1qH0475PEc7wdB3qChhsJYyzhgImKoink5Q2pAqUPloO+tx7DpHJgeyLOHGpuZwmqFE4KyzI5dDQHFTDK/pe1QcoXqBduBc4I5lxq7t8d7x2WunXDlcMEkFu01HXwtELLn9cM4gEmLluL5p0dryyosLXnf7lMefWHF2suFMSC5f2GMyiTBCEKUpoh6IdMcjRxasYFlGhKIknxY8eG1NvWlwecpkUZBOC2bDjiBGY6edBaUkVgbaXc8jJxWRVMxKRSw9sRmojOPKhSl13SN8IFjPoA3r3qIHw14u6b0chxDKGKsd62pARBEHkWfVWpy1THLBtU2DwzGJ4Ubb09UdmQzUWrLerJhlEc54chUoygkEjzcKhKBpeoIIDH6Uh9FaE4xBSUVnNXYw4AKDdjg90LQDQ69RSiLlzeCRJEzy0V3wQx/60BcUMn0uTegsy7jrrrs4PDzkj/7oj+i6jj/+4z+mLEsuXrzIYrF4Wi7GF7vkdes1t/w/Dg4OqOuak5MTrl69yrVr13jjG9/4BUUcv9zwZRdA/s2/+Tf8yq/8Cr/0S79EURTnlpenp6fnYopP5wlyizT36KOP8p3f+Z0v6NwvdOLjpbrwv9yyKJPJhB/6oR/i7OwMYwxVfzIyjwEdDG1nkFGHlIKMmFhGRIkkOIlSgvW2YraYMGhHnjqq3qJ6jRURwXqivqcyEEearMhZ94aSgJBy9DE3HiUcTXeTc+Asu/9fe28eZtlV1vt/1trTGWueh06nM5F0EhIIyGAgMl3RgKCCcCEhuYjXCA74IBd/6gM+jwpqLteLBEGcJSoCV0AvejFyHRkuZOrOSCfpTnfNc51hz3ut3x+n9+HU6arqqurqSnezP89TNKk6dWqdqn3Wd693+L6JSU/RQSsXQ0hqMYAPUYRILCaXVvHqsjF6t+YhE1ise1wx3kvBMZhYrBEkCmGY9OUFftwoFVVhzOyKS9Ex0MTYRiN8FkuoRzEq0fTYYHZ24NVXWawF5DsK9HWVKDsSFYQkUcPnKW8owihmzo/QCkoiJvQ9hBR09ndRkBFTkwvUw4RcwaGrv5NHjy/RbZp05S06Sznqfp1gKWS8v4QwJOVykcgKiWLFahihNPiGpuolLK/W6StZuFjUajFFC2ItqdUDCH3sziJ1P8YSEY6Tp+In9HbkyOds/CDEylsgIaiH1MOAUBno0AMrB56HbRlEkULmzUZjp1YYWhLEIXES47kB9WrDQ18aEss2sWyLob6LKZVK3HzzzVx99dVbvja3QioS6TCpubk5nnrqKSzLYmBggP7+/jV3/Gf7BJJ+T2vSvVwuUy6XGxWNy8sXZF7kvMmBAHz0ox/ls5/9LJ///OfXzEh/pmaCbKWRcCuP2wrPlBiNj4/z1re+ldHRUUYHLiFXzGGYBpYlEQiqlYBKzaNWD1mtelRdH9cPCJMYpKBa9YiiEM8LKDmSKIxwTE2gNDqJcWRMEEUsL60S+AHClHQWJMKS9PeVKDomgdAnZ6VDLmfj68adj2VALVTMVTxM06Knw6I7b4JhYFoGY/1FurvLdDiShdUqygLHVBhC4sceNc8jigJKZkwcK5RXY75So9RRoL/Twco5dDmC0I+YmK9QWa3T32XQ29uJZTdMA5fDhFXPZWJ6gYKI6OzrpH+oi4nFGstzq7heQILGDWP6SibdeTClgZsI8o6N5disrnh4VZ/ZSh0nD5XYRSeCkgkShSbBIaK/M4cpJQooFwsU8jmwJD19HdiOSS0WmJagVHZwhYGds+gf6CLUBqYt6enqwMeimBN0lBwSNFJolut1JmdXqdZdImVgJiG2aWKoGMOEShiTqIR63cdQEYEf48cRddfHrQcsr/po3djULdvCdhz6u/YxODjI93zP93DTTTft6jXZ+lgpJd3d3VxxxRVcddVVdHZ2MjU1xYMPPsixY8dwXXfN4892zmS9008qahdihdZ5cwKZnJzkl3/5l9m/fz8333wz0Bhr+U//9E/NmSCXXXYZtm3v6UyQzTgbArIbbPe5XvCCFzA7O8tf/MVfEEURU+oYuKB1hI4a+RCtGwaLQmriSBNGFjnHREmDjoLJcjVkqM+mHmlyiSZvKLRsrGO5ltBZsqDmsqShI2chYw8vloi8xHAjwkTS2V0gThR5x278ToWmXBBE2sKXkt6cRSI0E5WQcNFlpDtPb49DreIyU/ExaiE9jkUxbxCFEUfnasRJQn9nnksu7sKvBSy7EQtuTH/eYLjQ2GRPLNVwV+rI3k4uLeVZNTySFSg4DgVCKqs+S7WI0b4OxkfLhEGISEBHCm1pTEsyvRIg6iEHhkqYtiBvxQQiz1hXjlrdx3Esqq6PH0RMT9eRhqC3JKmGHquuprtgkXMEQSQo+TEdZoiOJWUL8rZEK4u5mRpdnWVyNjh+QEfZwpECP4hwpAIVY+kERMxy1aXic9LbywAjIQoVpgxJkhiMRrEDAghjal5AogV+FBEFMZEf4nkJ1YqPShSGITFzJqZj0dMxTH9/PzfccAOveMUrttQQCDvro2h9fKFQYP/+/YyOjrKwsMDc3BwLCwtNp4b2x59uLWfz8RcK542AjI6OsrKysu7X0pkg7fNAYHdngjyTIazNutp38lzbsYwIw5CXvOQlPP7446ysrFCu9qLs+eZzJHGC50ZYtoFl5nDsRq+I1pCzBEJIOosGtYpPPm8ShJpcTlKp+XSUCthGiJs4FEt5PD/AKFnEgWJhYQWZK5G4HoWiwotLFKRGaMVyqImihJJU9JcdlkNY9T1sx6TT0iwJSSWELqEoOSZLtZBl1yWwbUZ7HAwDMA10nBBiMNBdYlkKZiarJDphHgdLuiRK4eRsZAd0FGDmZCiiw2j4fxlGjpqSKKWZrgV0hx7Vqo+hfIRhcPnFPURBRO34CgKF222yHAl6usvUqn5j6FQYYBRzXDZYoFbxEKphRW91FXlychWpJR0FqIWCeTdCmhYrrks1lhRsB2lFRIFmuCdPErjUK4K6AouI5XqIpw3yIubJmRqmYZGXMfVYIg1JHCuk7dCJwizkqdZCgkQRBjEk6ZAoQaw1UZwQeCFJmFBzI9y6TxiEjXJdp1Gplzc6sW2b8fFxbr/9dmZnZ7d9bW71utzo8WmZ7eDgICsrK2uS7jMzM1tKaGcCsjXOGwE5F7mQQlgbGTX6vt90833hC1/I8ePHCcMQsShY1rNopUmUJoljalUPgWgMPDIkaEkURAg0lmli5SwClRD6ESFFpIYVN6ZgCOLAJenppkDc6DHJ5wlWVjAICZEIN6Sga1S0RpFgSoNqPUEX8hhGTImEMIC55YC8qSkYEuWHPD1Zw3FMTCJAY+RMQp1QLuQZlIKnZyLcKGFmeZWcbdKZS1hYjigUBPMrLsVinoGyid3fiVv3mV7w8YJVTCEZ7nIo5QwK5RJxopGJYuL4AtXYQEiD0b4iNopjyxHlUo4wiqhWfDxMJtyQbjtmsRJgmQ4DZYO8AZNu4+/S3deNFg07F7fu47kBUwuNgpHesiAMEsJ6hFEAEUesBJK8ZSCTgLmViK6OAiuex7IP+ZxN7PtYpkVOxvh+iFPuIKcC7JNmmF4loo7ETOJGTsuI8aOEUmeOmhejE00ShQgNfqTwvRDPCzEtE2lKTNPAyTsM9w8zOjrKK17xCmZmZlBKbbnwZCdmips9XkpJT08PPT09HD9+vNlFPjU1RV9fHwMDAxsmtfci6X4hkAnIWeRcE5DUqDEVixMnTpzSNQ8Na5VcLkehUFjTNe84DldccQW//du/zeHDh9GTmkU1g6k0MRAGCVURoAwDL9CUOsCybCIpkJHCjCKKRQfDFIS+hyHBVD4q14Fya1CvgmWwvFIn39NJR2eBMIgpdZWp1QOETLANm8W6IicSOm2oRxGBjHFsg/l6QuJHaMdhuN8hChJqMQSRojNv0debI04UURjz1HSdgU6TAyNl3LrH6lLIUU+TJ2aoL49TsFmpKo7P1cmXcjznijIiipj0XFw3oNxVRJuw6oWUDEWxx8J3A+puiBdCoaOANBUz80vYiSJfNCgWy4RRRG0pwositG0xvZKQLyQ4cchol0FHXpFoTY8VEMUKYRqUeooYIqKQkyghMAxNLAxMMyQJakTaBC2RiU8UNjyzIq+GRNNZ7kDGPiLvUC6Viesr5ItlAi/CcwMqgUns1xGGIEkUIorRQkCc4AkH4caNU0cUs+omyCShXnVxa0EjZGlITMvGzjvsG7iEffv28Y53vIMXvOAFzM/PMz09zczMDEEQMDg4SKlU2vT6PFt3/LZtYxgGBw8eZH5+nvn5eebm5ujs7GRgYICOjo5TqjWFENkJ5DRkArINzpUQ1unQWp9i1Oh5Hr7vr7kbjOOYXC5HV1fXGmuVzcaKDgwM8I53vIPf+Z3fwXVdaskqWsuT3eoa3w/RGrp6ilRXPQoFRRSajQY9PyaKImw7TyEvyBfsk70ELqZjn/zeEkKAu7RCmCuQMwQqdOku29jSwA8TtJAklkl32aRDJRimhULRKzUVI0eh0Cj7LXU5rMwH1L2YODYoFgTFgsGxuiJ0XaY8yf7xDiQJcysJoU4wCw5dXY1JiPP1xgQ95UdMTC2hNBTNhFx3ByODDq7rM7vgAoJ9I2VKBZNaZJAzNGjJ6nKdueUQ2zG59KJGk+Mjj60ShpJ9Q3lMw8CsxFQXVukcLrC0KnClhVcPEEYdU5h0503KJZPV5QhT+xTzNobtoMMYK+/gaIUbWZQLFiJOiAtFiqYg1AHCkBhGo4IsXy7gu1WiKCGILfyqh2FIKtUqMgrJ5W1kEKGlIIgbeSztu3hhjIoVbhCj45jVlRpuPQApkJaBZZnYOYeRnn0MDQ3x+te/npe85CVAI+w8PT1NV1cXruvy2GOPUSqVmuW265Xfn00793QezujoKMPDw00TxyNHjpDL5RgYGKC3txfDMLYdKs4EJGPX2e2wU/tztQ6RSgUiPU20VoWkwtDZ2Uk+n2d6ehrbtrn00kt3tIbLL7+cW265hd/93d+lVquxpOfQaAgE0OhXqK56lDoLxLFCiwQraVidSDSmoVisKgZNiTQNVKSIhIYEnNinHkvyMsbQAZXAoAuFGyVMRybdOU1OK5Q2SRKFJaEWhtQDSV5EdHXmkCrEiwRunNCRS0giiTQMFlcrFB2JQ8KKF+LkHSq1Gp0lm34pmVoEbIeFlRrFQo5OJ8a3LGwZsLoSEdoFSnmLQsHAsk2WFwXVVbfR26Ecujrz5NwKTpJQ6izguQZRGBNriFXExJyHlJC3YjoKRWqRpug4GB0Qx4qVUBAGdYa6Deo1n2pkki84CCthVRlUAsjbMQmCkqUQKGzTQddDwnqdrq48to4JXJ+Ook2oBdPTq1iWwXIQENVqkCviBy45rdFGjo58HW3bhJGmFjSsWCLPI4kT0Iq6r5EkhF5E3fVw61HDqsYwsCwLI+cw2jveDFu97nWvW3OtCCHo6OjgkksuaeYjnnzySRzHYXBwsLlhtz5+O2xHcFofm04m7O3tpVarMTs7y/Hjx5mcnKSvr29bp4/0+Xey/vOdTEDOgL3IgaQjadNyxMnJyaazb/sQqVwuR0dHxymnifaLOk0ongkvfOELOXbsGMvLy5TqJY7NPIFWikSBQUzoh9QNMChg25IwijGCCMsWJImkp2yzvOKTKxUIIuiUjSRxHIcUiw6uylE2QQpFpASJAhF6LCcOXQVJ5NVwhUUtsjGVixeYJHkLoQMcW7BYNfCjiIIIKHeXSGKfKEiYqJh0FWHfvjI6SRCG5OiSpsuIGR9wMC1FtRLz2NQi3UXJUH8HYLFc042ksTToKCa4q1WKRoxfzmOaBmEQ88hxFwJNh60RcYRhGXQOdhN6AXPTKyx5jUq1gW7JYtUlDjU5C/o7Tepuw503X8hhOpr6qolf9ylbMctLklVXIKSk4oVUvJhESga6DKYriiiIsaWiMhcTxAkFM2auLomCAFtApE1WF1cpFySJ1pQtjWU3JkL6fkJk5ogjj1zBJqh5ICWmDa4Hhozx6hGeF+DXQxAgDAPTsjBsi9Hu0Wa57q233npKGWtrKKi7u5vu7u5TNuz+/n4GBgZ2VMa7UwFJae3XSG3bFxYWSJIEIQTVanWNieNGtE79/G4iE5BtcDZDWFtJYgPUajUKhQJ9fX1rhGKrpZI7eR0b8YpXvIIjR47w6KOPorXmiaknsWlUBMVRglcL0VoSAXbOQekIIzaxrIRQNzq7wyiCOGFlJaSrs0g9lhQ12MqjHudAmOgkITHzmGaCZSlio0Qhp4jDCI1NFCXYtgZsvCRBh1DIaapuQpAv0C0E3T15plbBSCIWXcUV/Q5CxxybU0RJyFyoGO1XmDJhoS6IIkUlsBgsWhQNCGOPwBcoBFOzq7iJTX+HYHyshEBTr3pUFxsin+uCeiDJOQ69RQ05qLsS0xBoo7HBHjvhY1gGTjHP0LDD4vIyDhEDnXmEaWLn8jiWg22GLHkCIQU9JQhDQaXm0lWUBK5NraLocBod0MsVH1uHGN1FwnpMzlDk8zkqPgz2OQhDUKsEREnIalhARD6G5eC6EZbWeG6MME1qocKIIvwgJPRDPDfCrTVmfZiWiWGaSNtkvG8fAwMDXH/99fzET/zEKY1yG133qattumHPzc0xOzuLYRjbKvmF7VdJbUarieNTTz3F6urqGvfvnp6eDScOZieQjF2nXUA2mja4lSR2kiQcPXqUyy677IztEHbLFkUIwX/6T/+J/v5+7rnnHgCenD6K1hpNKiKNTnWtacwzTxRRmBCGIaZhYtomXZ05klgRxRG2giC2yVkGK6sRuaJDUK2hTEW5uwxhjUIhwcwX8asBlg4Jc110FBIcC2KdZ7Zm0CF9hgdyVCObmBjXjygXTKYrCeVijuWKi21C0VRMLDTuvpdXa3SWc4wM2kwoMC2DyrLL8ShHLvRxpKbUmSfwLSrLCZNLBs8estCBz2zdx7ZMrLyNk1ccn0swqOOUCxzoN1muBHSVLYpFmziKUUoT1n36ukyePh5QjWzCQGL6YIuIDgdKBUGlkpCTmmIph2UZLNUlPf02eRGxWIdC0abcIVmqagrlmA7bwQtiOoqCgpPDCxtWJSpRLNUtRKRJtIHruZg6QkQxyg/RtoUXJsRhSBzF+GFEEKpGd/pqnShJsCwTwzQwHZvhziF6enq4+uqr+amf+ql1S+hbr5P1SDfs4eFhFhYWmJycZHV1lW9/+9sMDg6ekthuZac5kK1gGAaFQgHXddm/f3/Tbbd1Rkm7p1cmIBnbZqOLOE1ip2GntOKjPYmdzgfZShK7Vqtt+jN3a+3bQQiBYRi87W1vIwgC/uVf/oUkSTi28DSJFljExFFMveKDFAjAMCWWYSCFQSwkNrC4GtFbNkmQ5B3JSi3CzJcQSR2vVqGzs9AYq5sE2LbJyuIKsdON6yWUrZhyh0UQarSQhJEiDhJqwqI/DwOFCC/STC4YGHqV7nKeOKxTq2iWA5OS8BkZyJMrWMShycSypK8UctFoAWKflZWQlUqAiBPGhmw6iiZeocjM8jIqTjh+fBnbkiRK0VmEfRcXqdVj4hNLBFFEZ1Gw4jqsJkU6gjrFgiRKJJ09JTy/cZe/UAGtY3o6JJUVFy+wMHKNvMyCXyT2A/JORKwjukoWtimIY4nt+pRzYGgDR8aUOi3iUKEU1KsJSys+kbbJSYUWEEYx9knL+aIVncx3RCTCZGk1RKiYKIrRSuEFisjzqa7WUYluhEJNiZXP0WV30t3dzZVXXslP/MRPMDAwcEbXmGmaDA0Nsbi4iGmaxHHcTGwPDQ2te+e/WyGszR4vpVxj4pielNIZJYODg2uGScH6negXMueVgLz3ve/l7//+7zlx4gRf/epXueqqqwD2bKBU+wWYJAn1en3TJDZAGIaUSqVmErt92uB2fva5ZswIDSF8+9vfjud5fP3rX0cpxdPqBIHWCK0hSXAr3skBVGajTyQBx5JoNAULqrUIZRiE+SJSKhKvRiItcknMamTQVc4R+h6e2YUgJqysYDsFaomN7fkYhsHxFYMOJ0aGEZ7Ms1LVmFITKwO3FiCkQT4XUyqZVHyJjDSBLtKTN+jssHliVhL4FaZcRS5UjJQSIiQqDskXi4RaMV9VGIZPb6dAJZooCJmrWAiRJ2/mqK26LNc1fT0WNc9BGnB8PiHyQ8ycIllW5GzoKBh0FSVuVZOzLLBsCgXdmKUe+HQ7MdVViV83MA2YXvRxY4uOsoMlQxarUHJKzC9WWXU15XKRas1jxdV0FnMoVcMNBJaI8XWIsAsUzQApTFSYIEyTqi+wTUi8CMeCKACExPdCQs+ntuqhtca0DKRpYDg2uVyBcrHMxRdfzI//+I9z4MCBDa+LndyV53I59u3bR61WY2ZmpnnnPzAwwMDAwCkNgNt57p36WrWua2RkpNnlvrS01DRxTNeVnUDOYX7oh36In/3Zn+X7v//713x+LwZK1et1vvKVr/DQQw/xyCOP8Nhjj/GRj3ykeVpoHSSVNijlcjkOHTrE0NDQhndpW+VctEVpfZ5yucxP/uRPEoYh9957L1prjs0fRyCIaXSrV5Y9Ch2gNIQ2+BHYjoMfxjgFGxVG1MIVtLAo5qGjXEDFYMQeoZXDTxSm9PFik1LewLE1DpLEsAnjBMeIWa0L+rrzCBTSsHAjB4sanR0W2iwg7IR6DJYlyBs+gSjg+hFB6FI0LSIjIFQmtqqx4jfmsw8P5onMDkhWODERoJKYfeM5bJngJg5GDFqAGdc4diLGV3kG+ywODNrUqgGykjSqlmyYnA8xDAPbkQwP2niJj23GdJUblU1OuQCmgWUleKGDtBQ5KyIxTPwIIrdCLAWmdBD4qKSx0emwYRLpODnQjR6Ngb48Xq2G0gZh4OMmISF5pE6IkwRDa3w/IEESeI3+mDgICfyQetVHJwqkBNNAWBZWPs9lowcol8vccsstHDx4cNevtzThnia2U/v2tJ+kt7eXwcHBPTmBrPf49LQ0ODjI6uoqs7OzPPXUU00B2Y5b94XAeSUgL37xi9f9/Oc//3kefPBBYO1AqZtuuolPf/rT/Mmf/AmwdqDUbbfdtq2fvbi4yHve8x4uvfRSDhw4wBve8AYGBgYYGhraNIktpdzTOR57/Tytz9Xd3c0dd9zB7/7u7/Lggw8ipeTJ2afRaLTWKJXgrtbRsSbfkSdJFCpRmJZJFEQUy3kkkC85VGohhqiRKzj4sUC6CW4osdw6dt6h6lvk8xKpFEuug+nkManS1W3h6yL95YAVXxAmilpoM9xrkMvFeJ5isW4Rex69nYLuXIRXD6i4Jq5fY3w4T1/eIIgUi26OsO4yNiDpKXj4niD0fTTgRgJdLJEEHmUnxhcFbCPh+HyIli6y0I82FatLPrYBwwMFrHyZ+dVVIi+gM69YWQmouTahH5AYipypKTnQU7YIvQRDhpRsSblsM79cpNypCN1VsIuUTJs4rEChk5E+RXVxFZlzyOVNQtfFyuXwPY9qAIZVIIrrJNogCHx04DXmECvVsDSJQyI/QClFrRoQuh5xGDWqreyGTYmdy3HV6CVcdNFFvOhFL+Laa6/d9WtsvU07tW8fGRlphoLn5+ebLtxn8txn8vh0mFTa4zIxMUGlUuGxxx6jt7eXgYGBba/xfOS8EpD12KuBUvv27ePQoUP4vt/8nGEYpx1ks5sJazi3QljrvcEGBga44447+OhHP8rhw4cBeGLuOEqIRsI2SvBdDy01hY4iKhHIXKPUOI4STEsQeB7d3XkCLyIOoeYqSpaPISWRdCiaBkYSUXXNRg9GvULoOuTyOVS1TrEYs1y1CYKQJIixzBKVWoTrJmhM6ifdY1frOUwzRjh5tBtSyNtUkhJdTkwkJOG8i1aKldWEak2Ry1v0DeapBhZaw/HpiHDVo7dLMdBnYTg5cquSMIywkworS7BaM5CGwXDOYqUW0dNlUrcM7JxmsWYQhhFdHZLaiseEC7aTo7/foFrPU1mt0dVlYpsGOTtExzGF3hz1SkhQr1Iq5bDRuFWPUDokkcXiwiKmbYORQBwTCRvfCzFUjGlaKOEhCw5uoAj8AHRE5AfESYJb9/FrLkmcIE9WWlm2jeE4PGvkAPv37+etb33rlspaT3edrMdm16RlWYyMjDRzJVNTUwA8/vjjDA0N0d3dvenP2W0BaSUdtVupVBgcHGRxcbFp4jgwMLBu0+SFwnkvIHDqBXo2Bkqt93O2+j3nUthpt58LTv2djoyMcMcdd/B7v/d7DcsTrXly7jhRw0AXFSf4dR+BQJfyIAWOYxNLhTQM3DDBTzwMaSDDgIJlUvMlvT0WIlSE2iJvS1aqIaaVxzA0QvlIkUOajTvvGFitKbqLEssMEBgs1CxKZkC5IMgXchiGJNQGQkK5w6DuWzjSY25Z4666FG2DXDFP6IesupKlWsz+fQW6unTD06oSI6TAjwXzFU2hqOnplKAEKhbMr2gwDMq24tjTFfzIprdLMDJsUa+GFMwIw8pT6hAsVetoFVKwI/y6YnVVYRomUT3k2HKjGbGrw8Bdlvh+hCViVl1FHHtYVow2CsRBgOXYJIlEhVVAYNoCEQdYjoVKEmJtEvga4hCBaIz2jSAJQoK6T5xoTNtCmgbStDAch6tGD3DJJZfw5je/mRtvvJH7779/R9fFVjjdtSmlXDPbxzAMnnrqqeZ46f7+/nWjAWdTQNLHQ2Pw3MjIyJqmSdu2dyWMfS5y3gtIas2+FwOl2tnKG+S77QSSMj4+zh133MHHPvYxHnroIbTWPDV3nEbroyCJItyaT6Q1UaLx/IScEyMtG9OU6DhBGhJBY8ZEoSiIFDg5g6Du4VolhFb4bhWno5e8GWAaPqadZ3bVIq/qdJYtPJ2nq5wAmlhIqtUcpmPTV27YdUzNawLPp79b0FWEMIiZqxjEvsDJmwwM2Zh9BVYerxKFESemIsrFBBUpOksC7XRQchKmZwP0rIeRL3DxsMlyJaGnx8QNbQqFmOmJGkp5zCQ5IiFYXBCUnJiucoQpDfp6HaJAYpqahWVNLp+ns5wQ+Zqlqk9X2YSTOYtSQWIYORYXPGxL4Vg56mFMIa8wZI4wUkSeJNZQqzUS4UkAOvBRKFScNCYdRo2y3dDz8VY9lE4wTBNhSIRhYeZtrtl3GRdddBFvfvObednLXkYcx6f922/nOmllJ07TV1xxBa7rMjs7y+TkJNPT002jxNa+lLNZ9tv6/EKINSaO9Xqd2dlZPM/b1s8/X7ggas6eqYFSW+FcFJDdfJ7Nnmt8fJx3vetdXH311QwPD3PF6CVIx0aYEmlboCGsB3gVF5XEBH5IFIZEcYQSAsuxsfI2YRKjo5ig7lHxDMJEE1RXQeaRhoGh6o153/WElSWX2PWoRSZCQMkOWKklTC8K3EoNywiwZcDcnM/iaoCIaqASVl2LlVpCgEkhr7CKBTq7HeaWQ2bnXIq5hJxtk7cCkkSzsBwjDclwvyRfMBBJjEoS8nbEimcytyTwah495Yhc3qSzx8GwbYp5hVtziSLF3JJmbtHn6ImYhWVBbNhEQUjBVnQUFbYj0NJidKwTp2AQhAm9/Tk6u/NIy6FvsEypZOP6EVL5JGHE3CpU3ZBAmwRJDiFACEnsuig0KtINN91IEXoB9apLbdVF6QSkiTAMpOVgFPNcd9Gz2LdvH295y1v4vu/7PoIgoFKpbPu6OBv+Vq0bfKFQ4OKLL+aaa66hv7+fhYUFDh8+zFNPPUW9Xj/l8dt9/q2wUSd6sVjkwIEDzRvaC43z6gTynve8hy996UvMzs7yute9jmKxyP33379nA6XOhRDWuRQO28rzjI6O8jM/8zPNk8iVWvPozHEIAxACHcXEfoCHotBVRimFISziKCKUEmFKbG0Q5nIkQUBSW0VpG9tI0CrAzNnEUYifCNzAoCACbANiUSTSAhFGVH1FHEikYVKyQRJR9RzqnkdnUdDTn0cKiWk5zC4EdBUFfb0WoetTqQjiKKSjDN09BqbpMDUP0hRUqoogqmDZJqVOC2iYRs7PR6g4ZqUmSERIPYCBPpsuI8CQAs8Hw5J0deZxqzU8L0EYJqaUeIFD7AcYtSrSkCiZx7FifDfCCwT50EVrhR9b5B2NijVBaEDsAYIoDsCIibAwkgqGNAiTBJnL43kRQsdEQUTih7g1l9CPkFqDaWGaBobjYOYcrhpqTBT8/u//foaGhrj//vubm6RhGFsaz3o2Qlitz93+WNu2GRsbY3h4mMXFRWZnZ3n00UcplUoopU47A6T9+bfD6cQyy4GcA9x5553ceeedp3x+rwZKtZOFsLa2puHhYd71rnfxiU98ggceeACAR2dOIAhJ0pyIF5MkNZyOAmGYYBUcXD8glzNxiZAVD7voYChBudPECy00MSo2WKqZFOwIoRLqOPT1Oug4JEkMVuM8OqkiTU0uXwRboQyNtBMclSMSNraVgNSsrLgE9YhKYiOlSy7nIKpVFAaeKmEkgsQNyFkKmZfoRBOFESurCWauwPCgJAkDco5CSodcPiH0A7xayFTo0NlnsToTUCjElEsFSmWbGHCDkFw+wZAGGkEuJwiCGJU4GGaMH/mEKkc+J/Dry4SJSTFvE3oeETnytiLCQGmQUY0kFugkJJFQ9RRCJ6jQQycJoe8Txxq/7hN5YePvaJlI08SwbYYKjca5/v5+Xv7yl3Pw4MFms2s+nyefz2Pb9jMewtrsmjMMo5kPWVlZYWZmBtd1kVIyNzd3ioHjRs+/nT6trBM946yw201752IIaysMDg7yrne9i09+8pPce++9mKbJgyeeQmiNBDSaKApRKwqnlAelMB0LzxMUCgKljUZyWkjq9Tq5Uh4/FAiZYOqIumvQ0+UQxwrPj3Eci8qqR5IoTDtPuaDAcFGJzeJ8jG2FWKaBaWhWK1B3NbYMKXcaGFIThIK5lYTOUo5Cp4VKIsJAszzfGOo0OJYjXygyP1MHrbDMgErFZGHOo6Ms6OzPUSwXmDwaYknI52JUKIgTzdKSIkw8al6MKaCjG0xp4YeaXE6RLxTIR0XqtRpxUMXJFxHKRic1OruLhLHAP3lCydsJSRAiTIlINLGZg0gSKZfYi9BaQRSTxAlhpAiDhMitE7ohSIlhGgjDQFgmo+Ueurq6GBsb421vexuvfOUrt+Wx1s5O7+K3+tjTbdatBo6PPPIIURRx/PhxpqammgaOG40u2ImAfLeJB2QCsi3OhRDWbrBbz5W++Y4ePcrY2Nimw4KgUfDwUz/1U/zhH/4hX//61zFNk/uOP4kWAaaUDY+oJMGr1VFKk5MC0zaIEws7J4niGKfgoJWkuuyjlSYMNV3deWxTESMxHUHo+fhhAUMKkqCOUy6hhUUYKsLABWXgBRb5kgEqwo0EcaTAtOgt21iWxex0QOz7rCYWnR0+lm1SrUYoDGzLoOZpZhZCSo4m3yGxbMnKskILk9VKQmz4TE6FdJWsRsgnZ7C8rLEsk3xek0QxS4shRj5PR5fB0nJMveJR6DTxI43vS7RWlCyLIIiJw2XsnEGoLGpuSKwEOjRJanWEBK3zCOGiESgdQpIgLBMVhnihgETj1+tErk8URI0qK6PRIGjkbK4aHKevr48DBw7wmte8hs7OTg4fPszg4CD9/f3bCv+0c7aS6Nt5rGEY5HI5RkdHmZ2dbVqSpI2J+Xz+jNayXcG5UMgE5Az4bg9h5fN5Lr30UiYnJ3nsscfo6upidHT0lDcjNNYdhiFKKV7/+tfj+z7/9m//xmXdAzyxukDiNvo8kiiCROHX6yA0jk7QOI3xqbaF58UYZuPOOZczyTsGYWzg5GK8ao1YFjFIiCuLmPlOTKcDlYREaKq1hnWHaWsKJYNYm9RcC0u6mDmB0CbVWoxSMagA29YYeYNAG8xPBeSLmp7hMkJo3JU6qq5ZDSxyJYGlwSlArmiiY0kchmgfFkOLYqdJWPMwDYldzJErlFiaq1IoWAgrJgoFQZBg5R0kCSvzbsPQr2xQ8Sx8z8MQESEQ+GCaMRgFEqXQkUIpgYqWUYlCGDYqCvBjE1RAEgboKCH0XHw3QMcJhm2BlEjLRpoO147uo6+vj4MHD/KOd7yDffv2NTvAp6ammJmZob+/n8HBwU2HjbWSJEmz8uiZDne1Pt5xnKYlSdqYuLCwQEdHxxoDx7OddL9QyATkLHMuCshuPk9XVxednZ0sLS0xOTnJww8/3Pxc6jyc+oSlSVgpJT/4gz9IsVjka1/7GuXZMo9NTlD1ag3XXmJ0rPFWXMIgxsornGKBOAgxLBOtDUAQRuDYgPTRokBEgAprSLOAVZRYdow0BGFsEHshOlG4oUmpJ0cSBbhuALEk0JAvCQw7JE6gutLwJunssrAdk+qqQCOo1xUYPh2dDlEiQIBT0FiOyfRUDVMKSt025b4ii7MVkiTBySdIYVCpGiRRhPSqWHmbnGWhjQiFIgygsz+PYYBbdTEdsM1GZU8cKYodJkksCLwIlE+SSNAeMomQxRxJlBBFCZEwUF4dUOikkSiP4wS/7hJ7AaBBGgjTQBoWwrJ5zv6LGRgY4IYbbuDtb397s8Ak7QAfHh5mdna2aSTY19fH0NBQM4mejiHwPG/NRxiezK8IseXTy3ZzIGeywZumyfDwMIODgywvLzMzM8ORI0ea1u1KqUxAtkAmINvgXAhh7cZz2bZNFEU89dRTjI6Obqmipp3UcTjdMHy/YWgohGBlZYWVlRWEEM3Ea2pL35qEvfrqq3nWs57FF77wBYrFIktLSxyaOE5Cw7kX2YjvqyRBJwqnmEdIAytnghSoJCSMHULXR4sAw7aQKCw7AmkRJRrihnW8jgyKHRaWECRh4/tUUkPFIY5dIpESpRVhPcQwBUI4JIbE8yN0EmPlGqcejWbquEuuoLGKBrmCRa0WIEQjkxOECe5UBdOQ5Mpgl2y8eoJCYtgKISVBJSaQAukkqCBGKxM/qKEBpRxKRYPAdYk8jZ2zUXGCNm3bLQAAQm9JREFU0iamY+HWY1RoorXf8KnyFUr5CCkg9gFNHDRecxJERJ5P7PuAQJgGwrCRloG0Crzg0kvo7+/nxhtv5NZbb1335GjbNuPj4wwNDTE1NcXi4iLz8/PNQWWpUKSPzeVydHd3N//u25lVs5OBUtt5/HrvXyklvb299PT0UK1WmZ2d5dixY01PrjiOtySAmYBk7IiteObs1t3+bj1XavsyNTXFQw89xMDAAMPDw+u+UZRSTaFodx1O12IYBvl8nmKxSF9fH7ZtU6vVmJubIwgCurq61u0QFkLwyle+kv7+fu6++26eeOIJrpOSQxMniAODJImBGJKE0PVQSpErJARehOWYCNPEkDFOIUfkJ9hFE2kJPD/CySncegyxxHY0mpiEMo4FqysuUnqIJIdTMsGMSbSkNp+AhmJZYDgRcWzgrzYa5/IlgZmzqVU8VAx11yDfIfCXPaTWOB02wtBEXoRX1Vi2jVU0cBcaPS65Qo5Cdxe1RQ9tROikjinzJNiNvIyIQNsYjqZaD9HaRGBQqdSRUmIZNqEKMR2HJIkJohitIIlrSCGIY00cJ8SRQuiYOAgIax4qihGGibQMhLSQhsXBkRG6urrYt28fr371q3nta1/b/Nukocb2E0Xr31tKSRzHaK3J5/P09/fT09NzRnmSnVQx7ba3VUdHBx0dHXiex2OPPUa9XufQoUPNxsTN5vBkApJxVjgXBUQI0XzTp8nE+fl5+vr6KBQKawSj1fvLNE3y+TylUon+/v7micI0zVPePN3d3QwNDTEzM8P09DRzc3OMjIzQ19d3SrLxuuuuo7+/nz//8z/ngQcewDRN/t9TxzFkQALoWKDimKjmkUQJdqFxp2xqjTYkwg+wijahF+AImzDWJBWNmTNJdIST60TkNHEQkIQ5JDmS0MMuJggrRxgrYi9AyBitcmjTIlEKz/WBBKFtYmkS1uKG+60Ddh6ElEQuRKHCKEX09Bbw3BirCFLEKGXi1RWW0RjW5M3V8FcVuXyEXXSIE4U2EpxOCTpHGMREYQXDMpB2jjg42UWuFVF9CYBYmmg0Vt4mDjVRpNFxQpzEREGIjmJC1yVOS3QNEyENkCbScrh2bJTe3l4OHDjA6173Oq6//nrm5+fXCEVrv8d6f2/LslBKsbS0xMzMDMePH2d5eZmhoaFNh0Bt9drcCjvtFN8K6WtMRTHNlXR1dTE4OLiuF9h2Q14XCpmAbINnMoQFjaqnmZkZDMOgt7d3R+tJkmTNScLzPEzTJAxD5ubmgO9MZEsTi5sNujrdesfHxxkYGGBqaorjx48zMzPD6OgoPT09a9Y/OjrKu971Lv76r/+a//iP/8CyLP7fk8dRuo4QIUIIVBKjghA/irGKORBFTAlxoohWG/M+Ai9CSoiEwAxzOGUTN6piywKhH5PEq1iOjWEVEYYi9Dy8mkYISa5ogUwIw4SwZgASuyAxHEkUBUQVjZYSowiWVlRXPTA1MpJYpmT+RB0MA0N6WHmbIIhwunKoJCL2I6K6wsznELki9ZqLX0kwChIr5xDWFUmkwBBYyiKq+GCBYZSJw4gkiTGlTRT6JGEMykCLgCRWqDgm9kOiICb2XFQUIQwJSKRlIQwbYdpc0d9NsVhkbGyMl73sZZRKJY4cOYKUsikOPT09zfBTGqZaDyklfX199Pb2srKywvT0NEeOHKFQKGzJ3LCdvbAa2UlV1ejo6JrGxMcff7w5A6T1NWYnkIyzghBizUzzM+Hyyy9nYmKCY8eOMTc3x9jYGB0dHes+tnV0buvd5UYx6zRWvbi4yOrqKnEc09XVteHzbwfHcbj44osZGhpicnKSo0ePMjMzw8jICIVCYY2gPf/5zycIAmq1Gs8ea1hnfHuxQiJCpIxQcaO3Iaz4xKFCWjZ2zsawHYQCKw+YuUYewxIoaRDWIvx6BWlIkkBg2BohPNyqRgkBJGitiUQJ2zQJ6h6KEMuxEE6eJIYkjIiIMISBFHlW5wJMQ2IUJWavRVAPiFUIkYXZWyQIQggEtVWfQo+BQmB12GhCfDck8WRjCqII8Je8hnV6yUInBkE1RIsAGUsiT5PoAOEY+J7faAiUBioO0UlCHGlUEBCFIcr30UohTAspDTAthGlRzOU50NtJb28v1113HS9/+cvp6emht7eX/v5+HMfZ8eaX9lp0dXVRqVSYmZnhqaee2nCa4GYhMmBb+ZKzLSDp41MDx76+vjUzQGzbZnBwkL6+vkxAMnbGXuZAHMfhkksuoVqtMjExwbe//W06Ojro7e1FKbXmzRhF0Zrvy+VyaxLZGyU3e3p6qNVqnDhxovn8Y2NjZzTbIN000smMWmuq1SpPPvlk8zHpXXCxWOR1r3sdN9xwA3/3d3/Ht7/9bbpmZ/nGkxPok8l1ESdoI0EFITqK8JMEO04w8w5RaGCKEI1JvBwiCw0hUHWN2WNhFS1UrFCxRCcxSRJTzJURRU3k+dRdiYVNEgqMvMarVInrgJCYpo3VaRL6PsrSjfJZJQgqPnFNNXIwJYMkjgjCBOXGGCUTP9DIOIcb1CFWmLbEKJnESUgSxmhDkuCha2BIC23GoARxrIjDGtIx0HVFFCQgbZQXkiQJSRiRBD6J55HECiEF0rBAGmCaCMPm2rHGnfKBAwd47Wtfy8tf/nI8z2N6eprZ2VlWVlYYGhqit7f3jPoYhBDN8a+t0wRPnDhBuVzGMIzmjUJriCz9m/f39zfDZVu9pnayxq2y3vtaiO/MAElNEicmJpiamtr0tHYhI1ZWVva2Jfkssp6VyW6itT7FTK5UKm1613TixAnm5uaaJYM76ezVWhPH8Zqwk+d5uK675nTjOM6a6pf0351sDFprVlZWmJiYIAgCent7GRkZ2bRiKxWK9lNP66aRCkUul0MIQbVaJQxDOjs7GRsbW1MJVK1W+dznPsfXvvY1ZmZmWF5e5ltHJ0iiBKECVByDSgCBtE2k7WA6OaRoJNMNs/G7NiyJUSjAyRCRjlWj091wkLYGQ2AaJkFNI8wAaZrYhQIIjUSQJCFKg13Io5UmXk3ASRCGBg1oE6ksZF4TBTGqGmN2mpi2II4jElehtMbMOViJ0Sh9jT2k0TjNKAKiMMKIDJSlUEkCiYWicaLQgSIhAq1JIoVOgka4yvWJI/+kA6zRKNE1TDBsDDvHiy8fp7+/n6uvvpo3velNXHzxxWv+Xq7rMj09zfLyMpZlMTQ0RH9//7avl7Tno/0jde5NyefzdHV1USwWd2SJ0sqxY8fwPI8rr7xyS49/8MEH6e/vZ2RkZEuPf+CBBxgYGDjt49PQ7+zsLFprenp6GBoa2pVhUqurq2f8HGebTEC2wXoCkiYaNxKGJEmad3uWZTE2NrZhfFhrTRRFp1Q8eZ7XHJUphGhuwOlHvV5nfn4eKSXDw8MMDAzsWlesUoqFhQWmpqZIkoTBwcFmnXy7oLX3erSK2EY+SlprlpaWmJqaWleolFJ84xvf4Itf/CJPPvkky8vL/Me3T6DiCOLGeFmSBK0T0AppOlj5HNK0kLaNlbPAkBhaomiUZpqGiTYURs7ByeUIPQ+dgGFKpLIwOg10FBF5CSICJ2+jihDXXeJAY1gWTqGAMCXUEyLpk4QKaYOKJDnDITYbr03VYjQao6wRaOIQkvhkGFEbqCgGqRDSJgkjtIoan9cKhEZHikRF6Dgm8RISHZP4PjqOULpRFgwNF11h2Ejb4nkXN6qsRkZG+L7v+z5uvvnmTTc0z/OYmZlhcXER0zQZHBxkYGDglGv6dD0fsPYmJv0QQjA7O8vCwkKzgGNwcBDbtnd8XR49epQgCHjWs561pcc/8MADDA4OMjw8vKXH33///QwNDW358U888QSe5zVvosrlMoODg3R2du5YJDMB2WP2QkDS8sV2f/80n7DRxeL7PhMTE6ysrFAsFhkaGkIIcYpQtNpCb7QBrycOURQxNTXF/Pw8juMwNjZ2xpPQWuPVruuyvLx8yutuFYrWtW737rJdqPr7+xkeHm4m7ufn5/mbv/kb7r33Xqanp1ldXeXrT0wjlI+OY3QSo5UCnTRq+C0bq1AAYWIYAmnaIAWGFMi83XD8jQOklkjLQPsJRsnEsCziMARfYpYMlNYYlsAwbMzYbFRO5RKUikk8jYgVRsFBWhZGBIkRE0YhWsUIZWJJC2UKVBIhQ0FinxxzHEpUEpGooLG2QBGqEGEaECQkOsTUECmFTmLCKEKFfkM8VAJaIqTR8MCSNsI06SyWuP6SEfr7+7n88st5/etfz9VXX73lv0EQBExPT7OwsICUks7OThzHaVblBUHQDB1ZlnWKUOTz+U1vXKIoYnZ2lvn5eZRS9Pb2NkdCb5ennnqKKIq44oortvT4+++/n+HhYYaGhrb0+Pvuu685AXErPPHEEyiluOyyy1heXmZ2drbh2ZbLMTg4uKMQYSYge8zZFpAU13XX5BhS0k0/jYemzXatIlGr1QiCoPk9692p53K5HSc2Pc/jxIkTVCoVSqUS4+Pjpx27257Y3Kh7PBWGMAyp1+tYlsXo6OiOK8LWI0mSpleR1pqhoaFm6C9JEr761a/yhS98gWPHjrGwsMCjJ5ZY9X1049a+YUOSJIBCA6blIGwbwzAwcjbCskBppJAIy0YEMWbRRNs5hEqwlIkyIUkiDNvCMHMkykcFEQKBMBuDnQgNrBLEaLTSEIEMQVmgLQMZGRi5mDiMAIVSElSCUgkCE6USNAphmI1TRhI0chcxqDgA3ejpSCK/YXkfhA1xlAZCNgY+IcxGj4dp86IrRuns7GRkZISXvOQl3Hzzzes2Brb/3dMTb/tHa47Bsiw6Ojqaoae0dHunxHHM/Pw8s7OzxHG8pbBP+zpXVlYoFApcfvnlW/qZ9913H6OjowwODm7p8ffee2+zgnArHDlyBCEEl156KdD43dZqtWaeaf/+/WvGbm+FTED2mL0SkDTO3/om832fhx56iIWFBa6//nrCMFxzx5YmDFOBCIKAxcVFtNaMjo7uatgJGhffiRMn8H2f3t5eRkdHsSzrFEHbSCjaBa39ROF5HpOTk8038mYVYdtFa92Mz6cd7ZZlEccxSimq1Spf+9rXePjhh1lcXKRWq/HAsUWiJIAkOSkiJ51oVQJCIi0HaZkYVg7DbIROpCkQtg0IJAIhGq7A0jARwgQRopVAShMRCbQjMXICfbL3AglSGohQoOyGYEECiSRRGqETpJSYunFyibVCGGAlGl/HCCTKC9EkKCGQcUKkY1AalSQkgdsI1ekEkCfDVUZDRAwTYdo8e7wb27bp7e3lOc95Dm9+85u57LLLTvmdpjm09o80NNp+kkzLeNPOc6BpY3ImoadW0lPnzMxMMw82MDCAaZrNU297PqXV3aC3t3fL19x2BeFb3/oWF110UXOS6en49re/jWEYXHLJJad8zff9DSMHm5EJyB6zVwIC37Hy+OxnP8v73/9+jh07Rj6f58UvfjH//b//d2zbJp/PUy6XKRQK61ZpxHHM1NQUc3NzOI7D+Pj4GcVM29fmeV6zLLe9amU9oThd7f96pBVh9Xp93UT46dbZnvNJ/39rzscwDOI4xjAM+vr6mqWnTz75JF/60pd4+OGHWVhYoFKp8NWHJ0hUhEoiUEkjCX1yU9ZopBSNJLNzUlCk2RAYKRunAa2QpkZaDkJbEPjonNkILSUK4SsM0yJxElAKrSRSWigS0I2eENAoKwEhQAm0OpmjkSZaNfo2QCF0wz5FJ40xsyoJGtYtcQQqQuvGNEHESedcwwAspGnyvEsH6erqoru7m9HRUa655hoOHDiA4zj09fXhOM6a32nribldKPL5/KYn3jiOm35YSqlT/LB2QnqNuq6L67pUKpVTTj7t+ZRCobDjk/l2BEFrzb333rutU8Pjjz+OZVkcOHBg22vbiExA9pi9FJCUJ554gkceeYRLL72U4eHhpodOK+vdwbfieR7Hjx+nWq3S0dHB+Pj4ljbhVqFoP1W0n3zSpLdhGM2w0G6deLTWLC8vMzk5SRAE9PX1MTIy0rxTba0iaw+TtQpFu5i15lJaTzz5fJ7R0UbIJn2z/+M//iNPPvkkCwsL1Go1vvHIJLGK0KphEa9VgkChtTq5MYuT8zBspGmjDYE0DQzTRmNiRBFYEm1YIBVGCMI2Gt8vNNK2MaOGe3AsFNKWCN2wYNdKIU0TpWgk+OMIZRoYoSImaZwkgphIhaASRKwa/z+JGn83LRBSNE4aWoJhIKWBlCbPe9YIpVKJzs5OhoaGuOGGG7jhhhswDIN6vb4moW0YBsVikUKhsOZmYad/9yRJmhVHcRzT29vL8PDwaS0+ThcmSx0O0mu+Uqng+z75fJ7h4eFtNyWut4Z77713ywKilOK+++7bloA8+uij5HK5UyrdzoRMQPaYZ0JAUqIoWhMKaicNEWw2wCYNOwVB0CwhNE1zXaFo9ydqDZG1bsStJ4ogCJicnGRpaYl8Ps/4+PiuhZ2g8cabnZ1lenoarXVzswqCYE0IYr3igK3eWdZqNSYnJ6lWq5RKJUZHRymXy0RRxP/7f/+Pf/7nf+bYsWMsLS1Rq9X42qETRDpCJwlaxaAVSifoJAFBIyEtBNJ0gMbYW2kbaCkRonHXL4UEDUrQOLEgIGls1NqQCMOGJMFINIlUgEQEEbGpmiKgVIxKIoRW6ESTJBE6iRqJf5UAGoGBELJxE2KYIA0M0aiweu5lDUeAQqFAd3c3z3rWs3jOc55Dd3f3mg04rQhcWVlhaWnpjMpzNyJJkmboKYoiuru7GR4exnGcTct50/dAq6Cl12graZ/QzMwMlUoFx3HOqFdlu4KQJAn3338/F198Mb29vVv6GY888giFQoH9+/dve30bkQnIHvNMCgis7YPYiPTNvt4bIa3ump2dZWlpqfn4tPILvlM23L4Jbyf0lDYK7iTslNJ6omgVtfbafyEE5XKZ3t5eisXiGXU9p6Tl1BMTE3ieR2dnJ6Ojo00fr69//et89atf5dixY8zPz1OpVKjVajx0dBGVRGil0brxO9XoRq5E6+Z/CykaYSMalU7aMBGSk4LSSB5rIRFCgzCw4phQKoQGToqNpiFOIkmIVQJJTEOFTooX+jtiISUnfwAyDVcJgRQ2zxovNxvsBgYGuP7663nJS17CyMjIhhtwiu/7TE9PN8tzUyE5kymD8B2DzXq9ztLSEtVq9ZQQ6XbDZBtRr9ebPUCWZTUHXG31NURRhOu6HDlyZMuCEMcxDzzwAAcOHGja25+Ohx9+mFKpxEUXXbSlx2+FTED2mGdaQFLSevn1KrXgO12uqS9Va/ipVSha7aTTUsDd6nhNw04TExOEYdhssmrfjNZrYGx/bRudKNIcz8LCAo7jMDo6esbhiPVeQxo6a51DUqlUOHToEN/4xjeaVWmpwD361BJhkhCrk3mSk6cScfJ33ygH1mhBo9dCn5yjAWgB6IaRolSa5OSNgJCNqX+cDJOhaTxeNz4ntDiZw2ik7BsCRKO3XjaEyhAm0jC5bLRIqVSiWCzS09PDvn37eNGLXsSLX/xiuru7t/178n2/2eeRhjC3sgmn4afWZHb7dWpZVrN8Pc21lMtlRkZGKJfL217rVl6DlJKBgYE1I2nT91z7WtPrVErJ5ZdfvqVO9yiKePDBB7nkkku2/Pt+6KGH6OjoYN++fTt/kW1kArLHnCsCkpL6Ua2urvL3f//3PP744zz00EM88sgjfPKTn6Snp6d5omjfgFOH23q9zokTJ6jVas151Tupm98IpRRzc3NMTU0Bjd+hZVlNwWi3RGm9o0wbGU8XVvA8j4mJCVZXV5tmfjvdXLYSU0/zKcVikVwux+zsLIcPH+bRRx9ldna2eSIJgoAHH5kmiAK0ECiVoFTUPI2gG+FIDY0keOPZGycMoRHfWVTLCsXJ/20qTUN8dOOZhEirqWSjE102TjpXX9aHYRjNE+rg4CDXXnst3/u938vVV199RgnrlLTPIxWS1obBrVRptZ8oCoXCmnLeVNCnp6fxPI9SqcTw8PAZO/S2v4apqanmCT3Ns7WWxtu2fUqYbLMerXbCMOTQoUNceumldHV1bel7Dh8+TFdXF+Pj49t7QZuQCcgec64JCDTeVFNTU7zuda/jWc96FpdddhkXXXQRN954I52dnWs2jY3uCFtPC1EUNTtqdxKKaHXj3chkEWi68e5G8jUlDTu5rrvp+NuUdqFoT7y3l546jkO9Xm+6Cg8ODjI0NNT8Pbmuy+HDhzl06NCahHv63L7vc+jRORKSk7mShojoRKFE4yRxsla3cRJJNEICiEboSzcE4+Q4LLSQSK0bJwxAShMpBFoYXHtFP7Zt4zgOhUKhedoYGRnh4osvpru7m87OTnp6ehgeHt52iHEjlFJUKhVmZ2epVqsAzR4baIhvuqb1ihm2QprPm5qawnVdCoUCIyMj264wbLVIaT1ZtK41vWlIHXLT99SZEAQBhw8f5rLLLtvynnLo0CF6enoYGxs7o5/dSiYge8y5KCApWut1N+pW0g1lo406bbKbmZlBSsnY2NiGTXzr2ba3206kpcatIgGNQVPpaWF8fHzLBndboT101tfXx+Dg4Jrxt+vV/q8XU99oU4vjuDmDxDAMhoeHT0kiVyoVjhw5wmOPPcaTTz7J3NwcrutSr9cJgoAwDJunL6UUDxyaQOtGSEtp1WgqFCdPJydLcjFOhrHEd/IbHQWHAwf6mqHHtEnUsizK5TLd3d2Mj49zxRVXcOWVVzI0NISUEqUUi4uLTE9PE4ZhU1y2evpcz/XWdd1TusnTqYJCCPr6+hgeHt61Po80VzU9PU2tVtuwqip9b7SvtfVaXS/5bts2SqlmU2Ka0B8aGjpt8+xmpD1dl19++ZaLTB588EH6+voYHR3d8c9tJxOQPeZcFpCU9K4qvYtqJ70D3OyOLwxDJiYmmtVUaRNiq1i0H+nbN+DTjRqtVCqcOHECz/Po6enZ8ejb9teeri+t/W8vOFivkmw74YdWwjBs5mBs22ZkZGRdwU1tYJ566ikmJyebA5JWV1epVqvNzSwMGy64WmuSpNEkKKVc89+FQqH5u01Pl+mNQbFYpKOjg4GBAfbt28eBAwfYt2/fpgKdNttNT08TRdG6ppat4afWO/X2wVDtH2n4KQzD5lAxKWUztHUm3ebtVKtVpqenqVQqWJbVHMrUXnbeapGSCsZWTr+p4M7MzBAEAR0dHQwNDVEul7d97Xiex8MPP8wVV1yx5VDrdr22tkImIHvM+SAgsLYvYiNb6jTmnL6JW43s0n/r9fqaHEW7P1G6Ae/0SK+1ZnFxkcnJSeI43nLobD3TPd/314hamk+xbZsgCFhdXcUwDEZHR9edWngmeJ7H1NQUy8vL5HI5xsbGmuGU9dZar9dZXFxkeXm56f+VikgURSRJ0vzeJEmI45g4jnFdF601xWKxeafd09NDX18f3d3dzUKInQhxmquamZkhjuPm3zUIguY1sN5JbaMm1vVoF5KBgQEGBwd3JCSt4wVaha21Sk8I0SxLTsViu0PL2mnPw6S+c5v5wrWuNT2F1ut1rrzyyi2fZLbrtbUVMgHZY84XAUlJ+ztaN1ZobHgPPPAASikuueSSpi1KSlr5kopEGIbMz8+f4h21WyRJwszMDLOzs0gpGRkZaTZkrWfd3nqqWM90b73TTxiGTE5Osri4uGtmkK2kG8vU1BS+7zdPCJuF9Na7+01PG1EUEcfxmpOkYRjNWfBRFG0pz7PRWtO+n3YRbqdQKNDX10e5XN40/LkdwjBsmh4CTQfm9YSk3XBzvbW2Fl+kQpFeU61W8n19fbt23baHz9IBV6VSac0peL21FgoFCoXCtpptt+u1tRUyAdljzjcBSUnvgD70oQ9x9913c+zYMUqlEm94wxv4+Z//eWzbplhslHa2V76kbMc2frukm0SlUmF+fh7XddeM8gROaWZrnZe+HVzXZWJigkqlsqOKra1UaRmG0bBaV6pZ8dTV1bVrIZv05DY1NXXa/MVGa20PP7XH/6HhUDwzM0OSJLuev0jXlp5IgKZQpWt2XXeN7cxGa91MFNp7VTaykt8urQn4NBTZLvbbXetmbNdraytkArLHnK8CkvLP//zPrKyscMUVV6x7ikirjtIS3/VotY3fqhtvykabb2uHvWEY2LbdvAPfbSPFlPaKrfXKl3dSetoqaq09JLuV52mlPX/R2dnZ3IDbQzqtJoHtXdqb3QSk9iIzMzMopejv7z9jw8PWpHbqVVWr1da4LLQmtVvDTzu9YQmCgJmZmaaV/FbDZ+udgNJigda1pn93z/Oo1Wpr+mHO9MZhu1YpWyUTkD3mfBcQ2Ho3+1aS4MePH8f3/WZ1SGt8ebslsu0bWvvEwvV+xm78LpaWlprly+Vyuelk3Nqjst0qrVbS5OvU1BRxHJ8yh2Qna07X1xrSad3QUo+qVnv0nRYKpLTa4CulGBgYYGhoaNPXkebiWteZnirak9qFQgHbtpvd5+lgqNP9jO2S5mEWFhYAmkJiWdZpy3oNwzhF1PL5U10f2psS0wFXO30dO/HO2gqZgOwxF4KApKR2Eacr+91s49FaMzs7y9TUVDO5C4030E5LZNdb5/z8fPNnpKN7d+pZtF7svz1HlMvl6OzsbG7AuxH7b72T11pvGveHrZ3W2sN6juM0w4Ba6+Ymv5vVTnEcrxmxmm7AUspTYv+tJ6CtNAq2/ozUnVdrvSsTBlvRWjctTFIn6dZeFTi1rHc7xQIprbkerfWOXYbTxsPteGdthUxA9pgLSUBStlL222pa2L6htduppJPmuru7d+XONyW1LZmfn28Omurp6dlwdG97j0K6+W428c40Tebm5pibm8M0TUZGRujr69u1RHv6OtKCgTTM0dvbu+7vtt0ksD1RvNEdbfoz5ubmEEI0xWo3Esjp77ZarTYbJdtJk9qtG/BOfKrabd53Ej5LkuSUE1B7DsgwDKKo4VS808KE072O9LpKB1yt17zZakHfutb0Jm87jYdbIROQPeZCFBD4zt1uusHWajUef/xxHn74Ye69915e9apXrZnM5jjOun0fQRA0faG2Yxu/HVpzMIVCoVn2e7okcftaNwsntLoKt5flngmtsf9arcbKysopJ8DdMgmERhhxenqa+fn5plhtZ7DYVvs/0iINaISEhoeHz9qpZyMhWS+019os2H4Sbs+rrGclPzQ0tKvXb7vLcOpHppQ6RdhaQ3ut/7bTOqhtu2QCssdsRUB83+dNb3pT0355aGiIj3/84+zfv5+5uTluvfVWnnzySRzH4eMf/zjf+73fuwcr3xrpBjcwMIBt21x11VWMj4/z5je/mWc/+9nNZrVCobDhBbuZbfyZ0BqfTjdg13WbX29NEreGH84k8Vqv15mYmGhau4+NjW2pa741/NReetp+ArIsq/m4s1FeDGsbHi3LYnh4eE0vTBraa79TXy8H1HqqaP3dtlZUpaee3W4WTIUkTeiXSiUsy2qeNltDe+0b71atcjaykt9sHO5mbCUJL6VsNoGmJb7rWdCnfUHpv2nTaVoevl0yAdljtiogX/nKV3j1q1+NEIKPfvSjfPGLX+TLX/4y/+W//Bf27dvHBz7wAb75zW/yoz/6ozz55JO7+ibbDebn55u19BtxuiFWrSaKQghGR0fp7+/fUtK5vfGu9Rif/ux0U0iShKWlpTWNiLv5+2y3dk8n9KUVWzut1GrfIOr1OpOTkzsuL94KnvedoVmtg8Baha29VyWd0rfVO9xUSObm5ppd5zsNn50upJPiOA7d3d1rpnOeKe1WL52dnQwPD5+2q79dKE6XhE+rw1JPr7QpcT2x2AjDMHZkB5QJyB6zkxDWt771Ld70pjfxxBNPUCqVOHr0aLMU7/nPfz6/9Vu/xU033bTLK90dznSIVfock5OTLCwskMvl2LdvHx0dHRs2s7V6KbVapGx2J9nu4ZXmLnaz27y1CilJEmzbbp40UtYLkWzHJBDWlhd3dHQwNja2o7vf9hNbe14lxTAMurq66O3t3TCpvRPau87T8NlGQpIKcevm23rttYd00t9t6lOVJElzguFulkqnlXrT09P4vk+5XG7+jFZ793S9Ke3X7XpJeH3SlTkdD5D6pVmW1RxytdVrZyeOxJmA7DE7EZBbb72V3t5efvmXf5nx8fE1YZc3vvGN3Hzzzdx66627ucxdZTeGWEVRxPLyMrOzs4RhiGEYKKVOGWKVvtF22iTYGqrZae5iKwn41oqd7u5uBgYGKBaLZ22E72aNgu1CnG5o6/UptP6ObdvGdd3mqWenjranIwxDpqenWVhYaFq8d3R0nHKyaA2Xta91s4IBaIhl2vS41VG4W6V1Dsjy8jLVanXNDVXqT9Yubu1C2RqCSj9a3wMpvu+ztLTET/3UT+F5Hr/wC7/Ay1/+8tP+Tcrl8ravv0xA9pjtCshv/MZv8Ld/+7f80z/9E57nsW/fPur1evPrb3jDG3jNa15zTgtIyumGWEFDSNo9itrDOZZlEUURSqlmOGg3pgi24rouJ06coFqtUi6XGR8fX/cuvj38tFHn83rVWq0JatM0GR0d3dC5eKdorVlYWGj2kPT09NDV1XWKwK1X1tt6Yjtd+KharTI5OUmtVqNUKjEyMrIrjZutZci1Wo1KpbImFGNZ1imlsmdSMp0KSeqcu10hac9btfespNVlUsqmq3I+n2dkZGRN3kopta5YbIe5uTn+7M/+jN///d/ny1/+cnOUrRCiWTkmpWz+u5PrLhOQPWY7AnLnnXfyV3/1V9xzzz3NoTHFYpFjx46dNyGs9Ug33SAIuO+++3jkkUc4fPgwDz74IO985zu55pprgM2ribZjG79T0mT+xMQEvu/T2dlJR0fHms13NxoFz0bFVnseaL1O7fY73jT8tNOfm+Z6JicncV2XcrnM6OjolmLr7Un49N/2U0UaxqnX603X3N2ep56uJz2RRFF0ygluvU741vW25q1aTxetQtzuheU4TnOs8m6R2u6nYdtULHaLTED2mK0KyIc//GHuvvtu7rnnnjUjK2+77Tb279/P0tISn/nMZ5iZmeH+++/nuuuuA+Cmm27i+PHjzbu/t73tbbz73e/e9ddxpqRd4gcPHmzOmdi3bx+vetWrGBsbw7bt0w6xgrW28YVCgfHx8TNKHKfhp/VGpKakXdqtG8NuNAq2VmyVy2XGxsZOu5lsZaRra1I7HWiVdjgPDw9vqyx3K6R/28nJSXzfp6uri5GRkeYJ7nR36a3rbT0FtQtbq0fVepVhu0FayDE9PU2SJM0bmPaZJa3XQno9tK83NblcL7HteR5LS0vU63X+x//4H9x666288IUv3NZa01NF6+liN2+o1iMTkD1mKwIyMTHB+Pg4Bw4caG6GjuPwjW98g9nZWW655RYeeeQRisUiq6ur3HPPPVx99dVAQ0De8573cPPNN5/V17Fb7MYQK2iEUE6cOIHrulv2jNqqSWBrzH9lZYWFhYWzGnJqPfW0vpb2pHZ7hU5rLL09XNZOmldImyrPRsNje19Euo71Ostb173dvJXv+80RsrZtMzw8TG9v77aF5HR9IK3k8xsbXKaJ7Xax2EoIamVlhQ9+8IP8xV/8BTfeeCN33333KX+T1hBU+iGEOOtisR6ZgOwxu91IuH//fv7u7/7uvBWQlN0YYpU6zE5MTKCUao6LFUKsKxTt4af2zXej/o8gCJiYmGB5efmsGDWmojo7O8vS0hJKqXVtMs60Wgsam+/k5GRzDsno6Oi2e0ja/apaLchbiwbShG9ahbSTQUqb4Xke09PTTSEZGRmhp6dnXSFJr7f2NW/UB5KeKoA1pbnpDBXbtjdNbG+X5eVlHnjgAV7xilc0Q0+tYnGukAnIHrMXAjI7O4tpmlx11VV88IMf5MCBA7v6M88WOxli1f79QRBQr9eZn5/f1CKjvat8J2/KWq3GiRMnqNfrG7rxno72U1B76alpmkgp14x0HR0d3fW+n/YektHR0XVFsbWiqHXjbT9VtItxWhwxPz/fDAedqSnkRnjed4Zz2bbNwMAAjuOs+R23Vpitl6tYb02tie04jllaWmJxcbEpnmkH/U5ITxWtYrEXIagzJROQPeZsC8iJEycYHx9Ha81dd93Fxz72MR555JFd/Zlnm42GWLV+PU0Otg+Lat14HcchiiLCMCSfz7Nv375db6xrnZ8eRRH9/f3rds23b7zrnYI2Kz3di4otoJkEr9frlEoluru71zS3nW4I01aq4dpNIXfLsLG9Ca9Wq625GUlDfK0f6/UEpeWy7VVQ693UpInwj3zkI3z84x/nHe94Bz/90z+9qWFhu0jsdmJ7L8kEZI852wLSTi6XY3JyclcdOPeKdEOIooh///d/55FHHuHhhx/mgQce4HWvex2vec1rtlTz3zo7vbe3l7GxsV2/61VKNV2FhRB0d3evaRRr3Xht2z7lDn2rp6DWkFM+n2+Gz860Yqs9r1Kv19fE7AuFQtNZOF37mRorpkaHs7Oz2zJsXM/mZT1xS9cppWwObMrlcoyMjDQHmW2W2N4OWmu+8Y1vcOedd/KLv/iLPPe5zwU4Jal9roWgzpRMQPaYsykgcRyzuLjYHFn5uc99jttvv52enh6efvppDh8+3BSac91Tq5U4jnn+859PX18fBw8eZN++fbz4xS/m4osvxrKspqXGZiWoWmvm5+eZnJw8Y0v3lPVmP7iue8pQo9QeY6MGsZ1Qq9WYmJigVqtt2qfSSnvF1mbjUtO1ep7H3NzcWZsoCGutS9oNG1tPma3lsq0hs6024dVqNaampqhWq82S2UKhsCsb+rmU2N5LMgHZY3ZLQN75znfyhS98gZmZGfr6+iiVSjz44IO89KUvJQgCpJT09fXxlre8hVe96lV87/d+75qTyvniqZWyW0OsUkv3ubm5LRsPttb8twpGu7dW6wYmhGBmZqZporidqYtb5XQVW+vlKnYy2rW1lPVszQgBml3tq6urCCEwTZM4jteU97YnttcrHDhdx3ZaMvvoo4/yyU9+kve///1cddVVW15ne67iQjtVbIdMQPaYZ8rOvT3Udb55aqXsxhAraGwiqW18uVxm37595PP5dR1wW+PorTNAtuLSmroK+75Pb28vo6Oju3oHn+aLZmdnWVxcPKsVW+0hp6GhoR2d4k7XhJfezcdxjGEYTev1jQonWoViOx3bTz/9NB/+8If59Kc/zfvf/37uuOOONV8/XxPbe0kmIHvMuSAgi4uL56WnVitbHWK1USluGvdfXFxkcXGRJEmaMXH4jsnjeqW92yW1E5mcnCRJEoaGhhgaGtp2KGsr41INw2hWbG2U0D9TWr2pTjc0az2Dw3ZB3qgJr7WaynEchoeH6ejoOMWK/Ew5ceIEWmsuueSSU3IVmVhszvkgIOdmTOU8Z71j//lE2g3eOsSqFa110y5FSnlKt3Z73N+yrOZj03LM3chVAM3NvKenh+np6eY87Y023vZu+I3MDQuFAp2dnac4tUZR1AzTraysbDp5cSfYts1FF13E4OAgU1NTPP3000xPTzdndq8X5mvttenp6WmKxUanivT0kZozzs3NcezYsaaH1LXXXrvj9bcntg8ePJgJxQVMJiC7TFqRNT8/3wxhPf300+zbt6/5mP3795PL5Zp9Db/4i7/Ij/3Yj+39YjdBCIFt283NPwgCFhYWOHz4cHMSYhRFvP/97we+E/fv6OhgcHDwlLh/ahs/MzPDysoK4+Pju3piNAyDsbEx+vv7mZyc5Omnn2Z2drY542SjhrZ8Pk9nZ+ea09BmYSPLspob/MTEBEePHmV2dnbXGh5bT0Lp7zQNCaavs1gs0t3dvWmYrz1XsVHHdjp+2PM87rnnHn78x3+cH/7hH+a//bf/1jQIXI/v1sR2xlqyENYu0J4DST210iT6j/zIj/DUU0817whPVx58LpIkCT/7sz/L//7f/5uDBw9y6aWXcvXVV/MDP/ADa7y1Thf/d12X48ePU6vV6OzsZHx8/IxtvdezyajX62ucidNpje3lyGe64VWrVSYmJqjX69uaD7KVSXitYT6ApaUlPM87xctrI7HY7slXa83Ro0f5zd/8TV7wghdw++23A5yS1P5uTmzvJedDCCsTkDNgvWqtJ554oumpdfToUWzb5mMf+xgvfelLm993PgoIfCf8cbohVqfzXGpvEEzH6m4lrHW6OeCpkKUbbxRFzM3NrWlE3O0+ldTgcGJigiAITknot/eCbGUS3nonofaf09HRQW9v767lYbLE9rlFJiB7zDN1Atku+/fvp7OzE6UU3/M938MHP/jBZrjrfOBMh1ilKKWYmZlp2saPjo428xbt41I3i/u3VkFtZJORlsoCZ8UlF77TBT49Pd20+QY27AVpna+9Xq5mvSqo9PPVapXFxUW+8pWvsLy8zLve9a5tneRau7SzENS5SSYge8z5IiDHjx9n3759RFHEL//yL3P48GG+9KUvPdPL2jZbGWLlOM5pLThaB0wZhoFlWYRheMq41K2W925EalkyNzeHbduMjY01u6a3y+ma8FIRFELQ1dXFwMDAKTMrUlKhaO+v2Moa/s//+T/82q/9Gqurq3zmM5/hyiuvPOVxF3rH9oVKJiB7zPkiIK1MT09z+eWXU61Wn+ml7Jg0rLRRWCu1RDEM45Rched5a04V0NhQ8/k8Q0NDdHR0nBVDwImJCVZXVykWi4yPj286mGkrMzbaQ1CpV1g6wjcVrPTk2Z6zOBOUUnzhC1/gB37gBygWi2vEIgtBnb9kArLHnA8CkiZ30ymIH/7wh/n85z/Pv/7rv6553JEjR3jb297GwsICXV1d/Mmf/Mm2Onr3mtTKI91YtdZMTk5y+PBhvvnNb3L11Vdz7bXXntI02D44SgjB4uIik5OTxHG8476OrdDq45VOxUtLlDebB56GnjabsdGa2K7X68zOzjYn4xUKBfr6+s5o7e2J7VQsMi4czgcBycp495jZ2Vl+5Ed+pBnPPnDgAH/2Z392yuP+63/9r/zET/wEt912G5/97Gd5+9vfzte+9rVnYMVbo7Xst1arceDAAZaWlrj00ks5cOAA11xzDb29vTiOQ6lUolgsbrjh9fX10d3dvaavY2xsbFf7LeI4blq4Ly8vs7y8zNLSUvPraRNe6um0mSlje64iFY6UdDqh53kcO3aMl770pbzlLW/hPe95T/NGYiOEEOuKRXaqODfxfZ83velNPPLIIxQKBYaGhvj4xz/O/v37zyuPvK2SnUDOQebm5rj88sub3cipQeHXv/71TWvzzyUeeOCBTa1FtjLEChoDpk6cOMHKygrFYpF9+/Zty/fqdJPwWuetR1FErVZrJvTTHpLW59oosb2d9dx33338+q//Op2dnfzxH/9x82tZYvv8x/d9vvKVr/DqV78aIQQf/ehH+eIXv8iXv/zlbXvknQ8nkExAzkHuvffe5mjdlOc///nceeedvOQlL3kGV7Y9znSIVStbsY1vtyPZqHGwNQTVnowPw5DJyUkWFxfJ5XIMDw9TKBR2bEW+EWk+qFQqZb0VFzDf+ta3eNOb3sQTTzyxbY+880FAshDWOcr5bocCjddgWRamaW44xEopRb1ex7KsTSurOjo6uOqqq5q28UtLS6fMBVmvCa+rq+u0jYOtXdqptXqhUGBhYYGjR4/y0EMP8aIXvahp5b/d38F6uQohxKaJ+4y95Wd+5mf44he/eMpohptuuonjx483XQbe9ra38e53v3vLz/uRj3yE17zmNU0zztZy/f3793P8+PHdfSF7TCYg5yDj4+NMTEwQx3EzhHXixIk1dijnE2mYyLbtNaWurURRRBRFp5T9rteEBw1BTXMWuVxuS3YkGw04Wk+c0xnm9Xqd97///fzcz/0c733ve7njjjs2FLkssX3+8qM/+qO8973vXTcn8ZGPfISbb75528/5G7/xGxw5coSPf/zjeJ53QdwUtpMJyDnIwMAA119/PZ/61Ke47bbb+NznPsf+/fvPm/zHRkgpKRaLG5b9xnHMww8/TKlUolAo4Pv+uk14HR0dzWFF6VwQy7Lo7+9v2n6cLrG9FdJTwt13381XvvIVvvSlLzVzElli+8Jit0PDd955J//rf/0v7rnnnmbIFDb3yDsfyXIg5yiPP/44t912G4uLi3R0dPCnf/qnHDx4cNPvue2227jnnnuaJaKvfOUr+e3f/u29WO62SbvZn376aX7rt36Lhx56iIceeoggCPit3/otXvnKVzYnDqYeVhsNZErngoRhSHd3967OM09PEZlp4HcH7TZDN910E7Ozs5imyVVXXcUHP/hBDhw4sOlzfPjDH+buu+/mnnvuobu7u/n503nktXM+5EAyAbmAuO2227jhhht417ve9UwvZcssLS3xq7/6q1x11VUcPHiQiy++mGKxuGaDTodYAad0a6enitQnamlpid///d/n5S9/Oa961au2tZb2bu0ssX1us1He4kzKZdsF5MSJE4yPj6O15q677uJjH/vYmuKWdiYmJhgfH+fAgQOUy2WgcXL+xje+cVqPvHbOBwHJQlgZzyg9PT38z//5PzcdYhWG4aZTEqERburu7qZcLtPX18eP//iP8/KXv5w/+qM/OkUEWhPbraWzmVicX2yUt3jf+97HC17wAv7hH/7hjEdKj4+PA41r5l3vehfvec97WFxcbI5taGdsbGzD3Mbg4CBf/vKXt72Gc5ksw3eB8eEPf5hrr72Wm2++mQceeOCZXs6WSedcpDPPd4ppmvzCL/wC9913H294wxua3lq5XI5isUi5XKajo4NisdhM7GcnjfOTl7zkJYyNjZ3y+b/+67/mne98JwDPe97zGBwc5N///d+3/fzpmOGUz33ucwwODm4oHt+NZCeQ84gbb7yRRx99dN2v3X///fz6r/86w8PDSCn5m7/5G1796ldz5MiR86ZcdL0hVints8jX+97W0NP+/fs5cOBAJgznAbs5YG2n5bKtoxle8YpXUCqVePDBB/nBH/zB5jTNvr4+vvjFL+5oXRcqmYCcR/zbv/3blh/7+te/nve97308/vjjPPe5zz2Lq9p9Wmeu+77fFIdUQLLE9oXHZz/72V2bj7OTctm77rqLu+6665TPf+tb39qVNV2oZCGsC4iJiYnm///617/O4uIil156KQB/9Ed/xDXXXINpmnz0ox9d831KKX76p3+aSy65hEsvvZSPfexje7rujWgNa9m2TbFYpKOjo1mZlYpMlr/ISGkdKZ1yIZTLnqtkJ5ALiNtuu43Z2dnmLO3PfOYzzcq05z73ufz1X/81H/zgB0/5vk996lM88sgjfPvb32Z1dZXnPOc5vOxlL+NZz3rWXr+EdWntvci4MHnLW96yawPW3vCGN3DXXXc1y2VnZmbOe9PCc5XsHbkL3HLLLfzcz/3cM70M7rnnHg4fPswDDzzA1772Nb7v+76v+bVnP/vZXHnlletuwp/+9Kf5yZ/8SQzDoKenhze+8Y381V/91V4uPeM848iRI7zoRS/i8ssv5/nPf/6mpa2n41//9V958MEHue++++jt7eVtb3vblr7vne98J2NjY0xMTPCKV7yiedr+zd/8Tb761a9y2WWXcdttt/Hnf/7nuzb2N2MtmYDsAocOHeKaa655ppexY44fP85FF13U/O8LwaMn4+ySjhv49re/zXvf+17e/va37/i50vCSZVn83M/93JZzfXfddVfT8mdmZoYnnngC+E657JEjR3j44Yc37bXIODMyATlDoijiscce49prr31G13HjjTfS19e37seJEydO+/3ttuUZGRsxNzfHfffdx1vf+lYAfuRHfoSjR49y7NixbT9XvV5nZWWl+d9/+Zd/yfXXX79LK80422TnujPkscceI45jJiYmePvb387Ro0d59atfzR/+4R82O1H3gu1UaLWzb98+jh07xvOe9zwgSzpmbM6JEycYGRlphoWEEOzbt4/jx49v269tqwPWMs5NMgE5Qw4dOoRpmvze7/0ef/7nf06lUuGNb3wjH/zgB/mN3/iNZ3p5W+INb3gDn/jEJ/jhH/5h7rrrLn7nd36naWveaovygQ98gI997GOMjIwAcPDgQe6+++5natkZO2Q3PNN2y1n2wIED3H///Tv63oxnnkxAzpDDhw/T1dXF5z//+abj5jve8Q6+8pWvPMMrW8unPvUp3ve+97G8vMwXvvAFPvShD/G3f/u3XH/99dxyyy1885vf5PLLLycMQ9773vdumAO59dZbufPOO/d49Rm7zfve974de6ZdaOMGMnZOJiBnyKFDh7j99tub4gFQLBZPsSp/pnnrW9/ajFm3YxjGKU1Ut9122x6sKuN85EIdN5CxfbIk+hly6NAhnv3sZ6/53P3333/K5y4U/vIv/5JnP/vZvOxlL+P//t//+0wv54LlbDd+nqln2ic+8Qk+8YlPcPnll/OhD32IP/zDP9z2c2Sc/2QnkDNgaWmJycnJNR5NU1NTfP7znz9nN9fT+Wml7qPr8ZM/+ZP80i/9EpZl8R//8R+8/vWv55vf/OaaEuCM3eFMGj/3wjPtiiuu4Gtf+9rOXlzGBUMmIGfAoUOHKBaLfOQjH+E5z3kOnudx++238+Y3v5kXvvCFz/Ty1uVMqrWGhoaa///FL34x119/Pd/61rcyATkLpCfY7TR+fuADHwC+ezzTMp55shDWGXD48GFuuOEGbr/9dl7ykpfwqle9ite+9rV88pOffKaXdlZo9do6cuQIDzzwwHndQHm+cqaNn5t5pmVkbIdMQM6An/7pn+af//mfueOOO1hYWGBxcZFf+7VfO69tEz71qU8xNjbGZz7zGX7lV36FsbGxZpnlL/3SL3H11Vdz3XXX8aY3vYkXv/jF/NAP/RDPfvazef7zn7+m8uxcNWg8W2yWs/jABz7AwMAA1113Hddddx1vectbntHGz9tuu41rrrmG6667jne/+91rPNMyMrbD+bvTZZwVNqvW+tM//dM1//33f//33HTTTeTzeR588EFuuukmpqenyeVy57xB426zWc4Cdrf8+UwbP++5555dWUdGRnYCydgxr371q8nn8wBcc801JEnCwsIC8N1n0LiZWeVukzZ+JknC0tISn/70p3c8gCkj40zIBCRjV/jjP/5jLrnkkuaI0cygcS3bLX/eLJR4yy23cMUVV3D55ZfzvOc9j1/4hV/gyiuvPNsvISPjFLIQVsaGbLXk95/+6Z/41V/9Vf7xH/9xzWPOZYPG/+//+//4m7/5G2zbxnEcPvShD/Gyl70MaORvfvZnf5YvfelLCCH4+Z//ef7yL/9yT8uft9v4mZHxTJAJSMaGbKUc9F/+5V+4/fbb+du//VuuuOKK5ufPdYPGG2+8kV/5lV/Zcv7mH/7hH3acv8nKnzMuVLIQVsaO+dd//VduueUWvvCFL5zSeX+ux+n3Mn+TlT9nXKhkApKxY97+9rcTBAG33357s0T18OHDwPkVp9+N/M12yp/vuusuLr/88rP3gjIy9ogshJWxY44cObLh157pOP1e52+2U/6ckXGhkJ1AMi5I/u3f/o2FhYV1P1LxOF3+JuVcy9+cTxw8eJDPfOYzQEOIL730Um688cbm13/t136N1772tc/U8jLOkExAMr4rOZ/zN+cTXV1dVKtVAL70pS9Rq9WaI2yVUvzBH/wB73znO5/BFWacCZmAZHxXcqHkb851Ojs7qdVqAHz0ox/l3e9+N6urqwD8wz/8A5Zl8apXveqZXGLGGSBWVlbOrQL9MyDz88nIOLf4z//5P3Pw4EF+7Md+jBtvvJFDhw5xySWXUKlU+KEf+iFe+tKX8vM///PP9DLPSVKhPZfJBCQjI+Oscccdd9DV1YXv+5RKJX71V38Vy7I4evQoBw8e5Pjx43R3dz/TyzwnOR8EJKvCysjIOGt0dXUxMzPD3/3d33H//fcjpaRQKPDhD3+YN77xjZl4nOdkApKRkXHW6Ozs5Hd/93f5/u///mafTUdHB3/wB3/Av/zLvzzDq8s4UzIBycjIOGt0dXVRr9fXVFp1dHQwNjaWTUC8AMhyIBkZGRnnIOdDDiQr483IyMjI2BGZgGRkZGRk7IhMQDIyMjIydkQmIBkZGRkZOyITkIyMjIyMHZEJSEZGRkbGjsgEJCMjIyNjR2QCkpGRkZGxIzIBycjIyMjYEZmAZGRkZGTsiExAMjIyMjJ2xAVlpng+eMdkZGRkXChkJ5CMjIyMjB2RCUhGRkZGxo7IBCQjIyMjY0dkApKRkZGRsSMyAcnIyMjI2BGZgGRkZGRk7IhMQDIyMjIydkQmIBkZGRkZOyITkIyMjIyMHZEJSEZGRkbGjsgEJCMjIyNjR2QCkpGRkZGxIzIBycjIyMjYEZmAZGRkZGTsiExAMjIyMjJ2RCYgGRkZGRk7IhOQjIyMjIwdkQlIRkZGRsaOyAQkIyMjI2NHZAKSkZGRkbEjMgHJyMjIyNgRmYBkZGRkZOyITEAyMjIyMnZEJiAZGRkZGTsiE5CMjIyMjB2RCUhGRkZGxo7IBCQjIyMjY0dkApKRkZGRsSMyAcnIyMjI2BGZgGRkZGRk7IhMQDIyMjIydkQmIBkZGRkZOyITkIyMjIyMHZEJSEZGRkbGjsgEJCMjIyNjR2QCkpGRkZGxIzIBycjIyMjYEZmAZGRkZGTsiExAMjIyMjJ2RCYgGRkZGRk7IhOQjIyMjIwdkQlIRkZGRsaOyAQkIyMjI2NH/P8D/fJ9Ap6P6AAAAABJRU5ErkJggg==' width=400.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"ceb32c967c0a432a8ebee570b1de6fa8\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"soup_bowl()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"This cost function worked well for linear regression, it is natural to consider it for logistic regression as well. However, as the slide above points out, $f_{wb}(x)$ now has a non-linear component, the sigmoid function:   $f_{w,b}(x^{(i)}) = sigmoid(wx^{(i)} + b )$.   Let's try a squared error cost on the example from an earlier lab, now including the sigmoid.\\n\",\n    \"\\n\",\n    \"Here is our training data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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XLl+b9Tv09SSmf+rue8j1M/V0PCQnJMMwBtm/fjslkomfPnhmWW79+PS1atKB06dKWdjhz5kyW2uF+JScnW9U7ISGB2bNnU69ePby8vHB1dbUcDsrq+hcvXkzjxo0pVaoUJUqUwMvLi1u3bj2W+j8qhtxDr1q1apYGxXXt2pUvvvgCd3d3XnzxRavnbt68Sbt27XjiiSd4++23KVu2LAUKFGDz5s1Mnz49zbJSH2NK2dO3NT0+Pj7N/CVLlrR67ObmBpi7jmxJ2aCmF3QZHe9O6UZN3e3l6OhI8eLF0xyrexTSW2fKtJTnL126BPzf60+Run1s+eKLL5g6dSofffQRo0ePxsPDg169ejFixAgcHLL+2zU6OpqkpKQ0Z0rcj++//x4HBwcuXLjAtGnTGDduHAEBAZbgiYqK4uzZs+kONrp69Spgbo/UbQHm9kg99gDMYX+vlM9JQEBAhuvp2rUr8fHxLFu2jIULF5I/f36Cg4OZOHEifn5+lC1blnXr1jF79mz69etHfHw8tWrV4v3336dhw4Y2l33t2rV03++U5+9VrFgxq8dPPPGE5bBBerJjHd7e3mzfvp3Y2NhMf+BFRkbStm1bypcvz6RJk/Dx8eGJJ55g4cKF7NixI8N5Ievv16VLl3BxcbHqUYS035P0vnfptU/qz096dShWrBgFCxZMt8zWrVvp2bMnL730Em+88QZubm7ky5ePwYMHZ9reDyIyMtLqNY4fP5758+fz5ptvEhQURJEiRUhKSqJ58+ZZWv9nn33GiBEj6Nu3L2PHjqVYsWI4ODjw0ksvPZb6PyqGDPSsiI2NZdCgQVStWpUzZ84wfvx4PvjgA8vzBw4c4K+//mLLli3Ur1/fMn3z5s2PpT6XL1+mSpUqlscpX2xPT0+b5YsXLw7AvHnzrOZLkXI82paUjVrqdSYmJnL16lXLsh+llHWmBPa9Ll26ZNmApXwpo6KiKFSokKXM5cuXM12Hm5sb06dPZ/r06Zw+fZovv/ySyZMn4+rqyquvvprlurq4uODg4JDuj6msqFmzJo6OjtSqVYv69etTu3ZtRo0axa5du3BwcKB48eK4ubkxZcoUm/OnHGZxd3e3uTecXnuk3rtKeS+/+eYbmz/yUt4Xk8lEr1696NWrF9HR0fz444+8++679O7dmx9++AGAxo0b07hxY+Lj49m7dy+TJ0+mc+fO/Pbbb5QoUcLmsm3tzaR8Bh7F5yw71tGkSROWLFnCtm3bMu0p+uGHH7h+/TpLlizBx8fHMv3OnTtZWldW3y93d3eio6NJSEiwCvXUn4t7v+v3juFIr30y2zsHKFGiBNeuXeP27dvphvrXX39NmTJl+Oyzz6ym3zso7VGJiori0KFDdOjQwTJt3bp1dOnSxXKYAcw9rlm1bt06GjdubNXtfufOnceys/MoGbLLPSveeust/v77b1auXMn777/P3Llz+emnnyzPp3Q53vtluXPnDmvWrHks9fn222+tHq9btw4HBwcCAwNtlq9bty7Ozs6cPXuWgICANH/3jnBOrU6dOjg5ObFu3Tqr6V9//TWJiYlWx54elQoVKlCyZEm+/vprq+m//vor586ds6yzdu3amEwm1q9fb1UudftkZX1jx47FxcXFcuGJlAE+mZ098OSTT/L000+zevXqLJ1pkJnixYszcuRIjh8/zoYNGwDzoZjff/8dHx8fm+9fyuCcOnXqsG3bNqsu8IsXL1oNAMpI06ZNcXBw4Ny5czbXY+scaBcXFzp06ED79u1tXrTDycmJJk2aMGTIEG7dumU1ovpeDRo0IDIykr1791pNX7t2reXMioeVHeto06YN5cuXZ9y4cfzzzz9pnr9z5w7ff/89YHu7cfXqVbZu3Wo1T3qfxay+X3Xq1OHu3bts2rTJav7U35OU71Xq73rKduzenZWsatq0KcnJyRleRCo2NjbNtT+2b9+e5kyPh5WQkMCbb75JYmKi1fH82NjYNL0Xy5cvTzO/k5OTze+4rfm//PLLLJ11YU+G3EM/evSozSs3BQQE4OjoyPr161m6dCmffPIJpUuXpn///mzfvp3+/fuze/duSpQoQd26dSlSpAhvvvkmo0ePJiEhgXnz5t1X1+392LZtG2PGjKFZs2YcPHiQqVOn0qVLF5sD4gCKFCnC+PHjGT58OFeuXKF58+YUKVKEv//+m127dvHMM89Y/WK9V7FixXj99deZOXMmTz75JC1atODUqVNMnDiRoKCgNMcZ78fhw4cpWrRomumtW7fm7bffJjQ0lL59+9KpUycuXLjAhAkTKFeuHN27dwfMQfzSSy8xceJEkpKSqFmzJj///LNlg5he+1+/fp327dvz0ksvUbFiRfLnz8/mzZuJjo6madOmgHmv19HRkeXLl1OsWDGcnJwoX768zbMBJkyYQEhICMHBwQwaNAgvLy/Cw8M5evSoZSDh/ejVqxdz5sxh2rRptGvXjoEDB/LNN9/QqlUrBg4cSPny5YmNjeX06dPs3buXFStWAOaBmevXr6djx44MGjSIO3fuMG3aNEqWLJmlz2KZMmUIDQ1l5MiR/PHHHzRo0IACBQpw/vx5fvrpJ3r16kX9+vUZOnQohQsXpm7dupZjnatWrbK03aJFi9izZw/BwcF4e3tz5coVZs2ahaenp80eIoBu3bqxYMECXn75ZcaMGYOXlxerV69m+/bthIWFWQ1We1DZsY78+fOzdOlSXnjhBRo3bkz//v0tF5b57bffWLhwIfXr16d58+Y888wzODo60rdvX4YMGcK1a9eYPn06JUqUsDrzJr3PYlbfr2bNmhEUFMTQoUO5cuUKZcuWZf369ZazMFI+G1WqVOHFF19kypQpJCYmUq9ePfbt28e0adN48cUXrQbEZVXjxo1p27Yt77zzDpGRkTRu3JiEhAT27NlDixYtaNSoEcHBwWzatImRI0cSEhLCyZMnmTFjxkMdxoqJiWH//v2A+ZDosWPHWLlyJadPn2bGjBnUrFnTUjY4OJgvv/ySypUrU6lSJX788UebvauVKlVi4cKFlh6FwoULU6FCBYKDg5k5cyYffvgh9erV48CBA3z++ec2t205iSEDPb3BGmfOnOH27dsMHTqUTp06WQ0U+fjjj2nQoAGvv/46X331Fa6urqxcuZJ3332XXr164erqSvfu3fH29mbIkCGPvM6ffPIJc+fOZdGiReTPn5+ePXtaHQKwpVevXpYR+2vXriUhIQFPT0/q16+f6Rd1zJgxlChRgi+++IKFCxdSvHhxunTpwtixYx/qR8uiRYusRrGnCA8P55VXXqFgwYJ89NFHdOvWjUKFChEcHMz48eOtDhGEhYVRuHBhZs+eTUJCAo0aNWL69Ol07tzZMsI6tQIFClCjRg2WLl3KuXPncHBwoHz58nz22WeWc0uLFy/OtGnTCAsLIyQkhLt371qNjr5XrVq12Lp1K5MmTWLkyJHcuXOHUqVKWX543C8nJydGjBhBaGgomzZtok2bNnz33XdMnTqVsLAw/v77b4oWLUqFChWsBkhWrlyZ1atXM2bMGHr16oWnpyehoaGWPcKsGDt2LBUrVuTzzz/n888/x2Qy4e3tTZMmTSzdsPXq1WPFihWsWrWKGzdu4OHhQadOnSxdlk899RTbtm1j/PjxREVFUaxYMZ5++mk+++yzdLtdCxUqxObNmxk7dizjxo3j5s2blC9fnk8++STNIK0HlR3rAPO4nF27djFnzhyWLVvGpEmTMJlMlCtXjs6dO1v2DitXrszChQuZPHmy5boS/fv358qVK1bXsMjos5iV9wtg2bJljBw5knHjxuHg4ECrVq14++23GThwoNX3ZP78+ZQuXZrly5czffp0PDw8CA0NtRpFf78WLVpEWFgYX375JfPnz6dIkSLUqlWLHj16AOarBl64cIHly5ezdOlSqlatymeffWbVhX2/jh07RnBwMCaTCWdnZ3x9fWnQoAELFy5M86Pyww8/xGQyWX7INGjQgK+//jrNBaRCQ0P5448/GDJkCDdv3qRBgwZs3rzZck2Ozz//nLCwMGrVqsXq1avp1q3bA9c/O5iio6MfbCiwPBIrVqzg9ddf57///a+ubpeBjz76iClTpnD27Nkcew5odrl58ya1atWiT58+Ns87lrxr+PDhfP/99xw+fNjeVRE7MOQeuuRuW7du5cSJE1SvXh2TycSePXuYO3cur732Wp4M8xEjRlCvXj08PDy4ePEiCxYsIC4uzjDXn5YHs2LFCm7cuEGVKlWIj4/nhx9+YNGiRVZX75O8RYEuOU7hwoXZuHEjs2bN4vbt2/j4+PDmm28ybNgwe1fNLuLi4njvvfeIioqiQIEC1KtXj82bN6d7BoTkDYUKFWLevHlERERw584dypUrx/Tp09Nc9EryDnW5i4iIGECePW1NRETESBToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIAOg8d8/mcj+sa7SIikvMkJSVx69Yte1fjkVKgY76RgQJdRERyM6WYiIiIASjQRUREDECBLiIiYgAKdBEREQNQoIuIiBiAAl1ERMQAFOgiIiIGoEAXERExAAW6iGS/S5dgyZKMyyxZYi6X16htbFO7ZEqBLiLZ69IlaNoUXnkFZs+2XWb2bPPzTZvmrQ202sY2tUuWKNBFJPukbJhPnDA/Dg1Nu4GePds8Hczl8soGWm1jm9olyxToIpI9Um+YU9y7gb53w5wiL2yg1Ta2qV3uS64I9JEjR+Lv74+LiwvHjx9Pt9zSpUupVasWNWvWZOjQoSQmJmZjLUUkQ1u3pt0wpwgNhbJl026YU5w4YZ7fqNQ2tqld7kuuCPR27dqxdetWSpUqlW6Z8PBwJk2axNatWzl06BCXLl1i2bJl2VhLEclQz54QFpb+83/+mf5zYWHm+Y1KbWOb2uW+5IpAb9CgAd7e3hmW2bBhA88//zwlS5bEZDLRu3dv1q5dm001FJEsGTo04w20LWFh5vmMTm1jm9oly3JFoGfFuXPnrPbgfX19OX/+vB1rJCI23c8GOq9tmNU2tqldssQwgQ5gMpks/ycnJ9uxJiKSoaFDoUyZjMuUKZM3N8xqG9vULpkyTKCXKlWKv/76y/L43Llz+Pj42LFGIpKu2bMzPv4J5ufTO+fYyNQ2tqldMmWYQG/bti2bNm3i8uXLJCcns2jRIjp27GjvaolIarZOM0qPrXOOjUxtY5vaJUtyRaAPHz6cqlWrcuHCBdq3b09AQAAAgwcPZsuWLQCULl2a0aNH07JlS2rWrImbmxsvv/yyPastIqndz4Y5RV7ZQKttbFO7ZJkpOjo6zx9sdnZ2xsEhV/y2Ecm9liwxX5ozPWXKZNylunixcU9DUtvY9hjbJSkpiZiYmIeqXk6jFBOR7PHcc1Cliu3nwsLg7Nn0RzJXqWKe36jUNrapXe6LAl1Esoe7O2zfnnYDfe9pRrZOT6pSxTyfu3t21NI+1Da2qV3uiwJdRLJP6g20rXOG791A56UNs9rGNrVLlukYOjqGLpLtLl0yX2c7o+O+S5aYu0zz2oZZbWPbI24XIx5DV6CjQBcRyWuMGOhKMREREQNQoIuIiBiAAl1ERMQAFOgiIiIGoEAXERExAAW6iIiIASjQRUREDECBLiIiYgAKdBEREQNQoIuIiBiAAl1ERMQAFOgiIiIGoEAXERExAAW6iIiIASjQRUREDECBLiIiYgAKdBEREQNQoIuIiBiAAl1ERMQAFOgiIiIGoEAXERExAAW6iIiIASjQRUREDECBLiIiYgAKdBEREQPINYF+5swZWrRoQWBgIM2aNePkyZNpyiQnJzNmzBiefvpp6tevz/PPP8/Zs2ftUFsREZHslWsCPTQ0lJ49e3Lw4EGGDh3K4MGD05TZsmULe/bsYefOnezZs4cmTZowfvx4O9RWREQke+WKQI+KiuLIkSN07twZgLZt2xIREUFERESasvHx8cTFxZGcnExMTAxeXl7ZXV0REZFs52jvCmRFZGQknp6eODqaq2symfDx8eH8+fP4+flZyrVq1Ypdu3ZRqVIlChcujKenJ5s3b7ZXtUVERLJNrthDB3OI3ys5OTlNmSNHjnD69GmOHz/OyZMnadKkCSNGjMiuKoqIiNhNrgh0b29vLly4QGJiImAO88jISHx8fKzKrVy5kkaNGuHi4oKDgwNdu3Zl586d9qiyiIhItsoVge7m5oa/vz+rVq0CYMOGDfj6+lp1twP4+fmxY8cOEhISANi6dStVq1bN9vqKiIhkN1N0dHTavusc6PTp0wwcOJCrV6/i7OzM/PnzqVKlCoMHD6ZVq1a0bt2a+Ph4RowYwS+//EL+/Pnx8PBg1qxZaYI/NWdnZxwccsVvGxEReQSSkpKIiYmxdzUeqVwT6I+TAl1EJG8xYqArxURERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBpBrAv3MmTO0aNGCwMBAmjVrxsmTJ22WO3bsGCEhIdStW5fatWuzYcOGbK6piIhI9jNFR0cn27sSWdGmTRu6dOlC9+7dWb9+PXPnzmXbtm1WZWJjY6lfvz7z588nKCiIxMREoqOjcXV1zXDZzs7OODjkmt82IiLykJKSkoiJibF3NR6pXJFiUVFRHDlyhM6dOwPQtm1bIiIiiIiIsCq3du1a6tatS1BQEACOjo6ZhrmIiIgR5IpAj4yMxNPTE0dHRwBMJhM+Pj6cP3/eqtzJkydxcnKic+fONGzYkH79+vHPP//Yo8oiIiLZKlcEOphD/F7JyWmPFCQkJPDjjz8ya9Ysdu7ciY+PD8OHD8+uKoqIiNhNrgh0b29vLly4QGJiImAO88jISHx8fKzK+fr60rBhQ7y8vDCZTLz00kscPHjQHlUWERHJVrki0N3c3PD392fVqlUAbNiwAV9fX/z8/KzKtW/fnkOHDnHjxg0Avv/+e5566qlsr6+IiEh2yzWj3E+fPs3AgQO5evUqzs7OzJ8/nypVqjB48GBatWpF69atAfjyyy+ZPXs2+fLlw8vLi7CwMLy9vTNctka5i4jkLUYc5Z5rAv1xUqCLiOQtRgx0pZiIiIgBKNBFREQMQIEuIiJiAAp0ERERA1Cgi4iIGIACXURExAAU6CIiIgagQBcRETEABbqIiIgBPNZA37179+NcvIiIiPx/jzXQO3XqRI0aNZg4cSJnz559nKsSERHJ0x5roJ86dYqRI0fy66+/UqdOHVq2bMkXX3xBdHT041ytiIhInpNtN2c5f/48a9asYdWqVYSHh/Pcc8/RtWtXWrRogclkyo4qpEs3ZxERyVt0c5aH4OPjQ926dalTpw5JSUkcP36c119/nZo1a7Jjx47sqoaIiIghPfZAP3PmDBMmTKB69er861//wsnJiX//+9/s27ePEydO0LlzZwYMGPC4qyEiImJoj7XL/dlnn+W3336jSZMmdOvWjZCQEJycnKzKXL58mUqVKnHt2rXHVY1MqctdRCRvMWKXu+PjXHibNm1Yvnw5np6e6ZYpWbKkXcNcRETECLJtUFxOpj10EZG8xYh76EoxERERA1Cgi4iIGIACXURExAAU6CIiIgagQBcRETEABbqIiIgBKNBFREQMQIEuIiJiAAp0ERERA1Cgi4iIGIACXURExAAU6CIiIgagQBcRETGAXBPoZ86coUWLFgQGBtKsWTNOnjyZbtm4uDjq1avHM888k30VFBERsaNcE+ihoaH07NmTgwcPMnToUAYPHpxu2Q8++IA6depkY+1ERETsK1cEelRUFEeOHKFz584AtG3bloiICCIiItKU3bNnD2fOnLGUFRERyQtyRaBHRkbi6emJo6MjACaTCR8fH86fP29V7tatW4wePZqZM2fao5oiIiJ2kysCHcwhfq/k5OQ0ZcaOHUufPn3w8vLKrmqJiIjkCKbo6Oi0yZjDREVFERgYyNmzZ3F0dCQ5OZlKlSqxbds2/Pz8LOXq169PTEwMAPHx8URHR1O2bFn27t2b4fKdnZ1xcMg1v21EROQhJSUlWfLCKHJFirm5ueHv78+qVasA2LBhA76+vlZhDubj50ePHuXo0aMsXLiQqlWrZhrmIiIiRpArAh0gLCyMxYsXExgYyKxZs5gzZw4AgwcPZsuWLXaunYiIiH3lii73x01d7iIieYu63EVERCRHUqCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAHkmkA/c+YMLVq0IDAwkGbNmnHy5Mk0ZXbs2MGzzz5LvXr1CAoK4oMPPiA5OdkOtRUREcleuSbQQ0ND6dmzJwcPHmTo0KEMHjw4TRkXFxcWLlzIr7/+yvbt29m9ezdr1661Q21FRESyV64I9KioKI4cOULnzp0BaNu2LREREURERFiVq1GjBqVLlwagQIEC+Pv7Ex4ens21FRERyX65ItAjIyPx9PTE0dERAJPJhI+PD+fPn093nkuXLrF+/XqCg4Ozq5oiIiJ2kysCHcwhfq+Mjo3fuHGDLl26MGTIEGrWrPmYayYiImJ/uSLQvb29uXDhAomJiYA5zCMjI/Hx8UlTNiYmhhdffJFWrVoxaNCg7K6qiIiIXeSKQHdzc8Pf359Vq1YBsGHDBnx9ffHz87Mqd/PmTV588UWaNWvGyJEj7VFVERERuzBFR0fnivO6Tp8+zcCBA7l69SrOzs7Mnz+fKlWqMHjwYFq1akXr1q2ZPn06U6ZMoXLlypb52rdvz/DhwzNctrOzMw4OueK3jYiIPAJJSUnExMTYuxqPVK4J9MdJgS4ikrcYMdCVYiIiIgagQBcRETEABbqIiIgBKNBFREQMQIEuIiJiAAp0ERERA1Cgi4iIGIACXURExAAU6CIiIgbgaO8KiIjkJTdu3OD69ev2rkae4+joiLu7u6GvCqpAFxHJJpcvX8ZkMuHj45PmltDyeN28eZNLly7h6elp76o8Nsb9qSIiksPEx8fj5uaWtTC/dAmWLMm4zJIl5nKSqcKFC1tuwW1UCnQRkZzm0iVo2hReeQVmz7ZdZvZs8/NNmyrUBVCgi4jkLClhfuKE+XFoaNpQnz3bPB3M5ewc6uPGjbPcpnrDhg2MGDHCLvUIDw/n008/tcu6cwIFuohITpE6zFPcG+r3hnmKHBDqKdq2bcu0adPssm4FuoiI5Axbt6YN8xShoVC2bNowT3HihHn++2QymZg8eTJ169albNmyfP/994wePZqAgACqVavGsWPHALh48SJNmzYlMDCQatWqMWTIEJKTk9Msb/Hixbz44ouWx++88w7ly5enXr16jBgxgtq1awPw008/UbNmTQYOHEiNGjWoVq0aBw4cACAxMZGWLVtSu3ZtqlWrRvfu3YmNjbUsv2XLlnTt2hV/f39q167N2bNnAejfvz/Hjx+nZs2atG3b9r7bIrdToIuI5BQ9e0JYWPrP//ln+s+FhZnnfwBFihRh3759TJ06lXbt2tGwYUMOHTpEz549mThxIgAuLi5s3LiRgwcP8ttvv3H27FnWrVuX4XI3btzIpk2bOHLkCL/88gtnzpyxev7YsWP07t2bI0eOMHjwYN555x0A8uXLx8qVKzlw4AD/+9//KFKkCPPmzbPM9+uvvzJlyhSOHj1K8+bNmTp1KgALFiygatWqHD58mA0bNjxQW+RmCnQRkZxk6NCMQ92WsDDzfA+oc+fOANSqVQsHBwdCQkIACAwMtOz9JiUlMWrUKGrUqEFAQAAHDhzg8OHDGS53+/btdOrUiUKFCuHg4EDPVD84KlWqZNljDwoKsgR+cnIys2bNIiAggOrVq7N582ardTVs2BA/P7808+V1CnQRkZzmfkL9IcMcoECBAoB5z9jJyckyPV++fJZTvWbOnMmVK1f49ddf+e233+jWrRtxcXEZLjc5OTnDU/RS1pt6XStXrmTHjh38/PPPHD16lOHDh1utK7358joFuohITjR0KJQpk3GZMmUeOsyz6tq1a3h4eFCgQAEuXbrEmjVrMp2nadOmrFmzhtjYWJKSkli2bFmW11WiRAmcnZ2JiYlh8eLFWZqvSJEiefoqfAp0EZGcaPbsjI+Zg/n59M5Tf8SGDBnCnj17qFmzJr1796Z58+aZztO2bVtatmxJjRo1aNq0KeXKlaNo0aKZztejRw9u3rxJ1apV6dChA40aNcpSHatXr06lSpV46qmn8uSgOFN0dHTaYYp5jLOzs6Gv7ysiOcO5c+coVapU5gVtnZqWkUfQ7f64xMTE4OzsTFJSEn369MHLy4sJEybYpS73tn9SUhIxMTF2qcfjomu5i4jkJPcb5vB/5XNgqPfo0YPw8HBu375NrVq1GDlypL2rZFgKdBGRnGLJkozDvEyZ9LvhQ0PBxeWBT117XL755ht7VyHPUD+ziEhO8dxzUKWK7efCwuDs2fRHv1epYp5f8iztod+nO3fhmz9hYzj8HQuJSeBaAJp4QY9K4OKU6SIM6+Q1+OIk/O8q3LgDhfJDhaLwckWo627v2tnP7UT46g/4zzm4FAvJQMmCEOwD3SqY20kEAHd32L7dcvnX/5YKYH6jAexo8CJXChTDcTF4eg6l7Zyn6TumIz7Rkeb5qlQxz+eeh79ookFxkLVBcbcTYdphmPc/uHTbdpknHaF7BXi/DngWevT1zKl+vgDvH4AfI9MvU9sN3q4FL5TNvnrZ2/V4mPRf+OwEXIu3XabIE9C7MowJhOIFbJcR48jqoLj9J64w5Mtz7PWsmW6ZfMlJvHhwNWFH5uGxeY3CPAuMPihOgU7mgf7PbWjzb9ibxfseeBWCLa2hhusjqmAONv9/MGgXJGXxUzQqACbXg6zcDjo3O3cTntsEx69lrXyFovDvECiX+Rk9kotlJdC//RO6bIP4u1lbpt+Td/muXT4qujx8/YzO6IGuY+iZuJUArbdkPcwBLtyC5hvhD4Nf3+CLkzBwZ9bDHGDqIXhv/+OrU05wJQ6e3ZD1MAc4fd38mbkY+/jqJTnfzxeg83dZD3OAiNh8tNhkPpwjeZsCPRPv/Ar7L6ed3sgTpjwNs+pDhzLgmKol/4mD7t+DjZsRGcLZG9B/R9rprgVgiD/MbWTeG/ctnLbMBwfNGy6jGrTTHNCptSgF04JgRn1o7QupOynCY6DvT9lRQ8mJEpOgx49wJ8l6ulchGFcbvm8DG1tBv6rmw3v3ioiB4b882HpNJhM3b958sJnTUbNmTW7fTufYpDw2uabL/cyZMwwYMIArV65QtGhR5s2bR+XKldOUW7p0KWFhYSQlJdGkSRNmzJiBo2PGY//S63K/Hg9eSyH2nssE+xSC1S0gyMO67F8x5gDfddF6+s720NAzq68y9xi2G2b9Zj3trQB4rzYUuKe57ybBvGMQutt6T75dafi2VbZUNVv9FQNlVli/1sou5s+MfwnrsievQedt8NsV6+mnuqLuU4PKqMv967PQ8T/W03pVhvmNwSmf9fS/b5kPAx6M+r9p+R3gr5fB48n7q5PJZCImJobChW38+jYYdbnnEKGhofTs2ZODBw8ydOhQBg8enKZMeHg4kyZNYuvWrRw6dIhLly5l+drBtiz93TrMHUywJSRtmAP4Opuf8041GG7e/x549TlWbIK5u/1evSrD5KetwxwgnwMM9jcPFLzXxghz+BnNp8etw7ygI3zXJm2YA1QuBt89bx4Yd68Fxx5vHSVnSr2tqFECPmuSNszBPOj22+esn0tIgoXp3Eo9M9OnT6dBgwZUrFiRL7/80jJ9//79NGvWjNq1a1OrVi3L7VLDw8NxdXVl7NixBAYGUr58ebZs2WKZ7969/p07d+Lv70/16tUZPHgwfn5+/O9/5hdbunRp3n//ferXr0+ZMmXsdgU5o8gVgR4VFcWRI0cst/hr27YtERERREREWJXbsGEDzz//PCVLlsRkMtG7d2/Wrl37wOtdk+qOfC+Usb1hTuH8BLxR3XraN39Cwn0cD8sNvj8P0Xf+77EJeKdWxvMM9Qfne07PSko275EYTerPTK9KUCqDHR/3J81dqPdarTtB5jmxCbA91WGoYTXMP4jT41MYupa3nrYpwnbZzJhMJnbv3s3WrVsZPHgw586dIzo6mn79+rFixQoOHDjAd999x7Bhw7h40dwNeeXKFQIDAzl48CBz587ljTfeSLPc+Ph4unbtyrx58/jtt99o1qwZf/31l1WZ6Oho9uzZw759+5g2bRqRkRmcLiMZyhWBHhkZiaenp6Xr3GQy4ePjw/nz563Kpe7O8vX1TVPmfpxLdVipQyY3PgLomOq0rLi75uPpRnLulvXjmq6Zj852fgJapuppTL0cI/gr9WcmC6fppf7MRN4yH6qQvCMqLu3g0ta+mc/XKlWZBx1U2adPHwDKli1Lw4YN2blzJ3v27OHs2bO0atWKmjVr0rx5c5KTkzl16hQAhQoVol27dkD69yQ/deoUBQsWtNxc5YUXXsDFxcWqTPfu3QFwc3OjbNmy/JnZDWkkXbnmwjKp76mbnM5os3vLpVcmq1KPNM3KecK2ytzPiNXc4E7qdsnixXSKpSpntHaBtAOastI2qdslZTkFc8XPbXkUbJ0p8oSNrvbMytzPGScZMZlMJCcnU716dX7++ec0z4eHh6e5J/ndu2m/0JndDx10b/NHKVdsMry9vblw4YLljU5OTiYyMhIfHx+rcqVKlbLqzjl37lyaMvcj9Yb2QJTtcveyNSLe1gY7N0t9NbzDV7J2WCF1+xmtXQBcUh0Pz8pn5kCqz4xTPiiQhY25GEcJGzsCP2Wh53lHqm561we8ONGiRYsAc1Dv2rWLhg0bUr9+fU6fPs2PP/5oKXf48GHu3LmT3mLSqFy5Mrdu3WL37t0ArF+/nujo6AerpGQqVwS6m5sb/v7+rFq1CjAfK/f19cXPz8+qXNu2bdm0aROXL18mOTmZRYsW0bFjxwdeb92S1o8/P5F5V+inx60fV3KBogYLrtTtciUOvs6kl2z/ZTj0T8bLMYJ6qS7WlfrzYMsnqcrUK2n8C++ItSJPQKCb9bQ5mQyovXEHFqcanNrU+8HW7+TkRIMGDWjRogVz5syhVKlSFCtWjI0bN/LBBx9Qo0YNqlatyltvvUVSUtaPBzk5ObFy5Ur69+9P3bp12bNnD+7u7lm6J7rcv1xz2trp06cZOHAgV69exdnZmfnz51OlShUGDx5Mq1ataN26NQBLliyxnLbWuHFjZs6cSf78GV8sO73T1n65CPVT3ShoRE2Y+rTtDe43Z6FDqtNOZtQ3D24xmobfwO57TtHzKQS/dDAP1Entejw03WAd6D6F4M9/pT1/P7f79k94Yav1tIw+A58cg/6pejRXPAvdKj6e+ol9ZXTa2ufH4bVU13aY+jSMDEhbNv6u+QI068Otp//eFSq4PJKqPjIp90MH2L59Oz179iQ8PDzTy20/DkY/bS3XBPrjlF6gJydDrbVwONWeZStfGFnTfEMWkwlOXIOPfjPvad3bmAXyQWQPY16je+Xv0P0H62luBWB0LXilsrk7PTbBfFOSif81X4jmXuPrwJja2Vff7JKYBGWWw/lUA/5eKgfDa0Cd/7/3/d8omHkEVpy2LudWAM71sH2qkuR+GQX6rQQotSztdf8be8Igf/PFrGITYVM4zD6a9jvVshRsff7x1PthLF68mFmzZpGUlISTkxOzZ8+mQYMGdqmLAj0PyOha7j+ehxab4K6NVirkaN7wXk3nxhuT68FbmZzOlVslJkGjb9O/JK57QdsjdwHKOMOhl4x3KCLF6j/MF4yxxTm/+XoG19M5DLmoqfmcfjGmzK7lbuuHclYUeQL2doAqxR6icnmA0QPdYB2ej14zH1jQOO1lOgFuJaYf5n2qmC99alSODrD+OfMYAVsu3bYd5iULmi/AY9QwB+hUHibWtf1cTEL6Yf5OLYV5XtetovnywPejcH7zd1FhLgr0LOhT1XzpztRX9LLFwWS+/OmnTYw/sKnkk7CrPTTL4kCc6iXMx9kr54ENz9uB8EkT85XiMvOEA8xuABPqPf56Sc43vCZ82TxrI9b9i5svL/3MAw6GE2NRlztZux86QHQ8LDllvkTj76luvuFWwLxX3rcqlC7ymCqaQyUnw89/m9vl6z/N3fH3eq4UDHzKfKGMjK58ZUSXY2HRSVhw3HwDjXt5FzJfJa5PFfOlPMX4Ll++jMlkwtXVNdPzs+MSYe1Z86WA91z8v/E5BfJBiB+8/hQ842X8HYdH5ebNm8TExODpab65hhG73BXoZD3QUyQnw+/RcPG2ObxcC0DVYpBfA5mIjodT0eZTagrlh3JFzJc3zevuJpnb5dL/vwGVWwFzF2le+4EjcOPGDa5fv797K99JgusJDjiaoEj+JPIpxO+bo6Mj7u7ulm29At2g7jfQRUQkdzNioCvFREREDCDXXMv9cbqfKx+JiEjuZ8TtvgIduHXLgLf9EhGRPEVd7iIiIgagQBcRETEABbqIiIgBKNBFREQMQIEuIiJiAAp0ERERA1CgP6AzZ87QokULAgMDadasGSdPnrR3lXKEkSNH4u/vj4uLC8ePH7d3dXKMuLg4unXrRmBgIA0bNqRjx45ERETYu1o5xgsvvED9+vVp2LAhrVq14rfffrN3lXKUKVOm6DuVir+/P3Xq1KFhw4Y0bNiQr7/+2t5Vsjudh/6AQkND6dmzJ927d2f9+vUMHjyYbdvSuQl2HtKuXTuGDh3Kc889Z++q5DivvPIKwcHBmEwmPv30U0JDQ/nmm2/sXa0c4YsvvsDFxQWATZs2MWjQIH7++Wf7ViqHOHz4MAcOHMDHx8feVclxlixZQtWqVe1djRxDe+gPICoqiiNHjtC5c2cA2rZtS0REhPa4gAYNGuDtrXs5plagQAFatGhhucNWnTp1CA8Pt2+lcpCUMAfzzUt0bwWz+Ph4RowYwfTp0zO9O5uI9tAfQGRkJJ6enjg6mpvPZDLh4+PD+fPn8fPzs3PtJDdYsGCBejFS6devH7t27QJg7dq1dq5NzjBp0iQ6depE6dKl7V2VHOm1114jOTmZwMBA3nvvPVxdXe1dJbvSz+AHlPrXcnJynr9pnWTRjBkzOHv2LGPGjLF3VXKUTz75hGPHjvHOO+8wduxYe1fH7vbt28d///tf+vTpY++q5Ehbtmxh9+7d7Nixg+LFizNgwAB7V8nuFOgPwNvbmwsXLpCYmAiYwzwyMlLHuCRTc+bMYePGjaxZs4Ynn9SN4m3p1q0bO3fu5OrVq/auil3t3r2b06dPU716dfz9/blw4QIdO3bUWJ3/r1SpUgDkz5+fAQMG8Msvv9i5RvanQH8Abm5u+Pv7s2rVKgA2bNiAr6+vutslQ3PnzmXt2rV8++23VseM87obN27w999/Wx5v3LiR4sWLU6xYMTvWyv7eeOMNTp48ydGjRzl69CheXl6sW7eO4OBge1fN7m7dukV0dLTl8dq1a/H397dfhXIIHUN/QGFhYQwcOJCZM2fi7OzM/Pnz7V2lHGH48OFs2bKFS5cu0b59ewoVKsShQ4fsXS27i4yM5N1336V06dI8//zzADg5OfHDDz/YuWb2d+PGDXr06EFcXBwmkwlXV1e++uorDQKTdEVFRfHyyy9z9+5dAPz8/FiwYIGda2V/pujoaB38FRERyeXU5S4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuog8EitWrNCdr0TsSOehi2SjzK4Qt3HjRho1apQ9lblPf/zxB++//z579+4lJiYGDw8P6taty9y5c3niiSe4ffs2t27dyvM3yBCxF10pTiQbnTp1yvJ/WFgYBw8eZNmyZZZpOeFyp3fu3OGJJ55IM61Dhw4EBASwZs0aXFxc+PPPP1m/fr3lal0FCxakYMGC9qiyiKAud5Fs5e7ubvkrVKgQ+fPntzweN24cgwYNsiofEhLChAkTLI9dXFxYtmwZbdq0wcPDg2bNmhEeHs7OnTsJCgqiVKlS9O3bl7i4OMs8ly9fpkePHnh7e+Pn58egQYO4deuW1TreffddBg0aRKlSpWzeBe7kyZP89ddfzJo1i5o1a1K6dGmaNm1KWFiYJcTv7XKPiIjAxcUlzd+9d8Rat24d9erVw8PDg6CgINavX/9oGlkkj1Kgi+Qy06dPZ+DAgezYsQNHR0f69OnD9OnTmTdvHmvWrOH7779nyZIllvL9+vUjMjKSzZs389VXX7Fnzx7efvttq2UuXryYcuXKsWPHDpu3oSxRogQODg5s3LgxS7cK9vHx4dSpU5a/7777jkKFChEUFATAjh07GDlyJKNHj2bv3r0MGzaM/v37s3///odsHZG8S13uIrlM7969adWqFWAO61dffZXt27cTEBAAQLt27di1axf9+vXj999/Z/v27ezdu5fKlSsD8OGHH9KlSxfGjx9P0aJFAahZsyZvvPFGuuv09vZm/PjxjBgxgvfee486derwzDPP0L17d5vjAvLly4e7uzsAsbGxDBs2jA4dOtCjRw/A/KNk1KhRtG/fHoDSpUuza9culi5dSp06dR5JO4nkNdpDF8ll7h1J7ubmBmAJ65Rp//zzDwCnT5/G2dnZ6vk6deqQmJjIn3/+aZlWvXr1TNc7aNAgTp06xaxZsyhXrhwff/wxQUFBVrc+tWXIkCEULFiQGTNmWKYdP36csWPH4u3tbflbuXIl4eHhmdZDRGzTHrpIDuHg4EBiYqLVtNSPAfLnz2/5P+UWo6mnpXSL2+oet3Vb0ieffDJLdSxWrBgvvPACL7zwAqNHjyYwMJBFixbxzjvv2Cw/Z84cdu7cyU8//WQ10O7WrVt88MEHPPvss1blCxQokKV6iEhaCnSRHMLV1dXq3vF37tzh9OnTD3UaW8WKFYmJieHkyZOWvfR9+/bh6OhImTJlHqq+RYsWpWTJksTGxtp8/ueff2by5Ml88803eHp6Wj331FNPER4eTtmyZR+qDiLyfxToIjlE/fr1+fjjj9mwYQOVK1dm9uzZJCQkPNQyK1asSLNmzRg0aBDTp08nLi6OUaNG0b17d8vx86w4dOgQM2fOpEuXLlSqVIm7d++yevVqTpw4weTJk9OUv3jxIr169SI0NJTSpUtz6dIlwLwHXrRoUYYNG0avXr3w8vKiZcuW3L59m19++QVXV1c6dOjwUK9ZJK/SMXSRHKJFixa8/vrrvPHGG4SEhFCtWrUsHdvOzIIFC/D09CQkJIROnToRFBTEpEmT7msZpUqVwsPDg/Hjx9O4cWNatGjBzp07WbJkCU2aNElT/vTp01y5coVJkyZRqVIly99bb70FQOvWrfn8889ZtWoV9evXp3379vznP/+hVKlSD/16RfIqXSlORETEALSHLiIiYgD/D68sthYOl7DuAAAAAElFTkSuQmCC\",\n 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OrqyrJly2xuN1LL6vu1f/9+8uXLl+b9Tv09SSmf+rue8j1M/V0PCQnJMMwBtm/fjslkomfPnhmWW79+PS1atKB06dKWdjhz5kyW2uF+JScnW9U7ISGB2bNnU69ePby8vHB1dbUcDsrq+hcvXkzjxo0pVaoUJUqUwMvLi1u3bj2W+j8qhtxDr1q1apYGxXXt2pUvvvgCd3d3XnzxRavnbt68Sbt27XjiiSd4++23KVu2LAUKFGDz5s1Mnz49zbJSH2NK2dO3NT0+Pj7N/CVLlrR67ObmBpi7jmxJ2aCmF3QZHe9O6UZN3e3l6OhI8eLF0xyrexTSW2fKtJTnL126BPzf60+Run1s+eKLL5g6dSofffQRo0ePxsPDg169ejFixAgcHLL+2zU6OpqkpKQ0Z0rcj++//x4HBwcuXLjAtGnTGDduHAEBAZbgiYqK4uzZs+kONrp69Spgbo/UbQHm9kg99gDMYX+vlM9JQEBAhuvp2rUr8fHxLFu2jIULF5I/f36Cg4OZOHEifn5+lC1blnXr1jF79mz69etHfHw8tWrV4v3336dhw4Y2l33t2rV03++U5+9VrFgxq8dPPPGE5bBBerJjHd7e3mzfvp3Y2NhMf+BFRkbStm1bypcvz6RJk/Dx8eGJJ55g4cKF7NixI8N5Ievv16VLl3BxcbHqUYS035P0vnfptU/qz096dShWrBgFCxZMt8zWrVvp2bMnL730Em+88QZubm7ky5ePwYMHZ9reDyIyMtLqNY4fP5758+fz5ptvEhQURJEiRUhKSqJ58+ZZWv9nn33GiBEj6Nu3L2PHjqVYsWI4ODjw0ksvPZb6PyqGDPSsiI2NZdCgQVStWpUzZ84wfvx4PvjgA8vzBw4c4K+//mLLli3Ur1/fMn3z5s2PpT6XL1+mSpUqlscpX2xPT0+b5YsXLw7AvHnzrOZLkXI82paUjVrqdSYmJnL16lXLsh+llHWmBPa9Ll26ZNmApXwpo6KiKFSokKXM5cuXM12Hm5sb06dPZ/r06Zw+fZovv/ySyZMn4+rqyquvvprlurq4uODg4JDuj6msqFmzJo6OjtSqVYv69etTu3ZtRo0axa5du3BwcKB48eK4ubkxZcoUm/OnHGZxd3e3uTecXnuk3rtKeS+/+eYbmz/yUt4Xk8lEr1696NWrF9HR0fz444+8++679O7dmx9++AGAxo0b07hxY+Lj49m7dy+TJ0+mc+fO/Pbbb5QoUcLmsm3tzaR8Bh7F5yw71tGkSROWLFnCtm3bMu0p+uGHH7h+/TpLlizBx8fHMv3OnTtZWldW3y93d3eio6NJSEiwCvXUn4t7v+v3juFIr30y2zsHKFGiBNeuXeP27dvphvrXX39NmTJl+Oyzz6ym3zso7VGJiori0KFDdOjQwTJt3bp1dOnSxXKYAcw9rlm1bt06GjdubNXtfufOnceys/MoGbLLPSveeust/v77b1auXMn777/P3Llz+emnnyzPp3Q53vtluXPnDmvWrHks9fn222+tHq9btw4HBwcCAwNtlq9bty7Ozs6cPXuWgICANH/3jnBOrU6dOjg5ObFu3Tqr6V9//TWJiYlWx54elQoVKlCyZEm+/vprq+m//vor586ds6yzdu3amEwm1q9fb1UudftkZX1jx47FxcXFcuGJlAE+mZ098OSTT/L000+zevXqLJ1pkJnixYszcuRIjh8/zoYNGwDzoZjff/8dHx8fm+9fyuCcOnXqsG3bNqsu8IsXL1oNAMpI06ZNcXBw4Ny5czbXY+scaBcXFzp06ED79u1tXrTDycmJJk2aMGTIEG7dumU1ovpeDRo0IDIykr1791pNX7t2reXMioeVHeto06YN5cuXZ9y4cfzzzz9pnr9z5w7ff/89YHu7cfXqVbZu3Wo1T3qfxay+X3Xq1OHu3bts2rTJav7U35OU71Xq73rKduzenZWsatq0KcnJyRleRCo2NjbNtT+2b9+e5kyPh5WQkMCbb75JYmKi1fH82NjYNL0Xy5cvTzO/k5OTze+4rfm//PLLLJ11YU+G3EM/evSozSs3BQQE4OjoyPr161m6dCmffPIJpUuXpn///mzfvp3+/fuze/duSpQoQd26dSlSpAhvvvkmo0ePJiEhgXnz5t1X1+392LZtG2PGjKFZs2YcPHiQqVOn0qVLF5sD4gCKFCnC+PHjGT58OFeuXKF58+YUKVKEv//+m127dvHMM89Y/WK9V7FixXj99deZOXMmTz75JC1atODUqVNMnDiRoKCgNMcZ78fhw4cpWrRomumtW7fm7bffJjQ0lL59+9KpUycuXLjAhAkTKFeuHN27dwfMQfzSSy8xceJEkpKSqFmzJj///LNlg5he+1+/fp327dvz0ksvUbFiRfLnz8/mzZuJjo6madOmgHmv19HRkeXLl1OsWDGcnJwoX768zbMBJkyYQEhICMHBwQwaNAgvLy/Cw8M5evSoZSDh/ejVqxdz5sxh2rRptGvXjoEDB/LNN9/QqlUrBg4cSPny5YmNjeX06dPs3buXFStWAOaBmevXr6djx44MGjSIO3fuMG3aNEqWLJmlz2KZMmUIDQ1l5MiR/PHHHzRo0IACBQpw/vx5fvrpJ3r16kX9+vUZOnQohQsXpm7dupZjnatWrbK03aJFi9izZw/BwcF4e3tz5coVZs2ahaenp80eIoBu3bqxYMECXn75ZcaMGYOXlxerV69m+/bthIWFWQ1We1DZsY78+fOzdOlSXnjhBRo3bkz//v0tF5b57bffWLhwIfXr16d58+Y888wzODo60rdvX4YMGcK1a9eYPn06JUqUsDrzJr3PYlbfr2bNmhEUFMTQoUO5cuUKZcuWZf369ZazMFI+G1WqVOHFF19kypQpJCYmUq9ePfbt28e0adN48cUXrQbEZVXjxo1p27Yt77zzDpGRkTRu3JiEhAT27NlDixYtaNSoEcHBwWzatImRI0cSEhLCyZMnmTFjxkMdxoqJiWH//v2A+ZDosWPHWLlyJadPn2bGjBnUrFnTUjY4OJgvv/ySypUrU6lSJX788UebvauVKlVi4cKFlh6FwoULU6FCBYKDg5k5cyYffvgh9erV48CBA3z++ec2t205iSEDPb3BGmfOnOH27dsMHTqUTp06WQ0U+fjjj2nQoAGvv/46X331Fa6urqxcuZJ3332XXr164erqSvfu3fH29mbIkCGPvM6ffPIJc+fOZdGiReTPn5+ePXtaHQKwpVevXpYR+2vXriUhIQFPT0/q16+f6Rd1zJgxlChRgi+++IKFCxdSvHhxunTpwtixYx/qR8uiRYusRrGnCA8P55VXXqFgwYJ89NFHdOvWjUKFChEcHMz48eOtDhGEhYVRuHBhZs+eTUJCAo0aNWL69Ol07tzZMsI6tQIFClCjRg2WLl3KuXPncHBwoHz58nz22WeWc0uLFy/OtGnTCAsLIyQkhLt371qNjr5XrVq12Lp1K5MmTWLkyJHcuXOHUqVKWX543C8nJydGjBhBaGgomzZtok2bNnz33XdMnTqVsLAw/v77b4oWLUqFChWsBkhWrlyZ1atXM2bMGHr16oWnpyehoaGWPcKsGDt2LBUrVuTzzz/n888/x2Qy4e3tTZMmTSzdsPXq1WPFihWsWrWKGzdu4OHhQadOnSxdlk899RTbtm1j/PjxREVFUaxYMZ5++mk+++yzdLtdCxUqxObNmxk7dizjxo3j5s2blC9fnk8++STNIK0HlR3rAPO4nF27djFnzhyWLVvGpEmTMJlMlCtXjs6dO1v2DitXrszChQuZPHmy5boS/fv358qVK1bXsMjos5iV9wtg2bJljBw5knHjxuHg4ECrVq14++23GThwoNX3ZP78+ZQuXZrly5czffp0PDw8CA0NtRpFf78WLVpEWFgYX375JfPnz6dIkSLUqlWLHj16AOarBl64cIHly5ezdOlSqlatymeffWbVhX2/jh07RnBwMCaTCWdnZ3x9fWnQoAELFy5M86Pyww8/xGQyWX7INGjQgK+//jrNBaRCQ0P5448/GDJkCDdv3qRBgwZs3rzZck2Ozz//nLCwMGrVqsXq1avp1q3bA9c/O5iio6MfbCiwPBIrVqzg9ddf57///a+ubpeBjz76iClTpnD27Nkcew5odrl58ya1atWiT58+Ns87lrxr+PDhfP/99xw+fNjeVRE7MOQeuuRuW7du5cSJE1SvXh2TycSePXuYO3cur732Wp4M8xEjRlCvXj08PDy4ePEiCxYsIC4uzjDXn5YHs2LFCm7cuEGVKlWIj4/nhx9+YNGiRVZX75O8RYEuOU7hwoXZuHEjs2bN4vbt2/j4+PDmm28ybNgwe1fNLuLi4njvvfeIioqiQIEC1KtXj82bN6d7BoTkDYUKFWLevHlERERw584dypUrx/Tp09Nc9EryDnW5i4iIGECePW1NRETESBToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIAOg8d8/mcj+sa7SIikvMkJSVx69Yte1fjkVKgY76RgQJdRERyM6WYiIiIASjQRUREDECBLiIiYgAKdBEREQNQoIuIiBiAAl1ERMQAFOgiIiIGoEAXERExAAW6iGS/S5dgyZKMyyxZYi6X16htbFO7ZEqBLiLZ69IlaNoUXnkFZs+2XWb2bPPzTZvmrQ202sY2tUuWKNBFJPukbJhPnDA/Dg1Nu4GePds8Hczl8soGWm1jm9olyxToIpI9Um+YU9y7gb53w5wiL2yg1Ta2qV3uS64I9JEjR+Lv74+LiwvHjx9Pt9zSpUupVasWNWvWZOjQoSQmJmZjLUUkQ1u3pt0wpwgNhbJl026YU5w4YZ7fqNQ2tqld7kuuCPR27dqxdetWSpUqlW6Z8PBwJk2axNatWzl06BCXLl1i2bJl2VhLEclQz54QFpb+83/+mf5zYWHm+Y1KbWOb2uW+5IpAb9CgAd7e3hmW2bBhA88//zwlS5bEZDLRu3dv1q5dm001FJEsGTo04w20LWFh5vmMTm1jm9oly3JFoGfFuXPnrPbgfX19OX/+vB1rJCI23c8GOq9tmNU2tqldssQwgQ5gMpks/ycnJ9uxJiKSoaFDoUyZjMuUKZM3N8xqG9vULpkyTKCXKlWKv/76y/L43Llz+Pj42LFGIpKu2bMzPv4J5ufTO+fYyNQ2tqldMmWYQG/bti2bNm3i8uXLJCcns2jRIjp27GjvaolIarZOM0qPrXOOjUxtY5vaJUtyRaAPHz6cqlWrcuHCBdq3b09AQAAAgwcPZsuWLQCULl2a0aNH07JlS2rWrImbmxsvv/yyPastIqndz4Y5RV7ZQKttbFO7ZJkpOjo6zx9sdnZ2xsEhV/y2Ecm9liwxX5ozPWXKZNylunixcU9DUtvY9hjbJSkpiZiYmIeqXk6jFBOR7PHcc1Cliu3nwsLg7Nn0RzJXqWKe36jUNrapXe6LAl1Esoe7O2zfnnYDfe9pRrZOT6pSxTyfu3t21NI+1Da2qV3uiwJdRLJP6g20rXOG791A56UNs9rGNrVLlukYOjqGLpLtLl0yX2c7o+O+S5aYu0zz2oZZbWPbI24XIx5DV6CjQBcRyWuMGOhKMREREQNQoIuIiBiAAl1ERMQAFOgiIiIGoEAXERExAAW6iIiIASjQRUREDECBLiIiYgAKdBEREQNQoIuIiBiAAl1ERMQAFOgiIiIGoEAXERExAAW6iIiIASjQRUREDECBLiIiYgAKdBEREQNQoIuIiBiAAl1ERMQAFOgiIiIGoEAXERExAAW6iIiIASjQRUREDECBLiIiYgAKdBEREQPINYF+5swZWrRoQWBgIM2aNePkyZNpyiQnJzNmzBiefvpp6tevz/PPP8/Zs2ftUFsREZHslWsCPTQ0lJ49e3Lw4EGGDh3K4MGD05TZsmULe/bsYefOnezZs4cmTZowfvx4O9RWREQke+WKQI+KiuLIkSN07twZgLZt2xIREUFERESasvHx8cTFxZGcnExMTAxeXl7ZXV0REZFs52jvCmRFZGQknp6eODqaq2symfDx8eH8+fP4+flZyrVq1Ypdu3ZRqVIlChcujKenJ5s3b7ZXtUVERLJNrthDB3OI3ys5OTlNmSNHjnD69GmOHz/OyZMnadKkCSNGjMiuKoqIiNhNrgh0b29vLly4QGJiImAO88jISHx8fKzKrVy5kkaNGuHi4oKDgwNdu3Zl586d9qiyiIhItsoVge7m5oa/vz+rVq0CYMOGDfj6+lp1twP4+fmxY8cOEhISANi6dStVq1bN9vqKiIhkN1N0dHTavusc6PTp0wwcOJCrV6/i7OzM/PnzqVKlCoMHD6ZVq1a0bt2a+Ph4RowYwS+//EL+/Pnx8PBg1qxZaYI/NWdnZxwccsVvGxEReQSSkpKIiYmxdzUeqVwT6I+TAl1EJG8xYqArxURERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBpBrAv3MmTO0aNGCwMBAmjVrxsmTJ22WO3bsGCEhIdStW5fatWuzYcOGbK6piIhI9jNFR0cn27sSWdGmTRu6dOlC9+7dWb9+PXPnzmXbtm1WZWJjY6lfvz7z588nKCiIxMREoqOjcXV1zXDZzs7OODjkmt82IiLykJKSkoiJibF3NR6pXJFiUVFRHDlyhM6dOwPQtm1bIiIiiIiIsCq3du1a6tatS1BQEACOjo6ZhrmIiIgR5IpAj4yMxNPTE0dHRwBMJhM+Pj6cP3/eqtzJkydxcnKic+fONGzYkH79+vHPP//Yo8oiIiLZKlcEOphD/F7JyWmPFCQkJPDjjz8ya9Ysdu7ciY+PD8OHD8+uKoqIiNhNrgh0b29vLly4QGJiImAO88jISHx8fKzK+fr60rBhQ7y8vDCZTLz00kscPHjQHlUWERHJVrki0N3c3PD392fVqlUAbNiwAV9fX/z8/KzKtW/fnkOHDnHjxg0Avv/+e5566qlsr6+IiEh2yzWj3E+fPs3AgQO5evUqzs7OzJ8/nypVqjB48GBatWpF69atAfjyyy+ZPXs2+fLlw8vLi7CwMLy9vTNctka5i4jkLUYc5Z5rAv1xUqCLiOQtRgx0pZiIiIgBKNBFREQMQIEuIiJiAAp0ERERA1Cgi4iIGIACXURExAAU6CIiIgagQBcRETEABbqIiIgBPNZA37179+NcvIiIiPx/jzXQO3XqRI0aNZg4cSJnz559nKsSERHJ0x5roJ86dYqRI0fy66+/UqdOHVq2bMkXX3xBdHT041ytiIhInpNtN2c5f/48a9asYdWqVYSHh/Pcc8/RtWtXWrRogclkyo4qpEs3ZxERyVt0c5aH4OPjQ926dalTpw5JSUkcP36c119/nZo1a7Jjx47sqoaIiIghPfZAP3PmDBMmTKB69er861//wsnJiX//+9/s27ePEydO0LlzZwYMGPC4qyEiImJoj7XL/dlnn+W3336jSZMmdOvWjZCQEJycnKzKXL58mUqVKnHt2rXHVY1MqctdRCRvMWKXu+PjXHibNm1Yvnw5np6e6ZYpWbKkXcNcRETECLJtUFxOpj10EZG8xYh76EoxERERA1Cgi4iIGIACXURExAAU6CIiIgagQBcRETEABbqIiIgBKNBFREQMQIEuIiJiAAp0ERERA1Cgi4iIGIACXURExAAU6CIiIgagQBcRETGAXBPoZ86coUWLFgQGBtKsWTNOnjyZbtm4uDjq1avHM888k30VFBERsaNcE+ihoaH07NmTgwcPMnToUAYPHpxu2Q8++IA6depkY+1ERETsK1cEelRUFEeOHKFz584AtG3bloiICCIiItKU3bNnD2fOnLGUFRERyQtyRaBHRkbi6emJo6MjACaTCR8fH86fP29V7tatW4wePZqZM2fao5oiIiJ2kysCHcwhfq/k5OQ0ZcaOHUufPn3w8vLKrmqJiIjkCKbo6Oi0yZjDREVFERgYyNmzZ3F0dCQ5OZlKlSqxbds2/Pz8LOXq169PTEwMAPHx8URHR1O2bFn27t2b4fKdnZ1xcMg1v21EROQhJSUlWfLCKHJFirm5ueHv78+qVasA2LBhA76+vlZhDubj50ePHuXo0aMsXLiQqlWrZhrmIiIiRpArAh0gLCyMxYsXExgYyKxZs5gzZw4AgwcPZsuWLXaunYiIiH3lii73x01d7iIieYu63EVERCRHUqCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAEo0EVERAxAgS4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuoiIiAHkmkA/c+YMLVq0IDAwkGbNmnHy5Mk0ZXbs2MGzzz5LvXr1CAoK4oMPPiA5OdkOtRUREcleuSbQQ0ND6dmzJwcPHmTo0KEMHjw4TRkXFxcWLlzIr7/+yvbt29m9ezdr1661Q21FRESyV64I9KioKI4cOULnzp0BaNu2LREREURERFiVq1GjBqVLlwagQIEC+Pv7Ex4ens21FRERyX65ItAjIyPx9PTE0dERAJPJhI+PD+fPn093nkuXLrF+/XqCg4Ozq5oiIiJ2kysCHcwhfq+Mjo3fuHGDLl26MGTIEGrWrPmYayYiImJ/uSLQvb29uXDhAomJiYA5zCMjI/Hx8UlTNiYmhhdffJFWrVoxaNCg7K6qiIiIXeSKQHdzc8Pf359Vq1YBsGHDBnx9ffHz87Mqd/PmTV588UWaNWvGyJEj7VFVERERuzBFR0fnivO6Tp8+zcCBA7l69SrOzs7Mnz+fKlWqMHjwYFq1akXr1q2ZPn06U6ZMoXLlypb52rdvz/DhwzNctrOzMw4OueK3jYiIPAJJSUnExMTYuxqPVK4J9MdJgS4ikrcYMdCVYiIiIgagQBcRETEABbqIiIgBKNBFREQMQIEuIiJiAAp0ERERA1Cgi4iIGIACXURExAAU6CIiIgbgaO8KiIjkJTdu3OD69ev2rkae4+joiLu7u6GvCqpAFxHJJpcvX8ZkMuHj45PmltDyeN28eZNLly7h6elp76o8Nsb9qSIiksPEx8fj5uaWtTC/dAmWLMm4zJIl5nKSqcKFC1tuwW1UCnQRkZzm0iVo2hReeQVmz7ZdZvZs8/NNmyrUBVCgi4jkLClhfuKE+XFoaNpQnz3bPB3M5ewc6uPGjbPcpnrDhg2MGDHCLvUIDw/n008/tcu6cwIFuohITpE6zFPcG+r3hnmKHBDqKdq2bcu0adPssm4FuoiI5Axbt6YN8xShoVC2bNowT3HihHn++2QymZg8eTJ169albNmyfP/994wePZqAgACqVavGsWPHALh48SJNmzYlMDCQatWqMWTIEJKTk9Msb/Hixbz44ouWx++88w7ly5enXr16jBgxgtq1awPw008/UbNmTQYOHEiNGjWoVq0aBw4cACAxMZGWLVtSu3ZtqlWrRvfu3YmNjbUsv2XLlnTt2hV/f39q167N2bNnAejfvz/Hjx+nZs2atG3b9r7bIrdToIuI5BQ9e0JYWPrP//ln+s+FhZnnfwBFihRh3759TJ06lXbt2tGwYUMOHTpEz549mThxIgAuLi5s3LiRgwcP8ttvv3H27FnWrVuX4XI3btzIpk2bOHLkCL/88gtnzpyxev7YsWP07t2bI0eOMHjwYN555x0A8uXLx8qVKzlw4AD/+9//KFKkCPPmzbPM9+uvvzJlyhSOHj1K8+bNmTp1KgALFiygatWqHD58mA0bNjxQW+RmCnQRkZxk6NCMQ92WsDDzfA+oc+fOANSqVQsHBwdCQkIACAwMtOz9JiUlMWrUKGrUqEFAQAAHDhzg8OHDGS53+/btdOrUiUKFCuHg4EDPVD84KlWqZNljDwoKsgR+cnIys2bNIiAggOrVq7N582ardTVs2BA/P7808+V1CnQRkZzmfkL9IcMcoECBAoB5z9jJyckyPV++fJZTvWbOnMmVK1f49ddf+e233+jWrRtxcXEZLjc5OTnDU/RS1pt6XStXrmTHjh38/PPPHD16lOHDh1utK7358joFuohITjR0KJQpk3GZMmUeOsyz6tq1a3h4eFCgQAEuXbrEmjVrMp2nadOmrFmzhtjYWJKSkli2bFmW11WiRAmcnZ2JiYlh8eLFWZqvSJEiefoqfAp0EZGcaPbsjI+Zg/n59M5Tf8SGDBnCnj17qFmzJr1796Z58+aZztO2bVtatmxJjRo1aNq0KeXKlaNo0aKZztejRw9u3rxJ1apV6dChA40aNcpSHatXr06lSpV46qmn8uSgOFN0dHTaYYp5jLOzs6Gv7ysiOcO5c+coVapU5gVtnZqWkUfQ7f64xMTE4OzsTFJSEn369MHLy4sJEybYpS73tn9SUhIxMTF2qcfjomu5i4jkJPcb5vB/5XNgqPfo0YPw8HBu375NrVq1GDlypL2rZFgKdBGRnGLJkozDvEyZ9LvhQ0PBxeWBT117XL755ht7VyHPUD+ziEhO8dxzUKWK7efCwuDs2fRHv1epYp5f8iztod+nO3fhmz9hYzj8HQuJSeBaAJp4QY9K4OKU6SIM6+Q1+OIk/O8q3LgDhfJDhaLwckWo627v2tnP7UT46g/4zzm4FAvJQMmCEOwD3SqY20kEAHd32L7dcvnX/5YKYH6jAexo8CJXChTDcTF4eg6l7Zyn6TumIz7Rkeb5qlQxz+eeh79ookFxkLVBcbcTYdphmPc/uHTbdpknHaF7BXi/DngWevT1zKl+vgDvH4AfI9MvU9sN3q4FL5TNvnrZ2/V4mPRf+OwEXIu3XabIE9C7MowJhOIFbJcR48jqoLj9J64w5Mtz7PWsmW6ZfMlJvHhwNWFH5uGxeY3CPAuMPihOgU7mgf7PbWjzb9ibxfseeBWCLa2hhusjqmAONv9/MGgXJGXxUzQqACbXg6zcDjo3O3cTntsEx69lrXyFovDvECiX+Rk9kotlJdC//RO6bIP4u1lbpt+Td/muXT4qujx8/YzO6IGuY+iZuJUArbdkPcwBLtyC5hvhD4Nf3+CLkzBwZ9bDHGDqIXhv/+OrU05wJQ6e3ZD1MAc4fd38mbkY+/jqJTnfzxeg83dZD3OAiNh8tNhkPpwjeZsCPRPv/Ar7L6ed3sgTpjwNs+pDhzLgmKol/4mD7t+DjZsRGcLZG9B/R9rprgVgiD/MbWTeG/ctnLbMBwfNGy6jGrTTHNCptSgF04JgRn1o7QupOynCY6DvT9lRQ8mJEpOgx49wJ8l6ulchGFcbvm8DG1tBv6rmw3v3ioiB4b882HpNJhM3b958sJnTUbNmTW7fTufYpDw2uabL/cyZMwwYMIArV65QtGhR5s2bR+XKldOUW7p0KWFhYSQlJdGkSRNmzJiBo2PGY//S63K/Hg9eSyH2nssE+xSC1S0gyMO67F8x5gDfddF6+s720NAzq68y9xi2G2b9Zj3trQB4rzYUuKe57ybBvGMQutt6T75dafi2VbZUNVv9FQNlVli/1sou5s+MfwnrsievQedt8NsV6+mnuqLuU4PKqMv967PQ8T/W03pVhvmNwSmf9fS/b5kPAx6M+r9p+R3gr5fB48n7q5PJZCImJobChW38+jYYdbnnEKGhofTs2ZODBw8ydOhQBg8enKZMeHg4kyZNYuvWrRw6dIhLly5l+drBtiz93TrMHUywJSRtmAP4Opuf8041GG7e/x549TlWbIK5u/1evSrD5KetwxwgnwMM9jcPFLzXxghz+BnNp8etw7ygI3zXJm2YA1QuBt89bx4Yd68Fxx5vHSVnSr2tqFECPmuSNszBPOj22+esn0tIgoXp3Eo9M9OnT6dBgwZUrFiRL7/80jJ9//79NGvWjNq1a1OrVi3L7VLDw8NxdXVl7NixBAYGUr58ebZs2WKZ7969/p07d+Lv70/16tUZPHgwfn5+/O9/5hdbunRp3n//ferXr0+ZMmXsdgU5o8gVgR4VFcWRI0cst/hr27YtERERREREWJXbsGEDzz//PCVLlsRkMtG7d2/Wrl37wOtdk+qOfC+Usb1hTuH8BLxR3XraN39Cwn0cD8sNvj8P0Xf+77EJeKdWxvMM9Qfne07PSko275EYTerPTK9KUCqDHR/3J81dqPdarTtB5jmxCbA91WGoYTXMP4jT41MYupa3nrYpwnbZzJhMJnbv3s3WrVsZPHgw586dIzo6mn79+rFixQoOHDjAd999x7Bhw7h40dwNeeXKFQIDAzl48CBz587ljTfeSLPc+Ph4unbtyrx58/jtt99o1qwZf/31l1WZ6Oho9uzZw759+5g2bRqRkRmcLiMZyhWBHhkZiaenp6Xr3GQy4ePjw/nz563Kpe7O8vX1TVPmfpxLdVipQyY3PgLomOq0rLi75uPpRnLulvXjmq6Zj852fgJapuppTL0cI/gr9WcmC6fppf7MRN4yH6qQvCMqLu3g0ta+mc/XKlWZBx1U2adPHwDKli1Lw4YN2blzJ3v27OHs2bO0atWKmjVr0rx5c5KTkzl16hQAhQoVol27dkD69yQ/deoUBQsWtNxc5YUXXsDFxcWqTPfu3QFwc3OjbNmy/JnZDWkkXbnmwjKp76mbnM5os3vLpVcmq1KPNM3KecK2ytzPiNXc4E7qdsnixXSKpSpntHaBtAOastI2qdslZTkFc8XPbXkUbJ0p8oSNrvbMytzPGScZMZlMJCcnU716dX7++ec0z4eHh6e5J/ndu2m/0JndDx10b/NHKVdsMry9vblw4YLljU5OTiYyMhIfHx+rcqVKlbLqzjl37lyaMvcj9Yb2QJTtcveyNSLe1gY7N0t9NbzDV7J2WCF1+xmtXQBcUh0Pz8pn5kCqz4xTPiiQhY25GEcJGzsCP2Wh53lHqm561we8ONGiRYsAc1Dv2rWLhg0bUr9+fU6fPs2PP/5oKXf48GHu3LmT3mLSqFy5Mrdu3WL37t0ArF+/nujo6AerpGQqVwS6m5sb/v7+rFq1CjAfK/f19cXPz8+qXNu2bdm0aROXL18mOTmZRYsW0bFjxwdeb92S1o8/P5F5V+inx60fV3KBogYLrtTtciUOvs6kl2z/ZTj0T8bLMYJ6qS7WlfrzYMsnqcrUK2n8C++ItSJPQKCb9bQ5mQyovXEHFqcanNrU+8HW7+TkRIMGDWjRogVz5syhVKlSFCtWjI0bN/LBBx9Qo0YNqlatyltvvUVSUtaPBzk5ObFy5Ur69+9P3bp12bNnD+7u7lm6J7rcv1xz2trp06cZOHAgV69exdnZmfnz51OlShUGDx5Mq1ataN26NQBLliyxnLbWuHFjZs6cSf78GV8sO73T1n65CPVT3ShoRE2Y+rTtDe43Z6FDqtNOZtQ3D24xmobfwO57TtHzKQS/dDAP1Entejw03WAd6D6F4M9/pT1/P7f79k94Yav1tIw+A58cg/6pejRXPAvdKj6e+ol9ZXTa2ufH4bVU13aY+jSMDEhbNv6u+QI068Otp//eFSq4PJKqPjIp90MH2L59Oz179iQ8PDzTy20/DkY/bS3XBPrjlF6gJydDrbVwONWeZStfGFnTfEMWkwlOXIOPfjPvad3bmAXyQWQPY16je+Xv0P0H62luBWB0LXilsrk7PTbBfFOSif81X4jmXuPrwJja2Vff7JKYBGWWw/lUA/5eKgfDa0Cd/7/3/d8omHkEVpy2LudWAM71sH2qkuR+GQX6rQQotSztdf8be8Igf/PFrGITYVM4zD6a9jvVshRsff7x1PthLF68mFmzZpGUlISTkxOzZ8+mQYMGdqmLAj0PyOha7j+ehxab4K6NVirkaN7wXk3nxhuT68FbmZzOlVslJkGjb9O/JK57QdsjdwHKOMOhl4x3KCLF6j/MF4yxxTm/+XoG19M5DLmoqfmcfjGmzK7lbuuHclYUeQL2doAqxR6icnmA0QPdYB2ej14zH1jQOO1lOgFuJaYf5n2qmC99alSODrD+OfMYAVsu3bYd5iULmi/AY9QwB+hUHibWtf1cTEL6Yf5OLYV5XtetovnywPejcH7zd1FhLgr0LOhT1XzpztRX9LLFwWS+/OmnTYw/sKnkk7CrPTTL4kCc6iXMx9kr54ENz9uB8EkT85XiMvOEA8xuABPqPf56Sc43vCZ82TxrI9b9i5svL/3MAw6GE2NRlztZux86QHQ8LDllvkTj76luvuFWwLxX3rcqlC7ymCqaQyUnw89/m9vl6z/N3fH3eq4UDHzKfKGMjK58ZUSXY2HRSVhw3HwDjXt5FzJfJa5PFfOlPMX4Ll++jMlkwtXVNdPzs+MSYe1Z86WA91z8v/E5BfJBiB+8/hQ842X8HYdH5ebNm8TExODpab65hhG73BXoZD3QUyQnw+/RcPG2ObxcC0DVYpBfA5mIjodT0eZTagrlh3JFzJc3zevuJpnb5dL/vwGVWwFzF2le+4EjcOPGDa5fv797K99JgusJDjiaoEj+JPIpxO+bo6Mj7u7ulm29At2g7jfQRUQkdzNioCvFREREDCDXXMv9cbqfKx+JiEjuZ8TtvgIduHXLgLf9EhGRPEVd7iIiIgagQBcRETEABbqIiIgBKNBFREQMQIEuIiJiAAp0ERERA1CgP6AzZ87QokULAgMDadasGSdPnrR3lXKEkSNH4u/vj4uLC8ePH7d3dXKMuLg4unXrRmBgIA0bNqRjx45ERETYu1o5xgsvvED9+vVp2LAhrVq14rfffrN3lXKUKVOm6DuVir+/P3Xq1KFhw4Y0bNiQr7/+2t5Vsjudh/6AQkND6dmzJ927d2f9+vUMHjyYbdvSuQl2HtKuXTuGDh3Kc889Z++q5DivvPIKwcHBmEwmPv30U0JDQ/nmm2/sXa0c4YsvvsDFxQWATZs2MWjQIH7++Wf7ViqHOHz4MAcOHMDHx8feVclxlixZQtWqVe1djRxDe+gPICoqiiNHjtC5c2cA2rZtS0REhPa4gAYNGuDtrXs5plagQAFatGhhucNWnTp1CA8Pt2+lcpCUMAfzzUt0bwWz+Ph4RowYwfTp0zO9O5uI9tAfQGRkJJ6enjg6mpvPZDLh4+PD+fPn8fPzs3PtJDdYsGCBejFS6devH7t27QJg7dq1dq5NzjBp0iQ6depE6dKl7V2VHOm1114jOTmZwMBA3nvvPVxdXe1dJbvSz+AHlPrXcnJynr9pnWTRjBkzOHv2LGPGjLF3VXKUTz75hGPHjvHOO+8wduxYe1fH7vbt28d///tf+vTpY++q5Ehbtmxh9+7d7Nixg+LFizNgwAB7V8nuFOgPwNvbmwsXLpCYmAiYwzwyMlLHuCRTc+bMYePGjaxZs4Ynn9SN4m3p1q0bO3fu5OrVq/auil3t3r2b06dPU716dfz9/blw4QIdO3bUWJ3/r1SpUgDkz5+fAQMG8Msvv9i5RvanQH8Abm5u+Pv7s2rVKgA2bNiAr6+vutslQ3PnzmXt2rV8++23VseM87obN27w999/Wx5v3LiR4sWLU6xYMTvWyv7eeOMNTp48ydGjRzl69CheXl6sW7eO4OBge1fN7m7dukV0dLTl8dq1a/H397dfhXIIHUN/QGFhYQwcOJCZM2fi7OzM/Pnz7V2lHGH48OFs2bKFS5cu0b59ewoVKsShQ4fsXS27i4yM5N1336V06dI8//zzADg5OfHDDz/YuWb2d+PGDXr06EFcXBwmkwlXV1e++uorDQKTdEVFRfHyyy9z9+5dAPz8/FiwYIGda2V/pujoaB38FRERyeXU5S4iImIACnQREREDUKCLiIgYgAJdRETEABToIiIiBqBAFxERMQAFuog8EitWrNCdr0TsSOehi2SjzK4Qt3HjRho1apQ9lblPf/zxB++//z579+4lJiYGDw8P6taty9y5c3niiSe4ffs2t27dyvM3yBCxF10pTiQbnTp1yvJ/WFgYBw8eZNmyZZZpOeFyp3fu3OGJJ55IM61Dhw4EBASwZs0aXFxc+PPPP1m/fr3lal0FCxakYMGC9qiyiKAud5Fs5e7ubvkrVKgQ+fPntzweN24cgwYNsiofEhLChAkTLI9dXFxYtmwZbdq0wcPDg2bNmhEeHs7OnTsJCgqiVKlS9O3bl7i4OMs8ly9fpkePHnh7e+Pn58egQYO4deuW1TreffddBg0aRKlSpWzeBe7kyZP89ddfzJo1i5o1a1K6dGmaNm1KWFiYJcTv7XKPiIjAxcUlzd+9d8Rat24d9erVw8PDg6CgINavX/9oGlkkj1Kgi+Qy06dPZ+DAgezYsQNHR0f69OnD9OnTmTdvHmvWrOH7779nyZIllvL9+vUjMjKSzZs389VXX7Fnzx7efvttq2UuXryYcuXKsWPHDpu3oSxRogQODg5s3LgxS7cK9vHx4dSpU5a/7777jkKFChEUFATAjh07GDlyJKNHj2bv3r0MGzaM/v37s3///odsHZG8S13uIrlM7969adWqFWAO61dffZXt27cTEBAAQLt27di1axf9+vXj999/Z/v27ezdu5fKlSsD8OGHH9KlSxfGjx9P0aJFAahZsyZvvPFGuuv09vZm/PjxjBgxgvfee486derwzDPP0L17d5vjAvLly4e7uzsAsbGxDBs2jA4dOtCjRw/A/KNk1KhRtG/fHoDSpUuza9culi5dSp06dR5JO4nkNdpDF8ll7h1J7ubmBmAJ65Rp//zzDwCnT5/G2dnZ6vk6deqQmJjIn3/+aZlWvXr1TNc7aNAgTp06xaxZsyhXrhwff/wxQUFBVrc+tWXIkCEULFiQGTNmWKYdP36csWPH4u3tbflbuXIl4eHhmdZDRGzTHrpIDuHg4EBiYqLVtNSPAfLnz2/5P+UWo6mnpXSL2+oet3Vb0ieffDJLdSxWrBgvvPACL7zwAqNHjyYwMJBFixbxzjvv2Cw/Z84cdu7cyU8//WQ10O7WrVt88MEHPPvss1blCxQokKV6iEhaCnSRHMLV1dXq3vF37tzh9OnTD3UaW8WKFYmJieHkyZOWvfR9+/bh6OhImTJlHqq+RYsWpWTJksTGxtp8/ueff2by5Ml88803eHp6Wj331FNPER4eTtmyZR+qDiLyfxToIjlE/fr1+fjjj9mwYQOVK1dm9uzZJCQkPNQyK1asSLNmzRg0aBDTp08nLi6OUaNG0b17d8vx86w4dOgQM2fOpEuXLlSqVIm7d++yevVqTpw4weTJk9OUv3jxIr169SI0NJTSpUtz6dIlwLwHXrRoUYYNG0avXr3w8vKiZcuW3L59m19++QVXV1c6dOjwUK9ZJK/SMXSRHKJFixa8/vrrvPHGG4SEhFCtWrUsHdvOzIIFC/D09CQkJIROnToRFBTEpEmT7msZpUqVwsPDg/Hjx9O4cWNatGjBzp07WbJkCU2aNElT/vTp01y5coVJkyZRqVIly99bb70FQOvWrfn8889ZtWoV9evXp3379vznP/+hVKlSD/16RfIqXSlORETEALSHLiIiYgD/D68sthYOl7DuAAAAAElFTkSuQmCC' width=500.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"4578660aa6a943dca5305be4cc0beae2\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train = np.array([0., 1, 2, 3, 4, 5],dtype=np.longdouble)\\n\",\n    \"y_train = np.array([0,  0, 0, 1, 1, 1],dtype=np.longdouble)\\n\",\n    \"plt_simple_example(x_train, y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Now, let's get a surface plot of the cost using a *squared error cost*:\\n\",\n    \"  $$J(w,b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})^2 $$ \\n\",\n    \" \\n\",\n    \"where \\n\",\n    \"  $$f_{w,b}(x^{(i)}) = sigmoid(wx^{(i)} + b )$$\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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QKBc4//3wuv/xytm3bRqlU4te//vWSrH3m+kwNDQ1x4MCBw55PJpOqBlBx3FECULFqadfltEVCJ4cOHaK/vx+YuSG2RUebsbGxYx5jaGiIm266iQcffJB77rmHa6+9lhtvvHHBheaFQgFN0+a8mfSaQqHAq171KiC8OUN4DeY639nPJRIJgMOiRG1B1ub73/8+pVKJf/mXf+Hqq6/mD/7gD7jooouOGF2aXZfY/t188pOfPCwyd8cdd/AP//AP8bphcb+7xXCkzuTZz8/3s3es/c6mnVL81re+dcxt22uYfS3aa2qvwTRNbrjhBn784x/z0EMP8f73v5/bb7990X52Qghe+MIX8s1vfpN6vc4tt9xCLpfrSisLIXjlK1/JD37wA0ZHR7n55pv5xS9+wZ/+6Z8edd/9/f0Ui8U5z/W8885j27ZtbNu2LRaEl19+OXfffTc/+tGP8H2fP/zDP1zUOcHcn6nx8XHWrVt32PPT09NzRioVipVECUDFquWss85izZo1fO1rX+t6/mc/+xl79uzhsssuA8L0mxCCb3zjG13b/cd//MeCj/e3f/u3FAoFHnzwQYA4WnGsaEAqleJxj3sct956a08jB3v37p3z+YcffhiYEVCXXnop9957b9f2tVrtMEuRdoq4HTlsM7trtZ3SM00zfm5qamreFiWXXnop2WyW0dHRwyJzF110EWeddRYQ/u40TePrX/961/tn/85Xmvl+9hbKJZdcwuWXX85HPvIRRkdHD3tdShmLw/YxvvrVr3Zt8+UvfxmAJzzhCYe9f3h4mJe97GU86UlPij/DAJZlLehzec0111CtVrn99tu59dZbufLKK7tS5J0UCgWuuuoqnve853Udcy7OOuss9u3bh+d5h73WjvZt27YtFnqXX345ExMTfOpTn2Ljxo2HeXEuhO9+97vUarX48a5du7jnnnsOq4s8dOgQzWZT+QAqjjuqCURxyvOrX/2KfD5/2PPPfvaz+au/+ituuOEGrr/+el74wheyf/9+3vve93LGGWdw3XXXAeFN5QUveAHve9/7CIKACy+8kB/+8IexWDlSF2ypVOJ5z3seL3jBC3jUox6FaZr853/+J8Vikac85SkAnHnmmRiGwRe+8AX6+vqwbZszzzwz7mDt5L3vfS/Pec5zeMYznsHrXvc61q9fz86dO3nggQf48Ic/vKhr88IXvpDBwUGuuuoqzjzzTJrNJj/+8Y/5x3/8Ry699FIe97jHAfDa176Wz3zmM1x11VW8/e1vj7uAZ9+0165dy2WXXcY73/lOgiAgnU7zpS996bDU3ZOf/GQMw+D666/nL/7iL5ienuamm25iYGBgzpv3bHK5HO9+97t585vfzOTkJE9/+tPJ5XIcOHCAu+++myc/+clcddVVnHXWWTz/+c/n/e9/P0EQxF3A3/nOdxZ0ne69997DjKB1XY87txeKruvz+uwthk996lNceeWVPPWpT+VVr3pVbAT9P//zP/zLv/wLhUKBK664gnPPPZfnP//5fOADH8DzPP7gD/6An//853z4wx/m+c9/ftyl++IXv5jzzjuPCy64gEKhwK9//Wu+//3vxzY3EDYxfec73+HpT386hUKBtWvXzhn5avOoRz2Kiy66iP/zf/4P+/fvP6wO8Q1veAOZTIZLL72UwcFBRkZGuOWWW+J/N0fiCU94AjfeeCO/+c1vuPDCC7teu/zyy/nkJz/JgQMH4lq/tjXTXXfdxYtf/OLD9nf++eezefPmru76I5FMJrnqqqt4/etfj+M43HjjjWSzWf78z/+8a7t7770XYNEiX6HoFUoAKk55PvvZz87Z8blz505e8YpXkEwm+djHPsa1115LOp3mGc94Bu9+97u7bDg++tGPkslk+Id/+Adc1+Xyyy/npptu4pprriGXy8153EQiwQUXXMDnP/959uzZg6ZpnHnmmfy///f/4pF1/f39fPjDH+ajH/0oz3nOc/B9n9tvv33OYvSLL76Yb3/727z//e/nrW99K47jsGnTpiWJhTe/+c3cdtttfPSjH+XQoUNIKdmyZQuve93r+Mu//MtY3A4MDPCNb3yDd7zjHbzmNa+hv7+fV77ylXiexxe/+MWufX7605/mjW98I294wxtIJBK85CUv4clPfnJXU8k555zDP/3TP3HjjTdy7bXXsmHDBl796lczOTnJv//7v89r7a985SvZsGEDH/vYx/jKV76C67qsW7eOJzzhCV0WI+3f3cc//vH4d/eZz3xmQQ0sc22bTqfjrtvFMN/P3kJZv349P/jBD7j55pv5xje+wcc+9jGCIGDLli0885nP7Ormvfnmm9m6dStf+MIXuOmmm1i7di033HBDV/ftE57wBL7xjW/wmc98hkajwcaNG/mLv/iLrhTwhz/8Yd72trfxohe9iFar1dVZfCSuueYa3v72t7Nx48bDxNAf/MEf8G//9m/ccsstlMtl1q5dywtf+MJj7vMJT3gC69at49vf/vZhAvAJT3gCuq4zMDDQZdh++eWX89BDD81Z/1ev1+fdfPOiF72IVCrFW9/6ViYnJ7n44ov5p3/6py4LGAij4RdeeOGcPo0KxUoiisXi4toSFYpVzsc+9jE+8IEPMDo6Gte+rTZuvPFG/v3f/31enoMKxUpw44038uUvf5n77rtvSdNiHnnkES655BK+//3v85jHPKYna2s2m5x99tm85z3v4WUve1lP9qlQLBYVAVQo5sG3v/1tHnzwQX7/938fIUScJv2zP/uzVSv+FIoTkXa5wm233TYvq6Yj8aMf/YinPOUpPRN/ELoCDA0Nce211/ZsnwrFYlECUKGYB5lMhttvv52///u/j9Ngb3rTm3jjG994vJemUCg6yOfzfOpTnzrm1JBj8fKXv5yXv/zlPVpViG3bfPKTn1z0HGiFopeoFLBCoVAoFArFKkPZwCgUCoVCoVCsMpQAVCgUCoVCoVhlKAGoUCgUCoVCscpQAlChUCgUCoVilaEEoEKhUCgUCsUqQ/WiKxbNXOPVFAqFQnHyUCqVjvcSFMcJFQFUKBQKhUKhWGUoAahQKBQKhUKxylACUKFQKBQKhWKVoQSgQqFQKBQKxSpDCUCFQqFQKBSKVYYSgAqFQqFQKBSrDCUAFQqFQqFQKFYZSgAqFAqFQqFQrDKUAFQoFAqFQqFYZSgBqFAoFAqFQrHKUAJQoVAoFAqFYpWhBKBCoVAoFArFKkMJQIVCoVAoFIpVhhKACoVCoVAoFKsMJQAVCoVCoVAoVhlKACoUCoVCoVCsMpQAVCgUCoVCoVhlKAGoUCgUCoVCscpQAlChUCgUCoVilaEEoEKhUCgUCsUqQwlAhUKhUCgUilWGEoAKhUKhUCgUqwwlABUKhUKhUChWGUoAKhQKhUKhUKwylABUKBQKhUKhWGUoAahQKBQKhUKxylACUKFQKBQKhWKVoQSgQqFQKBQKxSpDCUCFQqFQKBSKVYYSgAqFQqFQKBSrDCUAFQqFQqFQKFYZSgAqFAqFQqFQrDKUAFQoFCtCpVKhWq2u6DE9z6NSqRAEwYoeV6FQKE50lABUKBQrwsGDBzl48OCKHrNarfLQQw/h+/6KHlehUChOdJQAVCgUK4IQAinl8V6GQqFQKFACUKFQrCDHSwAq4alQKBTdKAGoUChWBCHE8V6CQqFQKCKUAFQoFCvC8UgBK9GpUCgUc6MEoEKhWDFOtRSw7/s0m02VYlYoFCcdSgAqFIoV4VSMxpVKJX7zm9+oLmOFQnHSoQSgQqFYEVQKWKFQKE4clABUKBQrhkqVKhQKxYmBEoAKhWJFOJ7ROCU8FQqFohslABUKxYpwPFLAUkoqDz9Mec+eZT2OSjUrFIqTDeN4L0ChUKweVkoASikZu/9+HvzqVzm4fz+1u+/m8TfcwMBZZ63I8XtF+3opgalQKHqNigAqFIqj4rouO3fupNFoLGk/KyViAs/j15/6FL+6+WYq+/fjVKt4zSY//ehHOfirX63IGnqF7/vcd999TE1NHe+lKBSKUwwlABUKxVEJgoCJiQkcx1nSflYiBSylZPSf/5nx3/wGAHtggMB1gVAY3nPzzey/555lXcNyoCKACoWi1ygBqFAojkpbfCxVvK2EANz39a8zPTKC77pIoFWrAeHaM4UChVyO0VtvJfC8ZV1Hr1DNKwqFYrlQAlChUByVXgnAXu3jSIzdeScH/vu/cbXway0xOIidSJApFOgfHsYyDNxyGadcZuy++5ZtHcuBigAqFIpeowSgQqE4Kr0SH0fbj5SS8fFxpqamcKOU7UIo/exn7PriF0HTqE1Pk0insTwPOT2NPz2NW6/TmJrCzGQA2PuDHyz6PBQKheJUQHUBKxSKo7LcKWDHcRgdHaVarcbPJZNJstksuVyOTCaDYRz5q6r8wx9y8HvfCx/09WHW6/QZBhPT0wCYhQJutYphGPhBgGaalHfupDQ6Sv7005d0TsuNSgErFIrlQglAhUJxVJYzBVwsFtm5cydCCM4++2xs26ZSqVCpVCgWi4yNjQGQSqXI5XJks1kymQy6rgNQ+v73mbz1VurJZLh/w2AAcG0bonV7jgOJBABuvU6yvx9naoq9P/jBCS8A2yxnCnh8fJxSqcSZZ565bMdQKBQnHkoAKhSKo9LLCGCbIAjYt28fhw4dIp/Ps3XrVkzTBGBgYICBgQEAWq0W5XKZSqXCxMQEBw8eRAhBWkqs++/H/dGP0IaG8IpFdNvGrFQIGg0aUcTQHhjAHxmJBaBhWWhAYf16xu69lzOf/3zsQmFJ57WcrEQE0HEc6vX6sh9HoVCcWCgBqFAojkqvU8CtVovR0VHq9TobN25keHj4iBEu27YZGhpiaGgIKSW1kRFK//VfNH/xCyYbDaTrUgUMKSlkszhTUxiFAq1ymVQ+T+C6pHWdtK6TWbMGUa8jp6YwEgnWDg6y7847Of15z1vSea0EyxkBVGlmhWJ1ogSgQqE4Jr2wcBFCEAQB27dvR9d1zj77bDJRU8aRaN1+O+799yNrNaRp4hw8iC0E2qZNiEOHcIVA6jqJcpnpsTG8apWmpmFqGlq9TqNWA98HKXFLJYxEAqTEazbJplIE999P8Oxno1nWos7pVBBPUkrVZaxQrEKUAFQoFMdkqQIwCAKmo6aMbDbL1q1bj9rYAeD89Ke0vvnN8IFp4goBQiAti+bEBAJIrF3LuvFx5PAwslLB1XWsXA727WM6m6VZLgPQMk28UgmzXie3di3++DhNIL1/P41f/5r0JZcs+tyWk5USmEoAKhSrDyUAFQrFMVmKAGynfGuRKfMZZ5xxTMHh795N8wtfAEIRFGSzyGgcmlsokAwCLMehXiwS+D5uMomoVOjbvJnGgQNQKGBaFkYmQ93zcCYm8CLz53KtRtayaB04gJ3JUPvpT09YAdhmuVPASgAqFKsPJQAVCsUxWawAnJ6eZufOnei6zrp16zhw4MAx3yPrdbwvfwlzsA+kxDcthONiDA8hdQN9YhIcB29oiKBcRgpBs1RiYM0aHM8LxUxfH8H0NHYigSsEGSHA93FdF9d1ado2zYkJPF0nu20b9ac+lcL69ce0nFlpViICuBLHKBaL+L4fN/coFIrjz4nzTadQKHqKlJI9e/bQ19dHNptd0r4WKgCDIGDv3r2MjY1RKBTYunUrxWIxXtfRIk7+bV9D7BxBB2QyRTA5ie4HSCBIZ8FxQNdplkrh/gYG6A8CmJqiEa3R03WS2Sy2lNR27MAsFEitWYM0DALfJ/B9tHwep1LB0DSKP/4xExdeCBzZcuZ4crJHANsG30oAKhQnDkoAKhSnKEIIxsfHSSQSKyoAW60WIyMjNBoNNm3axJo1axBCzEtkBA//juBH2wCQgC8M8IPw8eAQwdg4AN7AAHJ8nMTgIIHnIYtF3DVrkGNjSE3D0nX0Wg0vl6N9VGdqikxfH5aUUCphDgxQzmRI5vMkx8fpO//8uS1n0ulYEKbTaTTt8AFKJ3sKdbnXr9LMCsWJhxKACsUpTC+6dxeyn3bK1zAMzjnnHNLpdNc+4MgpR+m6+F/6wszjgTXIQ6ERNJkM3tg4wtARqRS6EBRMk8DzaESRxWazidA0smvX0ty/HyklToe/XWbNGoLxcfy+PnTAnZxkfSbFdDKDs3MnWrHI0PBwbDnTbDZjQXjo0CH279+PpmlkMplYEC53+nSlUsBKACoUqw8lABWKU5iVEoBBELBnzx7Gx8fp6+tjy5YtR6ylO6IAvPsHaIaEjWtAaOhNF3NtPwQBntAxg1ZY+5dMIA4dQkqJm0qFx0+nCVotBtNp6o0GAFpfH97kJABGMkkQ/exMTZEqFJClEn4qTf/0JJVEgtpPf0rhyivj800mkySTSYaHh5FSUq/XY0G4f/9+giCIo4Hj4+PkcjkSicSyCJ2T3Qew81opFIoTAyUAFYpTmLb3Xi/2cySh0Gw2GR0dpdFosHnzZoaGhuYULEeNADYbaHf9F7Sa4eNkHxRD2xi/fw0iigQGmRzuoXEEIPv68KLon8jn6ZuYQPo+TrkMQuBHk0X0RIJkLgdRF7EQAk/T0IGm41JwHAa2rqN4773kn/vcI649nU6TTqdZt24dQRBQq9U4dOgQxWKRPXv2AGCaZjzDOJvNYtv2vK7vkVARQIVCsVwoAahQnMIsdwRwamqKnTt3Ypom5557LqkoInekfRyRn2+bEX/ZPpgIxZ80TPyp6Gcp8XwtFH9S4kqJPdCHZejUx8aQnodXKEC9jtQ0/Hqd/oGBsGnkwAESGzfSnJyEIIijgK3pafxUEr3eIp8CZ2QEex4zcTVNI5vN4jgOxWKRCy64oCtCOBWJTdu2uwRhe9zdQllu8XQqCEDf9wmCYNHXWKFYbSgBqFCcwmiatiwCsDPl29/fz5YtW47ZLXvECKDnwd3fI3oRWn78kp8tQNT4EfQPISs1zKF+SNiY5TKiXMPtX4P0PCTExs+ZtWsJDh7EK5cJTBMRBPjj49iZDD7gVathFFAInFwOfWKC1JDA/5/7YB4CcDa6rpPP58nn89EpeVQqFSqVCuVymYmJCQCSyWQsCOdjOaMigPNnbGyMgwcPctFFFy3rcRSKUwUlABWKU5heRQBhRow0m01GRkZoNpts2bKFwcHBBd3cD1vPL38GldDShcIQTExBXx6ZTCL8AGPDIHguZr2KEE1krY4T5AnqNaQQtKaL4X77+hCNBsOFAo1GgyAIEJqGnJ6GaOScX6mQGR7GtC0amobM5WjUGySDAD8xgPHQL+A5L4QlihXDMOjr66Ovrw8Ax3FiQVgsFhkbC1Pa6XSabDZ7TMuZk90GRkq57DWAK3EMheJUQglAheIUptcp4MnJSXbt2jWvlO9c+zgMKeHu78BAHyQMaLqQDsArIaSBXgwbN8ivgWYoEoPCIMF4+LzXP0RwMBRTejLJYLWKbLVw6vXQeqa/HzkxQfsKpIeHYWwMc2iQ5OQEYt0w4+PT+JkMvqNhNCZh4iAMrVv0tZoLy7IYGBiIffBarda8LGdWglMlAhgEgaozVCgWgBKACsUpTK+aQACq1SpTU1PzTvnOtRaYFQHc8SB44+D7EORnIoF2EqYi8afpEM0RloBbb8U/O+UqyaF+bFOjuX8/AnCiSCCA7zjxoez+fkQUeXMcN/zyK5ZYM5yg6ibRJyax+kA8sr3nAnA2tm0zNDR0TMuZZDIJQKPRWLYO41NFAKoIoEKxMJQAVChOYXoRAWw0GtTrdYIgYOvWrQwMDCzpZt61nu0/C8UfgLBmnrczUAlFHLkBiLqA/b5B5KEJtEIOkcmQL5XQqlM4hTVxc0grmjks02k0KUn09ZErFLAMg3Z1oVcs4uey6LUqtjDJJH38gy2C5CD6yHZ4/NMWfX4L5WiWM9OR8B0dHUXX9a6Gkl4KwlNBAKoIoEKxMJQAVChOYZbaBDIxMcHu3bsByGQyDA4OLnpfh92cPRd+98vwZ92A6akj/FyEvjykbDQ3INFnglsiqAONsA7Qm4rSw319BMUiiUIeK5nE2buP5vQ0Wl8f8tAh9IEB/KkpEAIvmUSvVfFFkmRtjNYZG/AcH33nQ2FjynGaCdxpOZNOp/nd737HaaedRrPZpFKpsGfPHqSUPbOcOVXEmYoAKhQLQwlAheIUZrERQN/32b17N5OTkwwMDOB53pIjiYelgEceACdM55Lqg2rY7Uu2Hypl6OsH04TxgyBLoA2ilcJuWpnpg4liuNb8IPJANCLOtuhfN4Q5NU6tEdnKSEnQaKALAaUSWjpNUKvhTE1jaRp+uQEmWHlJc4+D1BzE7kfg9HOWdL69JJPJxPWDvu9TrVZ7ZjlzqqSAVyoCuGPHDgYGBsjlcst+LIViOVECUKE4wfA8j1arRSqVWvINbTECsNFoMDIyguM4bN26lcHBQUZGRvA8b8lrgQ4B+NufdxzUAV2DNf3gu5BoQL0BWhpkVMPYYQ8TBFq8r3ZNoLlxCLtSQTQa+Pk+gvEwfRrkcshaDWwb6fuIIEBYFtJx8IcGEZPjBOuSaC0Pa4MgmMqFaeB5CMDjMQqu15Yzp5LVzEp0Gk9OTpKJusoVipMZJQAVihOMSqXCyMgIF1544TF94o7FQppA2je33bt3Y1kW5557btyE0HM7mWY9jAACpLOQsCClg+FBuQgISOVhOmoKSWZhMkzdykQaJidBCIJsHithYyYTSF3iRc0frtDbFwCpaWG3cfv4jQbJTetpTRejZhCBb2bRitPoazyCobUwsh2ecVVPzrcXHE089cJyRkUA50f734BKNStOBZQAVChOMNo3l15072qahu/7x9yuM+U7ODjIpk2bDhMIvUoBA/C7X4Ghw4Y+kAKmxqIXOr6StI6mEC0BogKANFMwbCMSGkYA2mSU/pWJ8HVNw50Mo3/CskKx2E7XSUl63RrSk/tgMEFVSyNJ4zd9zMADPYuWqMPuBtQqoTg9yVio5cxSI7vzYaWicwvtTF8o7X+TSgAqTgWUAFQoTjCOOjN3Efs61n7q9Tqjo6M4jsNpp50WC4eF7mc+a4HovA79FgouNMfA7/C7q4STPNB0KLYbQcxQxPUXIJNATE2i+S7UDWiEN2Rpp5CT04DAz/eHncKWibl2ENttYvf1YZsmlqWTLI6HRs92lmx9HH/LOpoPHkL26wiZhPo4bBmCkQfh9y9d0jkvlV58Bo5lOeP7PhMTE7Rarbh+sBflB22klCsWAVxqxHw+xwAlABWnBkoAKhQnGO0bZS8igEdLAUsp4y7fRCLRlfKdaz89SwE3q7DvF0AAVhamq+ELqT6IInek+6A+AYkEDAxAZRKcIsghhO/ObFOLxsRZGaAOgBdAZvMa7OokTqVEqjJNUvgkhtZgmh1feU54XfSggbF1AN/xMRoe2BKyLuz53bwF4Mkyq3cuy5lf//rXcQfx/v37CYJgWSxnToUawPa/JWU3ozgVUAJQoTjBaN/EljMC6Ps+u3btYmpqisHBQTZv3nzUm2cvbnjtfej7fjPT2KGlgEgAyo6vI12HTYPQmIRWBZyoo7cZdQ1LCfXwOQkE0yXQBNrGITLFIlrJQSbTMFGZ2aVtwuQE2AloNUOxWTCgVMYaEnjVNTA9BsMABgS7w+Mcx5v9cjdoCCEQQpDJZNi4cSNBEFCr1eIIYS8sZ1ZKNK1EDeBKRQCDIMD3fQzDUGJTsWwoAahQnGD0sgZwLgFYr9cZGRnBdV1OP/10+vv7F7WfxawFQNtzf+di2q9CaQqyWehLwuRB8ASYqagRRITm0MVSKMhSeZgK08Uy04dmGmheCaFLcMPpH4GdmakbtCxEcTIUdJlsKAClhGQB6hMIq4Cer8K0B2YBWgHYu6C4H/o2LOm8e8FKRRg1TYsbRaA3ljPtz82pFAFc7uM0m022b9/OOeecozqOFcuGEoAKxQlGL2sAO42gpZSMj4+zZ88eEokEj370o0kkEvNeUy8EoPBaaAf+B0wdjDRUK4CAXD+YQHMCSMxE3cyZ1C5WCkQULdRssCwYLiACiTYVeQg23fhcg0o1PraezyHqUTRwchzSaajVwI3OKbDQgjHYOgw+UC2FqeDyjhNCAC4nR6vP64XlzEoJwFMtAnik46zU9VSc+igBqFCcYCxHBND3fXbu3Mn09DRDQ0Ns2rRpQTexXtUAJicfgcADIgFoOjDcB0EA5YlQ+DkdXam1xszPpahBJJ2CFIAHzUlELVqXbkGpCIBM55CTobiUQkOvFMO0chvLDgXgdBEyAhoeWMCQBjsdkC3QklDcCVueuOTzXiwr5dE3XxZjOdNOF58KEcCVsoFp/9ufq6tZSkmlEv4x076m2WxWCULFglECUKE4weh1F7Dv+2zfvn1BKd+59tOLCGBq7KH2A0gZoAfQGoOWHtY9oWPUp8MAoJGCYhkQkOkPZwYPp0AQRvEAUoNQDaNQJPNQCp+XZhKohFNABvvCsXOdN9PpScj3Q2kaUv1QLsMAYW1izoCWAJmB6R1LOudesZw396V06M7XcgZgamoKXddJp9PLIqBOpQhg27rpaBHA2T8rFAtFCUCF4gSjV13AUkpqtRq+72PbNmeddda8U75zrWnJAtB3SU4+gr9lE2QNKO0DBCT6cUoHKZcrOEYOq1LGNE2MXIaEH6AP9EPaDuvxGgLsjnnEbseaOtK/MvDQT1+DaBUxJ0sE+QFoyZmIomnBmiS0auDr4exfMwuNFgw5cCgX1iBW9oPXAmNxc3aXykrd4HslnOaynJmamuLAgQMUi0WmpqbQNI1MJtNzy5lTqQZwocdR0T/FYlACUKE4wehFF7DneezatYtSKZykcc455yzpptWTFPDYduSAgcYEiM2AQCKplhs0SyVM0ySTzeN7Do7jUCoVKSd9rMooZjmNpQks20IrR1Yxmg7F6GczCdPT0J+H/iT6oYOImkDqJnqtiqYLSKZg/RDogChC6yCclYQ97ZnBCahOQiGAwTxU62AEUNoDA2cu7dyXyIkaATwabcuZ/v5+Dhw4wFlnnYWmaXGEsNeWMysZATyekcbZ/w6V+FMsFiUAFYoTjKVGAGu1GiMjI/i+z+DgIJOTk0uOWPQkArj3R+hBM7R7adQJZEClXKFZKZFJpUhlcohGE/r7SQ/2k5/ah+v4NBjCG5+g5fu4zRzJehHTtDALa7GCcni9cvmwe7gxiZAzTSQynUOI/eECgiAcNZfWoBp5CdoGbG5BqRB2/hoBkIGcAwcrkBVhHeBxFoDLyXKbNHfWzaXTadLpNOvWreup5cxK1uZpmrYiAvBIx1FpX0WvUAJQoTjBaHuzLfSLXkrJ2NgYe/fuJZlMcvbZZ8edmku9yS9ZADamEeO/BSDQM7i1acrlMp7IUMgIbMuCZD8UBHgTIHw0oWHbNrY9CH7oi9YSWbxWK5xmcWgC3Xcx1uawnXFsr4Vh6Ih6R+OI50fXJnpsC/A7zkPaQAk298P/jEO/hCAJXgnW9IHmhwLwOHGiNYEshiNFzXppOXMqeQ1CeC2OJGZVBFDRK5QAVCh6gOd5jI+P09/fvyCT3COhadqCIoCe57Fz506KxSJr1qxh48aNXRGE4y4Ad/+I0LIZmq5GrVjE0A0GBtdiVIswNAgigPpkuH2znZaV4UxeQNd0Up4LuRzSSuAmEniV/TS9Ko3pGg1AmgnsehnLsjATSfSoKxiATDqcKIKApAWeA44bNpVoTSikwbTBATQH1mtw0DghGkGW6ya/EpYi8z3GUixnVjoCuNws5DhKACoWixKACkUPCIKAffv2kUqleiIAFyK4qtUqo6Oj+L7PGWecEVt0QO+miixJSAYB7L47jMJJiVOcJJ1IkMlkEelE2OARTEGjbeeSCH34EJDIwXTk35csQKUGG9cgNIk1OY6VSpFKDSH1MVzXpWnk8KsHqFaruIGBXQ3f63kufspGr9XCfaUKUB4LZw/ngEYTtphwIBFOGEkACR9ME+pj0KqGRtQrzMnWBDIXixWZC7GcSaVSXcdaLk4EAahSwIpeoQSgQtEDejm/F+YXAZRScujQoVh4nn322YeJz15ZyixJAI5vx6tOhClfI0MyYZHduD5Mr1YOhNtYg9CI7Fz0LBBFALUUUAlr+nJpsGvgjIHR0QnshWk5y7KwjCT09REEAS0ri+c0aTZ9an5AY+dDmKYR1g8aRSwZIACsHDRLsMaE3DBMT0C/AM+EPg/GZJgGHj7vsFM72QXaiRQBPBZHs5yZng6bgXbs2MHY2FicLu615cxKdBoDcXPMkdbQiYoAKhaLEoAKRQ/opXkzHDsC6HkeO3bsoFQqMTw8zIYNG+a8MS2HAFwotf/5byoNFy3XR+BLrEQDgjGgU8R17LfTCLpehzWDYFahPg5B1LxRLbYXBpV2V7AB5WL0o0EyaSJP30JlYoz0wADW1MEwSths0qjvw8+kSUgXPZMiIXyMoIDoK8FkABSg5UN2CorJIwrA2dfnZGMlawx7LZw6LWfq9Trbt29naGgIx3E4dOgQ+/fv77nlzEpFAI9WAzibk/Wzpzj+KAGoUPSAXqVa2wghjigmO1O+Z555JoVC4aj7gaUL08UIwCAIODDyC8zJn5HM6WTzGSYPHAKSIEyoTUU7N6De/tkMxZ2UUOgH3QubQrR82JgBYBegVgx/TvTNvDeRh0YR1g+AKWF6HAGYcgpTmiTXridZnEYi8TyfViqHVzxAdaJI0yjhWRrpXBU9uwbbMTCaDShIWN933BpBVqpD92Q/RpuBgQHS6TRSSur1es8tZ1QKWHEqoQSgQtED2p27vUwBz/6il1Jy8OBB9u3bRzqd5pxzzsGyrGOuq/3epbDQ/TiOw+jICOkdXySTTpBMJBBmH4KD4QZGAYimeZh9YXQPwMhBwQ9HvWlmOKkDQHSmtjvPOUqTGToUEmAD/hiYQ/EWvpVEOHWwDEgkEc0GpmFgSg/yefKGiWtLmkYS3y9RSTQRe8vYhQpmMonZX8M+WEOTcmZG8SnGyS4AZ3cBCyGWxXJmJQXgXN3OoFLAit6hBKBC0SMW2rl7NGaLSdd12bFjB+VymbVr17J+/fp53Yh6HZmcz37K5TKjo6Mk679jrT2NaSbDKF9jGohS280OqxbXC4VVYQA0DZqT4AHkZ7ZpdGxfr7UXE/68YQ3IaXDGQfrR9rV488BMAi74LqQT0Zg3Ca4Dff2I6hRW3wCWAfT1kRsYxGnU8KwWjbpJlXHwdKr3/5T00GZyudyyjTObzXJHe06VCOCx0sy9spw5Wm1eL1FdwIqVQAlAhaJHLFcEsFKpMDo6ipSSs846K7bJmO+aYGUigJ0Rymw6wZmNX6C3b6BmH7hhlC/Qs+BUo3nAubB2L20AFai3wu21RJTmFaAnoVoOtzdSUKuFAm7NGghqUcQvC/WoW1hPhN3C7WiQ70L7XtqqhJYzk9OQy0I+AVUJWhIa4yA1dF+Q3JSBmkO2rx8vMU1rvY9/6BDj40kOHDgQ15a1xcBypmpXQjgtJyvh0bfQYyzWciYIAgxj+W+bC/EBVCgWixKACkWP6HUE0Pd9Dhw4wL59+8hkMpx++unHTPnOtR9Y/hrAzqaUdevWsV78FlEXYPRBEEXlMoP4dUlgp0LB55fASkD1UPi6tQYIrT0wOjqBjQyIxszP/RZkAtAEFOvh81oSiASglQURvVc3MVrjYHZYuIgWPNqGoAiWA2II6jKyKcxCqwVDLWhkwJEYCQ8jb5NOaGxcc0FXbVmxWATg/vvvJ5fLxf8t9Pd0JFQEcGHHWGxUdr6WM5qm4fs+pVKp6w+AXrOQGkAVAVQsFiUAFYoeMVfd3lKo1Wozgmr9+kV90a9EBLBz9NyZZ55JQR+Hh78OBGHkzV4TeulJ0PQmhlcHLx1G6BpRjZ+U0JxJ2+K6Mz+3u4KzubA2sDkNngAp5t7e6xC76QKwv3vBeQtMDVp1aNVgkw//kwzX4JvQnILBAAb6oFoNfQK9NKR/d1ht2aFDh9izZw+Dg4OUy+U4lZhIJGIx2DYrXiwrcYM/GWcNd9LrKOORLGf27NlDq9Xi4Ycfjj8Ly2E5o1LAipVACUCFokf0KgLYjjwAC075zrUmWB4BKKVkYmKC3bt3x6PnbK0JD34aZHQdjHwo/uL3WMh2ZM8cDOv2AMxCZP5MZAQd1guCDloAG/vAr0RpXhGmjSulcBP0ma5gCdTKMwuP1hEvO5UBfwL8wfaJhcc7XcLuNDginAccpJAFFzFZC4WmY4C9K9yfmLkxt6/vxo0bgTASWi6XKZfLh5kVtwXhQoTCqRIBXO56yeWeBNK2nDl48CCFQiEW/JVKZVksZxbiA6hQLBYlABWKHrHUGkApJQcOHGD//v0YhoFlWUsSf+01tffdy/34vs/u3buZnJxkaGiITZs2oQkJv/s0uG0BpoUNHm3MPnTvEdDtUJE59Y4jdKRM9SwYPhQKYBpQ3R9G/KxBoG0WnQcRjY2z89BoW8HkoNk+vpipI2xTsCGohtvoUVdvYEFyHIaHodQIo4x+EuwmMplCBDo4PuBAsB/0jUe8ToZh0N/fT39/PzATOSqXy4yNjcX1g+26slwud0wrkpO9BvBkjAAe7Ti6rpNMJkkmkwwPD/fcckZKqVLAihVBCUCFokcsJQLoui6jo6NUKhXWrVuH4zg0OjtfF8lyCMBms8nIyAitVovTTjstTpOx/yvgHoJkZMEiTaiNh2JPCPC8mZuVNdDh/adBvQjJLCQS4eOED3Ic/H5iAed3nIPsvDl2REqEHdYV9kXpYs/HcxphWjiVgSASjb4bjnbza+AS2scMC9hfgT7A08GrE6wfRK8SNq0A+DuOKgBn02lW3CkUyuUye/fuPcyKpJf1g/NhpSKAyy1S2sdYCQE4W5j12nKm/R2iUsCK5UYJQIWiRyy2BrBtmwLwqEc9ilwux+7du3sSnel1E0i5XObQoUMYhsG5555LMpkMNyj9Esa/G/7sV8KuX28MbBGaOxtD4LXw0wU8y4akBYlBIAhFW+MQyEqo5VplQICwoD7dXkEoEiEUlLXSzOIa9fC5vgFICkg1wiYQYw00JjESZWQ6B0O5sKavjZ4KBaDTCgWg4cJQAYQHjo9ISeSghyyaCLcOgQnBDuDyRV/D2UKhs/N0rvpB3/cXdazFrG25WKkI4EoIofnU5i3Vcqb9b1WNglMsN0oAKlYtjUaDWq3G4ODgsTeeB+0OwfkipWT//v0cOHCAbDbL6aefHt8EemUp06sIYJv9+/fT19fH1q1bZ25QrTHY888zG2kW+HXiyJ1mRB59HjolhFgD7sGZ7WVypmZQSxB38xoF4q5gszDTMGLmoFGZ2T6RgCEBFKHZYdbsOfEhROCCVuk+mfb1bVYhAzgN2JKAAwlohulpgY7MGwhPgJsBY+fCLthR0DSty4rEdd1YDHbWDwoh2Ldv37L4D66USfNKRABXos5wMcdZqOVM+4+quY6j6v8UvUQJQMWqpZ2e6aUAdDu7UY+C4zjs2LGDSqXC+vXrWbduXddNslcdxb1oAmmvFWBwcJAtW7bMrFX6cOhLM6JLytCqxZmaeSySoVFzuCKEXyOu+TOHZqaACD3swJ058MzPsuOrSiRAq0N/P6Q0qEU2MmbfjEgUGjTCKKEAgmQW3ApkB6ES1RE2y1H2OACRAbcKgw04tBacGkgN4WoE/VW0Q4Brgb0fZGvWZJLeYJpmXD8opaTVarF7924qlcqi6wePxamSAl4JkdmrOsP5Ws7s3r2bQqFANps9quVML857ZGSE17zmNUxOTpLP5/nkJz/JOeecM+e2zWaTJz3pSSSTSe68884lH1tx/FACULFqaYusXt2g5hu1K5VK7NixAyFEnPJd7L7my2IFYGd6GqBQKHRfq+nvQ/O3YAJ2ErR+8B2Q6bBuzloDrfF4c18vYAZlYgHodFi/mP2xWXRo/hx1AksZizmSKUiYkNWAcZADHavt+Dqz8uBOxw+llQDqoHUIdN+FRBa8CgQJ0KpABobCXROkwXGRQyX8ch7dlYAEfxcYj1rglVwYQggSiQTpdJpms8n5559/xPrBthjMZrMLrh88VZpAViICuNDavPky23JmamqK0dFRLMtiYmKCgwcPxuUD2WwWx3Ho6+vrGnu3VG644QZe/vKXc9111/GNb3yD17/+9Xz3u9+dc9v3vOc9PPaxj+U3v/nNko+rOL4oAahYtbS/yHs13ulYTSCdKd9cLsdpp512xHmfvYoAtgvjF7qvrqke2SybNm1i+/bt3Rt5RZj6ZsebvHAqh3TDbxYrE6Z2jTXghaPYdG8fUo9uoOYg1CfaBwS3oytYZIFG+HyyHywTjAYEDWh1+Pq1Oixf3NbMz1r3dRVE0USnCMk8NKL36YlQAPpa6FnoWcjCGExoCM9GOKHw9AYK6GNRdDLYASyvAJzN7PrBzrqycrnM5GTY3NJZP5jNZo/5uVYRwIUdA5bPaqZN2zNyy5YtWJZFs9mM6wfHxsa45pprCIKApz3taTzpSU/iiiuuiKOJi2F8fJz777+fr3/96wA897nP5S1veQu7du1iy5YtXdv++Mc/ZmRkhD//8z9XAvAUQAlAxaplOQTgkYSW4ziMjo5SrVbZsGEDa9euPab1x3LNFT4Wnuexc+dOisVibELdrm3sOr/xr0DQIbqMwkwED0BLgxPV8GmAPQBpk0BPQXYIfB1yQ6HICwwIvDDCJ4BAh2QBggoYJjTHIACMQWhFotHIzQg5YUC9Qwx21P9J3UC4ZTBTgICEDe0G63bNpuuGUUxPRyR8gsE1CBeE7SL8BEHKJZASLdDA3zmz7xXy6ZvN7LqyueoHO42K2/WDR/rMnQo+gCe712Cb9r81TdMQQnRZzriuy2233cYPf/hD7r33Xt797nfz0pe+lFtvvZWrr756Ucfbt28f69ati4WnEIKNGzeyd+/eLgFYq9V4xzvewRe/+EVGRkaWfqKK444SgIpVS6cA7NX+5tpXZ8r37LPPjrsDj7WvXqWnFxJNrNfrPPLIIzNTPQoFYI5mktpDULln5o16rlv86dkZ8QehIbQ7gYaDJj3QhsCLGkEEYPXNpGz1PDSjfUnCqF2boPM8EkAk+owc0LaV0aEx8x7fzgAd9YTNCTDtMGLYrgNs1kPbGDe8+fpDNtruqGPYTaGbNRr9/aT9cmgF08FyR57ms/+56gfb0cG2UfFcvnQqAriwY8DyC8CjHUcIwbp167jmmmu45pprEEJQrVZJpVJLOubsazfX98Xf/u3f8r//9/9m/fr1SgCeIigBqFi19FoAzo60SSnZt28fBw8ePGbKd659tfex1BvbfFPA4+Pj3VM9OnzKDhOA9dshUwBphd2znZdQSpD6rMfQ7gqWkjDt2kbPhk0jcSNJxzUy+8LRb0TvbxZnXutM+XbV/+W66v/QBdEC2guCdB6KY+B7M3WA0oJWNKUk7RCIMHApXBMhp6kUUiSnLDRzPJwjrBVmX8Kes5gIY7t+MJFIsGbNGqSUsS9de5xZu34wkUgAYdR3ufwHVQ1g744z1+dh/fr1Szrehg0b2L9/P57nYRhG/L3Vnm7T5ic/+Qnf+c53+NCHPkSr1aJYLPK4xz2On/70p0s6vuL4oQSgYtXS/oLtlddaZwRwoSnfI62tV3WAR9vPnFM95jC7jdfj7ALn4ZkXk30QlIH+MJXrC6gfnBF01iC4k+0d4Wt9EDQ7dm7PbCssaE3OvNYpJI0+aLW7hPWOiR+A07G/WfV/elDlsK86rxSmmxu1mTpA0qFwDATCD/AHbTTPRLjh79TUTBrpFOlgHIKdoF041+XsOb34AyCTyZDJZOJ0frt+cHo6FMrbt28nmUx2zS/uRVkEqAjgYo7TTv/OZjk8AIeGhjj//PO55ZZbuO6667jtttvYvHnznPV/bbZt28bf/M3fqC7gkxwlABWrluVIAUspKRaL7NixA13XOeecc8hkMgveV6eB81JvxEerAeyc6rF169YjWuJ0CcDaD2a/GtrBMAXIMAJn2kAunLLhBzPTQBBofhWIDKRFCpyJmV3pBWLvP6GB0xHJCzpuvEYBiISiZobG0KYNmRzYRlhjiAxfG+0Qim36crClAW4CWTZgRCICM0wHB2l0t0F9PRg7UohIXFpSUEk6JOo2ur8DjAvnvFYnOp31g5lMhpGREbZs2UK1WmVqaopDhw4tqH7wWMzHPHmpnEoRQN/3V3wKyEc/+lFe+9rX8pGPfIRsNsvNN98MwOtf/3quuOIKnv3sZ/fkOIoTCyUAFauW5UgBAzzyyCPk83lOO+20uLB6sWtbzgjg9PQ0O3fuxDAMzjnnnGPWEQkhwC+D21H7p6XBnyQ2fTb6wC9GL06APQj+VNh4QRavmULzqoTJ1QD0dGQaTVTv1yHW9H5wJ+Z+rR0ZTOYgnYW+KuglYCoc+9bWzIk1yAEbKh2/Y12HgRKQA7MKa8rIygCiFb3PT4A/gbQS+Lksev0ASIERtAjMgGn7dAbdnUe9Vr1ipZpM+vv743F1x6ofzOVy2LY9b/GxUhHAxf5bW8gxYGXmDc93DnCvOOuss+a0ffn4xz8+5/aXX365iv6dAigBqFi19FIAtlotDhw4ADCnsfNC6dUINzi8CURKyd69ezl06BCFQoHTTjttXlFGIQSG82PQO1LmIgV0ePnN/kqRke+edEBOotsthNWEfAFEX9jUkVgDng+eFjVvSMKoYse5GzkQLqT7wDDCiGLBBFEOU8jNyCfQyIPXETWUHrqo4q9ZC5VoLcMDoI+F1i864NfwHqVh3hd6TOMKhA26l8LpF6TqINwMulUCmaFpaDT8OknZmz8cjsVyN2h0HmO+9YOWZcXNJLlc7qi1raoGcOHHma8AVGPgFEtBCUDFqqVXArCd8m1/GQ8ODvakcxd6HwHsrE3cuHEjw8PD816rED6W++OZ6JowwuhevEEYOZs5iWzYLBFHB/vR2IkkMlTWTJBj4f50IDkASZcwOpgOPf+wCDtMbHDLQCOyl+kQnV6HfyAdQkTKsLZPgE4R1m6FyQrkojUGLujR6hKSYDiFXq9CVPOneSZOooFn5xGuhbAq6PQhRIP9lsZWOT5zbsvE8bKZaTNX/WDbbqZSqXSNMTtS/eBKibOTZRLIfI4z37IPJQAVS0EJQMWqpV1ovVgBGAQB+/btiyNpAwMDjIyM9Ey0tY/Ri30FQUClUmFkZGRBdjSdJBKP4OYDfHsNwnHBM9C8TuuXHHidzR0dc30BkIhosEfYGVzt2NbGb43RbDUxDAPD7kdv1/gdtm2aOOooEuBViYVY0DHpw8xA0BmdHIet60GLjKT9eqwXtcDGO72I9mA2rvnTXAHJOtXCBnLR7GFbmtREGan1M86v0Lho/hdwkazETX6+x9B1nUKhENsDtceYlcvlrvrBTCYTRwhPlVnAR2vOWI7jzIWaBazoJUoAKlY1x5recSRarRajo6PU63U2bdrEmjVrqNVCsdGrtC307gu/0Wjw0EMPkc1mOf300+dtR9NJMvEgQq8R2GNgg5A5gqAPzTWhWUd40zPxMGFGtYERWiqKFopQAOr9EMykaptugmpxP1IGIMELAhJmE8uyMKwBLFlHtPfud3j6iQzQITrd0szPxkykULb/N+3NWALKJmADDsLXIeHirRnAPHgQAoHmuQjAyQiCevhVaQYBQhPYWJS4n4y2acHX8URiqT6AnWPMpJTx1IpyucyBAwfYt28fEP6bGBsbW3D94Hw5VUQmHL0JRKWAFb1ECUDFqmYxArDdPNHu8k2n0/G+oHdRu17sy/M8ms0mnuexdu1aNmzYsKibhpQtEvbDyHb+V2aQIjRR9nUQiUGQLsIfRLQAV0dzD83sQEtD0E7VtjuCQSJp1OuUK2USlk4224cUCdx6CcfVadQbOOUqFkVM08K0kthiCr09Tq7zfmjkQ3uXmFnXzs6DmIpLDMP/SRMKwLCu0R3SMA4JhGw3q4ApAiqpFBnAkHVAx6AJSEr23UguXvD1nC8rkQLulYiYPbUiCAJqtRojIyMEQcDu3bsB4vrBdoRwMX+MzGYlI4DLzUIaWpQAVCwFJQAVJw3tFOa5557bZVK8FBYiAIMgYO/evYyNjdHX18eWLVu6vqh73bgBSxMA9XqdkZERfN8nlUodZuy6EGTwAEJ4M923IgnMpGVDn+cW0mghdYkgTSCzCC8JLQ/NKRGawEAgbfCnCKSkUqlQb1pkU5BOZ8Mon17ASDokk0mkDHBbNm6rheM4FMsS059E03Us08TSBKYu0YQIm0HiBckOs+noJpmIJoeY2ZnXpBm+7Iem0sJo4fQPY3sO6BUIMuhanem0TtJNh7WEcgApSgjyOMZ+guQa4JJFX9tjcbJO6dA0jWw2i6Zp9Pf3s27dujhdXC6X4/rBVCrVNaFkMSJrpWoAV0oAHqkGUEUAFb1ECUDFSYMQAs/zembbAvMXgM1mk9HRURqNBps3b2ZoaOiwL98TKQLYOdUjm80uOZIkg3ujoF17PF1H44XUgalYZwmRAypIAdKqIKxBPOkgggKutHCrAW7gUCmV8H2ffGEdKSOq1ZMS/BlhKfQ8ll7BSqVJp9Lk6MOrSRzHoenoNJuhZ6BpmuiWRUK4GIaBMFJRE0kHVpQqjo2fgSC0fhF+PQpMVqgMD2Id0BC2QHeTSLsEWj9T5pkMeQ9gyQSOaJIgSZ0abt8vCfgjtGX4Oj3eTSC9OoYQYs76wXYzyVz1g7lcjlQqNS+RcypFABeSAlYoloISgIqThl5P7mjv81gia2pqil27dsV+ee2U75HW14sv6cXuKwgCdu3axeTkJIODg2zevJmdO3fiOM6x33wEpGwSyAdo1++1BV4bIfqQdHT/kux6XSJDVxe9iEiWkcJgggbkcvSnh7EQyKaDCNzQ4Lmzjq9zX1Kiyxq6bWPbNhltEL96CNd1cFoOjdJBmtJHaBpayiCpN7GsML0oNTPyCRQgvJnd+62oEzhAyDTIOl5K4tkJTED3DHxbYpHA0RqM6WeRkQ0cwIyKCaVRZ4qfMchli77GR+NEmDW8FI4UZbQsi8HBQQYHB49YP6jrele6uD26bjYrFQFciYibsoFRrBRKACpOGtppkZWKAAZBwJ49exgfH6evr4+tW7ce1Z7heEcAjzTVY76zgI+EDO4H2qJJcrjA655/LERHx67UgekwOigljpNAUgrrwLI6mtAJgnFISZAZhJcF30Z4Hrh1aFVnGkuMPLRmzKBFIDF0HUNPkkwPI5slXM/DcRwavk+1Hq1RQFPYWG4LyzQRVGbqAP0KSAFCIvwEUqtjSYNaQVCogeaF52YBLapU9SFkkIhsrIsIaeG7feziQQ4xziCPYoDTMZhbqJxorJRH37GOcaT6wbYg3LVrF9BdP5jL5eLZtadSBHAhx1ECULEUlABUnDSsZASwLaaazeYRU76z6bV1C8w/Ani0qR6zjaAXiiP/Bw0tjOIh6az96xJ4zBUd7EcyjoxsaBwnjWlq5HM5EAIRtH+XAkkVTA/MVlhTKDNoQQ1JAqQFXhb8qGFACvBAaH2gBaClEF6AhcRCI+Ol8Scs3EaFaqWCa7Qol8LIomlaGFo/Cb2FbugIkQUqaL5OYIIhoZqukm7l0b2wNtAg/P82gimtiWA9HnWcQKemT5PAp8ohqhxiH78gySAFNpJnA0kKMx3MC2S5BdqJIgBn064fzGazbNiwAc/z4vnFc9UPto+znJyINYAKxVJQAlBx0tDr0W3tfc4WlFNTU+zcuRPTNDn33HOPOSJtOdYnhJiXR+HsqR5bt249rINwKRHAgCZF4xGQ/ZS1FMnAIEWAhkQgDkv/CpLIruigj+95lEplpPRJJJMEvgjFnzRAFmfeqxVAzqR/hUiBqAJNEE2wJDPp4DzC70gVSz3alrAZxCyjb9TRq5uoNR4hk/dIpvpwHAfHcSnXHRreNJqmoaVNkskmuuFBAgxc0DRK2Q0MFB8CqaOJEpAFpoEkgioCC0eroAmXJBnqVLDI4tCgxW6K7EYiyDJEkj4KbCDPBsz2HOQTgJWqAVyqcDIMY876wXK5zORkaDe0Z88epqenF1w/OF9WYtyclFKlgBUrhhKAipOG5RKAruvG+22nfPv7+9myZcu8Hfk799er9R0rcjffqR5LMbtusZ0w1AaBUafBAEXLR5PrsaSN6YMpiwi8cK2iw/hZ6rRaB6lUyui6Rj6/mWZzgpbfPqc+oMNIGqt73bLV8cgGWY7tY0L/vvZxCF+L35gB2QIRQHYMd30GS6uHBtOGQSoFOQq4FR/Xcag7Hp5XwavuRXgOuilw0lkCExxzEOE3wWhiYuPikKKfBg2sKM0rhMRhmiRD+ICOjUsdDQOLBFXGqDLGOA9hkSRBhizryLOJLMNHbR45mWxgjrT/5ThGZ/2g4zj8+te/pr+/H8/z4vpBwzAOm1+8FI73uLm5PgtKACqWghKAipMGIcScEbul0N5fZ8p3y5Ytix7nttR0aydHE26VSoXR0VGAY071WEoEsMlvOnYEkjpgE4gWTeHT1HykzGPKNFZgYMoiumwgpKRSMWi2StiWTTabRWhZEJ3Rwlm1g52pZRLhGLn270DkgA5BGHSaQWch6Jw40n3zlLaBpxWYqWMEjQq2aWFbFhmzD1/zcZyAqu7htCYouQFSekyKJBtLHnrex9I1XAEmLg3Ap0oiSFKXDTT6qTMTkbTIoGMgkWiYBLgkyOJSoUqdKmMc4H4S5LHJkmcTeTaSpO+wdPHJ3ASyVKPphdDf308+nz9i/aBt213zixcazVuJFPBKzRtWKEAJQMVJhq7ryxIB3L59O5ZlLSjle6T9LWcEUErJwYMH2bdv37yneixWlAY0afFwxxNp0Bu0o28aWQKmEQI8UUVqA9QB6Q9Rm24S1ByG8oNkkm2/5rChQwJC6l2TQISWA9lRO0g2TPt2rGbmIugQlDrG8HY3pYQTPjowWvhWcpYvtA9mAbwieDV0WyOZ0NEzOaTWRATD1Lwy0y3BnppBPjiAG5i0UgaW5SDMNUgtAOEi3QySIO4rMUnj0cJlZgpJigISH4M0HjUEGjo2TUo0KVFiLxYpBDpZ1pJnI3k2nhIRQFheATh7Ru9c9YOd/oPj42HUOZVKdc0vPpboWkkBOFfmQUUAFb1GCUDFSUUvI4C+71Mul/E8j4GBATZv3rzglO9slpJuPda+PM9j586dFIvFBU31WGwEsMWDdEbNhLSATm+9jggekoAanutRKk8hpUZuXYammcYjheXbmLKGJJrEQR8cxTpGyA6rFimAYse2fYiOSCKd22J2CUmJBmYVDAfpDyL8jvXrVnh60onmFreiTuAmKdMCO42dHqZecPFaQ6ScvVTdcLKK4xh4VoGm2cK3XDyZxBQGJgUC3K7jJ0jT6Fi/SRIdDYGGQQKXBglytKLzb1FhgoexyVLqq5Bw1lAiRYZhdJY+NaOTlfIZXAmReSRxZhgGfX199PX1AeEYx7YgnJiY4ODBg2ia1uU/mEwmD1vzSgjA9neb6gJWrARKACpOKnoVYWs0GoyOjtJsNhFCsHXr1p58mS5XBLA91cPzPM4888y4GH4+LFYAdqZ/JRK0Vsd+dAJm6u40cjSaE1QqlbBgP78FYYQpUZ86jpGigUPd6qPZKpDWC1gE6MEUSImIomXtfRPMdBajFUDORAtDQRgvrKtxBC0HwcwMYmnkEV5oUu2nshiVDgEoZoQapIAWWmASAEZ0ngYSobkEySHKyRQ5klQ9l4bTImjq+K0WUoPxyQlSoh9S+7EsC03XsEmjYRHgQGQcY5HGp44fiWeJJEEBCEiQw6WORKJj0aKCY5RoGSVGqRLgk2aIPJvIsYEU/YvuLo6vzykYATwWduQj2fYfbDQacXRw//797N27d876wZXyGoT51wAqFEtBCUDFSUUvUsATExPs3r0by7IYHh6OJxD0guWoAZyYmGDXrl0kk0ke9ahHLbiYfTFRyTD9+7v4sUYWoe2nfWoaeQKmgHB4R7XcpOFUsO2w3k8XMxV+7ehguBgI9CY1o0jNACHXYPk5TBxM30IPptDo66oV7PqakhKCjlpBkQ/TwTHdEVypz0TMArM8MwYYkLJCoKfR/Vo8Ek5EDSp6lEYO6xJ1BFWagEOTwEhhG4SasW7TLJlkUn1Ug0ncalibaJLEtJsYtoZpmghNkKQfcCFuEtGwSNPqqB00sNHQowYSDSlLmEEah3DySom9lNiLTZYAP04Vh93FiytdONkF4LEigEdDCEEqlSKVSrF27VqCIIjtZiqVSlf9oOd5tFotPM9btm5glQJWrCRKACpOKpaSAvZ9n927dzM5ORmnfKenp3vaqdjLCKAQgkqlwsTERDzVYzE3ucWI0jqPEJBHMI0gQMMKGzLicwv/f+AHlMslHEcnm8uQSCQQYnZ0MBt6/LXf6WXi90vh4RoaLapgCIQcxg7ymEES069jeFOzmkPSCDrG0DFbDLe6Hvn6TLOIFC2CxCB6MxSXU9nTGE8lSbdc+hst0u4Y+GFUMDSLzhCIGjr9+DRIMUSdGhp1LNI4NBFoSBFg5D0KFDD8BF5L0nBq1N0SQcsPawBFgUZyL6ZlYRgGpkhjYAEBGgYBHhZpXBrI6BwkEsNPIUUYHfRw8HExsON08QS/o8QefFwS5MmzkRwbyLIOfR5f7ysVAVzO1OlCI4BHQ9O0OOoHdNUPjo+PMzk5yeTkJOl0Oo4Qzqd+cL4spAlEiT/FUlECUHFSsViB1Wg0GBkZwXGcrikZndYyS63/g97VADabTRqNBkEQdK13sWuC+d/sJZJJfkETH8hjksJCEARpAkqE6d8SrutRLpeRfoJCnxmPXeuMDoaPQ5vkziN0H28mLRuIAEefwtEDMEEL1qMhMAIdI/AxfYHhO4h2baLsTONqXdFAiY7UoseREAmSAXoTGvZ6DqTAFCalRJOqnWFTZRPp1sHwmskWGn2RMLNo4KFH6dkAH0EVmxwN6ePrNUKPQImmG5CqkkzppOQgwrPxmgF1r0StXoN6DT1IYibqmJaGaVkIXZCiDwgwSUXRPolNmrK+DyEETcoY2BiYUftIBpcmJgkcakgkdSapM8kUO3CpxVYzoRn13Oni1VADuBTa9YP5fJ7x8XE2bNiAYRgLrh+cL0erAVQpYEWvUQJQcVKh6zqe5x17wwgpJZOTk+zevRvbtjn33HNJJmeMeHstAHsRAWxP9QDIZrNLEn+wcAHYZD9NDkSPwoq0GmWqukngDJGVG2jVJ6jX9mCaGn359WAUO/YgO36SBF0ROw2hV2lH7gQpAmqxNDHIIensDk7iMYmntd+dJ0hk0KWJ4VuRILTR/SYiEGgdncXSyIEodngHQqBN41qD7MpnQcjYg88XTUayBc716iCrIBwsadIUHgbhTTmgRooBGjTQSNCKRKjupTDJoGHQpBifixA6mgma2SJDgqzMIFo2zVadulei6bSQEhLkqKf2YZkmhmliahYWCQSgBTaB3sIkhUcLiQ80o9rBHB6tOB3s0Yqig2H0tcReakywl3vRMedMF6/GGsDF0D4P27bp7+9naGjosPrBffv2xfWDnePqLMs6xt5nWEgN4MkcAezv7+dxj3sc//Vf/3XMbf/oj/6In/zkJ7Hht6J3KAGoOKlYSArY93127drF1NQUg4ODbNq06TCR12tzaU3TFiRQO5FSsm/fPg4ePEihUIhT00ulc0TdfKIk09w7e2XtPeEJh7HyBA2nSCK5hnR6CE/T0QmAEgIjihKGaGSQHQ0emsx3mUVrpCJvwfYRRFd8UHaldKPUshD4wgMtjWvOCD5NDmA7aRLNErpf6ar/62QsvxZPc6L9z/zeDZHg4fw6TvcOoonJji/HChITgcDAQSdLAw+Jiy9cPKOGhkaTIiYJdCwE4OHgRdFNicQWKVqJMlYCTHJxurjp1Kk3azQadTTfwrY0jISGZVr4oonl5dHQsUjh0ow8BWe6hn0cNMxYzLbTxRp6lFIOCHAZ5yEqHGSEH5BigDwbqRugu7k5r1MvONkjgG3mEmZHqx8sl8tMTYVR8Lb/YNuD8Gj1g+1/o/Pt7j9ZWeh3m4p+Lg9KACpOKuYbYavX64yOjuI4DqeddhoDAwNH3B/0VgAu5svKdV1GRka6pnrs2LEjnlKyFBYyV9ilTKWr+SOJSyX075OSwLFo6CWymSyJZAIwqVEGNDTWYJPBoI5gCoGLRhK/QwC25Z2UYWBOMuPZJxFd4lGQIKBCu21DI0fQER2c3fARiBY1u0bN0kl4GzCly2xJEIgEJS0NhAKw07JFQ6ehw27tTDb4oMfp5RYmGTxcPNI46FgkInHXwPDStChHoi+sC/RoEeBHli+hOOu0gtGxEDqIVItkSicpBzC9DM1mg6ZbplarUAUML03DmkZrOlimha4b2GQRUQOJSx2dBAEuXse1tMni0sAiDUgcmtgk4+hgnUlalCnlygQ5D8GOo5pRL5aViM6t5DGOJjKPVj/Y6T+YTqfj7dLpdNc+1Ri4w6nX68f0OlUsDiUAFScVx+oCllLGXb6JRIJHP/rRJBKJI26/HAJwofs60lSPXtUTts9xPgKwyC/p9PfTsQlo4DgOrVYLghSFgQKm2f7qmNmnj4MTTcKVpDFJYmOg0YdgGtCQ2kwtoCCJH5lDh8fK0en3p5HG7xA1nbc7iYSuukKTgGq4lRA0TIe6TFOYFYytifX4Ioj35XfUH2rRebeEz//oG9giW0jKgIlGjhoarSgi6UVnbvkFmhSxyOHRwMCOaviCWBCG4m8qWmUaHQuJRzMS1gC2yNIyy+gmpEmRlXloGhTLU3i+RqVSQfMNDNPEsMuYloVpmiRErsNguoVDnRR9NCkCghYVBDoGFgEeCXIE+ARRVDAQDpIwXdykxG5+jEU6tprJs3FJs4tXMgJ4oonMufwHO8XggQMHDqsf9Dxv3gJwNfDwww/z4IMPsm7duuO9lFMSJQAVJxVHSwF3pnyHhobYtGnTMdNCvRaACxFtUkoOHTrE3r17yWQynHHGGV1/6fbKUma+EUCfFiV2ItGiMW06rixRb9Sp1Wpo2GA3MM2wfqwzOghgkImjfeHED4NalKYUDISpSTmNH5kxC9KILgGm0XnlJB0dvEj8js5inXRXZ7EgBx3RQY0CLVGmqW1BdIjKKS2JRzO2Uw4rHBP4NCO/PvCoo4k0OwTonBEJOh+bNIIAiR+mdCkwpY/hGU2a1LAjwWqTBgQeLgKB02lwDbQoIQnQ0KMIoYXTdS4GujBwkw2CZgtDNxhIbcFpNWm6NRqNMrVGHdNLo6VKWKaFaYXRwaQoIAniCKAWRUnd6DqHUcEMPg4mCQwvjSfCOsIwOihwqDPJw5TYwwj1OF0czi5eG+9zPqxUelYIsSICcCl1wrZtMzQ0FNcP1uv1w+oH29dpYmLimPWDvTjfhx9+mJe85CVMTk6Sz+f55Cc/yTnnnNO1zc9//nPe+MY3AmFU83GPexwf/OAHF2RHdfPNN/N//+//7XruV7/6FRdccMER39NsNuOo6bOf/ex5H0sxf5QAVJxUtCOAs4vX20bJruty+umn09/fP6/9Ha8I4HymevQqAjhfAbiXbZQpI0hikcL0DYqV3bTcKqlkCt3PU/fG4+3b0cH4OLO+TjrTiBIfF4+a1qSqJ0nJIZIihQ5RdNDvso4RmNFjER0rP+v1RJcAnN1Z3I7x1bQWyBQ6LTzRT1WE22ok47VrmPg08akjsMPIHhY+DtBEoCPxaVFGw8QkhUTQ7Dh+gnQk9CQ+TijiMJD42LRr7GRod9NOgxMgEHF0UMeOo22tDtFo+hlco4owJEksknINppui0arRdKpUnSqiYmAIi2qygmWamJaFrWWiczWj6sUaSfI0KREKvRquXsUM0ng043WGwlXG3oN1JvFocpAHEAiyrCPHRgpsOma6eKUigCs1oq1X5yGEIJ1Ok06nWbduXVw/uGfPHlqtVtwElkgk4ujg7NrBXqzlVa96FS9/+cu57rrr+MY3vsHrX/96vvvd73Ztc95553HHHXdgmiZBEPCyl72Mz33uc7z61a+e93FKpRK7d+/uWnuz2ex6bi6y2SxXXnkl73znOxd2Yop5oQSg4qRidteulJLx8XH27NlDMpnkrLPOOmrK92j769X6jiW05jvVo9cRwKOdY5FRJnkQCKNijudwsLwX3/coZDeRThSoVaaQwUy8z+3y+jPxupo/ErhRXRyATgq3o55PYFKPt89hk8fEQaeCoIROFr/DSoZZI9AETiz5JES1gjOPvfYcXhFQ0QYZYA9lbU3H+23aY+1ER0TLxA7NnqPawACXJDnq0f7C51M0aWCSxgx0DEcQ4MUm0zomAj2uyfNxsMnRpBJG3bDwo87iTqGnodOiEnX6CgxSGI5PYPtRVDbcyhIpHKuKYUGGNH3BGtyWR6vVoNEq0Ww20d0kWqKMaZtxd3FC5AnwSZDHo4WHhx4k8PQ6PiY+LiYJJB4aZpwuBg0nEuNhungPTUrs4adYpOLawRwbDksXr1R69nhO6OgF7frBZDKJYRicfvrpcf1gsVhkbGyMRx55hC9+8Ys87WlP41nPehabNm1a0jHHxsb4xS9+wZe//GUAnvvc5/KWt7yFXbt2sWXLlni7ztnojuPQbDYXfB1e85rXcO211wLhZ+LCCy/k4osv5nOf+9yc2wshSCaTS3ZAUBwdJQAVJxWdgk1Kya5du5ienp53yvdo++vV+o62r/ZUj0QiwbnnnntUsbpSNYAeTfZwR/zYaTlUixUwAgqFPgxTx0fQ0FwawsZjAAsLSYWACgKBTqarQSNsTOis37MQ1GNHFiE7v3okPgEt6oRTN4ZIkEHHxqCEpDrLDNrA7xCXGtlZjSbZru5hX29S9zdSEZ2fjc5Unux41oiuSRWdZGSuUiJBniY1DFI0qCMBhwY+Pp5VwyFJ2NqRRkND4iFwCfBIUqBBKa4JlPixQDRJo6EjCL3+ZqKDoKPjWhUMXUcjgUUGgYbTca4maTytCUkfO6lhUcByczSdOk6rTqNeph7UMWQKPRnWDlqWha0nMbFANjGCcA3hXOK216GHQ50EeVpUsMiiRWsWkVAFcKgzzU6mGMXHJc1gnC7OMHzKRQBXYhawpoXTY/r7++NMRqvVwnEc1q5dy2c/+1n+8i//knPPPZcrr7yS973vfYs61p49e1i/fn0cWRRCsHHjRvbu3dslAAF27drFddddx44dO3jmM5/JK17xigUdK5/Pk8/n48cvfvGLOeuss9i8efOi1q7oDUoAKk4q2jU4tVqNPXv24HneglK+s5lPdGwhtAXg7BR1EATs3r17QVM9VqIGUBKwhx+HkZ6oLqlZAiMdkM32ITQNiywtSrF485FUKUf1c3lskrj4hF8nHgK9KzoIeld0EN/ApYgW9ehqWF3RQoFFPRJMYGMwiInExEWjiEEC2RUdtKFrlnA413fmAkBZy+J1/j46RF+A17HpjOWNGRlYCwQBjUggyXgLHRMhWnF3iobAx40bRQIC0gwQRGlglxoaJhI/bm5xqUXRwRIisnqRhNHAZlfXsIlHAz+KfJoksEjh4cQrkojQHiZqJkmmbTJyA9LRcZwmDadMxamgewk0s4Jp6zjCxdB0TPrwcbHIEuDhUe+oCwSHKjoGGgY+LnZkfB1E7SwyimhWGcfDYT+/RMNEt/LUMtAQW8gw2LPu4k5WIgK4ErWMEJ7LXDYxtm1zwQUX8KEPfQgIvUJ/+tOfMjY2tqTjzb5uR/q+2bJlC3fffTfVapXrr7+e22+/nauvvnrRx/3kJz+56PcqeocSgIqTivYX1sjIyKJSvnPtbynj5Y60vk4B2Gq1GBkZodFoLGiqx3LXAEoko9zFBA8TBAH1ko9Xs8jmLBJZIqNkPbI7iYSylOH4s0h0+Hj4iCh6Z2KRx8JGxNFBMMnixmJGIIJkV8WeTjpuwAgfd9cWgkaDEg1AkiRBAZ0MJg00JtFmj3/r2FebqkiRwoxTuz4zFjF+ZKwcehB27mtGGIb7LCDQ4mS0jqQuq/F7dey42UIiSZGn0TXn10JDQ8OOZFOdJFmakfgNqwcbGFg0qISTP7wUpjSjzt22LY3EwKbGVJxQt8igY0a+fzLyLLSRQhLYDQwbsmQZ8NfTajVDQdgq4UkPmjaHgnA2tmVZmIaNKdJIwCYXXZ/wnNxIuLZwo2kkNUySUR1laJDTbiYJ8KhrB6jnm/xWm8COuovDDuP1S+ou7uRUigAezZC+899vX18ff/Inf7LgueCdbNq0ib1798azjds+pBs3bjziezKZDFdffTW33nrrkgTg0dizZw9f/epXOXDgABdccAEvetGLlv26r1aUAFScNHiex/79+wEoFAqcdtppPfli6OX83s6UsqZpFItFduzYgWEYnHvuuV31NPPZ13LVAEokO7iLCX6H54Uj3XANskMpdNvAQ2CSxMDEo4zERQiB7obec20sMrSipgIQ+PjUKCGRaGSxSEIUswIXEEi9DrS7G7XDooXurGYQr6vW0O6atqGxDhOdBFbkPWhFY+c6GlCkQUWDDKnYZ9CnhY5ARv3Foeh0IluYUOK5VNCwCQgwGGC6I6WdIkOVMo4GrrQwKRB2Cod2Kzp6lNINMbABBzcSlRJJkrAmL0keD4cAHw0NlzoiWqNE4pp1AkSULjYQaDSYiqNp4SxhP+4k1jGxyAJB3MgRHq9AQy8hUmCndGwGKR9qoJmhfK3VK9QrLTRRx0hMY1k2lmWS0HMEeFHk1Y5Sw5m4mcSN/BAtMjhUsUgj0PFxEdLHN1oIsjjUKbGHCR5GEpBmkBwbKLCZDMML6i7uZKVqAJe707h9nJXyAVyzZg0XXXQRt9xyC9dddx233XYbmzdvPiz9u2PHDjZu3IhpmjiOw+2338555523pGP/0z/9E+95z3t4+9vf3tVMcs8993D11VdTrVbjP6JvvfVWvva1rykRuAwoAag4KajVanHjBMDQ0FDPvhCWSwDu3bs3nuqxdevWo04AmAshROyYv5Qv+9kRwACfXdxNhQN4Dcl0dQqLFNmBNJoRiUUCBBq1SDAZZNH9NAETgAaRjYlHixmxJaMUoRMfJxwjVyKMf2URgSAI3DgCaJDD60h1muRwO2oJDTKzHqdwO6J0OklaTNMCDDaQwIR4jF2IGwyABrKjkSRs2EjRnlLSFoAgsUjg0CSMrKVwcCl2GEabWF3izvLtSAyFJMhQp4JJCiP6ipU08TrEX1hbF+7DjY6vRcYwOlY859fTd2HGgrSOHQkvDTMS14IAN448htcoEaWPZZwubo+SE1GsUUQ1f741iTBMMtkMhWAA1/VwWy2abiVMF7sJsEtYloll2RimQVLL4+Nhk48MqJ3I/zAUoA41DBJRA4nE8FLYIkvbl7BNO118gPvRMMjFs4s3kqAw73TxSkUAV0KAtGsA50MvxOinPvUpXvrSl/KRj3yEbDbLzTffDMDrX/96rrjiCp797Gezbds2br755jhTcvnll/OWt7xlScf91re+RaVS4Y//+I+7nv/rv/5rKpUKj3vc47jooov4j//4D374wx/y1a9+lRe84AVLOqbicJQAVJzQSCkZGxtj7969pFIpzjjjDB588MGepWxheQTgyMgItVotnuqxmC/rzuaNpXzZd+7Ho8nDfIey3E+tVqPeaJDW+ykU+kALI0aSoGvUWLQXWnoVx3RxpYElkhjY+JSROAgENoUoGhhikaYVGRK3o4MtrUnTkDRkmhSZ6CZvAy0k4HdEFyUi7uYNH4PXtSbR9dijQRmbJGsxOBg/X4nmDnt4XZJCdAjCzk5gvetr0cMhjYwEYNicIWh/+gxp4ehTEM3WNbFpUSOMjDXx0DCiySAmqUjg6bH4C49nIwkio5wwymhHc34NP4Wph5NEdOz4dyLxovR72GgyM4JOi8Ro1HyBQMeMrWbC/uxcdO0aUUo//F25WgPsANMW6GQZ8DfRatVpOQ0arSKNRgvdT1BOlsN0sWlhGUkMYUaJ7dAMOxSw7RF0Dr5Rj9ZVjiejeDjomPF1CPAoc4Aq4+yKzag3zitdvFIRwJUQgAuJAPaCs88++zDbF4CPf/zj8c8ve9nLeNnLXtbT4/7ud79jcHCQDRs2xM/t3LmTe+65h7PPPpv/+q//QgjBS17yEi677DI+//nPKwG4DCgBqDhh6fTKGx4e7vqy6JVgg94KwEYjjMS0Wq2uqR6LYaEzfI+1nyYl9nMnpeAA1XIFx3UpJNZgZiROPJ9XkKI/SvnZcSQqwEWK8BoFUUKyFkXudNKYUR1fWB8Y+tv5UcoX2obLNlJE9iz4SAyqlAhNZPqwSNJsFUF4mKaBKXJdzSMm+a7HYfSwc3RcmJ52gTzD6BwCUtT1IOqebdFdMTX311/nNGKHJBXsqMYu7B0OcDDJhi0QQR2kHp2fHrVFzBjUWFFdnkCEgg6bepQit0hjYCEJusyiE1HtoAA8vY4mEmikAaKom0Mo4JrRtQ4NnnUsGhRpj4kTaICIUuYiOuMwhduuJ9QCA8vLQ7RtuGpIkqcVpYsTKYOkXIvmWrhOi4ZboVarUPdtND1sJjEjQRimi11s0kgkddlE99NxxNSlEV2HBA61uLM5wMPHiQWwQ40Se5ngd0hklC7eSIGNZGaZUa9UBHAlRq/NtwYQTu5RcJOTkzzqUY/qem7btm0AXHXVVfG5PfrRj+b0009nx44dK77G1YASgIoTkmq1yujoKL7vd3nlxWnME0wAdk71ANi6deuSxF97Xe19LwUhBK3EOCOJnyNcSaVUA9dkTX4dpFqxYAkbHJI0OlKuNjkEIopjhQ0GCZmjJTrr9HRa1KJuWgOTJCY2PpW4IaGd8mx/sesy0REtFLiyRalSot6qgBQYAaSMJokUmLaPoetxp2mb2Y8hgYiETQmHPGvwsCA+ToBOKo4yBh3vl3TOjIvEEQkOoBNEU4DDhhYNP9o2TYqqXqSpGbRIkCYdzf8VSDxMdJodtXo2mUjYiVggB1H1XruRw8COmm7aC9PRMbqes8jg0Yrm/BKL9FbcSR3g0YptW3QsTJKIqF4viKOZEt23cRIlnOhaWKTRsSNxGbbFaFgIIfCtOpoVnndfsAbHcXBaLRpuiUarjOGmIVHEtqx4VJ0RJGiJKgly+Lj4uOgYcR2pQxWTZGSerWOTQxIQOj3OGIHPpIt/hY5BlvUUonF1fuCja4uf0DEfViICKKU84nHm+g44mQWg7/vhaMkOfvKTnyCE4LLLLut6vq+vj9/85jcrubxVgxKAihOKtpDat28fqVSKs88+u6vTrdddu3Ds+cLHojNSOTAwwOTk5ILr/eaiFxY1EskBcT/Ta+4j4SVoVZvomsHg0CC+EYoKixQaFhI/6kgV0fNpXKqxQAwIMFoZpKQjOmhHHaoz9W36YdHBFAFeFJGC2fcyGQTUpn2aQYVMJoNhGARNi7o3RbnmISoGCWmRTNaxky6maaCLFF7HHGHQu/zxBFDGwyWHEJ1RQhsi8eHjdXQCd/gGUgNMqgwQdAynS5GKp3+Enn4zoixBkkZHFC9JigpVNJLY2BjocYSUuGvYjLtqIWzmqDEdyUMLixS65yKtmQtmkY2jhS1cJAKbTFSTl+v6XbSjaaHgS9CMJpWYpCJBrzNljGBHcdFw4glxulhDj+xemNX4k8XRKpAAKyEwKWB7WVpOk5ZTp94oUa810aUNpkOgBVS9KWw9iSZCKxmbbCRObVxq0WcsdF1s/7HQbnrxcdAw4nSxj0eF/VQ5hMePqA60yPjr6EP0tLu4k5UQgCvVaXwisHnzZkZHRykWixQKBXzf5/vf/z6JRIJLL720a9vp6el4nrKitygBqDihaNf7rV27lvXr18/5ZbhUwTabpQjKzqkeZ5xxBqlUisnJyRUxcD4WPg6j3Mkko+i+jVsRJBN5soUMvjYjXkJT4woBPjomBgk0zGhm7YwvniUzVOwD0Ug0DZtsNL83iAyEg2jUWLlj3yIyTHYBAyETiBZIGRmfuD7VKQdfr5PP58OZtpgEpo9FHzIIcBwHrykoelWCosQINFK6j51uYdsGuq6jUUB21QeCoJ+A7q7roCN16NHEQofIYkVgIPEiETtMie6uab+jESQUg+X4tc4IohbV/IXH86OpIUb0nIZFaMIcxA00MmruqMTiOzyeh2vUEcJCC4fARWIubMIJbVoysSD0aETdxhIdK6rRbKFhddVzhl28WRpMI6SGKdPY5CMbmhnDbSOqOQzT9eFjM7bsCdcAkCBLy6ggDEikTFJyPbg6rtOi2pgmoElpvIYwq5i2Ho+qS2h5fMI5xGEcdMYWB4i6ocOZJO0GGNCQuPh48TX2tDoNbYwRvt+VLs6zccGzi4/E8baaOdUigM985jP5xCc+wZ/92Z/xv/7X/+Kb3/wmY2NjXHnllV3z0EulEjt37uQxj3nMcVztqYsSgIoTisHBQZLJJLlc7ojb9DoCqGkarusee8NZTExMsHv3bmzbjqd6tPfTS/++xeyrQZGH+W9q/hTlcgnf8EjpeTJ9CYSQUUouNG3uTLWFkTpBM0oDtxsXQFLS2qazMooO1qN0Xdv3rl07mIhr0sJU54wvn4aJk6jRFBayaVCb8jANm/5CGmHIuFbQb0faNI1cYphWokRa9uN5Pn5Tp+pVKFXBKAoss4FpT2DaAtMyw+5pBFXCibZhGjrExemQAwKdRDxFJEx9ekCGfeQxcGKRkSTZEQUTXREx20t0dSYnSEbRtpAUaRpdTR9G3F0driCsJzRJRfuVWCTjiKaIrlu9I2VukY0semY8DMPOWy+upwvXkseJTJ3DJppmLOwApPCRuLjU8XFD70HsKK1f7kizh96DzbiZJFxDmM6d8R7UsUFIfKuBbkUNNbU8ub4sTrNFo1Wi0WxguGlEshQ1k5iYpoklsgQEkfegE9npGLGVTYsqBsnIwseIrptEcxv4yQYyup2F6eJWV7o4HwnChXQXd7KSEcAj1QDO5mQWgG9605v4z//8T773ve/x/e9/HykluVyOv/7rv+7a7rbbbiMIAi6//PLjtNJTGyUAFScUuq4fVfxBb5s2FrO/o0316OVkkcVGAKfZyQ624bUCytNNPD3A8NKIlIsUFn7UcRpOeqhgkYnqxVxAzur+lThRfZ+Ohe4msYMCrj4j/gAS5LpqB8NasraBsY/ExyZLTZsGBI1mk1a9RSKZJpW38bTQcsXCxo9Sgm1R47SFjxCYpoFlJrHQCQKJ6zr4jRy1RgMaRYQWYJoWljFMI+GE0cEgidTakzdaGMzUE4qOKSJhd6rBXjZQj8Rxjjwtiszc+nVSZAhww2kdvoUny7EPoIagxYw5dIJUl/gzseM0cnRSuLRioRm6/eWi6x16EOpBosvmJfRY1GNBqGFjk4aoonDmdzLTTNKKIofhrN+ABPnQDNurIO0gioCGqfBQ6E3HqwlH1WlhxJC29bQejYurxNfOIkPYItNhSeOncIwiIpHATmhY9GG5ORynTtOpRaPqGujSxmiPqjNNbCP8w0OL4o5uFN1sj6rzcPBwwrnGeoOkzJEghxfVGLY/wz4eJfZTm9Vd3K4fNJififzxTgEvRwfw8aSvr4+77rqLz3/+84yOjrJhwwauu+461q5d27Xdrl27ePazn32YXYyiNygBqDjpWI4U8Hz3d6ypHr1q3ICFi0mJZB/3sY/7aDYaVKtVDMtkOLeBickJzCCHgYUfRVZm5rmG0xxCESCiQnw/6pztqAEUHiBoiGkMjDg6KIB6R6dpOELN6YqSJegDfHTfRiJp1lvkUn3YGY14xlxUAxe2o6SxSKLRnsIRRPsuxLVgmiZI2jl828eWKYSXQbYatLwyB2p1vEYVXdOxXRvsgCCQaJpAJx2bS8uOaFAATHJaXFkokZRwyDBIE2iSpIkbJYwDIECgUzc9SlHELYNFCwMNCwsr6pc2kLHJtOhIq4NNuitaaJOm2uGLKPw0mBZmNHVD4pMk1/Ue0OI0e9hdnMIiGc3tDVPMRA0+biR2XQh/58LHkImoJq+BSSoWjSBxqUd2PmV0TEzCKS5hQnzm9xtGFctRF3ToRWiToiHHOj25w5S1WUZEo+rScgPC0WlFo+pqTpW6Z6FF6eK2IAzTxU5HurhGIhqhB5JAa9KkghVFUa3IYqgdCW0LaIcaRXZ3mVG3p5NkWHPEdHEQBF2pyeWgndVYDSlggFwux+te97qjbvPOd75zhVazOlECUHHSsRwp4PmIrPlM9TheEUCPFju4i1owQb3sUHUrZOw82UwOX2sSGC0crUKaIcCIoysutWjW70yUKowO5mhRxooiQB4OUhr45gGImgZk5FznR55uYaepjkO1qykkrAucwvcDStUimm+STw2SyYZp1YAgmiJRoTMVHRDQpEYoL1IY6LGACfcdxqjaU0owJcJMkGcjflDFdV0cx8H1PITnMTk5iWmaBEYe0/YwDJ1AtH9PggnWMtYlCdvXQ6cSCYgkFm5HBM8XM6UDiSi6F84ODo2yqx1CLc0AHh4mKcBFhy4hp2PF6c4ZBC29Hs0GgTT9BASYUQpeRMPlZiKIoQlMNY7W6dik0DDxOiKrbWNoqfm4ep1mFBV0aZKIOnHdKF3crgsMYt9BP5obnESPJrqE59G+lhITmzpTeHoDEWhzjqoL08Xgd4yqs4MNNFsNnFYzShc30d0kWpQuNi0LwzCwRdjwYpND+GXaAredNneoYkQNSlpXd7GMo4jQObv4F+iYZFkf1w8mO9LFK+U1CPNrAjnZxZ/ixEAJQMVJh67rKyoA2zMy5zPVo92lvJI1gHUm+R3foeFPUy6X8TyPgdR6kukkgnYXqMT007HAaHeIWmSReFH6zMHDxYqiOdCe6hBG7aTw0N0kpkxhxjfzcG0+LibJjtrBdFQ7KGgwjeu4lMtl9MBCECATDVrRpIgwOiijG3i4vgRZWp1CCx+XFgE+Fjl0tKhWcabDF0AnS4kWumYgbIFlW5SDcjiNIqXjOC1K1QpGo4Smadhmk6QVMGFeyD49G0m0atc+/Y6IXRItTnDamJRFZ3pX4HfIx+4OX40mDfwoRash0BEYZDExCPDQkF1NGElyTOqTcZrSJEGdcixeBAbJKO2qR79TmwytjmaSdrNII+7KTmKRIpxz3AIpaY+Ja19LnxbhpBA7jgi3I2lhlDE8S5dG1CxUjpo1Qk+/znSxlGE8V+LTiM6t3UwCQdfv2CZLS6sgkhI7qcfp4marRtOtU2+UkbU6RpBCb5tRW1bYnS4TkdDL4NCMyg+cuCvaoxVdm3oUWTaicoD2Z11E6eI91JlkNz/GIhOlizfiyiYpbf5jHBfD0WoATyUPwE5arRZf+cpXuOOOO3jkkUeoVqtkMhnOPPNMnvrUp3L11Vcvad6x4ugoAag46dA0Dcdxjr3hAvZ3JJHlui6jo6NUKhU2bNjA2rVrj/nlK4RYsQjgJI+wl/uoOlOUy0WkgKHcJqTdwImicAIN08uBITCiMWPtkWOdBsQGCQwsBEZ0I63FkSZJQCBcPLOOkEORiW8YHXRpRVMdZiJ4fiQ6XVnHqbvUSg4JM0+qYDFZGmsPqsAmH9ebQVhLFqb5XNqm0npsNeMT+sPVo6hlnQR9eJQIDZET0Up9kmS7bFl0UiSTgmQyATkN4Uhc16XZ8vmf6mYmzSam4SOMBMmEh2EYCEFUaxdG1ySyqxbP7EgXGhhdtX3JWandBCnqs15vUI6MT1okyVClih0JQg1iQSaiuJ6M/m9mn2nqHZHbBPnommYis2f/MCEdTuSozjR3+EkSfl8Y4aU9s0WPPicz0cjQysePvAdl1FiSidPFoZl1LU4Xh80kCQzPx5NO13Vri8b2GsKReYmooaXdFx3WlbbMCroJaRJkZQ5aOi2nQdMtU3GqaL4RClyvhGi5mKaJrWUjP8ZU1NlcjyLapfjzE47jy+FSi6xuQmNtDaMrijjFKFOMMj58kIq+Fo3fI8/GJc0uPhILTQGf7PzqV7/iFa94Bbt37z7s/O6//36+9rWv8eEPf5jPfe5zXHjhhcdnkac4SgAqTjqWqwlk9si1SqXC6OgoUsoFTfVYiQigJGA3P+Wg/DWNRoNqLayJGiyswTda8QyOsEZPxzcncLQWFlkSkblzEE3skEhssnG9n9c2fCYXpWfT0ZQGD8NN0RJlTEwcaugYaJiRnUcOogShxMORdarVKs1mk1x6EDsnCAIPzbMxZYYEaRpM0vYHFGjRNIuZZpIEeSSH+9C1xVaDMhoGFmkasakyNKmhRZE1AI8A2jdsEZCwCxi2ye8y52F5koxbxHEcxpoVCtU6mB6WZZEz8gS2j6ZrpKI0aptWx5qSmDSjiF9o4dI5rziM/rXR0A4TZU703lb0fyY2HibSy6AH6cgmphi/xybdJXDDaRoOTtxxLUkxgOzoyjYwIwk5Ez0XgUbTKCNI0fYeFGi4HfsO08XNSFq34t9B2ISRi4yym9Hc5PC82ubWvuYQTk4ZRItuN63/n71/j7Ukvcv70c/7vnVf133p6/RcGDvjCzjgkwSHoHMRBwL2SSwIjpLwjy2k2CLBCEUmSiQioigKSIkEkon5gUSkIP6KjIQJkc4xh5CEQwiXOL+ExHY845npme7p7t29916XWqvu73v+eN+qVWt3z7i7p8d2m/W1bPfau1ZVrVq1q556vt/neXrtYito8Vhz4tTOys2WboCYPWY+UijqKMOPbApM0FymLApm82PqumC+WODVMTJyYpIgQHmSSEydCKlluXMHjJfd/kgUEh9D4z5T053H2lkDFf4pxzzPDf5PJMqpi9vs4slDqYv79SAikMedAbx+/To/8AM/0HmnfvjDH+ad73wn58+f5+joiC9+8Yv8yq/8Ci+//DI/+IM/yO/+7u9y+fLlr/Vuf8PVDgDu6rGrt0IEApsYpn6qx3A45NlnnyUIggda36N4Yn89BrBkzUv8J070i6TpiqIoGIYThqMhhTM9VgSEDB0kWoIQaEw327cpm+FrJ90sO2hhWLKlBlb4+CYBcnw9ACon9qg6dqdVFjcUqCYkPVmRNzX7o4uoQd1tT8sCMOScdikVLTA5yzoVHVtlXI5ssgWe7Bp9chp8QioHgKzSdQOSKlUgmNBm+mZc5L9yhQrFOU8QezFxHGOMYVAaiuoGZVlyms7J/Qbf81AqhrjC8zxGIiFnjTAxupbUBCj2wZmRNOQOYttYtPUZk+i+Mji+6/fDrl1aqZJaDFiwRjlTaevAtwGYtoUbbx2XgAHrHrMqCfFIsA1t4/ZrSO0fgTN7No5lLV2Ci0/iVMPVGeaxF1VHRutt2LaLKyc+aVlj2y5eORPr1E0pjhzbJyk60QluO5qM005M4hNiI+02xywkoVJriDX1qmA4GDP2DsnLFXmVslovSdcQNEOWybUuu1gpa7tjz/uhO9+syrkdi7C5zTHG2eJIEmSzxNObc6+h5pSr5My4yu8RMnS5xU8weQB1cb9apfH9gLvHHQD+7M/+LLPZjL/6V/8qv/iLv0gc323e/RM/8RN87GMf4zd+4zf42Z/9Wf75P//nX4M9/cauHQDc1WNXb4UIBDZP4C+//DKnp6dcuHCBK1euPPDF9q1kAFOOeJ7PkjULlrMVplQcjJ/AGzQd+2WVoLFLdHCt1SYmMBENRTeEb7MoIootZmnYY85sMy5wgoFaFjR+RilSRhy4lmBCRU5FTsyUgjl1VXNncRsQnNu/iAoMXmc0nCN1QClSQsKeb12FpnapJLatV2wZSttoNpuUgbMcgQaoqNDOsy9m3IGn3DGUQgiEEXgMKZkx4zmuc6UXR7ZhXIQQrEPBXji1zFYj8cuCoixI8xnNWtCYIbfDBh1HNDQIFEc9S5wRARkNNjgOZ699iMJgE4E3zJbCc0IXt/0eG9h+l22/vKFhzdoBxIaAIT4BHlD2WFPlRDt9+W1A1AOdxjKrBmQVdyxwv13cslkFqWu9K0JiPALH8rXN2m3xBeDAjx0kUHVEo/LOcshuvSFn2al4fSIUQWdh0/c/FLCVXey7/OTazYPS7fuQMliiXFTd0IwRRWDnB+s5y2KJ0AJfxqh4aQUlvo8nInuOIN04w7rncWk2opxGYlRFyAFg5x/tzOuiO05HfJEZr1CyYsA5JlzhgLeRuPd8pXojq5lvtBbwb/3WbzEYDPjUpz51T/AHEEUR//Jf/kt++7d/m89+9rM7APgW1A4A7uqxq7eiBQw21eOVV17pUj0eNn7oUc0AtvvWXvyP+AIv8/+jKHIWS5ure+7gAjpYYxNbBwjX5mwzZwEHggylt6Bm7IbwE0BvzakFjhHp+/vF7GFokDQYUWAMBLpVDQvXapbETBAYmrXHaXqHQMXs7e2hPZsy24pELKYVBM0QD+mG8TeqzIKViwKbo9wcmQWjfs+exLbtWuZSdKkUwv08oXACFQsUT917Ar7Mn+c6AzctZ2tFQ+xAcVuCMSFralURxzGT+AIrI1nUFVVR4pcLytSCkpEWrNJVxzJlYtPujYnIWHUN4BEJK2oChoRIfIRTFbcq42020K8iqrhw35f1EWwBbklJ7QQZDT4RER420C3v2fK0KSMbNlBRkdt2qF9iiIi7dnHo1L++ZfB6RtCgOnWxIsbHCi/6DxAeEQ01hpqajFpl+HpI6z1YU1CSEzvluWUR886ayGYcD92n0A6AbR72JMoJWmzkoNQhXjGmGmYOCtulfBFRRmkXVec3MVXeUJQrsnxBlmUoHeAFKV6oCIIATylCMXbnjbXFqSmJzITaOwKxAbEBQxoqIiadyMTO1K7cOXWbmpxzvJP7raZp7hsAPu4M4M2bN/nmb/5mhsPhGy43HA557rnn+PznP/9V2rM/XbUDgLt67KptAZ+d2XvYai+6L7zwAlEUdakeb2Z9jwoACiGodcVL/CeOzBcoV5AuSsIwYW96QK023nINFcLdjNohfNt+y9GqQBh7rDyCLubNmi0PHLOz2gJ/dsi/Z+5sEryqxKCRTpRgbUAEhVmQrlZkWcbAnzKeTPGkR01GRWEH+llaUOEVFHJBxAUqMteqNi7uK+mpMq2KUzn/utDNJLaxYJtcXkWEbclCa2ZsPfgKJFl9ketmwstcYuUsbIpeS7MGgl77GGCG5hwhigk3CRkjWYsVgR+w50doJLrRLNMltc6o8op1tiapfUh0p1BFbbZjGSXbui2pqBBkWA2vZMyAgAqDR0LN2s5pqhohwu79Z4UHVlxiQUlO3olJJAMiQpSb6WzBn3Gt9D4L6Ztoq13sEeMRg7N8MW4OtK8urinxCLpttzYvxs0Dds6OdUITrLv8FYMgYexY6pFTEltI2bZgS9Ju7tDOBA7cO+XWQ0DjzLKbcImREzxnS3NWHe4RYpRGDEqigU9kDlFVQlnk5NWC1SpltRL4zQCVLDu7GSEFCXto0zimVHWftZ11tF6Lnpu1FZ2a3iPiXfwVJ5q5v3oQs+nHHQAOh0OOjo6+8oLYeNB7WW7t6s3XDgDu6rGrszN7b6a01ty6dQuwxqRve9vb3rTj/6OaAQQwfsErg99G6DXL5ZKiLBmN9kiGMQjdmTa3MV/tgL8FgRE5CwvydAJGELpki357UCI6oNcCRzujt2nBekQ0oqIJVlRCERJ15s6FXjNfzCmqkv34Et6gphYratr5tCkGbW04RApa4Oth1+LNXXpDazdjh/UzBJKGTUvQmhG37OVm/63kYknEIQWKHEFJwk2sce9SwKmAKeAhqDHO986wYcVC6AFAjeEVxmTd7/upJ4I1IJVkqELWas3BwQF1XePnmmW9JE1ThIbUO8UPAycoGVL2MpgTIjIHWrVzDlzaNSMZMWZA0+QIHbjjONxqF/s9AGZLdJF0umsXD8iAkDE+CgUULag3BqEltSgJ2Jgce4RkPQAVO760b9Nz1oy6cfm8DZUzwbZ50jPxMkq035MgYrh1Xkl8FGGXNlKSEZK4NBGNQVM4pi1j3nkP2sSPTRpKa3fU2tPQY8Tb86MFr4EYUAZrVKAZMGCop4jcJ6tSsnJOXiwxxhCLCevkOr7nof0cISJCx3S3x8JzIqua/vd6wDv5f+Fz79bm69Wfphbwt33bt/E7v/M7/Nqv/Ro/+IM/+LrLffrTn+b69et813d911dx7/701A4A7uqxq0cFAPupHgAXL158JHFPj4oBXHCD48M/IGhCijsZpSg5HF5CxpssCmvcO6Fi6dgGScXasTxt2oWhkSVS+xTMnN1LSIPGUN9DkWqj31rDYOk8/1rT41ZMknNCXTcs5nNM7XFh/DR+LDvAZhu88RYbIwlQdYQwdgLR5s/aFJJti5UhDRqfoLPmCB1j1CpRPQIUERWCBp9T1sCUNTUSmw1rP4MGN+cXIUgd+xfiUziVsD7DrFlwsYGZFRvDZ9P7d2ikhUQCAt9H+TVTpjY5IpcsmhlFkZNla8p6jUlMl39rPN1bp9naRoNmRcHKMyAVMMTHJ8CnZo2mxMPfYi0ThluAMCQh68BzQY3nGF4nJjEaUWdoaecV7XsGDqC1kW9tVF3pvj/ftT3LblbUOhqqzozaWvZAzoxGajw9wGeCQnU+ke262rSZzT6PAGMFKuTUFERMO+avIqMicyzcGl8PUFWGR+TGCdpzSDtOPMe4GMONUfm2CjmUCXkyJ0QSsoesQ3QOWWVbxWtjEFqQpzWNuWm/Z6UIhAWjxjXLNRVDLvAO3o/Hg3vXvdH17ButBfzRj36Uf//v/z0/8iM/wuc+9zn+9t/+2zzzzDPd719++WV+6Zd+iV/+5V9GCMHHPvaxr93OfgPXDgDu6rGr9iL5ZkBWP9Xj7W9/O88///wjndurquorL/g6ZTDc4k94hf9C5a8o6yVSKS6On0b5srsRblpzm0F05dpRmoqIsZvH0kgyamm5oJoCiedabqaLzbIzdBt2xtrEyJ65c4QqE8J6Qh7OKIuSxXKJEh6HB+fQXuakB8JZwrCVbNEKRpogo5QeNsN26tgbSensNiL2OgaqtaRJ2HMcmdeZKUObvuuxJkMgCKhZY0HICGnBnmjAtb/7Wm7l7JMBctiCgIEzQQbLGuZUTjgDeV+BK+ru3wMCMrc+KSVREmAYYQzouqEuC4qqYJWmZI1k4Z8SBiF+4DPyh5Ryc87ExJ2tjD32fpdGYlAM2HdxboqK1FnNbNhBu9y2UCok7gBiRkZoYtZBQ2SmeIwQ1E4Ys2kXR71Wuz1moWsX2yU8YgIiNCW44+ITbb53A40sEQjH1rXJJNIxc4vuOPsMegIT+7/t9956G9rzddCBxoolWpYYYTr2uIVjNUV3DKyIpG9UbsVGws0U9qc/Pc+nHKbEhMTmAib3mB2folXNKrVHwyMkCHO8UOL7PlIK9vgm3sZ3OQP0B68/TS3g7/3e7+XHf/zH+bmf+zl+4Rd+gV/4hV8gDEMODg44Pj6mKJz3pjH8vb/39/jLf/kvf433+BuzdgBwV49dtRfJh1EC3yvVo61HObf3sOtqqLjKf2ZurrFarS0rIDzO712mluvulm4FEvYGZm+4WefX17IglUuGqKhQJoDGpjTY+TlrigtQsnSt4Xk3R2VvmHLL3FkLjVE1hZxjVoZsXpME+4z3EmrZBytJN+/XggSfmIYSI3rMy5l2tEARMHJAz3ctPeUUzTP3vtY4OCZljXZAY8yUJWtKUhLHAiYYUkCLCoznjtuGSZE99e+KxjX3WvCxuYkPEZS0UWyySwbxEORiw8CJ3ro9lAOlNup47A/JfEFMhNEGv5DM6zlFWZDlGUVdYGLd+dcJJUA4FbAQqL5SGevwuOwAX8SYEVCiKKhYOzZwA9zu1S4uhb3JNqImZeXUxYqIIR4WEGddpJw9Xq2vYrsOOwfYtmHte+1xbgEYhHrUaxcbCjL8zpA8xndguw/+2k+V9XORkQRMAN15GwoT2OMkKhoHKX0Gbg40cg8MBQLPjUPYKp3YKOe08x5sP2OfsVZCUXsNdbhmONkjVAfowqMoVmTljHVhj8e4uUKi3002LhgMvIcCaE3TvG7C0DdaCxjgp37qp3jf+97Hz/3cz/FHf/RH5HnO9evXAXuN//Zv/3Z+/Md/nO/93u/9Gu/pN27tAOCuHrt6WAbw9VI92vU8SgbwYdaVM+d5Pkuq77BcLimLEr8Z2igkaTrRhrWp2LRCLVMzBTTCCSZsGsakA3q1t6Y2DT4Hzq9v4qxTqq04rNbw18KZ0rXkrK9eTY5WJXmuqeqayfCQYCSoRYZP4obh1dZNu4V17YyhQKKqmLCeOK6uBRcegqC7+dokiBECdSZFwgaorTh1c2GZmxObMWDKijURjYOFORBRywbZtDfQzY1U079JCwIiCrctm71rl/XZTAeG0HGaCT45IIzEZ0CNxCdEY7M0oHag0EITgYehRkiBjAUjRhhjMI2mLEuKsmC1WpEt1yzUHD+0fKXUomvl2mMgu9lBu+eCpTPxBlAMUQQETKlZW19GvK12ccyA1ZavXtyzz8nccQYYEhEgaNz84OY9QWeDsmEMG2o3v2fnRlUxxAysgKNNMgl7tjGNS3xpnNegFZPY1n3mLH/asg8Ws63XjTHIJkDgs2EH7fxgmxsdMnGAz57LFbmzP2pHJKz/pEdExtKp5KOurdvO9wlASR8TF4SxJGQfWYeE6wMGR89xe3mHm6/dQinFeDzu/nu/UWZ/mlrAbX3f930f3/d938dqteLFF19ktVoxGAx49tlnGQzuX0Czq4erHQDc1WNXD8MA9lM9nnvuOcbjcfe7+83cfZD9e9An9hmv8BK/y7qasVjM0cZwbvwEs+yISlXUjqcIXEyaOcOUFFtzdqob3LdMSY40PlKL3rxV5vzXPGyG6sjOVBFvWcHkLIgYU7JGNj6qSqibioPJJWTctiwtcLRpHLOOtbNzd2YLNEiUZRG9OZLImTtbP72ctLvhhwxcZJfl2+zwf0hJScHKMUYLAgIgoiCnZt5FwMVMycg6JXSi7fGqnacdwN1Neh+cYnbZO76mx3q1/7ZK6jFzIzkSBR77WybWUzxSdwwtuK3cTwUTPMvK0iBEQegZ1t6aKIkxWuOXPrNq3sUdFvOcoloT+K2YZMxabLaVkLDqAcSAiNTti0Ex5JAa49rFKySCvGe90zGNveqri1fURAxInRI7wENTdGkfm/f0mT5blb9GKpv9G7rIt4pV9zCj3Fxie75ZEciInHk3twcGCWfOo8DyfWJF468xMiLiAAsCIwoH4UP3ECTAsdLWALqhdqryxgFkn4oV1lKnwD4A2VlbT8eoqiBwJtft/CDAgfcMbxv/XxFj+ze/Wq2Yz+csFguuXr1qj0sUdWBwNBq9Lsj7Rm8Bf+ELX+Cll17i3Llz/IW/8Be2fjcYDHjPe96z9bM/+qM/4vbt2zz77LO88533b6ezq/uvHQDc1WNXZ42b36jaVI/r1693T5ZnUz2EEI/UuuVB1mUwvMbnuMYfk+c563mBJ4Yc7B1Q+gtEjlVrIvEIuxkmywSOsbFd2ya8Br2ltIyYUJg5jd4krW5HvxVOZTpx7OCQ2rWSrSffgqosWSxuoxWM4328GHzGWEhUOj+41H2mpmN0bCsuwXNSjIoVRlZgrH2MzYW1ljQeCo8YSUDOsjfDZpD4zttOEjOmcFm3BgFENEzIO+5t4JriMEZwCgRGo4GCBuOMRzL01gWw6oQiYcczGQxFBxUNttF4kReIOKAgFcp9F/SWgnWPbRuiWLnfWoc84cCh3fcBHoKEEIEnK2RUMogGDLFWGX7iUYuK9XpNuk5JWSFD6exKPCq5DWX7Vj7W5Fl084NWv2uTXIyx339sBlvm0z5Bxwa21Tjg20bVxQwp0EQMO4PrPhsIOEPvPs+qWDlzcuVYNtmNGdjq283onmH0mgUBibOa0Rhn/N0+aAWmFa+0oFsSYc28PUI372pVyO32rNLcw+Zfyy4qDvdX0ba7K1YYoanlyj2EDTDAHk/xTfylrkUuhGA4HDIcDnniiSeo65rlcsl8Pmc2m3F0dNQt0wLCJEk6MPeN7AO4Xq/5a3/tr3F8fMy//bf/9r7eo7Xmwx/+MBcvXuSP//iP75tJ3dX91w4A7uqxq/ttATdNs5Xq8cQTT7zuBfZRe/fdz7pqCr7M7zAzL3ceenE0YDqaUMkUiYenB9AIvLvSFixTp53ZiufYNNuCs3N7bZ5vwZxGrahVgTUfsdmoNsPXsjhhz56jci3AVpFZrySz1YJAxdSmQns52t242xg3e3OdOCWopOm1kivWKCaULJAoVJPg6YFLh9jMIhpn6ty2iwMGljFkw/607V5JhMceM1bAmgDlIIxmyQX2KAlZ0ip2pVOmagwBkgpD7f6tHWhYYwgxrp1oa4CyamljCNnjRRG47A/It+bhmt57vM7vz257U9uA0oaQrd2E4RqICMgR+MQkRqDKHG/okyQxGJClYlktbMu4yPFqn0W06HwHYy8hE1kHShQe67vaxSsHcQRlM6IyEZEbHTAut2S7XTzcAoR9gNiu2yNAMiFAIahQCAfi2/PVdyIOB3ao8RGsWWAnLhM8gi221W570lkZtWpkC+gafEYYXSLLklpaP8G2AoZdvrDdfkxA4s53axwu3CNHn7n1nHWLjc2zSSCeTtBqAWLoHpeWXOLP8jTv647zvcrzPPb29tjb28MYQ1EUHTt448YNrl+/jud5HRh8EAD4KOr555/nwx/+MEdHR0wmEz71qU/dxbT9x//4H/kn/+SfkKYpUko+8IEP8JM/+ZMPDEA//elPc/PmTX74h3+Y973vfff1nve97318+MMf5l/9q3/Fr/3ar/FDP/RDD7TNXX3l2gHAXT121X9ifr1ar9d8+ctfvu9Uj682A5hxypf4/5A3C9LTirKEyegc0VBRCwtlDBohBJW/dk55MRIPibc1nG8zGkIHnNqItwEKz7FywioRtCQgIe/NUVmQ5dEf7veJ7Qi/WVrFap4zCvYZjUcc3bqDNIEDl5MuN1ZTUbqfVU6QYq1NrGlzC+A0DVrmGDOgYN7tZ+Oas/0WovV6053th3CgImCPNRmGUwZMWJFRsiRhjzU1YzTXCZkyZMB1QGypdWMEFa0VjCLr2o+GIQE5inb+LwJyA5qLHJN004UjcNDbgrq8bw2D6ODTWcAXu/ZvWwlBLycEAjxybLjdDFjLkEpEjEgYCEkQGkahj6GgaRpUrpjXc2tXsl6TNwWmNaL2AxIVsqb/2WPW/Sg6rUhVToGPtf6OaTB4GCpWKNQWOwjcNU+YMOraxSU2rcQyyVOockTc5pP0j9GwxxiKTpFeOcFG6M71qgc8TTfjt3LbWlMZTePVSCICYgy1E3JsBB82U9h30Yjt9kedEn5jiB455rruTKlDRqxMiqpjZ06dcYX38iR//g3B39kSQhBFEVEUceHCBbTWpGnKYrFgsVhwcmL37fbt2zRN07WLX2+c5FEwgB/72Mf46Ec/yg/8wA/wmc98ho9//OP81m/91tYy0+mUX/7lX+aZZ54hz3O+//u/n09/+tP89b/+1x9oW//u3/27h7Jz+ZEf+RF++Zd/md/4jd/YAcC3oHYAcFePXQkhujSQe9Xx8TFXr14lDMP7TvV4lPnCX2kG8IQXeZHfoagyFosFGs25/SuYyCZ4WN87O7+2UNcwjWXSanI3HzVD4Xfsm51v6nu32ZttxhLLrsQEtaKslxSkbGw3bNpCG/dl22AToKFqCuaLGVVdcZBcRg4qyzj6a2oMEy5j0Pgk7obcttdaS5rKtWNtlJtNSLD5rRKfWq6dLcwaQ9S1eyOnArZsXdEBjZLUGQyPaRzkBKhZEjEgpyB2shKPHBiQERIxIdELFrIVZIgtKxibrNFn5EJOe/NwysCaJ7huYp4QfQCzgcyRxvGLtnTv3xGKsgeWojMAsK8cbo/bpgylqvGxmRwLjGtyJoSMGCiBN6iYotCmpq5qqqKiqCuWyyXGwFKleJGNOlOeoup9BjY+2L19F6QOkCrGRMQYCozL4w17iSvt8St6r+37PCoKMjJyr2Jg9shQ1mOPhsZ5+fU3HvbmB63gqKRxs6iW3Q6wSt6Nj6DlYRuM1DTChtuFDMhYd2ISK/jwt9rMAkXjmDy7v3a2to2+a7+RwGUjVyKj8dfUIuMp/hxX+HO82ZJSdswfQFmW/I//8T8IgoCTkxNu3bqFEILRaMR4PEYIQRiGHfB7swDw6OiIz33uc3z2s59ltVrxwQ9+kJ/4iZ/g6tWrPP30091y3/qt39r9O4oi3vOe9/Dyyy8/8Pb+5E/+hIsXL/Lcc8890Pve9ra3cfnyZf7kT/7kgbe5q69cOwC4q8ey7sWyaa159dVXuX37NgcHBzz11FP3bRT9qBlAY8xdUXUGzav8Icd8mWoNs2WK8iUXJ09Se/YmWrLuDJQbCgI9ompKbMSV6m5amtraqmBoKAmcr5nNT816IMNaIVfeCY2/RjFyTKJPyaIDf1adObTmzlXDfDFH1j4XJk+gIkPpUhaEUfh629zZWtKEtLN62imLWwYQcJ51CRIQ2sfTEa2iuXQtSfv5qy4iLHDWHAUrt1xB7ZZNmLB2AEE48UrJkpg9clYIB26f52meMP+dOYYhwkHVDegSbLfctAmQSHwT0OiAazLijlum7z8o+iBPb4O49Vb7VG3JIuoz3nyrXqvYxyOn7mBRbAKO5eYcSghJHTNV0BAQcUINjBkIj1Eg0EFGxRLTaGQhWdSLjh0MdEAapW520MczHqVXMWSTEbzusZENDUsyGscJhiRYR0LZMXN9sQjgAKJjGA0YDZXKES69w75njLXuFhgKJ0rpzw+aHmMoqLHnf+FYb8ta2+9kJey2hRDO/Nx6BbZKZDsCscJnTOufaQ20+21xaw6tqbE2Q9a2qDW/tg9zgifFowF/96r2OnX+/Hn29/fJ87xrF1+/fp1PfOITLBYL3v/+9/M93/M9fOd3fuebMq1/9dVXuXz5cmc7I4TgypUrXLt2bQsA9uvWrVt85jOf4d/8m3/zwNs7Pj7mW77lWx5qXy9evMj/+l//66Heu6s3rh0A3NVjWWcZu36qx9NPP83h4eEDPSU/agAI27YOFRlf5reZmWtdWzWORuyPDhBSIVy0lGX1dNfmqtUKU9l12Lk8G5NmEzA21h+F8zWzZtA+HokblpeULBACMAZNgzUzPsWygwOnxBTkzCjzguVyiScj9g8O0N4ajWV1AoaopqAWhVP54uYOq55Cs7Wk2YBUsFFk7TKNv6aUDSMuY2icICWjoSRkSubad4WbIYsYuyadBBpaBXBEjCZBO8hcIUhoyDBMgRMq5oTsMQFOiTGsEN1sIsBZzneuB3ypPHCfxJCEbaCd6VJD7PfZZ/E2580Qj7y31j4bqBBkW+KQgHWvvRrjU/W2EXyFFmMfdtrgvZAVHop9BsojTjRTQmqT0dQNMpMsmgXL1IKmWCcICVVdoTxFJKKt9nBC0s34CTdaMHOvJQMGRFRoJIEzgt42nzaA30RbxyAk3jKWtjIgD58Dxw6mbkxhAyo3c3rCzYOuO7W30AGqxqnjy62jEjJyCSKCwj04hAycsfQUm6ZTO+avdvtj/8427WKJ18RM5pd58txbA/5gM88spUQIQRzHxHHMxYsXqeuan/7pn+a3f/u3+S//5b/wyU9+kiAI+Nf/+l/zgQ984KG3efb6+EZdi8Viwd/8m3+TH/uxH+Pbvu3bHnhbYRh2iUsPWlmW3SXc29WjqR0A3NVjWf0WcJvqoZTine9850P5Rz1qEQhsAGDKba7ye6TNMbPFKU3dsJecJxhsYtPADrxrp5q0cVo1gR6RyhMayo7xa9up9qZXO7XuaAOwqNA0jsVICRhRaw/qyrEk7Q1YoKmtFYbJydOSbNmQhPsM9kK03IAVj4iCJY2fOp+0xA3jF87LzYpO7ND+jH47OmKMpt7MGhpB0Azo23pYk98ptkUbOybUI+hF2rXHqKQGElaUKDyOsV57mitMOSVC41Nx6pzwPi8v8GQ9w3NQtnH/D/30X1srvclvTQRdQzABarHxBcwdkAao5ea88RFbE3N9wDfAZ9UDSPIMwKu2wJPZeq9VLRdb7+2zhwrJ2r1uXMN2QQMkhGLEwBcov2SfgKapKKuSMiuhhjRNSdOUlbdGRVZdrJTaYjztNjZsujVukazIMChCJgwIaMixfK/GM4rayxGi9ZI03QNLWzFD1iw60Yz1UzQE7INjfBV+x+gBrs1rGcOGitrP0WhKSgIG3X72U0Y2gqilO9Y5AoXCJiR7DKid+KncamlrRuUTyNUTvJXVXnvu1bEQQvCud72Ld73rXfzoj/4odV3zxS9+kWefffaht/fkk09y7do16toC39Yg/8qVK3ctu1wu+dCHPsT73/9+fvRHf/Shtnfx4kVefvlliqJ4IDVvURS8/PLL99yvXb35evPBp7va1degWgbw+vXrvPDCCwyHQ9797nc/tHnoW8UA3uZ/83l+ndPyVY5ntzCV5Pz4GZJBvNV+jZz3WUnqAJomZg+EQGgPg0G6FI+CJQ0lBQu0a/+2+anS2cXYFq9lPUpSjGgwskYbTcgYnwSfAZqaWttZxDRfMBoNSfYVyIaAAQEjZ6SbWuZMWKCn8Mg4cQpfn4AxCfsb0QnWisMnIuOUgiWaioAhXjVws1atcXEIBBQsyJlTOS+4gKFjaEx3nDSSGqvAbWgcE2hvmiENX2Kfmn2Mu4kPqChEQJNPafm+ordtKwBxoMTArWYzLxr38NlAbIDLiE2z0jeGSm3Om/4s4BAfvdVu3pRlFDcAzwK4vrAi2AKEFlyZ3utwC0wlBFvQKukpmS1E97iN5IQxtbrAOHqSYbKPwTAej9mP9qmpSNOUk5MTFreXHKfHlEWJMcbt3/asXxtVZxnZijU1CxpyYgR7KD0B7Xf7mTDcEpTYc2hb5CGRlJSkrEipURxQE+EzReIjUVTOp68tv04cQBSUZBRkFOTgZvtCRsRMtuYAgc4epmDlGEfpVOIjAkaA5Dzv5Fz2Z1Hy4TPH76f6DODZOsvMeZ7Hd37nd3Lp0qWH3t758+d573vfy6/+6q8C8Bu/8Rs89dRTd7V/0zTlQx/6EN/1Xd/F3//7f/+ht/cd3/Ed5HnOZz7zmQd636//+q+TZRnf8R3f8dDb3tXr144B3NVjW4vFgrqut1I9HraklN3T8JstKaWb9/sv3DGfJ89t/qvnBRxOL1GrVZeZGzBE4m1FrknHSuTMqNUKLSunWlRn1LrW1qLPIoYM0Wg8l6Zg2cExubwN0qZ51OTETClYIquA+emaioZz4ycgak1w7fye9WWboYgssKxWKBltzWxZZ8HKsTI281c4v7Y+C+O5ubwmSKllSMKUkJHjOvtGxsMtM2qfBEVABZ3hsY+kQaFpGIKDFWsEE24RcsjKSUusLvW4OMcFcwIiQGOIkBSuSR7gUdIQmojKbG7AQe9eHKA7vm3T8ITECCoExliz6kr4CMbkKAyStWNHbXO0RjlYaHMrajyXNJEQsNzy4pP0G2ZnFadnm3VnmbXmzOsWiBogpcYQMhMRqT5gT51jMHIqWF1QVRVyLVkUdn5QIBjKETq26mLPU8QkW2kkASFFL5kmpyYVFWvlETPBY+AYRWvBYt8TbQHAmMEZgYkkI+selGzjeYJNJampWCJ1SO3Pwc2LGoxL+bDnSc4K35mh++4cbpxNTUE/XUW5edqqm/3b52m+ie/kmr7+pubt7qfacZb7AYCPygPwF3/xF/nIRz7CP/2n/5TRaMQv/MIvAPDxj3+c97///XzgAx/g//g//g/+63/9r6xWK37zN38TgO///u/nE5/4xANt64d+6If4lV/5Ff7xP/7H/KW/9Jfui9F79dVX+amf+imEEPytv/W3HvwD7uor1g4A7uqxqzRNWa3sxftsqsfD1hupih+0aplxcvEPKSQUi4pypRnGBwwmCbXc3OCUYyAaKqyXX4LvrChaM1wQqHKTntBWzJ5rE7ezV4aYadd+rbrlphg0AQMwSzCCSNgs4LIsWSyOUFJxOLmI5yuki4hrXLBZy+jZdAQDytDInIDz2Hgvm/lanYErpcsCtu3iEFBOdOJYRGMVxJkzBha0wDHasI2uJB45GR6By5C1JsAJQ1IaCuYETLB5yBMKKk55gglzZpQgfDIiTJ0Q+IYS4QCgvel6DgDWvfav/Rh99q5/bvTEINqnqaYcmacIhNpq0x6gWTp2coDghDaFRHGAxyklNlxuSO3mG0MacHkn3TbEtrBEIrp2rz2PJOmZ1/32sHfmddsuNsZQS0MqjGtNJyRyTBwaZFjhGZ+mqSnKkiovyVc5qxUoqRh4JUQG3w+QUrhjuNlGTMTC7XMt7NmUuWi+hBDfPZy0Fiz2qG63nEOSLYAYErHqsXiSmFpXyNqgiByQnnQPIvZ7E91cYkVJRUnEkJyVe/iS1OQI9/9tDdjnOf4f9qHrARI6HrbeiAE8W48KAL7jHe/g93//95nP51s//+QnP9n9+xOf+MQDg7171fve9z6+//u/n1//9V/nu7/7u/mZn/kZPvjBD97z82qt+cxnPsM//If/kKOjIz74wQ/yF//iX3zT+7Cru2sHAHf12JQxhqOjI65du4ZSCt/3Hwn4g0fXAl7wGs8H/2+qcMZyYde5N72Alwg0BQEDZ19B56EHrZef3wGi1ualaQLW6gaGTWvbDre3dhhWyNHO47XOHgIPv5etW8kC0XgEZgBGUKwrFus5kRowno4xsnSgMcN3Hmw2Js6ycTYiLsOoCiM8SlaEDClZOZsOy0a26uKWHayx1jZWwSkJGODVBb7c5A9DCwGtzYeNfkuQ+BiEE4VYwGCTQKzvXEnKiAlLMkbAMTBBcxvJHcYcMHPHJAFfM1tcYnxwRAl4PTDdKkoXOtn6LuseAOy3Y0tqpJHU2XleMTnHas0BihGmg8ESszXvlyDo32brM63h046zs+xaTkDEkKrSBPUQQjCsEYgtNbBdd9hL+rj7dXyGXTzLNibG74DrmhpJTEqDFFNGns++pymTJQNdUFUlOjek9RK9sH8voQrJwgw/9O0Mm6BjA9sP2H94WVOQMGCNQDEmIcADyp4/pYe3URPTvn8b+IQkLORNKt8+HkXsUSLwmVC7B5CQwZboxEb4rVwL3j6MRYxclOGYNiH6XfxllGuja63f8uSNN5oBfCtMoL8W9alPfYobN27wB3/wB/zwD/8wh4eHvO997+Ppp58mSRLW6zVXr17lD//wD7l9+zbGGL7927+dT33qU1/rXf+GrR0A3NVjUWdTPZqm6VjAR1FvFgAaDLf4X1zjD6krjapitFdwYe8pm1Xqbl6ly1PNmbss3hBN5drAfSsMO2Zf+nNMoR1w9LDueH2FpM1EbQGhVeuOnMHGZjnfJCBTchaslyvyImfsX2A8GWFE7YQV22rd1iS3jYgLGCD1Am0at5xN8WjbyiEjZ/xsI+IsIxd37W2bPpGD1FRqyZBzeAQ0zmik3V8BLvHBgr6EEYVrBdqmbUXIRVbgch0mVBzhM3I/iRhQc8weDXMS3VB4hi+dHvId09ugjMsXab87iTCCo2ZbaZiJzfmQueMTYBB1xPX0Aqn28PysZyO4WX6EINvaxgYMKgRpr5U/wiPtqYojFAU1mTHMfMVIxuQofEYMsUyZj6B0gFCzfd7e/fre7eBu38TdDeV2uTklDREpMZEckYSCMGyIzIymqSnLEpkJluslrO3f0VCNKOOyY3d845O5fQUr+Gnbxw0NC9YEhFSERITO8c9Q9zz/YsfatWUj5ByD2HrjoVh1YC9gyIiaCo+ENufXstZ9pXAbISc6m5o/y/sJew9cX2sG8HGPgWsrjmN+8zd/k5/5mZ/hl37pl7h9+za/+Zu/uW2V5T7rcDjkox/9KP/gH/wDfN9/vVXu6k3WDgDu6uu+sizjhRdeoKqqLtXj2rVrj6xlC28OAGpqXuI/ccd8iXWWWWCqBPvD8yhf4jOhokBTufkky2jVbgpNoBxwsmrJxqkoS1bdTVMbjRYrNHXnu2cTWFO3vC2r8p27m9ymrZwyQ8uGxXxO3dQcDC4jk419i3DRb1atq7q5q4jxVq4worGMFPYWW7Ky8W6EHYArWHbmHgbtVMtl19ZtVI7nUhjsjdeKMkLGtGa8Vt28cuubA5KIPVbAkhLFkiVDNDYGr+IZDlmwYIUhJKDiS+zzzawsLPUUWiuybALDmQNJ9thWGHwT00+t9QVdWshImA6XB+ke/7NMEAhGqmHVuxf37V9C6HFwZiuNY4hi2Vv27EW4LyQxGDLRniOGGbBEoEnwGDABSho8rLLVQ93VHn691waD1IJMFD1wtq0uFoiu/Zy7b8xDocWEoecx9Gq8pCQ0IVVZUZYlVVaRVZtRhyZrQILylLU4OZNGEhE70QbkFFQoaho8hsQECDa5vJvj69rDDhudnScEQ0HeGYkrhgxIMJRIp5K3ghJ7Drb1JH+WCRe3tvXVAIBvNAN4th5XAAhWwPKTP/mT/NiP/Rif/exn+YM/+ANu3LhBmqYMh0MuXbrE+973Pr7ne76HyWTytd7db/jaAcBdfV1XP9Xj3e9+d5fq8SiTO9r1PQwALFjyIv+BTM9ZLFYUVcYgHJOtM2qVUbqbuUeMR4TEw3eMhM+Axmk023UFDGko8IiJiKjRiCqkMuvuwl+T4xNSMHPAbUjrYFb0LFgsrLOxcaVeIxsfUUdcPDikCVbdclY5HPUi4gwhIwQeDUU3p6XwEfhUKqVwbey2Xdy2rRuqrh1cuZt6hY2c01jDEK9JQJUEjNzQv3aff+Gsbew6NF5nB6OYkLIkYNqxXlOUM0LOOWLCnH32uc0YQUFBjSJrpiSklEoRCMMrp4fsD2bUoqG9/BUY1Jn271DQQZQEzRoYLEfM0yEitKhj4BlWHQCBvAcq+yzcyAlO2jp70c3PgMGWDTRA1EhKYWiNMyxb6GbrMNT4zgZnSMSYfSz0sSKa+u52b/+1gbDZbjkO7tFeXp153QLIBRUxARmGSBwSh4JBWFKOUuImIstysnXGrJxhKoOUEs/30b6GcAN21BkzipCIhhUNmpTcAcaKiCnWprvqxCLGWHB+ViRzNr/YYFh2s6UeIQNs9nCOdssNOeApvo2zpbXuDJPfqmrbzPcCd98oLeB+jcdjPvShD/GhD33oa70rf6prBwB39XVZXynV41HatvTXdza9441qzjVe4P9LUa+YLxboRnNu8CRhFJCtr6KNm5Fi5IQNxsk9bFvVoFEuCaOhdkbOtq1asaZygAk5I3RZuLaFPOraVm3yh09EycKlbfiOMbFt1TzLXbtcMd4f0ASWWfRJEE5Z3FcSW//AgsbdGBW+Uxc3GHHc3WqtWrcvTrFzWB3T1yU/bD5XTUGtVkTNFNBE1qiFmrJjGzfRdhCxR41g7falZOZSQAq0Y/sqMnzGFAhqLjEk70DKuhkScAKEDELNnSzmwnyPYnrKBgBqyno7LjAQpjsiHprhasDz16dc2N/cjH2v6dq/Q2FYiY3n3LoH6iLoT8RtAb4IufV6cIYd9PX25Jt3lxp489pCGZ8FPjBhiCTC4Pcydc+2fxFv/Dd09i/hLFjz3INAyw6OSEiRDJVH4udU5U0GFwIwNu7MZHBcHENq2aDYj6nCCs/zEM5iqNw6Wu1+CAr3mwEDcjQxgROANBQ9vlUgOZtfHBF355AtycIx1pKYmJh38H9DcvcM3lerBfx6qUXfKC3gXX391Q4A7urrrowxfOlLX2K1Wr1uqker2n0QwPZG1V7g72d9BsNN/gfX+WOyYsViuUQIwYXpkzT+msJkaFWjdEDMnmv1thdx4Tz/+kpF5dS6Bs+JLRQeipCVPMWohtws8IjwiLp2qU2+leCAIW5mzgcaajCaYmFYrSrCcEyhU7QsXWScce9LMTR4RC5uTrp923jvWbXuzO2sQTYRMXtUrJxxdd8E+pQWNig8QsZuxlF1bVevGVAFKWUHJgQxtt1jbTosXxYxdXnGMHAMoGV6VviEVJRMGTKnYormNoo77PEkr1AjCWlY6JCxtu3DOGxY5opXXrrIE392QagEBYZoOSHPBzDctBmF7N101yEvvDq2IKT3c90zgA7YAMYRgnJr/q+fDSzIegAvRlJumUNvVyXoQRLDaiuBRGzNDkoEy16rNMOQYoAQRcQEK2Zp28XCQKkaKzPmbnPpfvt3s87Na+O4xn6Vrp2/pKISmkwleGLK2A9IghJ/2JA2C9sqLiuqZcUyWyKEJAh8xt6YMiyRyh4JD491b34QrP+gxrCiYKlqQhMQMSJyn0zBVpKIR7AF/trxiLY0mn2eIHEJNmfLGPNVAYD3u40dANzVo6odANzV110JITh37hxPPvnk6xo73ytu7c1Uf31vdCFuKPkyv8OpeYnVek2+aIiDKfvTPXLlPO8ECK3AyE6cIZBuxs+CrrbsLB9b2brW80+iaRDC3QhNAi71o62AoWMRfWeguyZiTElKoxuWiwVFVbI/vghhRXknwDcDoOklbNjR+IocRUjOzLWpY9f6VWcSO8TW5/IJ8YgQKPezzUC+R7ilVg4Yo1As5Qrj2FGFj8HrPr+FKwMkEeueKrRgzoAhGQWamojAMaRtEkWFzarQpIyRFEypWHg+Og8wvsH3W6DlUd+8QPLEKTKP+eKL50HA9FlN6kydWwXwqPB45fqAVsSy7gkmCnXvyLeYTcKIhat173dqC/D1BQkCswXofCPIPUPLTQ7xWPd+P8Rj2Xs9wmfeA2gjfGuDg/UFbAi40WsXx80K6hLhvPnupS5+vfav/SwheW97If62ubWRFKqmFIZTKqe2liTqAqPY0MQpclyTVTlVVVKWJbN0TuWXeJ5HEARMvKntr4t2mzFZ729AaUXlWWOgVsjk4RGw5x6EMhSy8/azn2NI1mO8FR7P8vo5tV+tGcDX28Y3Ygt4V18ftQOAu/q6rIODgzf8fQv63goA+HqVccrzfJaVPmG5XFKWJePhHvEwphBLvA4MgRCn1GQupcO2VUtSJ64wztw4QlNQ9MQerbVKP/3CK4YIIzHOK27DtrXt4k1bWbuZwdn8NbTRXBg9iYkK6tqgvZySlAF7NK7lqmmoyLcAoaZxbeWQgjk+g04YIk1OrVbg2BKDtXqpKbC5rgkSSUO9Fall28oZJbVlIY1PxB4Numv7tss1VJSs8AlQxDbzFeHkMvuk+BRY8FxxkyF7biYsQlHxv7nAc1yjoeJIadbpiNG4QMVuHtOHL728z3tHJS++et62UQ3oWwFctoCnFJqwkcz/54B8ZM+NUEHZikGEdjN/9tj3LV9ED9SNkVtqYLYA3zZjN8RjtQVUlD0ujvHxz/CDZ6PkztZdat/e6xxNLTyO/CFwwBjphBd2/EC4//SrHwVn90dtNVoDvC1GMDK+TbLpPo8FmKlrSMeMqUXDINgjCSrMIKccr6kqKybJ1zmZvgEKAj/AD3yrCO3dtVSjaPx+IorNL67dcbWiK03EPoKamrRT+7b1DO8mYHsEYOs4fpVawPcLAHcM4K4eVe0A4K4ey2ovlk3TPBKbgK8EAE94iRf5HXQF6UlFDRxMLqGiBu1uKK3nXc4SIQ2ejp0li+hm4KD1/AvIneefR9AJRNacbt12I8bMw1cp8PFQHXBsyNl4/ikCbDpHWRQsFguUDLgwuYLne9RALWpEo1Am7BjImgJFgOcoFjurmKFcKoIFcML5AEYWvEqNVyUEDLDJCUVn4GtbnYaiayvHTjgitxhOhKDOJLfT1wjCAN8LCIX1R7Tt53aG0Bo+x5wjRbKkdFBrnzUNPoqCZ7nAKSklEdBQkBKQsUeIzU4+XiYMRguMYwCVByC4+uIFCrG56c5TxaWVz2xYURiD+uIQ2UsGScINszf2rOefMZAYQ9370jLTEBkPrw7RjUcjh5RGUWpYBo3jVWEiNJVqCDAoNIKGCI/SGXH30zxsu3VbCdtnFr9SO/her1Nh84yNgAWGBRLDkJARY6AgR1Bh3Pv6Jtf3av/2vRIB6jOvz/JYCkVGycwtN2IPo0YMlGYQ5QTGsKhtu7gsS+pZzdJL8ZRHEPgEQUghraK8rbNm0j4+a1ZU7jPYkQyNzxBDjkTzFO/gjeqr5QN4vw+yOwC4q0dVOwC4q8ey7oexe5j1nVUWGzTX+GNe479R5DnL5RLpKy6Mr2C8Cs+JI2rnf9d57wlDJTIEIwonzrAqWetcl/cAoUZjvfyWyJ44w7YcT91+GBQBYNycXev5ZxXAuZmzXq9YrdfE3pDRZEgjN1KEyEwQTYEwmyguq9YtOqsMwAE73QE3GyU36lhJI2oaVSA4pGLp9lVSkhGS9D6XcLOMgWsr+/hEGGAcxczFEVlesc5WCCEZqBoRl0TBgEhOqBzANSQsSF2726qRW1VuzZrbDMg5YMwpExpuUaLQ3NRjLnCLdJWSrwWXgoTMtXdbTFdX0rYXe3XrNZ9Lb6+prg44OvU4GG/UwL7X84/zm64RGbtzMGwUQZpwsw4ptL2ZH8QlM/f7qTSse63ioZDO9tp6EQ6QlHhAQowF6LJaEZmEGMGavHs4GOJvAb6z7eDBfbSHVyLrvfZYdGp0Q47PMQHgM0IxoqEhwzi2+l7t3/5rD9WtXwjh5gm3xR395QEH91u5is9IRPj+gNivqAcrIu0zK2eWHcwLmoWm9mtMaciyjIE/JPfyLeayH1cHlhXtm1S/m/fg8cYPkDsGcFffqLUDgLt6LKvfAn4UdS9AWZPzIv+RzJyQpSVpviTyY/bG57pIN5sr6uGROHHGyFqXaB+pvS5KzcZeaQdqqp7nXY1NrHW2FhjaeDVNZcUZZYJfD9F+veX55zmTZa1rlsuUei2YJheJxj61yHpt5TGZmKGDNaXwCIlclFztIt42LeQNgLMmuwlTNNplAq8wArx60DGarXo4YEhNQcSEhtq1v4e9tnLt2roDRJhxPrwIRpGWS5rCsKpnNMuaJUs8z2PgHeCHA3Lf2t/Y1I89UjKbAes8AKfADEnMRSJmQMKEhmXj4c+PyYzHhfMXSFTMzM3sVe7+uSxBBNvMlDaC8uWI23ecRUkfG/Svlj0BiCwE4fGEG4uIwwgKt5wUhmVPZRsp04Mj27+bIFjTb2VKbiM49UNqEXKJmIXz/Qtp8KjAKb3h7nbw2df3nCLbAhZn272bny/RSELm7uFkjEDRIFl0/nxn278xAanZfNp72cv0AWF45v0KuWVfAwMG0mcQJQyjgsosUbXH0fwWBkOappRVRRPVBH5g5wf9CZncgFwfn5zN34VE8iRP3evI9A6R+aqIQJqmeV2rmd0M4K7eqtoBwF09lvV6jN2bXV8LAFfc4Xk+S9bMWSwXVFXJNLrIcDhCi6pjozaed5ubXZvaIbTv5tQ2KRmtIrSgImRIQ4lPjEdMzboDdbYEtcxBQCmXRMSEjGnQLglhTtNoZ+7ccDh9AhEXLinCKj9t+/V0c383ds7QRs7ZH9goOZulumkrCwIGZL3WrcQjqEdUlCgCGiqEG7nvt5WtCXSMQBC6KDl7S5fdciUrlPCJwggRSiZMyZuUvFxTZz6z/BiT38E3Q0yk8EMf/Bm+HFBRM0FxSkNMzQyPGQPOc8cC2TrnhUVFtPC49MSQgYxIFxXVqMRXmkLL9lAwFrA464xSbW72vQ4wde/flbTn3fjU4/atPVKXIyzUZmUTZfoW2hQ9wDcVkrQPDmGLq6rOmEGnaCoEdwBQREhqAsaI7gy07Vpzz3Zw//XW7x2btK0u3n5tbW0qt19wB02KR8GEIYrETXz2q0FvAKYQdwHQs3YyAf5Wi/vueLuIVZcFoojFeQLfIOo18RCSJKQsK4oqpygLsjxjrdcQCoLAAsJEJVRi87kuc5mwc1i8dz1IRu+bqR0DuKuvRe0A4K4ey3qrWsBaa+7wAlf5z+TVgsVigTGGc+MnEWHpmC+Qzlijod4SbLQsmvEKKmnHzVuLFysEWd3FtlkWzapkGyqXnFFRkxEx4dR/GZxgI2dJyICCBaYIWJ4sMNLjwt4ldFCwaSvXSBqXOqIIiZHlGr9JtiLipGsrt4BQ4Xft1r7615rmCmrvlNpUNASETBCIu5JIbIpHXwBikzNajzUrOhlQUmF6ZtFKhUzi81RxSWy8LlmizhuWWY6RCyKVU8UBA9/HeHZizrrj1Sw5T1guODl5DfxDRsNnGIaQFQ03r8cETxQMQs1svbnRnukAA1D3nilqSUeftQpgiaEQmsFrA156JaAMsw5GFL3lQ7U5N0MBqdhkjQRie74vZRsM9r0AJ07r3NYekgUNINwZFDBDIZ3Vy5CaitKxu+ae7eAFpbU8cq/P/j7tAboh/ta84QDP+RwKB0wVBQEeh4wRBFRnYtu4R/v3rH3M9nxjPw3FrmMb9Hgo5mSceopCxAzkPlFUk0QFlUnxasWyskKt1SpltYSFWuCHVl3s+wHPyG/iK9VXEwDuZgB39dWuHQDc1WNZb0UL2KC57v0BK66SZxmreUkgh+zvHVL6m5zeDTu2ySoNGDgzEtvyFUKAtgxYXwCh8PFdu1jidZFUipDSAbOaHInv5vEEqorBWI2tRFGQkmUZq/Q2QZiwPzlAKGu4rF1slhVx2JuwQaNFg/FKSpkyYt+ZRVtI2beWUY7N09SO4Rwg8anJacg7NifozK21OybKtbVrN+do66yqGay5s81jkJTUCFpT6awD2IkYU4Q5fujjDQeUVcRpqanyjOK4YhW+ZsUgUUYZXsbzC/5HMeXp05cI/CnT6R7F7YipOSYNNFnhM6kUJmhg7REHkJUgGu4y38t6ZFZ7ZHxlutCwkQb95RFHqSLxC3LR+h4alj2ypuwBwInSvZwVQ9YDgFPklop4gKBAdASaMts3/LO3/1btq4FTbI7LDA9FwhSDpCHApqPcre29G1ydhSHeXWydOmN07TlzHjjBMCFmjkLqAFNLEjFi1XvoONv+Pds+VsgtwQncS4DiAKPz7Sxo4/YUUkw58EPG/hidpNS6xCs85vWMqqzI85yoijla3qacVEwmE6IoesMUjq8nBnBXu3pUtQOAu3osq71YP6oWcC1zTi/8ESuvolnWZHlOHA+YjiZUcrkF3Az1FoumCLbSNBQhXj1CCLYSNqyJs+4BQkPEFANbkWt9KxQjDY2fofQYjwChJfN0xrpIGQV7xOMA7dpaDUUn4vBcXnDJ2lm8pBjXtqzICPGoWSHwOjsYgdz6XNoZr9iIOJu56zcGXfpbXoYGQ8SgUzVvMohjKtZbZtERU9bMO8hhmdQxNTmmBypy5vjsUxFxLAoIGlRwwGpY4zUBQbGGYsFRsaZJT7jhLTgxikN1yGQackdJaunhVYLSs0AsWgXUgf13CwCzCvpdQAWsC9sZlQIyFwM8CJwps4HkhuKGGyQMfdNqahgFdCBPCsOyx2L1jaVHQpKJDePVN5E2GCd/ce/ThoXUHXsaAvOtzGHBoredEDpVbQPMEMyQGBJiBi7tuUL2GLfVGTaun1tsMHe9zs+wddUZtq5xgwQrYVh5AXsMnURK4FOhqOhno4T4WwxgTPCG84IRQQcYraJ+GzBqrO9gjQYSYjkmiAV7JFQsaeqGZxbPoFaK1157jWvXruH7PpPJhMlkwmg06ubxvloM4Ov5AN4L/O0YwF09qtoBwF09liWE6NJA3mwtucUL4rM03ppyEVCbhsnokCjxqIW9EbXMmgVqmsBFs4GhJNsCLx4+2l/TmIYhB52qt2XW2gqdQrhlxxQBIQMaanTHPRlUlVCJFaKpWSzmlHXFYfwEyTB201cWPsRMHVizbVkLuCbW80+Mkc2MxmgXuWabh4aKjNK1pBcEzvOvYE1I0msD2xg3IQS1nyJJekxmmx7Sh3WqaytbmxurFu6DP9uATsicFjZwx6lyjfOUDN8dY5ugUrFAUKuSm8ll3p4kHOo13qkk1TCl4ct6xKWTV0m9IaosSeeC4rBBCANLD3lgj79yV760AhmAdjs1kLBoTYejvgLY7sbkFUntGDltNFVTIBwbHfSuplNP93jfbcHHQJgO3lgPwZ4yGLnFrg0ay7G20GCE5LS3/ABJcSZKrv962ls+wxDiceQ4V6klUWXPu9oBqLNm0wP8LQAY421F1/lIt3zLgrbzhe3cn00vabDsoDXtDvBJGKAR5JRn5gfP+hfKu7KCfQsATbtPwZb5y1nA2AB3qNyapky8gG/Zfw/BfoDWmuVyyWKxYD6fc+eOnbIcDodMJhOCwA4J7JJAdvWNWDsAuKvHtqSUb4oBNBiO+AJX+T3KqqDxChpyzk+eQgYGn9ABrNSBq411S8GyA03W/NhzvnqhBU3CgDY01PjQRaRZkOMhEGScIno3N5+4S9NoBSZKhByrF2iagNnsFK3hwvhJTFh0c1QegfM1q7H2MTZyLepUuLatbGRD2OwDtuVasqLvIQiWsZRIPCIaChflVlNT2kgtdRtT4QykMxQ+OcueWXSNRjv1c3ucQVO4tBGJzwCQNDQd4yjctkP2AEnRzQauGbPHgoKSJZIxGmtD/Xk9YXJnRdMsGE2f4DDe4+UmYi+tqHTDokp56bWKhczwKsHihk90zp4vonflG0mYt3N7vfMjCjYAUHiG88eCoxuCvcvQ1A3z+ZxkqBkNbYxdXyTi99q/UwkrY4gaSVR7aAQjBEYLPGmopSYGEIZINUihMbIhb4yDRpuM4WyLbdsGj5yZJbx7+Zads7UQgkBF1MT4LozPo0bRdDN4AXJLnBKynV2c4DHfArAe8xZAGhhotQXO7HyhlYysEIQMqImZuNlBzeoN27/Ahv0zm9nbfmP8rMAkIugYRo1hyIiADbBrmb8nn3ySwvlozudzbty40T1gvvbaa+zv7zOZTF5XrfuwZYx53RnAXft3V29l7QDgrh7bklI+NAOoqbnKf2ZurpGvC5brGRgYhYeYwN7i7BC9IGKCQfeUvMbN9lnWqyKjTcGoyaztitY0TUrI0IEce4OyZtGCghUesQOZ9hZZ0J8zlO4GfoKRDUXa4MkhB/v7lF7aTXJZvz7VeQMCnTij7t26bVycTynTLsGizf+lm0esXTSd6UQcrbm1QiFR+HpIpnM8Zwxt5wc3ZtGNmx1s28r2vzWVA3TGTR7WlGhqF48HJRkeA9YOckXEFA5MahZOJa079W9QF8wXGUU94psONGUYEVIiVUIYnOeJuMEfjDg6Djj0b7BQOTeuZyQXlszmEHqKpolRytsSgvRG8zqWEMBPBddesN/OvKqYzS1gDvcPEJ6HMabXPLdJIp4RTCtJUCgWVczMCGIJa68T33IubLjtvg9pDGEgOj4srD1u1hOeyIcMjUciGmpZE0o7kTkVagt8TVHMeq8nKNItZa01bmmhkqcNqYJ9rAjnGJjhUzFhhGBATU2NcbODcHe7tz7zehuOmnvMF54FZ/YznGAAjwmH1NQMMEgyfOozdjH+1ryg1JCpmrD3LWZnGMWK7Zb105zj9SoMQ86dO8e5c+fQWnN0dMS1a9coioKXXnoJgMFgwHg8ZjKZMBgM3jQj90Zt5l0LeFdvZe0A4K4e23rYFnDBkuf5LKm5TZqm5HnGKDikXFcY0bhJPMuNyTNzcR4BisBpWkNq8i7+rZ2LK1hiZN15iISMqVkj8DDormXbuBuroUE7gAk4iFRSmRWrlZsr9DymBwMqseqSQ0BSsbbiDFc+CRWrrtWsCF2rtsJIm5cLVrRSkVP3mpGWfTNoCmp3FOxy1sOwpqBSa2QdOZYwcNNkrc3NRuzROJsb62VoLWEqMjxipxA13bHyiJGEtHm77bJhBwIbhg7ceJRUlWZ1ch28fUbnDlBewoQNM5RVA0bxHXJPo2XMQbjP6ErMXHtEQnKM4XS1YnWSo5TErzzKQYTvB1S9SA/jCJlEw+0XLC9rdMGt0wWe53H5cEzm2YVGPh3nOWoMau5RLRS3jGAygsrdx0f+RlgijHHAx9ahb0FYW4lDo5mQlI1gX3rMjIImxMMwkoahtCm/uajuUoecvbhHyC0j6oERrHvvmTpwLYAUgyTglhMoTYGIioK+r55084Otrx5bdjPGGHKlu5A1+0CzDcbuBpCGVTcTGbOHh6AhoKJhtSUYMcYQNN7Wxz7b/vXwtgChQnKFQ+6npJREkd375557DqBjB4+Ojrhx4wZKKSaTSQcIHyaV6Ks1Z7irXZ2tHQDc1WNbD9MCrljzRX6TdXPKfLGgqWv2kgsEA0mm50jh4bHftVT7YocNaNr8zFq86K0ZwIAha7FGy3LLKNkybJHzw9tO2AALhkJG1GSoJmQ5y8irCmUivBFoJxxonEF0wbwDabaVLLasWwzGRdNZdlAgUU1MxB4li26f21nBwrWfAdcEHtO4OLV2uUCPWHs3t4yrY/ac8CR2IBC3vtbmJnWs6ZTamWDXZDSUBIwpHKsKOTHW56+homJNxD4ZNSVzYibM8mPSuUT4PtPJGCMNL3KBP2NusBL2xp8Zj0lt0B7EoaFeBahIUymP/WLCxcOIpgTlbGbmq5xVXSIEFKWPUSFBEFAJhTEQvwp3GkGeZwg9IxgmjEcjBpHcpIF4UBiYplCngjuuTRhImOkN41e0I43AgW+40wOA9RlfwD44nAjhxB12RQPgBgahJSAZCp+l0AyFnQAtTclM9qPftsUjNmVmu3Va3jV7hzvfLDDdJ+YYnyEwRDsPxrRbwxC/SxMBGDSK0x5bdXaeMEQ6e5k+gOwDRMPSGS3ZW9WYCkVAgqQgY44RZnNwuTuvOMbfisC7xD7BA9z2+uDM8zwODw85PDzEGMNqtWI+nzOfzzk5sfOuSZJssYP3A+p2DOCuvla1A4C7emzrYVrAPgnPLN7PF6//MUlym9FFRRmeoLGeaNpofJJO0dpaoXgEltk7A5pa0QVYgBUyQVN1dikbcGXBkFW7mg44WsbOKmVbb8Cmbjhe3EGbhsPpFU6Xt/H1wHnnrVzkXCvisHYvEWMy5s5UOqB2CSVbgFBoMMIBQuPm9tp5xNmWQUjQzSPijsEQj4BTcbR1PK2gZLb5TvAIndZU4Lu5RIHPqJszbA2nY5cy0reIKVnhMcSwx8yJBwznyU2NWmak+ZKD4Bx6OiIWkhmaTEfUIqamJKTB+Jo6C2EEg6TBpD7slxRAfsNn8Gca7lQ+gzgkDEOMGRLHNWVZsl6V1JkF+FkgOah90tsh67BinWU8cTmmGI/tmeGunsaAnwHHcLsWnJ/SgbxxCHfcYY3OgEHdmxMcScPcmA6PjSWses82Xg/oGAxCbhN+gbAKZGtDozhkQG0MCRpBjW8aTmVfHOJxTWz2ZYAk7beH2aiJ7TkAC8cOroAUwYCIgoAp4FPBGTWx6P7Hln+GooydfUx3DPA384NYwLjaAoyqB4IjhPbJjWFkxkgqtDMS6tdZP8E3av/eq14PnAkhGA6HDIdDnnjiCaqq2hKS3Lx5E6UU4/G4A4StoOT1tnE/M4A78LerR1k7ALirx7aUUg/EABpjuHXrFteuXWM6epJnL//f8XyPjFNOeZms/hzGFFtiD6ATbFjQlCDxAUHeA03tDGDHtkmDLEMS9h0L2C7Xii62EzZixnb2rjDMlzMUHuenl9BegVnXlGJJjXTZv5t5xNaBL2fh5v5y13YNXRt1hDWuyfF0TCVT12oWTthhWVFrGxM5y5ZtEYedR9SsOUHLCtH4ztRjGzhKPBQ+Wc/xLmSEwKd0wHfTXI864Ojh4zOgpMSQkLJ2uRB26UBXXFutyfOMafQO9kcr7gjBgIYZkrGouc4ePreYUJP6mvlsQDRq8CJNfscnuGBzdPNTj4FfIoA4hGVmb6p7vo8OfebVAG00ui6oZMmt/5qTJClr7eMphR8H5M57rhQW/J1fwrwQVO5U3DRJwTj1MMDIg9ydVoHYbv/G0nTtWGXYmu0bC5yS1y5wgNgCZxt20FaC4BiNMbB0Z2ZIQGAMsRswMFSbHcOKPcQWGLPt4G4bHfja/L4VnBxjY9ZqfBIShmggZybSDrBY775tMHZ2nvDso5x/Zl7wLGCMjceRr1DSJs/s42Go8SkoSZ1f4QYQSiRXOOBBqgVgXwl4+b7PwcEBBwcHGGNYr9cdO3j16lW7v3HcgcHhcHhXmtH9sIU7ALirR1k7ALirx7aklJRl+ZUXxF5kr169ysnJCRcvXuSJJ57oLqYJ+yTsk83HVKw53A845Sopt8+0gYVjrAonjgjwiGk1sf12sSRAi7QDjh4RHjGtD2ALmqzoAnIzY51lrFYpidxjb3qAURUNFmxIHSDwtrZh28keEolxkXM+kWMFbYvWztiFSCRCSPwmcobSckvs0VC6eUQLEEKG2MyLiqaX7GGXERzPb+GHgjBICOUA23jcNpW2wo2Cxu1zQIDHgAbdta/bbXsMAI/8jPp3rguOZ6+RNyGj0Qji86xYELNGUwI+ERVfNgOeMwlSVtzxGmanI65cukMRNmRlSKDtjdz3Bb5DYX3bFp9NcpkUkskkxjsNeKnRSL8mCAJ003BnOSddrfB9jzLwOZcGnJ76VFP7aXwFyxarmY26GKDqpYRMfcMROKUsNMLO4EkjLBjUkM8yhpUgEdLyawYQhrrnKWgwnPGJJmQ7V3gfyR00mYE5krFQZCiCuiCsBQmK094s31lrGvsdbTNRZ9m8IR6n1KyAFZIJI05pSJqCiCGSYstexkdszQ9aZnE7fu6sP2HJ9sOebvdRCOz5pzihwUpLIs4h8SgxrGiouMQeIQ82o9faszwI8BJCMBgMGAwGXL58mbquO3bw5OSEW7duIaVkNBoxmUw64He/LeBd7epR1Q4A7uqxrfsVgeR5zpe//GWKouDZZ59lf3//nstJKRFlwDneyTneiaZmwWuc8jIzrtIqftv0i4bKpX9YcGRBk23L5hyjVWVRhRBI1441aBRezxtwRWNKlsslRVEwDg+JRopS2HapR4BfjRC+sK1lV604o+4JQNrIudavkDMiDq0ySlUxYoSmccBQUbJ2mb2bz1aQup8Vjh2MaSgZBDHN8JiyzMiWDYIlkRzjxQY/8Im9kRO7QMVyq70rCchdG922qkNKciRxp/6NGbBy+27qlNPFCqM1T44vkYcKn5rnGfN2E1CJOYYERYVAoOsJfrBGC5BK4RU+ZaDJBOylEikNKhKYhQQfVA8LNDVI14EzQLyo+MJ/W2K0Zu/8AZ4MMQbkYQNliSozzAspL5wq9saazEsIgoDp1Gcm7I18Gm2MoQNh27++gWltE0hGlceqhiQU3G53xBiCoOHWPKVpPK5M97heKgR2Ru680tTSMJUGhCFQmmNTb1rHiC2fwBDByRltLkAjBEslafCZugcTyxU3+NRbauKz6mF1hoG0ZtHb4MwANTBTHuCzR0RNQ4JGUhDSbLV7R2fmB8/Gz4VINz+4AYztvKDgboFJAyyRrNzs4ADJFS7yoPUg/nyvV57nsb+/z/7+PsYYsixjPp+zWCx49dVXO5B38+ZN9vb2GI1G3Tbfihbw888/z4c//GHu3LnDdDrlk5/8JO985zu3lrl69Sp/5+/8Hf7kT/6EZ599lv/wH/7Dm97urr7+agcAd/XY1v3MAM7nc1588UU8z+Nd73oXcRzf9/okHlOeYspTjpG4w4yrzBw7GDHesm7JWRIxoWRpLVaqGoxPIGLK3nLWG9DO4jWNZnVSUleSg8ll5GBbdOETU/tHaKWASW8ecRPDtskWnnXbAEnMXo/Zc6KQauBYxLPziA0BCSUrNHprbtECwzUxE0q1Zn94EYMma+aQB6TVMfmqwqxAqhlDtY+MKyJ/iC8iaioE3lZbuXbBagZFmxJi1b8rRow4rhYs58cMmgnewYTEU+SApETgc5Mxe2bGSNAZCa/rhEkwAyCJG4o0ohlZW2Ev9UgSjVgrymMJFzcqX4B1BZG2/oL5yyU3T04An+ne1FKFNSQxZFIRRTFP1DHXlWEyKRkMM+ZlSZ7lqKZmFcUEYch+ZMGP0HDeQLqGeQ4rH1aBvKcwZKJqXpzNwcB0b0oSC/IWBBjD3EDeCGgEyhgCT1AaxUhCLDSB0iRKW7WvsLFy/VbzOSE5Fu15A7GGO26uLkMAiiE+AYYQTUVNgN7yApwgHdNma4q3JTCJkO61nWkUwNLp0pdIIGbswgjDM8rits7Gz7Vxc22N8LljnEuiEL18YlvbDKNlRJ9ietd2vlI9CgDYLyEESZKQJAmXLl2iaRpee+01bt261c0PCiE6djCOY5vZ7E6WRwEAP/axj/HRj36Uj3zkI3z605/m4x//OL/1W7+1tcx4POYnf/InWSwW/PRP//Sb3uauvj5rBwB39djWG6mAjTHcuHGD1157jclkwjd90zd9RQPXNwKUAsGQcww5xxX+PAVLZrzCjKvMuY6dd9sYLzcqp1FrPHPeGSpPXDs565arKtsaQsDh4WVMkHfefNqZOucs7EW/zSRF9trKCdbFrjWV3twcQoZdEgcYAgb4VUPjlxjCbslNKsjmc7aqXkXY5fpGPRFH+xkTtYcZNCQ8RalX5NUak/mk9Ql6rhFiQRAEJN4BMqoIpfUGLEiJmJI7QNhQkDCkILeuf/ltihONihV7ezGpUjSsMCQOjCbkNNTmHEPRcJMShWGNYlSD8Qxh3DCbh6g9uw0994inDdpXpC8JuGjbrm1lFcTX4eZLBcv5goNDxd50ipSK1J1icWTn+y6soVrbm3EQhMTTkP3G0DQNfpxjipo0TbleV8SlR1HEnO4HpDKwN/eQDlCNPNsmFkBVVRytjxGez2Rvwr4vOTZN910denC7RwgdeMKqiAUsDERCcr0BGokHnJMGIw0jCSkaJbZnC40x7gawORAHKI67ZSQRAWsMI6x194pyy0rGnl3blSCt16Sx6z47P5ggmXeMoo9yEpIEDRTkrLfUwnC3Xcz2lsVd+cQDfGY9+5cnGRI/xO1Oa/2Wzt0ppTqrmfe85z0URdGxg9euXeP09JR/9I/+Ed/93d/NX/krf4Vv+ZZveVPbOzo64nOf+xyf/exnAfjBH/xB/u7f/btcvXqVp59+ultub2+P7/iO7+B3f/d339T2dvX1XTsAuKvHtl6vBVzXNS+//DKz2YxLly5x+fLl+7qIP4iqOGTEBb6ZC3wzDRVzrnXsYEWOZwKk8SlZIFE0lC5POEIgaFaKeXoH3w/Zmx6glW171i5qTqBonL+epyv3/qjnSShcqxeXyBHgu1aedfLbeBdKfNvcC61lh8cY32ZPnFExe3gEPVWv6RjH2tnBCKwDotcmnrR7IyWT8BwmhJFJWNcLqqKhzAXHxQ1Yge/5+EHA2D8HvgVQGwucFGUC1kvFaVrjxwnjyUWEPGFCwJySER4pDSMgFZrr+jxvN6+A8JhQk/sezWrEYKJRoWZ9IyTGoJShnCn8CyUrBdlKcC6F9WjzfR5qePWFjDRNLWidjMgqySCBlcMZXgB7Go7+N0yubN671BY4J6FHEQ2ZRHBQN9w+rVjmJVWZsyADIfEDH+UF6CBASkXk2/cXZYFenJIPQqaTMVJIpGo6Y+pIwEmvHTiVhjs91fBE0ANutl18KqA0AhqF0IJDKdHKUAlNKmomjWGuBBP3ngC2Wr/WukcyBzdoYA2SGgxjxw421CzuoRZu3y8EVGcgYnjGj3DiZvcWjh2ckNBgiJwVkOrmAbfnBbskEGG+ImD8M4x5mHrUDODrbUMIgZSSOI6J45iLFy/SNA0vvvgi3/3d383v/d7v8S/+xb/g8PCQD37wg/z8z//8QwHTV199lcuXL3cPw0IIrly5wrVr17YA4K7+dNQOAO7qsa0WsPVbJFmW8cILL1DXNW9/+9uZTqcPvL4HLYXPPt/EPt+EQbPkFjea/81C/7fuJhWQWCNlsyZNU7I8ZxhOGY+nKKmosGIIn4SGsjN3LliC0GBs5m7ImIrMJXdUnehCU6HxnGta3WUVaxrnqZe1ugREZyBdIVBuHlF03nttWVVwTeZ+pvCcCpmtdq5HgEF0DCECYn/KwA/Rw5qsCSnLkiqvKZYNN9XLFvwEMQN/DxMalBny2vKUfG1oJmO8JKEQIa9yiQto4JQETQqM0KQoBJLSJCAMQ2pOlebOfMJksmQdNBRGcLj2MHGDmXt4Da7xDP4rkH9zS6waTl9ISdOcKIoYjYZklT2foqQXBycg/bw9iEuHN0ZJa70Co8iufLgAqRXKV4z8iL3EcNRYmxlfZ9xcWmYq8BVlHVAB63XGhUFANJkghGAsDOvGMMQ6PA6kYWUEWkCjDc720Z63xqDlhg8zxjBUomfOAwdScQPjaE9FYhR57TFGk2jBSmiGQm3NC+4jXUpze54bUqDEikkg4DwhPtqxgxVjZMf2GWPNpvv2MhKY9cCand076z+oOKYGfAwTLiEJqYCSgowhAcserBzgd+k2dj/Flp+gAN72dQ4A72UBo5TiypUrfOITn+ATn/gEWZbxR3/0R7z00ktvipU8+96d0ORPb+0A4K4e22ovzO0F9PT0lJdeeokgCHjXu97VtVYeZH0PGy3XlkAy5hLSjFi+NuRt0ysU3m1OeZm5vs58MaeuKvbjC6hBQyVaraMhcq1XMA6cCUJGLNUS3dSdqXTIiIbKgcXWMHnbVLrsRBwZPhEBManOMdq2XDdzhtotv3ICAOsNCA589m7O1nw6pfVCtIa8gVP69tW/CRUVxv3MUz6DeA8TQ2Zm1GVIUZbkZUaWWnvs3EvRaAbTIUE8JqfGkGOIuYliwhU8bmGIXMNQEYmKq+aAibmN5ZkMyyrmsFkw9xuEgDD1qGONDkCspJ0jVFDegUEJpW/IjufceLliOEiI44RBLDrQp8LNIchfhKqA4RBS97M4puNagwbMTTip4PAAWgzi+QJf+vi+z/nhAE83lGVJ0mTcWmd4WrNfNyjfIzgt0CrECwUL41MAsYQbHh3gO+dpjgpDIKy34FhqagyBMDTCEIqG416m3Qg4MT0DQmPwhOQUiRCKqvY5D1ZcIiQrWdMIc9dk3h6KOz2gNURw1NnTeAgUEYIxmpIagyFAbsG7KR4nPXA2xmPZYxCt/+C2GfQMQ47CGsHEJLhUEm2BdCjUFgAc4jPvtX8vkzB4QPVvt3VjvioA8PW20QdncRzzPd/zPQ98XevXk08+ybVr16jrGs9FGF6/fp0rV6585Tfv6huudgBwV49ttU/NTdNw48aNTkX3zDPP3POJ+n7Wd5ZRfNhqL+i+HrLHRSbZ2/nSl7+A8W8yeVKxTl7bMqkNmTgPwY2nnk3iqBDuJvqVTKUDBhSsMD1T6VbEUbJGmYBSrAkYYdwsXn85m9qx6rahnDegBXzKCVk2ZWcUZ2xsbgJAsj4DHH1iZ6Kt8URAEk4pwwxhBszKE6plSlTGrH1YpSl1eoc8CQiCGt+LqQSkxASMGdC4XOEIRcUts8fUDEBYGJ3EhnodkQ+twbReegR7mtSDei4QApIEZAH+iw2zZI5+LWc82u9urHEEKyeuNgqo4VwFd9yoZJRsYt+0Aho4bGBxBFVtma+FwzTGwEJv/r0SIKViEkR4y5zBvKbxBgwGhtdWDU2TEogFtwchfhAQBgHDgdeBMYVhLqxpeQUEBl7TYLT9/obCKo8jKUikwUMjPVj1QNY5IXllvaKsKgZJwsBYL7/GrYPG55KQaKEJpGYhaoZCdmIR92kw7pxp66ADiJYdlHVEKQX7BKwoaTBkZ1qzZ/9Kx2cA4lmBiQCuu7NqLcaYBkoREyApHb+rz2zj7V2T+8Hrq8EANk1zXwDwUdT58+d573vfy6/+6q/ykY98hF/7tV/jqaee2rV//5TWDgDu6rGt9qL54osvkqYpV65c4cKFCw8N3vrWC48KABpjOmYyDEP+L8/8PwnDEE1Dyk1OeJkVt0m5Rd8s2ifqkjgQBlXFJOy71mu7b4KQ4RlTaUnAHoYGhU/jbqYRY1L/NUxdd7N7ERNsBrFtK2tqB/6sYrmhpKbolM1tW9naxgy2gGNFjiKkYOZaxQPnHbcNHO3kWAl4lGVJurRmwZPzIQdij1m1oCxyipVmtV4jGlgPYoaezxfDA94lZ6zJ8YHagb6smeB7NqHEixrmixg9ykkiTTHz8C9k5BLETQkXIBxAkde88h8XTL6rwKwOiaJNSoPsoZISa+FSXQfjFhEBHbu3MnC+htk1KCb2aIx7beFJZAUaYHOAVQ37a831m3NSr2I0mpJEMf4Q9htodMOYjNfKmtVqRblassg9/DAgCAIuJx533L4ZY/DUxr9QGINUoI1gbWBVwzlPclIbhPEYSRgLw53VgiJbEQ8GjOMEI9gycTlEckM7xlDbROxAGfaEZCU0pTAcnmEDQ9iynwEL7k58jwoPUFzE+kxG1GRUBPewkznrP3j2r3DUB4jGIIXiNRSQ4JGwh6amRqI7IPhn3iQAfJiHyQfdxv0CwEchSPnFX/xFPvKRj/DP/tk/Yzwe8/M///MAfPzjH+f9738/H/jAByiKgve+970URcFiseDd7343f+Nv/A1+6qd+6k1vf1dfP7UDgLt6bKs1gc6yjOeee47x+OHmfNrqt5Tf7FN/+/7bt29zcnJyFzMpUYx5gjFPYH0DZ5xylTmvsOa0a/faZX1qWfbSSGKkU/L2kzgUHgL/jIhj6IBgAa7xK+4l4kASMbaWKURuzlAREHfLtfYxAQkNNaFjKGsnVmmBo2UXF/hMnZ+gVVsUpASMyLGpHsvlkkRMCfcCd7xWDNWQKqrZ11OO6jUmU8zLitPVEbdUTSUlh0nOyK85VuBhWFQRE6WJ0Iig4fprMeNLOYNY08x9okqQA9VtibpsaFTFtaM5TSU5vHXIsdxuD7YQxBhYa5gcO9Dnqm0uBh4Mc7j1Mpw7oANmceTMoIEotAzgREOSwfUTzWIxZ+jljMf7hGHIwLeMoQCUVDTRkEkCRmsmsuB6WVMUJfV6zf9egxdYMPhE7HFiNufpOU9w22wA1AVPdCDNCPuBnk+XrPOcwWDMuThhDJQGSjS1sIKQO712scEwEnBkDBgwSA4RaGEYS8mSBiPEXXYzEwQvK9E1Xm0WsSBz7GBEwBgoqFlTooEJ3pag5GwcnfUb7AFEAz1PbGqgIeC228YYwVNIRg/Z/gV7LfD9h3///W7jfkHmowCA73jHO/j93//97vV8bh8gP/nJT3Y/C8OQz3/+8296W7v6+q4dANzVY1nHx8ddxNLTTz/9psEfbAPAN1vtOk5OTrh8+TKXLl163Yt3a70Ss8dlvo2KjBlXOeUqa45Ziwwtym5pkFSs0NROWZwgMFTk1GeSODQllQOTygSYUm7Z1djlzog4sEbTCt+5t9myANM7A04t29dG4VWsEHioHnC07V9BxAhjBOv1itV6RRiGDEceoRiQUWBoiNBUGLRMCYMQESj2GVPXmnWRsKwqghNQ4jqzaIwnYxbemHPZiEnSsPI1xkiGuYeJGtaeb42fgTAU1KuM03RGQ8TFi2OWNxVyuB1Dljk/4jCCxMDt5+H8s3RRt0uHSfY13HzZcaDh5veVpFthreHcCo5mUE1qZrMFxmimlw5IlUWVg3CT+7sf0pn3BEqyCmLGAQy0YUDJoijIs5xqvuK2As/3COOIQRRQRx4jBIUwJMCx0R2FFhrDnXTOuiwYjUaEYUgsBDccsAPJoRAoDFMhWBiNFoLzgs43EMAzkAlYG6wXIR7nhaARhsCxgxJDSWtdZHdg/wxjCLaVq/GQeEywkg9rDW5DAMddsoetswBRaUPqS0L3+ixAXGAeWvzR1ldLBPLVagHvalf92gHAXT1WpbXm2rVrHB0dMZ1Omc1mj6xF86gAYFEUvPDCC4Cdubl8+fIDvd8n3kojeWH9f3Ij+yLBgUG4RJG2LEDTFA6UWTCmAENJ2ok9AKQJaNScnACBIGCIRFGTO4aw3b5VIvfj30LG2BnBjb1MOx/YB4QeCZLQiUssEhJ4+ITkZsFyuaTMavbiQ9QIPBHTEKKZcuqmyiKGGG4xdvYvEyRzT3DJizgmIYwvMtYvcVp5NMsZt2SDOWrYfyplFh6iTYK/8GliTaYgOhEYaahVRv7aDH8UMp1OGFSS2R04H8GRm/kTwMph7SSC+nn7s9rdn4cjKwDZk6B7I5HrNkZOwtzNAV4QcOcGaA2eqHj12L5hf2/KWtlLb39GEMBIiBoYNRBIOM1h3UAhBesoRKsQhmMueg3XiopVWVIsSgbrNZnybBpLECIiRaw8QglSNyzmx5S6YjSZEkUB54Tgdg9cBNh2dgaWWUNyUYIWhiGSpfMjHAm1ZRUTIzgyhsaxg0NgKiSZbNDOpiaCLXWxnWWV5O7ctLInyas0QEjiEqytF+YmhO7s49NQi96ZdzdADJG8h9c3fr+feqt9AMHOAL6eR+lb0QLe1a7a2gHAXT02VVUVX/7yl1mtVjz11FPs7e0xm80eCWMHjwYALhYLXnzxxQ6UJkny5vYJj1HzBKsTxbc+/a2sudMZUKfcJma65eW3EXYs8Eiw4WkFioCVuoGpN+khAunm+Aw+iVP/ii2ACThxycKBSbusR2Rtbci6FrRlAEt0F08niFz6wkqfsJwvqOqa0WREFPnAhGNyl+5RUzNwToOCY67wFCsMBRGaOYKIGggoVYTvDxnHIeFwRFokNCeCJn+J03JOPdfcXArid8C6CQluKfS5BatyxVjE+IdTFq8JfCemXJyASCwRNkg2Ct9oCTePLUhbOnYvTqCRkH4Z4kN3fAJIXRrbdABLAXsZmMaCv6oqaZpjZBAymUw4N1Jdu3gvgpmB0EXEpYVlIDMDo5FtQbfLHbn3KGAuFFGoiMKIA19zsykxZUlZlojsDq+uPHzfKo8H+YpjYDw5JFYB57VVC+8LnDQHQiFYbvkMwi00xggw4BvFOSkojcEHKmEFKHVvflBgo+euGY1oBKZWjKRi0ihmsrGJJ9xtLyMxLuXDLrAGYhS3EC6NRxCiyXvKXoNhJXrKZuAsh/ZtJAR3/fTB6mvNAJ6tHQDc1aOsHQDc1WNRaZry5S9/GYDnnnuO0WjUAbWvBwBojOH27du88sorjEYj3va2t/Hf//t/fyT7JqW0dhRIhpxnyPleGsmrzHiZOdfdgH2b2CGoyahZEzKmJsc3I6ra+dt1CSCt+nfdU/8GzgOwQaC24u6srMN3KSMCjwCPGIFi7cBkWy1wrOqK5WyFqEL2DiZEwQFrcjQLxkxJyTFoJvjMqKlZkzPhSwy5TIhmBQywbokBnqi4WV9kEpySixLfn7B3PmI/eorTSCHOKfJrNeXymJMTRfFqjTeoGe6NiI/HrKW7ibrRrryAc/twO7cze2kOex40x/b38RBy9zUKD/wbUJTg7PwYjzbzf5GA7AiOczg4hGKds1guOXcuYDqaIoTEeEDT+uSBWsPJCszQtoIFMI621cNpa+IIHARw1O4PhrUy+MrHD3wuigFzPWJYlRR5gZcuuONJpJCURUGC4SjyaBBgBMLAOXcXCIQgNYZYCBZuts9u39rDvNa2dI1kgiARhkwYG1UnhMshNp3fZIMFZdcNmEYxFoIYgxEahOnA2z7SJpq46gtKNIITbAv5lJABgiEGn4arYtXhP5/teUGAP8/gdf+m7re+lj6AsGsB7+qtrR0A3NXXfbXAKkkS3va2txEEdnaqfRp+vTi4B632Qv+g69Na88orr3Dnzh3Onz/Pk08+2Tn7PyoAeC97GptG8m4u8O57ppFIZybTpoLUck0tckw+pQkaQjlxs4SaqJsLtIbQDRWh8xYMGFnTZdaEJB3ABKgp8YjJOUGh8Bk6LkdSsKAsKxaLBVIIJuem+N6QqqfQbEhRBDRoFAWgaGgYIUiBGYdMWBIhKMkxJBhR8mJzyHv1ilJUgIFQc3w6QF0pmBzEpGXIIPK4oxr82CDXirRZM7tWEF4IqcqI3PdpjUhWMyC0CuBQwuoLEB/Y45yM2XCaM1gcw3SfTmrTzv8dKFjc2swQ3liuWSxXxFGAGNsWuhRwWsOeAT2HowAqd48ve+ROFGysZPYjOO2UvjY6rq1zoemYQYxBKJBC4hufsk6pfcVoMKBuGuoiZ16sKFJJENhUlieigDtms+EhgsYYpkJZ4EnDVEhubzF2gBDcANACz0j2AaTBw1BiWC6XjMqaxWhE6M6WpdYoIZkZiYdkKgQKzUJuZhXBRsX1FcUThGsh25avfRzwKUxE0khGhPg0W/YxTxNy4U2IPzaH9GvnA3gv8LdjAHf1KGsHAHf1dVt9YHXu3DmefPLJrQulEOJ14+Aeph6GAayqihdeeIH1es0zzzzD4eHh1v49in0TPSbm9W4AZ9NIUo6Y8yonvEibPBt5CYVouLN8DQEEQUDoJwyCCcKzymALHD2nErbAsSR1Io/IWcWMXfu36AlKrMVHyYKACTkpeu2RzlNU7HE4ukylagrXMo4YkDvrmQEJC0pnXr1HgWaAIXWK4lOe5IAZdzAWGIoSBdypDlDhTSY01H7D0XzA+csVKmxYmob1F5aEgcfBE/uEXkAa1Ry/UKKOM9Z1wdUbAnHqE4YhdR1wYaJojCC6CfM11K57r0KggX0PblsTOoKYTvSxNnBOwvJVyIYABsWS+bIgjmOevDDg1CGcQw/KBZymtl3cgr/Is8Cw/XbnvdNGemzYPw9KDSMDUoMWgj0jaLBpIctaEFUl89mcA08wOjyH70k0hnA0ZNZUzMqCoioJFnOeXws85REEAePQp/QDSmCp7QYPUawwHEpJjSE1DUMpmW0ZhMNcQGEERgvq+YJhvibe36foKWgPxUYIUgMn2hAJyaoRlh0UoNDcFjUI+7doxU1n/QYlxxhqYOZ5+ChAEhIwcLODf443N3rR1tfaB/Bs7QDgrh5l7QDgrr5uK01TTk5O7gJW/ZJSPnIG8H5B22q16sQe73jHOxgOh3et71ExgHD/7SCBZMRFRlzkCn+BnLlNIgmvIw9fpW5iyqKkygzLfMlCzvA9jyAIGfh7hH5II2ra4DiF3zGAYHGPxCdk4KxiBpSskEgkCZlZsl6vWK/XBIOQc8MnMVLRUCCw+b81KRFDcjIKFsSMySgZIhwPWAARDQXX2WNMDaQMaUiFYk/WnNYRF3zFSNTc8HwMEKYBmSq4c5oxvuVx+X1jgjsR+jWonwpIkoBL3pDRUzVH1ypEmpMuVqxWK6oK9gY+i1cT9g89Uu3OB8+CLXkCDhd1ohDfh6SGoxfh3AXIakOaLomjlMFgShIneCH4FUxzMDnMnN4miOj8BMcR5O403usxfpG05tLnG9t2LryNxcy5GO5UbboH1KFgllnftrHnUe5NOTXSspOe5noDEOIHIVciQzWqmVQlWZmxXK+ZrQylUviBTxAEXApC7rjPmWoDxrAvPLSGcwIyYSi1Rkhrs2O0Zr5YEJYlYv+QLAzxtWEsJJEw1ki685G2P5+5lvHSGGotqIV91BgJgRQGiea45/Vi84pNtw4BTJDcQVMBKYKLRLznEQBAY8yOAdzVN3TtAOCuvm5rPB7znve85w19uB4VyGrXBfcHAI+Pj3n55ZfvakufXd+jmOHpM4APUxETLvGtXOJbqUXB3HuVuXeN08FVymZNWZaUZUGxaFir60glCILAZfUeICR3qX/1Xepf603Y6IblcklR5iTxiMngHLlYuf2I0UDlFMcNK0KmDjyW4LhLQ+TAZkhJRYjmS2bERTICYWPghrLiWPrIdIo/rGhExDAyZDc1N8d3kPKQg71zDJVh5UE5t+BN+dCcQPh2jzjy2H8y5vimoSxLpCm49j8rjJkjo5q1TAiCgFUVcFgr2k6pMbBw7N+BgpsvOc9kqVksFpRVxd7FPTwV0zSgSuA23K4hmGzWkfZOs6J3X5c+RDWMa9ugvpHCAhgE9j3O0o9CsmEGfXgts/OGge9z/nDEzAkgVCuYcOVjLVJyFPgxeBEXR4Z1XaGKgrLIadI1z/sKz7dgMPB9zvuB9fqzWiAibOJIgCDSmldOZ4R1Tbi3h3R/D5UQGOA1bTBIxkIQYPAF3DJNNwco3UigO6wcG8PESJbAyAhCYSiEJjCGU9keQ8PAcCahBH6ABHWXZvjBq70OvJUA0BjzujOAu/m/Xb3VtQOAu/q6rq9kwvooW8BCiK/Ytm2zM2/evMnBwQFPP/30694gHhU4bQHgo1iXR8gBb+eAt9s0EnWT0/gqaXzEcnyTqgwtGCxLyqVg7r+IH3gEYcjQn+KpmJrM8S22fBJKKhqdsZjPqaqGw8EVokHMmkWnEq7IMBhCzpGhWFLjEXOHoRvu96i5yZgRS2pr/4JhguFIKGjOo9UxEBFIu/1FOmF/eMPtyZLrr6wIv83n8GBMqBTMGzIJRQX7ORQj4Bh8x7Z5oT2+YRhyfhDiLQxNXTOcZixnDetsQXOkufGFiAvv9GxWytRjpQUHPjQuE65pGq7esZ/9/MGYSoXEAvaAG273pkOYue0OY1i5rzP0YOYI1z0DZgH5ys4djqeb767vFzgJNgkjxsA8X7FYrgmjkKemQ067ow4HgeaoW9Yw8eC4hy0ueHAbwPepfZ/JcMRcN4zKkqDIqecp0jS8EAb4wf+fvT+PlWTv0zqxzy/2iIzIPfOcU9utW1V3ecdvQ78D3bQ1dEMj1I1b3WIEjdHMCIYxszDTuC2kEQapkbAthLBkyZZMw9iSh01GII8MGJg2MsKyEPaoLRvoxt3vrapb69lyj1xij/j5j19mnqy6VfdW1T1Vd3nzkUp1MjMyMjIyMuOJ7/f7PI+FZdn4lomhw1xAVRaEYYhbFLTbLTzTIkMyl5KO0NZ+hGprQinpC41TKTGlTkOo0QGBZLKTX+xKwUqoSdFw7VXYRmeOpCkFiIq53Hw3Lsje97C4fQmzf/DuCODrvMa+ArjHZWJPAPf4RuMyW8Cb9b2MaBVFwaeffsp8Pn+l2LnLmgF83RbwK6/3+TQSMWNqP2JqP6SQCYtiRJ4J0ixjvpgT5QWaV2BZFp4VYBvKAHrFnLIomIchlZT0mocIqyRlho2LhknCCh2XEovFmt5VQEqERYMVkhIbg+sEa3WyS0GIvrZ/0ZlUdTraEEOwNcYudR2ZGKzyCD1NELUO1yqdZU0iEkiGGkkHDAPsFeAr/z5jXdCsdgov8Ux5HhqGiVMzaQF+sySdpIy1nPEiIikSNKtCCIezuw7BdYuyLMnzCZmu0Ww26bZNZAWzcyhbF+u3bNjYLda8CzJXt8EroVqArl+QM9dUnoKbI2zXL9C2UMkcUmKmS07jBNd18Go+paYMm03AFZK4Ym0VDoEGg6raqmc7GsoPcH3bBeXPp2mYjkNl2xw1G5wWGV6aYiYJ1XJJrAtGro1lmiRJQq0ssTttIsMgWpclewgqoCM0QqmSRvo7gpJcCEZS0hE6I1nRqAS2gFSWlELuSDrYKowlkEoJUuBlAgyddmUQi5KCip8VlzP7BxcE8G2Srs1v16u0gPfkb4/Lxp4A7vGNxmW2gD9vfUmScPfuXYqi4IMPPqDR+OJ80a9jBfClr/F8GomImZmPmJqPCGvH6IXFIh+RZiXRasVytcSRdTQvx9BsojDH0B1a7S6lsaEArJNJEhzaLMko1npaD0m6ft0AnTEFGQknBFyhjcZsPQ3oocLXbIRWkhQH1M2UqciVzbRV8vihpGzOaXRaVFVAcVbgHFbEBqQjgdZRSl4Rgt5R7VYxBCxI1xtas5WCV6wDLBZr2W9NaiznHp0uZM06dpHj+RHDezlVFTM7VQyy06vQ6y0sw0Rfwdk6ziPeOYevdj6+ZH0ub0mQIUzX3fRem23WnO9CvOMDuJkLNIQiiVJKisUCEc25UWvgGD5GBqMMJEoY4rmCRb5uBQvJzFRKYluAp1dASUesjcNlpYysd46LQ10wpNp6Cuq+j1WWTLIMM0kwoiUNWZKaFqs4xrJsTMukKzSGzyV/XJFK6RwA8/X8XkcoQQdCEKLSPWrCIJeSDsokOpUlibZraQ4tKbivCUxNR1YS0Pj3NY+GuLxq3buoAH7ea+xbwHu8bewJ4B7faOi6TlEUX7zgK+JFpG02m/HgwQNM0+Q73/kOjuO88rou40f8bVUAPw+7aSQlBQvjhKnxkJn7iLRaIVOTVTEliRMkCRhg6x2iIiTQ2khNzQiqODqTiDkWDuVaBJIxx6ZJSoFqdhpUlARonGJyQJ+Mc3Q8UlIEPrmW8f2kx/eMp0ghaYqM01XM+NzhO0c60nMRI0E6M6hdTYk0SFNoF6D7UDwBcaBMm5NHEusjWK1FFDUdVmtSVqur9A3PllQLJYTx2zAVAtuysCML35PUmhGPwgghIBOSdDqGpc7DwEU3bYKawTxTrVjPgWWuCm2OCVUOrSUsVlDshFVMdw7l3blA3WTrHdgzIU8qzodz4jih6HRAd4kzaLuwiQP2dRjvOKx0TLat4ESCLySjavOooKcLRoWkpoErwBaShApNQiXUnJ6vCUJdxzRNwihC0w1ErUlR5BhRAvMVUkge1rx1u9hCaBp9oXGyGR5EzQ+2hUYKGFIq8YeU+EIQrpcZSmU2bQodp4JAQESFJiuOoyVlUeC5quL3k5rNj2qbULjLwbskgK+SZrSvAO5x2dgTwD2+0bjsCuDuTKGUkrOzM46Pj2k0Gty6deu1Yucuqz19mRnFbwIdgyY3aHJDJTBoI6bOAx4vfp2EEyzTxtICVkVIuSiYE2KaJoHRp7R1ClOV0woSavgsSBUpWgfQKX/BJikVHpIlMKdGixoNYLKeA5xRoAmY5C0qfUIyOWMha3RabaxEMA0ShJAYrkCbaeSA64K3JlmFDm68FoPogvqoYtgGLxAsBxfv1wsgSiVmVLDK1U+ksS5EHlhwOlZxf0UUYlk1Ws0Avy2ZPcrQ7YThKkHKGE2URHkNy7LoNA2iQscR0JZwfKKIWa8Fw/U4ZcuH6fojdk0I16JZTah2cb+A1RKWTsXTqRKb3Dqos7AVg3Q0mOzoIRzzItXEEvKZub+WIRmuFbigWsGjSrWCV+uOcCgkZSUQUlAXkpomyCSILCOcz/GFoNZuU2iCAAeCgEZZcJ4nyDRDzEIKwNF1HtaUoMZcR575QuN0TfQE0JQqVm7Ohd2RIaUyp95stARXaiznS4wo4mqzTm7bfCwMfk5cLvmDd0MA9y3gPb5K7AngHt9oXKYIBC4IZVmWPHr0iMlkwtHREVeuXHntH2BN08jz/IsX/AK8ixbwq0Ig8KoOg4cL7Mlv4Yeu/ThWN2EkPsVGoywr0jSliixG+SlEYNOAWoFt2WAsCESDJTEZC1xaxOQEW/uXDLAoyXjKFd7jHDDxKZkJk5ZecDey8bMQQ9i0WldplCb5TEA3IfAqZK4TnevQADcAZiCPVJ3Rmqn34TRBRtCkwm5rnN7fiRQzoW9UzJ6qkT0hFHms25CFkjiOWS5XNDo+dS+g5wsGT0HTXLqHLlUsKfIcq5YQrnKSJAGZ40mbcOViHliI9U9vtXPeN0y2Mthg3f71gUYJJ0PVTraNirNJSFkU1OsNKtdi02lt2BeJJY4Gox3+0DBVRU19hpJUK9loLixgyUWpUJcSocttroYUYAmNk0qppefzBR1N46DdpNJ0FlJJgrqaYKJpeFYNajXKsiTIcs7yDG21gvmCVNNwbYsTx8G0LDRNUy8rYCPlcYUgkCrReigvEkMcCbN5yDzLCJpNEtflptT4o8JBewvkaN8C3uPbjj0B3OMbjbchAimKgu9///skScKtW7dot9tvtK63YQT9VWM3j3l33xzxQyQsmOgPGHuPmHsDnKpDnuWkWUK51AjlAmFIbDPDtnqUdoSj5cSwngg0yIiRWKRkzGkQUwdiVD3PRCuWjMOKRtGgfcNiXug4bsV0adJcGkivJFrpLM80tDqYNcjGqMpWAfYIpKbujyeCxamkvaxoBBqzudrXmi45v1vSamtkS4GUEOWgnUpSe85qldE7cMDz6diCaqlas1JCmK1JsmtR2BYtE9rkjMKM8SpHyAWfngl03cB1TM5XNrphghTPiDxEBe0VjBfgtNS6y6okXY2p0Gg0m7Q9c+sJyHMJIXVLtXkBGlJSlNBlnderQ1Zo+KzJnSZJpUaOJJMS34DJzmfeE4JhJcmylPl8Qc008Ft1RoJtv/kAtZ+aQmMuK0rgUDcYujp1V1UH8zzDTzNGeY49m1FJyC0Tz3YYOTaGroMQFFISC1ii2s5NwKwkw3nIPM8JggDHcbgqNP4zw8V9S5Wx11XovglehwDuK4B7XDb2BHCPbzQuuwVcVRWr1QrTNPn444/xvDdXFX5VRtBvCxshTFmWLzS+dgi4wm/hiO8y4C5PtH+J5iyxHAszqLNMBcdFQZZmyDgiEi5HRorlGCTOEltvk1KtY+AkDUq+T8AVWVCKlCTWyMIJhnEF03wftGOVP2JXxBJaxxb61ZIlSgTRTQBHzdLVFor8VEswO2svvyW4DkzOASr8Gng+RIMKWYFhrecDG+CVFZ8+WSD6Mb7foH/FpRAwuQvOkXr/jeZFfFujrlI+5BJMx0Q3TZpN6DYrjlc5WZpiihXTWYIQgnZgMI8cupaJm+mcLS/av9MSyrJgFob4PjQaTQzdwLVhsYmLs2EiwZZQL6FKICjU3KHtCQZrYYkG6I7YJpA0DMlA7MwJGhWjosITSijiiopcSPQ4JlwtcW2bw4bPYoeLtBAMkMj1iJ+QgiNNo0BVMBdUCODAcphYNg2gKkvKLMNIEsLVCnuxINd1pGNjWDYLy0RoGpUQxJXkfB6yyHMOgoCO4+ILwX9sONTeIinazwDu8W3HngDu8Y3GZbaAh8Mhy+USXdf5zne+84UehF+EyzaC/ipbwPP5nPv372+FMLb98pkrgcYBH9HjDud8n3v8JjORgQNHtFn4GUVRIhKXHm5a3wAAn/1JREFUp3mGHoK/HGFpksiz8U0faZq4oqDCoSh7ROld0mWG4ddoaIGSk4ZNWrWC1FS2v2lo4F4tkIDvgzmHpKfm5+yRyvk1PQgKSWYIpUBuCpIz9RmtVmAYkvm6lJauBSKeXXDvV5cIPcV2W9i2jakr8hfUYbFu2zo1mMdgCjASGI5U91LsaIZKXcO2bGzLptMOkFFBlmWYxZL8OOLTlclBVxAbHrZl0W0YDLKceRjimGA0O+iaIgubuDhPgptDkMI8hcCD0XqbhITZziHYsS9awVJKhMa2FbwxjJaoOcBkbTY9jhKWywjX8bhdr6mqIRAj0ZHMeVah29U0TtbefQAOGm1diZs3gg9L0/Bcj7nrUpOSPMshTamShNUqQgMKx8G2LGZZyqooqNfrVLaNIQR/3HAILlHx+yK8CxuYfQt4j68SewK4xzcamyrb5+XkfhGqquLJkycMh0Mcx0HTtC9N/na37TLWA1/dCWE0GvHo0SN83+f27dsYxqv9bGjoHPFvsSDnAb8BqCYvgGHoHPg1dDz0hs4i7eEmj4mjiKw4YWIFBEZG6vR5sqjw0oqjuk3k1zCSitiomDxtcv3WjIlWoWtK/CEmihw5PhQDRQAroByCdwhaCeaiYuTp67aqgB36YlkXfy+WAkHB+HhIUZi8/2GThWbj2TD8FMoS3AYsYrV8VELLgHQEg0iRP8eGWaz+tgyYru1lEDArBF3bROQmqVYjc0p8LaPQYlYrFU8nqopZqaHrOjeuNBij3l/LAb0AL4ZlAif2JrgPSp3tXGDHhdHmDUlY7rzdtnlhLQPQMSU7WhjamuTxasUqinBdl1v1GufI7bpdBBJBXYAulH+gI+XW50+9psQ3BGc797Uk2AhWysgQIQSBZWHYFqvApyhLsixDJAnpfI4BBLpOluccCY3/wq1Tf8vkD9TvghDirYtANgb0z2PfAt7jbWNPAPf4RmPTOnlZnNIXIc9zPv30U5bLJe+99x5xHLNYLL74ia+Ay54BfNcVQCklJycnnJ6e0u12uXHjxhudDO/wXSacEzIhY4VLg5gCQQaYFFoJbp3KvcORnBFnOYvMYpmuWKQzjFJjXr3Hd7Q5y0pS13MelwaaEKSTGrQK6rWKvNSZPzAwr0iEK4gkeBFkJmiailfLdcinIGoSxxdMZsokWjkJScJ19a/eFEyWJVr5lGjh0Ww2sQODZaTsSM7XLdW1rzOOA04MwzPoH0K0frxeh2RNEBsBDDPVfj70IF3BbAmNGsQ56JpOM3BJbZeuVxFFEfNSGQSWZcnxeERp2BxoFlpiMSrUz3fPh+G6FWzrqmW86ek+QwbtC5NpKaXKM17f9jXJYMe3r6VJHi4XxHFMzfM49L1nkjp0CbomWErJat367WmCUEjaQmAgWFUljq5U3NvnAYWA6fq2iaApBRqSyVrwYRgGvqYzSVIiQ8fzPMqypL1Y8ZOjYz7VNOr1Oo1Gg0ajcSkXa88jjmNOTk6+1AjIq+B1frf2BHCPy8aeAO7xjcauRcrrEsAoirh37x5VVfHhhx8SBAFPnz691GzhyyKAmqYxGAwoy5Jms/nKXoRviqqqePDgAdPplKtXr3J4ePjGJyANnR/ix/h/8k8oKXCRxCj7F50GJRAgOcfiSPQx7HMOdI+neUhQFKS1gPnM4Xw0ZuqNqVcpkdanZwvmUwuzUWK7FaOFTr7Q6CaSwoRlCc6pMoHWMxAzycoSpAuNbpmRNW3SlaDZFowGklZLMl0zE00vqPIJaVTguh10XScpoeeAXHv1WTaEMVgatIGT8wvF8AbxzsdfadDTIZ4rEjRbE0PbASL1d91XtjBJmqJVCzTXoxsEWFWJUSQMznMepEuMQIJhYVsWiWsiMREI6u5Fi9czlC3Mxtw61xRx8yuoaVDk4EooJZgmmOXFhi9XU5w4ot+o4zkuhpBUEiIkEklT0xjvVKhaGkxQ7eOJlEhZ0dN0FlVFW6ghw6gqMHWN3csrDVgKdTwgoQ44ZcXZdMoKSbPZxDRNrguN/7x9CJ2IMAwJw5CHDx8CUKvVtmTQ87wvTZTiOOb73/8+pmly586dL7WuL0JVVS+9qNq3gPd429gTwD2+0dj8eJZl+VqVgOl0yoMHD7Btm48++mg703aZopLNDOCXaU9v8P777zMajTg+Pubp06c4jkOz2aTZbFKr1S61OpDnOffu3SOKIm7fvk2r1friJ30BatT5mO/xb/hVMhaY+ORU1NGZUmJSAgZzHGq5TTQeIE2L6+0GA6tG2woYp9+hYRxTpQXRImK2XDKb+lyrKtIDm1wa+DWBNReMO6q4VYzBvg25AdpMEnUEhiYQsxKjtm6abuxP1vwnTTPG0xlFknH9+iGTiY7lgKXD+T1oXFXL1duwAsQYFg11n23DNFKrrXkwT8DVoWHA5BTyQr1OtH4tKWGeXeynQpNEUcQqirjad9G8OkEuEFJjkJsEdbjilgzzjDTLSOMVD0YgNB3LsrAsE2nYCCHwLUgraJZgVRDOVaUzFCBqgtmOYfRIKMNqicRaTTnJS4Kgy8pyqImK0+LiO3GkSXIkXQSlAClLlrJC7hyDfU1nuFYIp1KiS6ihU1aCrmCtOK5AE8SbJwlBVlachDMSJAeNBm3Toic0/kPDwRMCfB/f97l69Sp5nm/J4NnZGScnJ5imuSWD9Xr9tS8Kd8nfhx9++Faqi7t4HQK4rwDucdnYE8A9vtHYbQG/Cnbbmq1Wi5s3bz5zkrhMW5nd2b0v++PdarVotVqUZcl8Pmc2mzEajTg7O8MwjC0ZrNfrX2pmKY5j7t69S1VVL1T6fhlc5RZDThhwTIDOhAqTHNAoSQCfMF3y6dTifb1SlU59nRxh5KSZz/XyCqvWmEOrjeGWrFaCxaMVM2fMfJJhFjqzmQ1NB8fVMAswlzCXUIWCoF+htTVkpmH6inSGC4GmVcxmkjhOmM9DjmoVmlNHW6dL1FsQPlFVv/k6ts2yYP5E+ffN1zN/jTYM1nN+dRfcFEZDqHcV+QNoN2G47h23Apit7zcMydPJiiiO6bgODc1neC5IJDR3nIhcR8fRXRzHpVOrOE1ysixDKxOOpwmaWNCTGnPPocBhLHR6AUSV2kZv3SbeHJKuBctKkb/lPKSscur1BrZt42vymUg3X8AAyebrZkiJbWhoUlBHzQKaSM6qC1/BbYLI2itnKVUSiIGGVQlqAlZUiKribDYjl+qzrwyDQAj+I8PBecH3xzRNut0u3W6XqqpYLpdbQjgajRQJ9n2azSaNRuMLq+ZRFPHJJ59gmiYfffTRK8+6fhmUZfnK39c9AdzjsrEngHt8o7FbAfwilGXJgwcPmM1mL21rXoaoZIPd2b3LGiTXdX1LBqWULJdLZrPZlhBq6/mozUnvdSoYu0rf3aroZUEguMYPM6CkYITAJmeFxCcjR0YJs9US3eriNm5S6BkZKZIauabKZGHsUnccMAuGnkU9cOg4PpKIqlZR5gnDMCf4jRmaZSFyB+epTeKb2KYgyEvyukYxNCBUpDPPBUeHFednM0xzxLVrJlmmqp7LpcrJ1cqSPDPoHMI4Bs+SDB9BnguafYjWcRWxBN8AO4XJANIcEM9W+dKdfWLaQKTIF8UMI0q4ZtaxRY3hTC1jmTBbP19KnvELlLqGZdlYls2h65PFBWfjjELGnMcREGHoOrmwwLTVfJ2t5iMBLKHi4qSsCOdzgjKibHWwLAspJaZRsS1ESYmubUcKkVLSMlBZvqhc5TaCQQWaEDQQmEgMIRmsU0YAbEBbW/2wVgt7UjKdTvCrikanzUrXeX/t8/ci8vc8Nsd9vV7n+vXrJEmyJYNPnz7lyZMn2La9rQ4GQfDMd3JD/izL4sMPP3wn5A/2LeA9vlrsCeAe32i8akxakiTcu3ePLMu4c+cOzWbzc9d3GQTwbUe4CSEIgoAgCLh+/TpxHG/J4GY+alMB+aK5wY3SNwgCbt269dZOgB18TgmwsWiTsmJJIHXOoyVlmGAHAd1awD3N5D05IREZPpKlKHFFRWYJxk87tK8tqIRFvSbJVxrRQ4+jWxWlE1DMS2qzmKyRMhqsyD9Zod006JgmyUDDOPCYzXVIU+p+RJqWPPh0zmIRUavVODpqMRxCrWawWml0uysWCxW3pllgZJK6XXK2UvsoXlfwap4ifqNzsHuQrklbpwXj5GKZML3w3QtTsERFOZsSpSnSbJMKh3qD7Vxg04fB2talWVMRcQC6BtNcGSWLFSwSwao0sRyTXqMGeUWWZbgyZhipeDpdE2SlCZaDZZo0PI3zqiIMQyhytG4H3bQA6JrymTm/vs4zCt++LhjvVAdrwFwqolcBk6riQFcKYE1AUwgsKZFCMKyqbQnSrSoG4YRCE5itNgvD4AOh8Z/qDvYbfgcdx8FxHA4ODrZV8zAMmU6nDAaDLWFsNBpYlsWnn36KbdvvlPzB588u71vAe7xt7AngHt9ovEoLeFPZMgyD73znO7iu+9Jld0nbl63avWv7Ftd1cV2Xo6Mj8jzfksHPmxuUUnJ8fMzZ2dmXUvq+KjQE12hwnzEFLQKZEc8mREVOO+gx930MUVBisyoO0I2n1EXJSmi0zJxTqSFii8bMhRo4bsVwqSFzgTXRGDrKYiYIfKyaT9GuqJoZNS0i1iMGxzmWDJmHAd1Gha7DYDAly9J1woTNfK6m4TzPwjQLomhFHNeQEpaJpK6VlFLtI89X3n9NB1zg7Fy9T2kCm6rdzu6s1WAVq7nApg2rqOTR0zmGKLDaHZx11XW2UybMd55v2UCuCmd9HaIFzCKo2WoeUb2gqhJqQsOxHTp1h6qQ5HlOjZjzvKRM5mhIkkRHywrqecVRt01eWhQ5ZCWktoYmVVSbrcHMkngCjLVBtJSSrqZRikrN8wnl8bfBoS627eMKWEhJTQjmUmIIQV0IRFFwNhuT6xrNZhNd17ktNP5Tw31j8vc8nq+ax3FMGIbMZjMePXoEXFxMJUly6TO1n4eyLF+ZcO4J4B6XjT0B3OMbjc9rAUspGQwGPHnyhHq9/kqVrcus2r3tCuDnwTRNer0evV7vpXODjUaDJElYrVZcu3aNg4ODd3KS2RDAqEo5nXq08iF+4wDXNZkDMSngsdQEjfQA01kCJo6RIzObli95MnDwrmVUdkWFsljJZwIOIQiUACQ9UySoc9XBzR2y65LpLMMs5+RFQRQvefo4IQgyNM3FNC3abYvJRBF2IQySZEQQ1IgiCJrgGRXnxxVeR1141JpKVTt8DGlPvT+vBuOVqvJ5LowjpXZtOiAyqMWwXIHZKnl0FlJWFe+/32RWKPLXDmCyriqaxrPt37iCHrAKIffWXWyg5qqoO4CGd2ESvUkSEUJgWRa+Z+GUYCY5aRzxcJShtk6QjpdIW7WTD2oGE3lxLNQsyVAKMqm+V7opmJQCFRis0TMqprIiWCeIWEKykhUqGkQgJASaYLa+GCqARZ4zns0oNZPDeoO6pnOI4I8Zb175+yIIIfA8D8/zqNfrW8GH67qMRiPOz8+3342NkORtVQQXiwWr1Yp+v//Cx/cVwD3eNvYEcI9vNDYmqs+TrKqqePToEePxmIODA65du/ZKP6DfFgK4ixfNDU4mE0aj0bbVvVwutye+t618vE6DslRtxyyv0W/dwbdLYnJsIKWiScVMyxnEXb5rJqBDsZ4D1N2Kcm4QnJlMr+ZIJHZNMJlDdwWyBqslRE/Afg9MH9JjEIGgqCzc1KXVtimSFCFShPBI0xVJEqNpLlHkoesGZbkgSQo8TxGzwCs5OS7x64JVoqqEIoXBiaDVg+maqPlNNROoa9CqKSHILFR+g+ehWsZzch6czAFoNptEOz/FmoliSEAzULYwpoCOAbOh8hLUNUh2OofJxZ9Y1sXzWy6MUXYvQQHzMSQlFIVAFgm6blCv1wm0nEFRkCUJURRTxRWZ42JZFnXbYrRDBtuGip3b3tYlI6lEH6FUldBSSAop0NHxpRKPJFKiozijnhdMZzNKQ6fRaJBqGj4a/6FpvzXyt4vVasUnn3yC67p88MEHGIax/W5sZgfH4zGgxigajcZ2jOIyiNhiseDu3bvUajWuXLnymce/6t+MPX4wsCeAe3yjsfHI2/3BzLKM+/fvE0UR77//Pp1O55XXd5mk7esQ4fY8xNpoNwxDDMPg+vXrpGn6RnODbwotLqnCJdKsuNppcGLa3CRFEFIHhoBPyUzodOyce8tDasGEWJTYoiJeZ/QWkU4Ql2BLCgSFhOoMeG8d/2ZDp4DMUpm4wRw0XaBrEpEfs4p9Wq0Wuq5z5UqD09MlUSSQco5tR3zyiY1tWxRFl3o9I0kqhNCo1TWWE8mVTsnJiYqh010gV3FzSQ49GxZzOBuoxBCE2gaAoshZVmOEMGk0m/TbOuM1edQ1mK7bv1KqH+huAZMZVC1I1ss1fRivSZ5jQlioiqOUsNg53OwKWjFMIhWPl1TK5idazKh8g0ZDtV091ySoVNXJkRnjMqfIM5IkRTdylo6LZVs4pk2mXxAgA0ksdqrvUmLrMF/fLAFbCI6lBCkQUlArc1aTMZ5poDcaSE3jhqbxn1v2Kwk+viyWyyV3797FcRw+/PDD7RjJ7kzttWvXSNN0SwZPT085Pj7GsqxnqoNvMi6xef1arcadO3c+sw4plRXQZpt0Xb+UmeQ99ngeewK4xzceu9Yty+WS+/fvA/Dxxx9Tq9Vea12vayvzRdt1Weu6LGzmIS3L4s6dO1ul7+vMDV7G6x90PLSehqUrdfCYgAYxJgXKICQHLCwz5yQJaIddyuaItpFzWmnYppo3i3/TJLhZMU4BoQiYHisyFLShnELUV2pcewCmlzAZhmhFztFRgEVKOMsIQ4Nr1wIgYzhM8f0Oq1WObQtms5A8PyPLrmBZNlHqcdAqKUu17boBk3XF76AJx2dK39A/gsGaCXXbMFpBnmfE0YzStmg2G2iajtyp5DV9NS9nSMgzODtR7WwpYZ7v7EidbZUvqEGyqRh6igD2KkgWcLZS6xNAoUGeZITzOT0fymZLxR5qMFkfokIIAs8mrdRxYcmCucwgzcgWS5xywsixsS1L5Rm7OuN17K8QcGCIZ2xjWgKGOwrgrMjIZlNiRylypRDc1jT+E9NWPn9vGRvytan8fZ5PoG3b9Pt9+v0+VVWxWCy2s4PD4RAhxDOJJK+iml8ul3zyySd4nsedO3c+8/ob8rf5Pdv4iL7LucQ9fnCwJ4B7fOOh6zpVVTEej3n48CGe53H79m0sy3rtdb2NFvDXxc5hOBzy+PFjgiDg9u3bnzn5vMrc4JfxG9x8PkEQ8Duu3uS/1T8hIUHgsKKg4Co9jgGDdD0HGBspEp9FYeHPGmhBDrlN3a8YT3XkSlAfapSWIkJFAquHoPuge7AaqRZs4UC5iIimM6Rw0IWPLUZYbgtmGUFQsVzOiaIcISBJbHzfpttt0estiOMW5+eSNJ0zGc55MtPpHNYoCpfukUEqBVUIkaOIkKYpccYGcQFpmjKfzzk40CitplKi2iAKJejIMijmEK6f1++CXP/dDmC6JnmauJgLBMjXvMBEtXnjBQwL6ASwWB96pg7nq5T5Yo5hGAS9BvO1OqVpXaSHWCgBSK8CSjB0Ha1ySTQXLEmlZRRZThRm2NWMU8vEskwsy+bINYhMaGk6mpBooqTUQFAhEWRZhj6bsHJUFU0IQVMI/phl47+DbN9d8vVF5O95aJq2JXrP28w8efKEx48fby+WGo0Gvu9/hrCtVqvPJZ8bgUpRFM/cX5blG0dd7rHH52FPAPf4xkMIwXw+5/z8nE6nw3vvvffGStZv4wzgrtK31+tx48aNL6wmXKbfoJSS09NTTk5Otp+P1FSdr6CiAcyQFBikNLHIyTZzgELQMUpmUjA8rXFHW6pqklNRodNswHwkcI/A8WGyUlW/bg1SAxYZmI8Bd8l0NceTFrnTxqhWWKaBqEK63ZLhcLE1Rm63XdbjX1hWzmiU0et1SFOTdrfk9CTB8zLOBymySrHcksWkRqNtslzYCE3Q7cFg7Q3YbUO4jNCSGVcCm5rdIkk1khSsLgwHarlm/YL8Pe8daFiwicxoBRftX8tQ1c1+DqMJjBuQrR8TBrCuGtoi5iRcYlkWB+06YSW2EXEp0C5By8HQ4Gy9DZoA4Qo2Dd6eKxjiENgOEklD5JymyoR6vlhQRTmZZWPZFqZp0nc1ZqUEqWPkGdZ0jO5atOtNVoAjBP+F5dB6B+RvM3P3JuTveQghtor7w8NDiqLY2sxsLpZ0Xd9+P+r1Onme88knn3ym7bzBhvzlef6Z16vVanvyt8dbwZ4A7vGNRlEUZFlGWZZcv36dfr//pVol37YZwLIsefjwIdPp9I2Vvl/Gb7CqKh4/fsxoNOLKlSscHR1tX/8KdR4zw6Nkho5NwSe0uS1nZCIlWN8fWCmTwqDlCe4/9WkeFCwNlUlreYLZDA6nULRVrm2jCeYCxjUQmmS1nJOmCzS3gUxqdOsF0jFIEpv5/Jxez2TdxARASnW71ZKcnc2REsLQwLIqNCpsy6F/GHA+0mnVlzx5AlmeYVcLZhMTy7LQayZt14ZcZ3S6YjiJcJyAK4c+w7l6Hd+D4Xz7soqwref/Og22c4FCwGTHFkYYQAFtTQkujs8UN6x7MC8unrOpGCZJTFpOsW2fIAioe4K4gkYJXgHnEVTrCmCwE/zScdQ8ptonEIv1bgICXRAKi1rNolar0RIFJ3mKyDKiVURTpnwaGVi2jSYELBdUNaW8DRHoEv5j0+LKW7Qc2mBXcPGituuXhWEYtNtt2u02UkpWq9W2OvjgwYPtcqZpcvXq1RfO/H0e+XuXvoR7/GBhf2Tt8Y1FHMfcu3ePqqqo1WocHBx86XV+myqAm0zfOI4vLdMXXuw3GIbhZ+YG6/U6p6enLJdLbt68SbfbfWY912nwmBnKMM+lIEPiMCl7eNoAoak5QGlmQA3LlRBpuGcm0bUM34F0TYzKBdBQ/MSuw/wUWk7FRJ+zkhF+1CCpBZgOmFVC5TiMzwVBYDMcpvR6DqNRgmXpTKcV/b6K8KgqnUbDZj6HdnvKZNIEICs0eu0CgU0QWAhdQq2gJjLqzpKzByln1Yp2t2ISaWtTYp/R/IJ8Wy6wrhLWa6p6uXm02uEonTqMMkXCagboCTghTDJo7HykjgvzTcRcTSl14zgiXi0wm4r8GQiMDIKlMqG2Gxfkr2bCvLzYhkJnG/vRsmC2M8lg67Bc39aRrAyBazi4roMpKzKZYKUpaZIiqhJ7HemXpCmWZfEHHJsP30FV622Tv+exiZ/b5BXP53Pu3buHEIKyLLdRc7uJJHme78nfHl8J9kfXHt9IzGYzPv30UyzLIgiCS5uze5mtzJvieYXyu8Im01dKyUcfffTaYphXxYvmBsMwZDgccnZ2BrD1UnveXPsaDQBSUgQuKQUeFaFeMJ3d4FrjCWiSpZZjiYqloSGR5EIQPDXQ2xWDRMfQoTKgPAbPgaKARVqx+vUpRTvFqrfInzjUOyW2oVPGBqaVrT3hfJbLlNEop9WysW2NOF5xfp7g+yqA13FcbHtOWWpUFZimQBMV42FGvalMxbuHgqQyqeUmhl+jWZXMF3OiXDGyJEmYL2OS0sO2LXodk/FCbNvOpgNiTQZrLkzWQhbPUL56PQGLpUoSOV8LS1wLZsmO+ndHjKtbktUsIlpFXO24yFpAqxQkERwv2bZ/Vztfm5qt1NMArgGTnbxgzWBLBn0dRpWqPlpAXYeoElvi6OiCpWZjFpJxEdNxdFaOR5ZlLBcLPk5Tjsqck3XF2HXdtyJw2JCvd0X+nkccx9uEkY8++ghN0z6TV5ymKb/8y7/MT/3UT/HTP/3TW5N6z/P25G+Pt479EbbHNwpSSs7Ozjg+PqbZbPL+++/z+PFjkiT54ie/InZVxZexrvF4jKappIM3Eaa8LsIw3JLjDz744J28JlzMDTqOo6LFUB53y+WSe/fufWZusGE6+NgsSddzgNCk5ERoOAYch9fx6zNWOnTtnJPEJrAgzCA/0blqKEZSr8M8hPwUetdhlJeEYUhQZhz6LUapi+NDQEFu64RnDnp0ihA+s9nF7GKalqxWEVlW0Wx6rN8Cul5ycrKg31cVzFZbMh6lSiEcKiZlajB8KvDrMF2omUnfjynNFn7DxnNSwjjDlCtkPGc2BtNycRybWs1GFjo9B2SlfPzkCqIENAeO14RPSkUUNwh8iNdzgQ3/ov0rpeTpdEkUJXQ9l45Z43wkGEjoNy7UxIENy+pi3btk0LdVrjGAp0FVQL9S5No0YFkq78EciGxBsaZ/dR1GhUaSxCyXKX03IK/5tDSwLeiIkv+gWBLPZpyfn3NycrKtiDWbTYIguBSitiF/vu+/0GrlbSNJkm21bzdebpNXfO3atW2LWErJL/3SL/En/sSf4Cd+4if4uZ/7OX7hF37hnW7vHj+Y2BPAPb4x2J1nOzo64sqVK1ufrMussl1m1e7GjRuMRqOtUtDzPJrN5pYoXXblYzgc8ujRI+r1+guVvm8buzYzu+QzSRKm0+ln5gbrhzDzS3xDzfsp+xcT28k4nwV4ky5ua4ppZpDYeJ5kmQnqPpw91fD6Su2bT6HegHyYM6nmuLWSVqfD6qkJrqTWEaRzgd4uyDKDhmvQvrJgOvWp1WqkaURZZmSZ+twtS1l6NBqSwWCGEGI9B1hS5JKy1Gh3DJKR4PAw4/iRqSLT6hVPn4bkRcHB1RaB61KkUOBiSRep12k1c45HOVmYUhRLav6cTDrYlkW7ZTFaGds+rOPCcqH+bgUXEXFSwmJHLOo4qv0rkYgipEpjrup1ylWNcy7avDujhHiuInIAdXvHP1BCIqFXQpEqBfFgkz8sUKW/Ndr2xZwggKFDEsUsl0scx6HW8lgCkVT//keBxRWzA50OVVVtK2IbcdGutcqbXjDN53Pu3r1LEARfGfn7/ve/j67rfPjhhy8USKVpihCCW7du8Vf+yl9BSsknn3zCP/tn/4xf+7Vf21u+7PFOsCeAe3wjkKYp9+7dI03Tz8yzXWbFbrO+yyKAm+Hwoii2J7qzszNOTk6wbXtLBr+sz5eUkqdPn3J+fv7KSt/Lxq7Ny/Pk03Ecjo6OtnODm30hjjOmvSmJiFm5bSqrBNNlaWRoQjKVOtn9DtffC5FI0rUJtFOD+RKaM5irTi2anfHgQUirXtF5v8PsxKCK4LBTUVSC5YmBpy+wnAa27TMeDyiKR7RaTZIkI03VMSSEYDKBbjdD0yAMdRoNh/kcms2Q2Uy1rrPcoNdNKQuBQCBEyYPjGVQV33k/YDhzSELo9mC6bu+6jmCRXYgn2s2C43GOSFMWyxVoIXGpfPZaDYvRwmTDBg2bi4SPAGbrSp6UisiZSORsSpZECKND5rh0GzBafzXsdazc5rCIdz4714ZFDm3AKeF0DIv1uv0dYUjb/TxhCDxdRaxWK1zX5WbTY8RFWfHHbcGH5sUxuakI1+v1rbXKZp708ePHPH78GNd1txXjV/mOhGHIvXv3vjLyl6bplvx99NFHLyR/SZKQpukz9wkh+OEf/mF+9Ed/9F1t6h577AngHl9/LBYL7t+/j6ZpfPzxx3ie98zjlz1n9zbm9gzDoNPp0FlXPjYee+PxeJs/+qYee2VZ8uDBA2az2aUooV8Xu235V7HhMU2TbrdLt9vlWlnwm+ljKB6QJDFRHCPLgkXNJ8BkJgPqhuDhp0167YqRKbF0SbomRUUGTgpLUs7CKWg2TVnHsHRKCa0eVAtJ4kqSRMMyJUE9ZbVyiGM4OKhxfDzGcXT6fYvJJKfZ9NC0hLOzkCBQAb+W5dDthlSVQVUJbFtgGgWD85Rm06esSjRnjIvEFm2ywkQIRbZWO/P9fgCjdUvXc2EWG7iOgeu4tJsVp2GOmaYkScJSn7PILJXhW7MYLizEer+aa1sYW1M+ftmq4vTJnDwraB11cda94sqAjY9Lo6YsYwA8S7WMhQCjAi0BdwmTHHqNLad7tjIIZBrbWcC2BdP1ghJJGi9ZRQme5xF4LgtxQf4aAv6A+/nHpOM4HB4ePmOtMpvNGAwGnJ6ebqMKN9+R56vbG/K3qX5/VeRP07TPrfw9T/5ACave1ajGHntssCeAe3ztcXZ2huu63Lp164U/ql/nFvDL1r8hexvbiE17dOOxtznRNRqNzx0Gz7KMe/fukSQJd+7codlsvrXtfhGklDx69OiFNi+vAkc3OHAPWKFzm4RhXqAnGg/ynCg6Y7zKMCudOHVhYNE8EJg1GIZqFi0BZr8eUwZTMsOj065jGhrluVq/GcDyqYanV9DRcXWHqpyTph2CwGY6TTEMjSQpSZIYwxBUVcRolNJq1bZzgIaRc3q6otNRhLDZlJyfJ3iexmhcMg9ndPoVWtaj1tEJ10KLbl9ZvUgJ/Q7kpTJoRoJdU559VbXmVJpG07cpXBvLkoRxjqNlZFlKmS1IE52abeI6NuXKoZYZLGMoWhUPTkKKouDm9TpLocifob/YMBrAd0CW4KeQpxfef1LCfGcW0LUuxCWeCbMdYYhYC0MkElYLztOYnufRcWu4miSt1PsUAv5dX+Bpr35cPG+tstsqHo/HW7Xt5juSJAn379//ysmfEIKPPvrohWQuXRP757Enf3t8VdgTwD2+9rh169Y28/dF2BC2y8rLfJfK3V3biGvXrm3bYNPplAcPHmw9+DaEcfdEEUUR9+7de+tK35ehLEvu37/PYrF4oc3Lq+KHMPnrMuAOJqa5xLVM2gTIeol/7LBIMhajJY+qguqBzeHHBmXp0Ql0Hh9H5LMlfdPBaDZouoJwCskD6N2AVQVRItDTilpbYzUyWc4r+n2VOLxcpvT7AYOBYnrttslgoJqjluWu74OzM2XYN5vp1Gr52n5Gw7YlsyczDg8SsvwKuq7j1GERKiNlBPR8lQu8ipSwA6DXg5M1SZVSmUWPRxf7xGsIjMLC0Cxavk+O8rucRRmFM+fxSYxhGHiuwb3jjLKsaDQaWJ617e02/Yv2r7lDBuuAWEEcqkX7LbaG0U0Xwh1hyGKHDPqWmuMD8ARQQK+QTCZzknSF7Teh8pgswPAE+bpKe8WEf/uLU9Jeipdl9M5mM54+fcqTJ0+Ai+i2dz36kGUZn3zyCQAffvjha5E/x3H25G+Prwx7ArjH1x5fJGTYze+9DNHDV2XdspswcHR0pE74azK4EZHUajWazSaGYWzjp96l0neD5yuPjUbjjdf1HUxMNE5kjVpUI6sNsURFZujc7JicxT59vWIQZuirlJN/EyNbEbKSRCuDowMLmTVpC4HuQjaCZgeMBWQOuDVwdY08LFgsFGmSMsI01bEyHmtYloHvC87P43WUm2A8hn4/Q0oNKQXNpsdyWaHrIbNZkzRNOTkpOOiXtFp9RiMd04LxAgJX/TtRTjj0Dtamz4DrqOzgDXrti7bw5vZwcXG7UYfhwsDzDALfA6NOYGakSYouFhSliSYEZZFwNpOYlqWsjHbav01PVRrLJSQZhMaOFczOoW7bO89xLsggUpHFbg5lCqYJ56WqzKVJSr3TxHPUaEbHhR0uy88EF1XDy8BuRu9kMtkq3suy5O7du+i6vvXZ+6IK+pdFlmV8//vf316EvSgPOMuyl5K/V8kP3mOPt4U9AdzjG49NZbAsy0shgLquv9CY9V3DsqztiW5XRHJycoKUEk3TCIKALMswTfOdVT52PQZfNJP5urCF4GMMfp0cWfmk0xsc+hFDq8CyM4htnJqGm7n0rrucDyvs5YiBL0FAVMUsRiVHCx1x3UFKG6epMX0C9RxEF+KhYDmR9Hsp4LJaFkTRCUdHLmUpME2X2WzBJhGk3fYRImYwWGLbXUBiWTZBMMUwbJIkJcuWtFoueR6QJGo0odkHIeH8KZSH6v3pOoTrqpyUYHsQr9Rtz7kQiIDKM94lf80ABvMLAtVpwGChYZom0WpFXJkEvk9RFNhmxGieIgQ4jklq2DQdi0DqlHMYr1+z34JkPYYW7CiBpbwwdwawTJAFtCW4BZwOVEVQSqjVJcvFgiRJea9bI1qTPykh1dgOEfYN+G07quHLxKZK3mq1eP/99xFCEEXRVkgymUwAnmkVX6byfhPvVlUVH3/88UvJXxzHn7l/T/72+DpgTwD3+MbjshM3vqoK4OdhMxMVRRFSym3+7mQyYTAYYJrmtk0cBMFbm4F6mc3Ll8VvweLXyXGdlHFYYzxt4FQZeS9CF5LQUHLTzKiYzxcEacH1Xgtx02Exy3GmGWkZkd+PSaROqVsUCwfXsvA9neFK4HkCTcRU1IgiaLdrzOcxSbKkLDf2Lxq+b1CWMdNpRqfjMx5LpARNi5lOE2xbZ7FIuXbdIok96g2D5ULDMCqypSCcCVo9ZdIM0OnBIATPhnYDMgn9QD2mWaoyJ4GyAt2GuICiUqQvry7In6nDOIKiLAhnIc2ggFp3e9ETNHxkXGIVCVoZMT2OeZwYeK6O8G0sy0Y3dFJ5QYBcB5ZrZXHduRB8GJVKHKktYJqB0VTkDsC3K07DBWmaEdQD7JpDtKka2hDukMj/QbBuhV8yptMpn3766dYLdHO812o1arUaV69eJcuyZy6anj59im3b2/la3/ff+HuS5znf//73Kcvycyt/LyJ/tm3vyd8eXwvsCeAe33jstoAvA19HAvi80ncTe7cZkN9k8w6Hw62IpNVqbVM4LgMbmxff97l9+/altta+g4GGIDRyLK1iioYTWqRTi6NGxYklCOySh6dzqArsTpf4sUXvDiwMi6vXLFZzn/JxwVEv4um8xKhCRsuKJ/cdagcWTt0kiR0MM0TXNYTwWK2WdLs+o3UPNs8rTNNgMNgoNR2krDg6EpyeLkiSmNFI5+BAxzJ90kTgOhbRStLppJyfq3KX7gK5In2U4ElIZjCoIFsXl/s9OFt7qkgJvS4Mx4oMWgb0WpAW4HmAUAbRyzQjjSY0bJ2jXhuEDlIJPpYrgb4yyCufetPHlSW6meG7EWeriNUqwrMly9LFtm1M09yqqUGRwaIAP4Myh7PFxbbNt3OBkjSekeal8uvzbKY7DkzmJlYZ6OjwO75ccfiF2CV/m/ngF8GyrG1KzUZ5H4Yh0+mUwWCw/Z5s/r1IYPYibCp/G/K3m329u8zLyN+Llt9jj68CewK4xzceuy3gy1rf14kAfp7S9/kB+V3D5U8//fRzRSSvite1eXkT1ITGHQw+kTk9N+N45VD3YTATpKFGS88ZJ1MqafD+jRbzpYlvQXYG0gSrBrMZdA8NiOq81wWzVXJ2L0OIFCMbcTKwiUeCen1MtxswHltYlsFoVNDr+QyHSzzPYDRSagmVCyzp9RLK0mK5XGHb0G471Go587naB1Gk0WyukFLtW8eF8Rx66+rX+XoOsH9FtXMB6oFSMm/Q717MCApUNvDx6CKXtxnAySAjnM/RdZvbN+sMxhfjDq0GzNct3mZd5fxqmo7tuDgNl44jyfOMwIk5WamZNMesmFeqFdkxTVjoxPO1MKTJM/ODYaWOgzAM0Z2cer2JbdnULUjWXxVLQFVCv4A8h9/dWhtHXyI2M3/tdnvb9n0V7Crvb9y4QRzH21bxxpi8Vqttq4Mvi6crioJPPvmEPM8/l/xFUfSZ+y3L2lf+9vhaYU8A9/jG49tcAYyiiLt37wJ84bzdrojkypUrW7XkdDrdGutuRCSbJJIvgpSSx48fMxwOn0lfeRv4LZh8Qk5mZ8iVzdIUSCTDPGf6GyHdA6FIrKbDErw6nJ/CYQfiNa81PBiOoLkA/ZqOabkcfexSLD18M2YYF2iazvHxUxzHRAiHNM0Yjy1qNXNtKaRKdM1mjSxbMJkkzOcGSZJydHQDyHDdGouFwPc1HCdhOMy3n02jC14Cw2Nw1ibVlg2j9ayf0JSf86alWvdhtDP357swW12QP8uA2TIlnM8xDJNOp84ivSDgTR8mO8sbJheqXl9F5wkhsCwbK7Bp25KyLPCtmHiVEg8iBhXkNUctY1osiotTg22DzCvC+RybFL3RwjItZQQNdCuQKegaDNa8RwA/euHVfil4U/L3PFQGtIfneVy5cmVrTB6G4dakfTNS0Wg0tr6cz5O/TW7vLj6P/L2N5J899vgy2BPAPb7x+LbOAG6qeI7jcOfOndeu3u2qJTcikul0yunpKcfHxziOs62KvChloSxLPv30U+bz+ZeyeXlV/BAm/w0QiYquXTJODVwt5Wm4oB0IyqzDtUow0gEJ2UbJOgfRUi3ScG1ubPuwfAC2JZGWIDw2se0F773vs5h5FIXEtufk+YIsW6HrykLINE3qdQtd11mtFiyXObpeEccVjUYdx5GMRjmmqSo5QVByelrRaBjM5xqeVzIfacSxoHsAo3VVrtGBeawqe5YFywSC9fZrQiludV39E7q6f5mq+UBdjxkOlliWRb1ep9UQW8IoJUjtgvy59lrssb7Dstnawni2qgy2TIGFSZaZJCXYXkndTzhZFqxWK6Q1Z16YWLYig/PSIAyVz+B715rMdIuGBC+H89GFVUwzuPgsP/ah+Wod1VfCeDzmwYMHdDodbt68ealEateYfBNPt6kObkYqfN8nSRKKouDjjz9+IfkrimJP/vb4RmFPAPf4xuPb2AIeDAY8fvyYRqPBrVu3vrS6+WVJJKPRiLOzs8+ISDaWGpdh8/KqCITGTQweyALHSYnDnDyZYVk1+p2A0VCjCMFrg6hDuFBm0LGA7D40rqkZulYHwhmkIVw9qjiPNTRDEAQuebIgSQK63TpSWsznQ65fb3ByMmK5zLfq74MDn8VCJ01TPK9Jq1Xn+nWL09Nk3RoWdDoRYWgCGo5jkyQVtVrKcKgqgeX6I+s0IF1CEgIu5LoidoAiiTut4FYDppOL243aitEk5KjlUq8H6Kb4jCp4ssM5Av9CYSwETFNwdfA0cHSQC1UR9NeCD2V5o4Ndo6GDrCoaXsrxsiBNEkS+5EmpgwDf8bArg/oSwgTs5kXGsKPDtLjYrv9++7KOirdL/p7HbjydlHI7UnF2drb9TXj48OG2Vex5HkIIikKR5+dhmuae/O3xtcWeAO7xjYemacr37BIrgFJKqqp654kCUkqePHnCYDCg3+9z/fr1Sz95PJ9Esql4TKfTbcUDVKvsgw8+IAiCL1jj5eG3JS5TmXBWhCRLDavu03IC5pUACUtA3ofG+4rMNBqq5etrIIdKDGF4kI6gcwjhTKPhFIhDg2xuM5stODoKKUuf4TCm0wmYTJbcuHHIaDSnqiS6XjKd5mRZhOua5Dk4zpj5vIuUJo2GT5apx6OohZRqDtC2l4h1CofnK4La8wEB81CRo6B5Qfg6HTUHuPl0+20YzC72hWOuuP8kwnECTOkTJ4IyAa1QlT7HVmrdnruTzpFDz1SzeLal/AXjHBIBpgfZJtXDheWaONY9WKyVwELTqCyXIICy8tCKEXlR0cwhnYb8m1GEZZqYpkXoW2xOIXUPkvW6HQ2+d0nXC++S/D0P1Ta3CMMQIQQffvjhtl28G0/n+z6PHj3ie9/73jMXaqZpvnSWcI89vg7YE8A9vhXQNO1SK4Cg5u9eJYD+srBpuYZhyI0bN+j3+2/9NZ8XkYzHYx49egSolvonn3xCEAS0Wi2azeYrKyXfFN8zdf73/7Jimeq833EI/YBWpohM01ezcb4G5UDFpwlHkR+vCcOn0LEgbgECpAvLU4FeVrhBwXCmzJSrKtmeqIvCQcoFo1FOp1NnPA6p1WrbSL4rV7qMRnMWi4zhcIamadi2TpJE9PtdFgtoNHQ0LWY2K0lTtX8aLWACowEYdfXeav4F+TNNZffiWqoFHHgXZtASicGKp4MI1/Wo+TUMTaAbsBGWrmLlITiYXuy7XvPitkC1wTeK404DRjtexLOdOFrXg8X6tmfBIgdZleTTKbaoqBs90AyOrhZUq5wsS5HZnKdDHcPQsSyLhW0hNeVF+duaYF3CddNoNOLhw4d0u13ee++9d06kdqvgH3zwAb7vA9DpdLYXTtPplH/9r/81P//zP0+32+VnfuZn+Omf/ml+8id/knq9vid/e3ytsSeAe3wrcJltW9d10XWd3/zN38S27a1o4m2SwV2l7wcffPBOWq7PYzKZ8OjRo63NS1mWW3uZR48e8ejRo9cWkbwOyrLk+OEDPsgF369dAeHSOpdUax5sesAKvAAGZ3ClC+N13uwKNQ+nG1A+hnYLJiE4HpiWwdnjjE5HYhge02lBWZ7S7/ucnxfbKuBkktHr2ZyeDmg0lOAkikosy+Pq1aucnJSUZcp8PmKxyEgSkyxLCQKLycSh3baZzQT1es7ozCDPBd2jC4GH54MrgRKECcOZut91YVRAUYJlSIScs4pXXOv72G6NKBEEAUx20kL6rWcNo1uB8hrcHJ69NgzWHUkpId3x5msHMMkuHlvsXDcFLlhpwfDpBFlA2elhaMba4NnAdQ1c16XbKDmOcrIsQ2YRjycxmqaEJt/pVpSl/6XGFr4O5O/evXtEUcSHH364JX8bbIQkVVXx4z/+43zyySf8i3/xL/in//Sf8qf+1J8C2F5I7bHH1xViNpvJL15sjz0+i6+CpLwMv/7rv06j0eD69euXsr6qqlgsFltLlaIo3prZ8q7S94MPPvjSyRqvi1exeSmKYksG5/M5VVVtRSStVms7C/WmKIqCu3fvEscx/o07/K9GdSTgTEEzJIYPOYL4DJoGhMfQtyBvgoxgNod6DKsFMIVWDQpLWZOMn0iIEjqdlDyvMZlM6PdjomiBYaR4XkCWzZnNQs7PJxiGQb3eoN9vMBplSCnxvB5Q0WpJTk7AsgSzWUCtNmI2s8gynX4/QEqPZhNmM/XdaBxCuITDNpxPFeHy/Yv0DYBmF2ZLVfmLVwsqGaGbdVzXQ0rodyBKlU+fEMowehZfzN/pmqooJvnFbc2BfP0arboyc96g3YTJupIYeDAX0NTBKiDKCx6fqTLlnZt1poWqaPoOLHfKBUHzom3c8yUnaQ5xSiNa8e+3n6Jpgnq9vlXSvo6AaTgc8ujRI3q9Hjdu3Hjn5K+qKu7evctqtXrpCERZlkowI589fRqGIsgnJydcu3btrWzfL/7iL/IP/sE/4NGjR/zar/0a3/3udwE1N/xH/+gf5f79+9i2zV/9q3+V3/k7f+cXri8Mwy9cZo9vJ/YVwD2+FbjMFvBmfRuD2BfNyem6viWD9Xr9jasdX1bp+2XxqjYvhmFslZJlWW7J8ctEJK9DjtM05e7duxRFsa22fCeG31it/fImgiMJgxo0ahBGSkk7L6H8BLp3FAF06mrWrn8Ag2PwUnCvQ1EI+j2D1UKi6xPqdYssk6xWIf2+xenpGa5bMZlM1yrgOs2my3CYIQS02z5SliyXExaLDiBpNhsEgSDP65RlnSzLyHOTKDqjKBzm84J212SxtKkbFpUUW9sXqwasq3m9vvL/k1Iyn89xrBWa3sJxlMr0oHsxF7hKwFGaEyiUP59lgblWFZeVIn2dFgx24uWEAawJoKXDNAZfV7N6joA8VMIQz855MpsjBNTrTTJxcUx7LizXBNO1YF6oNrOUoJeCw9RivLL4sfcCfuj95jOVYwDP87bHx+fNxX0dyN+9e/femPxtLoTeFvkD+Pmf/3n+9J/+058hd3/mz/wZfuzHfoxf+ZVf4Vd/9Vf5+Z//ee7fv/9Ws5D3+GZjf2Ts8a3A21TuPj8nF8fxtjI4Ho+3ysFWq/Va4fPn5+c8efJkG2d1GTnGr4PdmcP33nuPXq/3Ss/bJb+7s1CbJBJd17cqyUaj8bnva1P9FELw8ccfb9vKv6elCGC2HjlcFdCcgtYAorX4YwjNGiwegVe7SKtYVutKWxdO/g0cXi9JC53VqqDRqPC8lNNTpQoejRKkLHj8eEqt5nLtWpvJJKGqDIQoME2B6wpOT8f4vsNi7XmiaYLRaEq320bTNA4PVR6vrneQ0sc0czRtwfhJwhiB3bbRDZtuz2Q81xAoxS9AN6iYTqe0azFO0MEwXbJMJYCczy5EIqYBhgHLdfVutlRpIU8HzxpGJxG0DGUpY1mQpdARSnnsGnA2U5m/iQ5YKnauKAuW0RghLPXZ+TqLnTjsnfFBAlcJProSZAyn84vX/96Byrk9PDzk8PDwmQzr8/PzZzz2nr9Y2Cjf35b46YtQVRX3799nsVi8NvnTdf1LV8FfFT/xEz/xwvv/7t/9uzx48ACAH/mRH+Hg4IB//s//Ob/7d//ut75Ne3wzsSeAe3wroAx83751y66J7NWrV59J3njw4MEzyRutVuuFooldpe/BwQHXrl175ye7PM+fGXB/03b+Ljm+fv36NmFhOp0ymUwQ4qIV+LyIZJMrbNs2H3zwwTOPfbcGHRNGuao4hRm4IbQdRe6qdaHUXCeA9AqQTWW8PJtAtw/jsTJFLuIKzQLPF1hWjeFwzNGRRlF4nJ+PMQzVznZdn/E449o1nzjOse0M09Q5OVHlOsepsViA7wsmk5CyhOXSRAiJbafMZiolZDp1eP99jenUx/cLgnbEybggX6ZYTo5pOKSJQ+XbDM4FYRhi2zl6rUcZ2xBDw4f5AloeWKaq7mn6xdwgQLv+rIrY0FUFcLUReEhlC7NYE0bLgHl60TpuN9ScYFHkzOczDN+k2WygaTqOA4s163MtCHP1OpYAPQVzBsMC+o2L1/ctuPOc+fPz9kMvii3cGC2r1vxXR/42npd37tyhXq9/ZpnPI3/vUiz2IozHY6qqeuYi7ubNmzx+/Pgr26Y9vv7YE8A9vhXYOPW/aziOw9HREUdHR2RZtiU/u8kbrVaLVquFbdtfidL3ecRxzN27d5FSfmG6yOvg+YSFNE0/IyLxfZ9ms4kQgqdPnxIEAbdv3/5MlVAT8Lub8N8MFYlJJhDU4eQRdLowK9c+d2vvuVTA7Dfg6hVIDKjWpKl3BUZPDGxnRrvrcX5iIqWgqjIWiyVJssJ1fW7e7BCGSxoNjeNjNRMlpVy3Ygs0TTCdQrtdYZqC5VIQBDaLBfR6C6bTGgCGYdFs5kiZUZYWhmFieg0ONGjWch6d5YRZim2H3HtgIAQIIam16lQoc2nHVtYteQmztdCj01KtYM+GmqPIXpQpMiyE+r8RwHhHGNJvXUTPATTqzwpH5hnkRU44C+k2ofJbawukZ2cUfQ+sEswMVhGc6Bekb2e0kN/aV5/by7DrsXf9+nWSJNkSwSxTa1qtVpydndFsNt+Zf96G/IVh+FLPy6qqvrbkb4Pnt+H5bd1jj+exJ4B7fCug6/r2JPJVwbKsZ5I3NuTn+PiYp0+f4jgORVFQluVnMn3fFRaLBffu3cM0TT744IO3mk1q2zYHBwccHBxs/dM2+0NKuW2bJUnywvbZv9OEfzCCfF0YLExAgrkAO1BGxNOp8tMbjxQxPDtTCRVGB3xfskgFUgrqgcN4sMQ0HWq1Go8eHRNFJ9y82ca2bXQdOh2D8/MLM99eL2A0UhcVrVaArhcMBjNctwfINdlNyDKJOvQERQHL5ZxmU1WQmmtDZD0FGZh4nonrevjtnOg4pJKSZqvgfLhC12Mcx8K2LNLCQiC2IpBNpS9OFeErSmXxYpsqXcRxINzxIa45z5K/mvNs3Fy3CafzTGX7GgaNbp0wU63Yhrc2izagJtczgmvPwH4L4nVl0DaVuGTzqf3bB69+bGxiC+fzOVmW0ev18DyPMAy3STW2bW9HCXzffyuenFJKHjx4QBiG3L59+6Xkb7lcfq3JX6fTAdQM5aYK+OjRI27cuPFVbtYeX3PsCeAe3wp8HdI7dvG8aGIwGHBycrI9iTx58oTFYvHW7WV2MZlMePDgwdbm5V0Oh5umSafT2VZ9NrOSw+HwGRFJq9XanuxrOvyYD/+PhWpDTjM1AxfmoD0Ga3Nucy7+l4DbguED6PcksSGpXdFYzmySZMXR0YLT04zlMuPWrevM5zGdTs5otEJKaLU8NE0ShgnzeYXranieQIiMwSCh1fKZTtVnqOuSk5MZ/b46+XY6BqvVkrKE2Uzt25onOT1RHn4bG5dGs+DhyRzHqrhxtQmGQd3PSOIEyZLFVLWuLdshqNtMFg5K+QGOpVq4G3+/zf9P1nOAmqb8Em1LVQuzQglEHBdWa2GIlLBIUsJQZQsf9OrMU42aAZ4Otq5I3yoFXGW+vTk8k52vWOBBur7mcgz4uPN6x8TZ2RlPnz59Zgyi1+ttFfibavpgMNjOlW7+XcaxuyF/s9mMW7duvfCC7GWVP03Tvjbkb4M/9If+EH/5L/9l/vyf//P86q/+KmdnZ6+kAt7jBxd7ArjHtwKXrQK+TCwWC05PT3Fdl9u3b2/nBsfjMefn5y8kP5eJV7F5eduoqopHjx4xHo+5du0ah4eH223bnOyfF5G0Wi3+LVnn1ysdY90GbjZgNFY5s+kZ6ALGqbJBmUTQbMJwCkEDRiOBLQr8Ixgda9TrJsfHI6SMODhok+c6QsSEoaBWs1itMmZrh+R+32U6TYnjEs/zGa1dlDXNBSTttsb5+QwQLBY6ui7R9ZwkUXOAk4mg10s4P7MVsewptW9ZFiznQ2rCQK86lJXObALg0G47jBcNBAVxmgIRJ58WwIq6b9Btm3ieyyrVt63fdkMZSIvtflb/n44u9n2noaLxXEMJSUwt5vR8Rse0COp1AlPjbAYrCakO0lLtc1BClNVulvBOxa/c4T4/1APjNQ6pDfk7PDzk6tWrzxCpXQX+jRs3iKJoWz2eTFROXhAE2+rgm/hRbsjfZDLh9u3btFqtzyyzIX/PX1h+1eTvF37hF/j7f//vc3Z2xu/9vb8X3/e5d+8ef+kv/SX+yB/5I3zwwQdYlsXf/Jt/c68A3uNzsfcB3OON8XXyATw9PeX8/Jwf/uEf/qo3ZQspJYPB4KVK3+cVtFmWXZq9zO5rvIrNy9tEWZZbdeX7779Pu/3ioFgp5TMK6ziOKaTGf3X3Ju0PLeZLl76pMzqFngXDYzisQeaBIZU5dNuDyTG0azB5DL1WyfC0pN2uGI/HjMdLjo5KOh2P09OKfj9nOJxRq+kkyZKqkrRaJpO1UZ6uCwzDI00rLMsgz+v0eiVgMBhAo+EShjVarTnLZYM8h263sSYNOZOJj5QQHECaZmTLMbPIpt5o0O3pTNftVNMEzYJ0XdELarDKIc8L0jSjzBM0IyNKdQzTIPAsjnomQrdYxKo1LOXaIHp2sU99VyWObAhdWSQsl3PQber1Op4jSCRbi5r+joG0ECj/xfV1Vb8Fg832Gqolvzl5/Ce/FX770asdD59H/r4IWZZtyeB8Pl/PaTpbxbnv+1+4PiklDx8+ZDwec+vWrRcej19E/t71BdTbxN4H8AcX+8uDPb4V+Lq1gHeJ18uUvs8raKMo2ra9voy9zAZvavNymXhebfwideUGzyus0zRlOp1y/Tjj3m9kVN6ClWWjxw7DykLTDRY5LL8PvQNo2xDGSiwyDlUc23ylUVUZ4/EZWWbT6bRotUqGwzH9vkqskBKWy5KDg4CqihmN4u02dToBg4FiZa2WT54nDIcRhqH6nZZl0+2uKEuNPAcQlKUkDJd0Ous5wA6UZcrg0ZzWgUGj0UQIjXKH2zeaKiZOStXmzaWq5um6Qa1m0Kx7jOclupWRZhlFvuA372sITceybJp1i4OeSZoJLFO1hg0dpLggf2maoFVThOERBPX1/oZ4udn/EO5ExLUbMN4Zq413vl4ND5YF1CUkK/ih7qsdD5v5vje9GLEsi16vR6/XoyxL5vM5YRhu/SgNw3imVfz8BZSUcluJftnFiJTyB4b87fGDjT0B3ONbgQ0BlFJ+5XM5m4rXfD5/ZeIlhKBWq1Gr1T7XXuZVM3kvy+blyyBJEu7evUtVVW+kNrZtm8PDQ37f9+C//n+XtK2ERxRYImQ4NugCg4XHlbbJcGDS9wTlOXh9MDxFfpaDAk2bsAgdDg4q6nWP6TShqgSgM5sdc+2ay3g8ZzDI0TRot5VRcZZJkqSk29XRNNWqjqKSbrfBaKTImmkWnJws6ffVZ9xu6ywWS6SE6dRQYhdi7v3mCtfTEXoTDY1OX7Wsu4GyeZnMwaggL6BWVx5/NUc95njKqy/wdFaaS6/tEiV1hJmRpRlpmpCs5vyrgYVtW1iWTbth0qxrpIWaGZyEMVo1JZY+QRAghKBRg9HyoqXbacIoutj/1Q7P8R1YZMoGpqGDHkEyU/6AP3JTCUK+CLvk7+rVq691LLwIuq5vFfYb0jabzQjDkPF4/Iwl0yaN5PHjx4xGI27evLkVTuziZeRv8/3ck789vk3YE8A9vhXYXOlXVfXODZV3kWUZd+/eJcuyL0W8XmYv87ydysZeZhdvy+bldbBarbh79y6GYfDxxx9/KbXxDx2qz7cUNe7oUN4I4EmOVkbE45in6YJsbJLGFnZlcXZmUUeQlSmynFBi4/sBQRCRZQuKwqDTqTMchgSBxdOnSw4OHJIkp6pgMokxTYFl6azWA3D9foMoUorgPDeRsuLwUOPkRLXPZjMd05QYRk6ei+0cYKMx5t69AstyuHk7YDTXsAw1u6gnypalcQDJuvLW76pZQVCzd7Xas/N89ZqatesEAt+xCSObTsPnbFTguilpmhHHKRoFJyMPy7KUgKEI8QOXa+2ASgqiRCWEiJ1M4HhnhNa1VWJIzVRpIa6hSF+4hIUFyc411o/c/OLP8OTkhJOTE65cucKVK1fe5DD4XAgh8H0f3/e5du3aMxZET5484fHjxxiGQVEUHB0dfS75e36WeLPuPfnb49uGPQHc41uBzY9zWZZfGQFcrVbcu3dvm2rhuu6lrHfXXmZjpzKdTrf2Mq7rbisheZ5z//79d2Lz8jKEYcj9+/dxXZc7d+58YbXyixDYcKcDd0cgQ/ANDcuyQdgcHUpWUU7HiTk9ydCtBeHQQgYwfijp93WCThOtkuS5y2y24MqVnNVKCQd03Qdizs8zDg7qnJ+HGIaG65rM54odeZ61tYOp1RyWy4puN6Kq1OfbbHqEoaTRCJlOm+v1WnjemMFgjGEc4vsBSSmo15TKdnCu3lu3D+P1zJ1jw2Qnwq3VuIiBA9UaznKY79i99NpQVoKrPZNlbLKModMsOB5myCpluViiaxWOrRNGFvH6+3HQFpxP1CyfbSoPwSiDJqplXNMgDZUwJNFgvpMtXPdV2xfAs+C/9wV87m2Tvxdh14KoKIqtybOmaZyenjIajbYikk0aycvI377yt8e3FXsCuMe3Apsf6K9qDnA6nfLgwYNLIz0vg2maz9jLbAbiz87OODk5ARRhvH79+jvPFQaVSPDw4UPq9Tq3bt26NDL+Ww8VAfQacPopHPVhWEGzI8hzC6Nm0U4kRV7QbC0Yj0v8ZslgoJOmx/SvNBkPbep1kyTJMIw5vm8xm2V0OnUmkwWDgSKBZZkzHiu1gxIZeESRmgOs122qasZ8XlGWikSapku7vaCq9O0c4GKx4OnTU65c6ZOmAY2WwDFheArWmgNJeWGkXJZQb0CUqNk9Q4dKV/Y3cbaOgdMvYuBAtWyHO/P7UsKVLpTS4Frf4GQoKYqcZt1gERvkScRqFdGqSz499rBtCzDRNcEgVN6CAJ6j4uI2aDdhuEM6Fztzgt+7rrbrRZBScnJywunpKVevXuXo6BVVIpcIKSWnp6fM53Nu3LhBr9d7Jo1kNBohhOD+/ftIKfnJn/zJ7UXThvx9lR2FPfZ4m9gTwD2+Fdj8SE8mE3q93lsjYM9DSsn5+TlPnz6l1Wpx8+bNd3bC0HWddrtNq9Xa2rxs0kbu3r371u1ldrFrNdPtdnnvvfcudRbztx7B/+nXVd6vAJI56HMwr6r0iVUGvT48fpghRYrj+vS6LlWW4Tg5937zDM+ziGPBcJjQasU0mxa2DXluY5pLWi2N6XROnlfU6zaOY6BpBlVV0e2qi4uzswlSQq/XYjhUpEvXU4bDiF6vi5RgWREnJ3Mcx0NKpY6ouRWnJ5ryA1yTtt6Byjju+cq/7/x8szOh0YFwN8WjoRS3nqPmBHXtWWNnKeGgBWcTkEiiVUQcr3jvyMbz67QKwWwp0URKlGakSUIcxwgE/Y5BXLpYponQNFwXop1K5GJHCNIKlB/jBj/y/os/r68L+Ts+Pub8/Jzr169vU3eejy4cDof8vb/39/jlX/5lyrLkp37qp/h9v+/38ft//+/fk789vtXY28Ds8cb4OtnA7AovgGcEE2+rErar9H0TS4vL3oaNshJ4qb1Mq9XaZq9e5jZsso3fptXM/+yfwskcWrlKAGnp6v+uA5ORpCxD8sWKRjugGficDARtU7CaSZJkQqOxZDKxCMMTGo2MxWLB4aFGFOX0+zUWi4g8v6ggB4FFHFcUhfqJ7HbrjEYZUkp8v0cUVRwd6RwflwghMM0DomiOECFp2uLq1TazWYNeb8lsViPPBb0jVbVzbPBrMJwAEuo9WKwFGL3+s5W9fvfZVnAjgOUKat5aMVyqCuFgtiF/K6Io4vqhS1IG2+fZpoqDQ0CSSSbzgpodczoptq3PTlMnFS62baFpOt0m7Iii6bYuhCKBA//LP6jI6y42xOvs7OwZz8d3jePjY05PT1+6DVJKoijaRkjmec6/+lf/il/5lV/hn/yTf8LP/uzP8hf+wl9415v9zrG3gfnBxZ4A7vHG+DoRwA3yPN8KJhaLBVLKz+TxXgZ2ve02raV3jVexedmc5Db7JEmSrdHuRh35Zcxiq6riwYMHTKfTt55t/Pf+f/Dffh+6FoyeQt2B8BwMXRKdz5nPM947dImmPrYl8ZwK3dMYPhB0uxGLRYLrrtA0h8FgSlGckKYJQswpConvKzPm5bLA80yqSiNNFTFqtTwmE0X0ms0aReEgxALLqjMeS5rNGicnkqp6guPcwDA8er0WUFIUGdNpAymhfgA6oFVKBQzQ6V3MAXouJFIpdwG6bSUK2dDpmqu8Andjr/ttZQbd8CWr1ZzROKbf80kKf7uMJiAIlIhjg0YN0BR5LMuccJlSyITRQiKEsqBp90wK4aDrOrYpyDQwNfAM+LHb8Ad++7Of0deF/G3mDl+V/O3C930lOnrH88Q3b97EcZytsfWf/bN/lj/8h//wW3/dPQH8wcWeAO7xxvg6EsBdFEWxFUyEYYiUKr91Uxl8U5FGmqbcu3ePLMu4ffv253rbvS3s2ry8LMbqRYjjeEsGoyh6bXuZXRRFwb1791itVty6deuFaQqXiYdT+Iv/d6WglUPlk9cyKh4+XGATIZIWtu3QMCCcQq9ZMRlJ2i1FmKbTOZ3OCk0rGQ6h1ysZDMZ4XslweEqaput2r4lhWESRwLIsNE3DdT2iqMQ0oderc3a2wLJ00rRBVUlMU2e5HNFotElTRcS73Qbj8Zxut8lwaOE3VLLG4BS6V2A0U63behcWMRhCtYWLQnnyabqyYpGo+bxyHQGX7LRge20YhACS5WJJHCe8d9UDrUbgqXbxfKWEG9Md8udYar3pzrr6nXUVUZYImeEYK2arYpvb3GxaRKWHxETTBP+L/wB6Oz8BUkqePn26bbkeHLxGOPAlYmM387LW88ZwPM/zzzxWq9W+svSMmzdv8g//4T/ku9/97jt93T0B/MHFfgZwj28tDMOg0+nQ6XS2gonpdLo9QTiOs60Muq77Sm3Ljb2JpmmXqvR9HSRJwieffIKUko8++oharfbKz3VdF9d1t/Yymzbxq9jL7GLX7ubDDz8kCIKXLntZeK8JTRdmMXQ7MBhUnIcz8qzErbe5dmQzOIFy3fFfJYKykJRlQVlq+L5OVbmMRuccHWksly66rhHHcOvWDc7OxjSbOsPhjOl0gZQVtZpBq+WvSZ7AdW2Oj0Plo9eoc3YmCcOQWk0ihEu93mY4hFZLZzpVSRVhqPwAA6/i9FRH12G6nt/r9KAS0HUBAadnF+83aF20hTUBtUARwKav/AENY2MZI1kuFsRxyvvXPFaZOh7iVFUO23VF9npNZS2TFS8WlJxPFfEUQsexXQrpYnuSLMtAphyPMooyRNMEv/2OxKgMylKl1XzdyN+VK1e+UeRvjz2+CuwrgHu8Mb7uFcCXoaqqrXp2NptRliW2bW+rYC/L+XxXSt/Pw2Kx4N69e5du87JrL7OJ2PI8b0sGHcfZ7pONzyDABx988E5J8P/tX8I/ewy5LHn4GyFlWXG7FxCGNkKAX8FiDv06DI6h26gYnVcEwZRazef0NMbzJth2wWo1o9cLyLIUISSmmXB8PNu+z7Is8X2Nk5PFulUoaTabFIWBZdk4To/ZbEqtlpBlLSzLxjQP0PWKeh0GA41m01nPAS4IwxpZptE7VMTNdxWJm60TQBoHMF8Tvn5/U9lTj/W6Kilkg3ZdmUdLKamKGVUZcdBvMotr7ArhN8ttoGtKyCE0ZfcSrtQ8YrITFyelSgHZrRh2WzBcKpV1lqX8sX/nKU17iRCCer1OVVXbcYi3OQbwedhEzL3MaPrrTv5u3rxJo6FiBH/H7/gd/MW/+BffyWjJvgL4g4s9AdzjjfGuCWCWZeR5jmmamKZ5KUKDzYlrUwkrigLLsp5RzwLPKH3ff//9r8QXbDKZ8ODBA2q1Gnfu3HlrJ6zdamkYhlRVtSXItm3z5MkTbNvehs6/S2Q5/E//dwWT5RhHq2i3utiGSbkEWQAliBwyCdkcNCFZjCoODmIWiyVFYdFqFQwGY1qthDBcUa9LZjPFvOp1C02TzGYJzaZLGGZIqY6Tet3k+DgiyzIaDQ8wiKIlBwfXSBKbdrvGculj21OyrE2WQb/fRMqSNE2Zz1WLvHGg2thFoqLrhFjPAa4rcp4HSXUxB9jrPCsKafiwWEFZSeaLOWmacfumzyJx0YR63DRAA86mF8/ThDKSnu0QO9tUZtOGodrMYaRI43BXgVyDWXYxh/hD78Gf/FlViZ7NZpyfn29JVa1W22ZZ7140vG2cn5/z5MmTl4qxpJQkSaKqmc/h60D+AB4/fsyNGzfI85xf+qVf4td+7df4x//4H7/1190TwB9c7AngHm+Md00AV6vVM0PbhmFcKhmUUsV9bWbk8jzHMAwMwyBJkpdm+r5t7FrNtNttbt68+c4IaFVVzOdzZrMZk8mEqqoQQtDpdGi329tYsXeFKIr4P/7jE/6v/7LNtYM68cxASugEMDpRZKrnw/AYOt2KuBA4ZkW0qEiSc65ckcxmNeJ4gO/DfD7EMDRqtZL5XDEwKSVXrnhUlYqCWy4LDEOnLE1sWyBEyWKxZDRKcF2TOK6haTpXrhygaSW27TKbBUgJ/X6d0WhGr9diMHDwairqbXAC/atKBbxb/atZKsM4L9X9hgFpBbqu1LZCQF7BdC6ZhSFplnPnps882anCCrU/xnMVJ1dzVdtXAyY71jGmAbYDy534t0ZNzRo6Fggd8hyEBdOdZf70H4DbR88q0K9du4ZhGMxmM+bz+faiYUMGfd9/a8fJYDDg8ePHL/1+fh758zzvK6nkfxFOT0/58MMPWSwWX7zwl8SeAP7g4qu/7Nljj1eAlPIzir2iKCiKgjiOt2TQMIw3Jkebdla9Xuf69evM53MePXpEkihT4NFoRFEUb8VK5WXYtVj5KqxmNE2j2WyS5zmj0Qjf93FdlzAMGY1Gb9Ve5nls2t8/+pHNvUmD6VKndwDDMxgv4OCKEljMEjAtGI8FQRBh2g7xQKPd9pjNImx7iOf5jEYLer0Go9GcONap1WxWq5R+3+HsTLXBFQTNps9kEjOflwiRMp1KWq0Wh4c9Tk5SkiRhtZownaa02y2iCHo9m8lkRlXBYqEi2RqNitMTHU2D2frc3u0pstU0VGv2ZMcPMGgpgQiouT3bhWVcEYZzLD3lzp06leZil0odjIBOHcbrc/oqUUbSjZpq93YaqhK4iMCyn00V8Wy1bJZfkMJ+GwbrKqJrww/d+iz521Wgd7vdZy4axuMx5+fnGIaxVZ7X6/VLU9cOh0MeP35Mv9//RpO/1WpFnudbMdff/tt/m+9973tf7Ubt8a3HngDu8Y3Ai+Z2drEhg8C2amea5hsTkizLePLkCWVZ8sEHH2AYxrZNPB6P0XWdRqOxJT5vwy6iLEsePHjAbDZ7qc3L28auoW+v1+PGjRsIIbY2Grv7ZNdeptlsXuo+mU6nfPrpp/i+z507d/h3dZ3/+ldgsIB+D4ZD9XezBbMpas7uscB1LUanKzodGyE8oihCjSzOsW1YrSxAkucqPuzKFX09B3jx/vv9OsNhQlEULJdzdN1Z+0sazGbQatnU6zYnJ5Jm06UsG2haTBRNmUwcWi2P6TTlypWMeajEMu2eqtAFHlQ5TEeq4tfccSzpHzw7B1ivwyismM9DiqLg+lGTQWgjUCKPwFU+f1GqKoVSqqpho3YhOhmHag4w8JSKut9UxDFOAaHI3wadBpzP2O4LTcDP//jLyd8Gm4uGZrO5zdfdzNuOx+PthdZmmTclYaPRiEePHtHv97l+/foLL4zSNP3akz9QLew/+Af/IGVZIqXk1q1b/I2/8Te+6s3a41uOfQt4jzfGu2wBV1VFURTkef5C766XQdf1bZv4Vcngcrnk3r17aJr2GZHDpqIwnU6ZTqfEcfxWiE+e59y7d484jl/L5uUyIaXk0aNHjEYjrl69yuHh4Uurj5dpL/M8RqMRDx8+fGb+sqrgf/434HSyVsgCi4VSx2oxpAl4ErJEUhYL2u2KOPYoigmelzOdntHtQllquK4gSRZMpyuqStJsOhiGYDxe0e/XGQwS8jxnPp/T6fiUpfKJ6/XqCCEYjUIajQ7TaUmzWWO18rGsKWnqM59n1Go2WZaRpglFcQXbtji4bmHoBnkM8zVha7UvUjZqHsQ7c4D9LpxNlHipLEtuXquzSiw2P95SqlnBjVBE06AdqBm/yWJN8Lggf7tzgIau7tM1QMA8Vu3hXWEIwH/2s/C9Dy6OiZs3b9Ltdl/rs9zMDc5mM5ZLtRFvMje4OSZ2L0pe9Fppmn7mftd1v5KYxK8r9i3gH1zsCeAeb4yvSgUspSTP87dCBjdCC8/zXknpu0sGN8SnXq9vic+bDJcnScLdu3e31cfXsXm5LOyaTL/uiT5N0y0Z3Jzkfd/f7pPXUS5vlJ0vOtH/f+/CX/2/KEPomg7lEqoCDA3iSJGgyRPo91NGo4RGY4Ft1zk7m+P7U7IsQ4gVaVpwcGAxGIRcSB3g4MCjLCvyvGA0mqDrFs1mF9vWEEKSpgZhmOH7NouFixDQ73dIkiWapjOb+UgJh4dNzs4m+L7NyYmBJMWvlyxmDlduGCxiF00zaB0JZkto1cD1lUdfXoBhwjwpiVZThCg5OmphmBZZrlq5efEs+QNFKJu+qvxJoO6pGDkh4Gx8sZyhq/t3W8GWqQioYSgCuojhex/Af/T7vhz5ex4b5fnrzg2Ox2MePHjwuZGDaZpuRzd2sSd/n8WeAP7gYk8A93hjfB1sYL4sGTQMY+tjtsmyfVOhxYuIz6YK1mq1XqkK9rZsXl4HRVFw9+5d4jjm9u3bX+pz3iSzbE7yn2cvs4tdX7mXxctJCX/t/wz/r/+Put1tKPGHENB0AB10JGkiWcyXHBzEFEXOaCQ5OKgYDgd0Ogaj0QQhBO22RRguKYqSgwOP8/MleZ4xn89xXZt2u02aqpJYv99gMFDHW6/XZjhU97fbDpNJQq/XYTjUCQJBkgjyvML3+ywWGkdHOY8eVaRpBnZCmgkazYqg6SBzB7dmMcvEtvXq1Quens6RsqLTbuL6xraiJ4ErHSgq9fc8UgKSlv+s4GO38mcaihxWldqHnxGG2M96BF7rw3/5hyVnpw8Zj8e8//77dDqdNz0kXojducGNGv9Fc4OTyYRPP/2UTqfDzZs39+TvErAngD+42BPAPd4YXwcCuIuNUGRDCF8VQgiGwyHT6fTSsmzzPN9WBjdKvk0VrNVqvfBE9K5sXj4PaZpy9+5diqK49Orji+xlHMfZVnw2/otSSh4+VGTji0yFyxL+6t+Gf/2b6nbXh9G5ysudnUGrVVKWoBspeZazXJ5x7ZrBcGgj5ZA8L+h2YTRSn1G9buD7kuPjkCzL1+TP4vCwz3KpjinPs0gSk6oC09Qpyzr1usR1JaenAinBdfuApNVS5s+eZ7Baden3l6Spy3xu0OrAZCkxjRRkwvmwpKoqGgcVaelhWRaHh4L7TxdKKdxo0O8bz/j6dZswnF3ULaWEg3VhLisU4XtR21fXwPdU5a/pq0pglIBuPlsNbNfhT/97ktn47ZG/5/H83GCSJAghcF2XKIpoNpvcvn37tcif4zhfycXUNwF7AviDiz0B3OON8XUjgLt4VTJYFAW/+Iu/yLVr1/iTf/JPbufVNm3iy1DcFkWxrQxuqmCbuaeNt95XZfOyiyiKuHv3LkIIPvzww20m6dvAiyo+pmnSbDaJoojVavXKZCPP4X/7t+A37yu7FLuEaAUtFxUF155hOx5npymtVkiWxQixwvcDzs6mmKYAVjQaGvP5kiwrgIKiiACdTqdLGKrhPCkl7XaL6VQdUwcHDaqqYDCY0273mU5LGg2PKKphmhOE6LBaqYphUQjiOCSKDpSVziHKamZZEVeaIo61jFGUk2UZppGTCUFZCZoNjytXHMahvp37e97kGZRh82jnPstUs4Ab0+csvyB/4fOE0FWVxJoDWalay//lvydJ5g+YTCbvhPy9CEmScHp6ynh80bt+0dxglmXEcfyZ5+/J3+djTwB/cLEngHu8Mb7OBHAXu2SwKIqtvcdyueSP//E/zv379/lbf+tv8fHHHz/zPE3T3goZ3E3cqKoKwzAoiuJzZ5reNubzOffv398aPL9LheTGf3EymTAej5FSomnatlr6KvYyaQr/678Gnz5RZseLocq/nZ3BwUHCYrFASgvfLxmNhnQ6OfP5kkajIstKDEOSZQnLZUocxyyXKxzH5ubNA+bzlFrNxDR1LMskzwVSVlSVZLUSJElBEHjM55s5wBZJskRKwWLRBBRRHA6ndDoNhkMf3YBOr2JwKulfEQwm6v21jxSpa7g5s8WIKNUoSxNNT4krHdO0qHkm/Z6N0A0MXbV8V5FSAO8aRj9v/CxRZNC11ezgfKXa5RvyF+5U/mwT/if/Q4nIFPm7desW7Xb78j7018BsNuP+/fs0m01u3LjxwrnBsiwBPtMW3pO/L8aeAP7gYk8A93hjfFMI4C42ZHC1WvG7ftfvotVq8df/+l//QpWtEGJLBnVdvxSSlmUZ9+7d24pHpJTblmir1cLzvHdCBjet5yAIuH379luxtPki5HnOJ598QpqmXLt2jSzLtu2/jcq61WrRaDReun3LFfxv/gokOZi65OkptFzBai4pinMODiTjsQuMsKyK1WpIrWaQZXPyXM3wuW7OkydDPM/j6tUO0+lFRanRcFgsCqq1NLfbbTAapeu/e4xGFbqu2sjTaUq/32UwMPE8KEtJmla0WodMpwZHRzknJxpCQNDRWKwENV/i1QSzUU64mJAYpnq/mk6tWTCa5mRZSlmkGLaklDaWbWFbNoddnTgVeK4SbqxicJ0L+xf4bOXPtS/Uv6c7whDLhP/xz0uM4qsnf2EYcu/ePRqNBrdu3XrmQmBTRZ5MJvydv/N3+HN/7s/x3e9+l5/7uZ/jZ37mZ/jhH/7hrySr+5uGPQH8wcWeAO7xxvgmEsBd/KN/9I/4Pb/n9wCKgFwY/34+LoMMPm/zUq/Xmc/nW1+9siyxLGtbBXtZPvGXxSZC66tsPadpyieffEJVVXzwwQd4nrd9LI7j7T7ZVVm/zEPuv/vv4P/w10DTJJ1uTqmZjI/FOot3SRDEGEaNwWBGt1swGoU0mwbT6YTVakUcxxwc1Dk8DBgOY/JcVZY8zyTP2RLFRsNlNpMIwboqGNDpCHS95PxczeJ53iFlKel0Sk5PNSxLJ8sO6XYjqspgMjEI6rDIdDxHUnP//+3de3icdZ3//+d9zzkzmckkmcmhaRraJilIaalyWKWIIK5UwS8ih70ALchCf1Bd1/XsspcHtLDLuqiAeGlRFKwsReQggidOallhLdWlNUkPaZrmOMlM5ny67/v3x3TuJk3apmmaQ+f9uK5eF5l7pvPJNGRe8zm839DbV5gh9i/QwVaFqloIBmDgwLKuYRT2NfYP5cwadxXuHCMpBw67A4fDjtVqo8qvkNcLHT0y2cKhjomWfYt7A62WQgs5qwX+4WIDu7abSCTC4sWL8fv9J/Tf/3CK4c/r9bJkyZIJfzZzuRzJZBLDMNi/fz8vvPACzz33HC+88AIXXHABv/jFL2Zh5POLBMDSJQFQTNl8D4CjGYaBpmnmnsETGQaPVual2J+4uG+wuD+uWEZlOtqvFd8w+/r6Zq3FHRzcd6iqKi0tLUdcrptMeRnDgG9+C3b8DYLBHPF4DofdgaEbRCKDVFenUBSDvr4cXq9KIjGIYSjY7Qn27+9jwQI/imIhm82jKIpZE9AwFEKhwuECRYGyMg+JROEUcG1tBZlMlnA4SUVFFZGIRnm5i1TKi8sVQVF8RKMQCPjI5ewkEkPk80EMQyFYr5DJKqRjOmlNY2g4irscHFWVqKqK3Q66FTDAYS8cbomlCwc8Mjnwl+uFMJjNks1k0XUNf4VOKufG7rBjt9mxWhR87kJZHDg4K3jowRCbFf6/y3Wcxp5ZD3/RaJSOjo5Jhb9D2e128xR5S0vLTAwXgI6ODj7ykY8QCoWoqKjghz/8IaeddtqMPf9USQAsXRIAxZSdTAFwtNFhMJ/Po+v60R9EIQyObkk3UaAqFpm2Wq2TKvNiGAbxeNw8UVzsT1xcJi4vLz/mWTtd1+ns7GR4ePiop2xPpGLJm6nsOyyWlymesi6Wl/H7/WRzFfz7f7hQLQa5dBK/3yCRcGGzRVCUDNFoH/X1NrJZO4aRpbOzG6s1Q0ODm2g0Rz6vmc/jcFiw2SzE41kURcHjseP1ukgm8wdmBxVSKRVNMygvdxGNOlAUqKmpIp1OkM3qJJMVB26rZHg4gs9XRihU6AhSU68w2Gfg8ebYvTeG1Wpl8Vu8DMcsVJeD1QGhSOFAhrsM0sbB4tAeF+gKOB2Fmn3ZvAFk2T9YmB3UNL0wE+qzkc4XSqCoqorVUjhAYhiFvYDp7MHw59ALtR9nM/zFYjE6OjrMri/HGv4mW0x6ul144YV8+MMfZu3atWzevJn//M//ZMuWLTM+jmMlAbB0SQAUU3ayBsDRDMNA13VzZnCyYRAwZwaLYbDYzmyqZV6K5TGKS6KZTOaYe/FqmsauXbuIxWKccsops76xfzpK3hQP1kQiEbO8zJ9eq+aPW6qpr1MYiRhUVydQVS8DAxF8viipVApNi5BOx9G0GIpSOCxgtapUVjoYHk5htSpYLCqJxMFWYoGAh4GBg3s2q6t9DA0VrldXVxEKFQ6UlJc7D+wDrGRgwIrDUag9mUxqVFVVMzRko6ZGo6/PQi6XBWuEZNpFIFBORcBCPFw4tJE+kIkNo9A+rjh7Z7EU6vUlR1U8CVYV+vb63AeuZbLkchn6hvLkc3lQwGG3UlVhJ513YlELJ4qrvHDd3x8Mf0uWLJmVzjNwMPy53W6am5sn/Hku7uE91GyGv4GBAVpaWgiFQlitVgzDoK6ujldffZWmpqYZH8+xkABYuqQXsBBHoCgKFosFi8WCw+E4pjA4ugRNJBJhcHBwTDuzqYzF4/Hg8XhoaGgw98eFw+ExvXgPd1gil8vR0dFBOp2mubkZr9d7zGOYDsVODhUVFeM29k+F1WqlqqqKqqoq82CAzxfmjW0xenoNUok8DoeGzZYFLFitHjKZOKqqMTKSpKqqCp/PSjSaJp/XGRhI4fXacbkKS8HFrOH3lzE4mDIDRnW1xwx/DoeNoSGN6urCEvHgYOFwSCplxWo1qKrS6ekBVVUIh634/Wl0XSWbzZNOj4DNRU3AS2UF9O4vPF9gIaQP7P0LBOBAqcLCPkDv2M4fAT/0hwv1AEcSQBIqfXaSGTtLGgFDIzySQtMy9AwkwUhitVpxl9m54p0Kdm0/I9HorIa/eDxuhr/DzfwdLvzZbLZZC38A+/bto76+3vwgoygKjY2NdHV1zfkAKEqXBEAhJml0GHQ6nWP2DB4pDH73u9/liSee4Mc//jFVVVWk02lzqXiqb1iKolBWVnbgtOqCMYcldu/ejaIoY8JgsbuHrussW7ZszEGLmVRs7XaiSt6oqmoeEFl/m8a37s3jLR8hFEqSTndTU2Nh714fhhEjk9FoaKglnc4Tjyv4/WWEw0n8fifRaIpotPBvarGoBAJlGIaCz2cnHs+hqioH6nvjcKhUVbmIRpOEQlkqKqoADbfbQSJhUF4eJZUqhO3KyrIDzxchk/ERjcapXWDH6fSSz2hEYoVfyTY7DI2a7YunwWUHpw1cLsjkCzN9eb2wNzA0MrqJ3YF6gAcC4nAUVNWCz+shl/cQqNKIJzOER7K87617IZlkBMzWhYZhzHiQisfjtLe3my0YJzrpfaTw53K5Zi38FY3vVCOLa2JukwAoxBSNDoOjZwaLNckMw2DDhg088MADPPDAAwQCAeDgzGAqlTKD4PGEQSi0uXK5XNTX15PJZMyZwT179gAHw+uhp2xnyuhDJ7W1tSxYsOCEv2Gf/haVJadkiScqyefKqK9XGR4ewmbrwWJxkclkSSQM8vk0druDkRGVhgYvfX0jaNrBQO/x2AmFkuTzB2/z+z2MjKQwDA2LxU5fXwxdL+wDDIc1FAXcbhc2W4xkUiObLXyvFosNTQujqgZDQ3HsdjtVlV7CQ3nc5QqRWOF+/mDh5G9VeaEsS2+o8Ly6o3AIpLhN0eWEaLwQDl3OwkEPqw36wgdfB0UphMXi8nEibcFqKeOzH3Fi1waIRsHr9RKPx/nb3/5m7jEttmA70SfDE4kEHR0duFwumpub52X4W7hwId3d3WYLO8Mw2LdvH42NjbM6LiGORAKgENNAVVUcDseYZeLPfe5zbN68meeff55TTz11wsfl83ny+fyYMGi1Wo/rTdfhcFBbW0ttbS2hUIi9e/eiKAr5fJ4dO3aY/YkrKipmpDeqYRjs3buXUChEQ0MDtbW1J/w5obDceuZKld+9EMftsaGqHmKxPnw+A7dbweEoIx7PYLXqaNowmYzBzp1WysocBIMeEoksVqtKPJ41A6FhGASDhRIxRTabk2SysNTvcJShKBpWK+TzKSKRDIFABYODoCgGqVSccDiB1argdLppbHQRGtDRdbDaCsFHUQ1Awa1BpB8Uz8HvqXxUpw9FAZsNUtlCIEymIVgJvYOFIFjhAbsVFEuhzl8xI1ktsO7/6di1ncRiMZqbm/H5fOP2mIZCIXNbQUVFBT6fb9rbEyaTSdrb23E6nbS0tEwY/jRNmzD8Wa3WORH+AILBIGeeeSYPP/wwa9eu5fHHH6epqUmWf8WcJodAxJSVwiGQ47F9+3a8Xi+BQGDMzOBkTEcYLIY/r9fL4sWL0XV93MlZt9tt1ho8ER0TdF1nz549hMNhmpqaqK6unvbnOJJYTOeznw3h8+ns3JnBbg9RVVVOJjNIdbWdwcEwuq5RXg49PcPkcoXSLjabjWDQg91up6zMTiqVIxrNUFNTzsDAwfAXDHoZGCjsA3Q6beRybqqqCt93KFT4d6uoCDIyYlBXl2fXrjyxWJyKikX4/WV4PDn6+x0oKqgOG4oBwYBBT1/hscEFMHBgKbfCD+H0wSAXqILBUTN91RUwGB27FFzlh6FoYRbR5y489qoLdRz6TuLxOEuXLp1wL6hhGKTTafPnpViDsby83JwdPN4PD8lkkra2NhwOBy0tLROGy2L4O3Q51Wq1zlih9Mlqa2tj7dq1DA0N4fV6eeihh3jLW94y28M6KjkEUrokAIopkwB4bHRdH9OSbrIsFsuYlnRHYxgGfX197N+//7B77SbqT1wso+L3+6elD7CmaezcWQgas3m44L77BnjppUEqKzX8/hqGh2NUVaUZGooSCFgYHCxMqXm9drLZNOFwnLIyg8HBGIZRCBsOh52FC/0oihVVVchmdfJ5HcOwHyj/o+B2OxkaSpNO5/D7qwiHNZxOG5mMD78/RjKpsm9fmmDQh822AFUdxGarJhZTqQqoxJMW7Goei8NCZKTQJcTuLxRyBiivLJwMdljBboc8hQMhea0wqxdOFHr+cuD2QNXYgyKqCrd8QKeMI4e/iRQ7sxRrMI7uZz26H+9kpVIp2trasNvtJ0X4m88kAJYuCYBiyiQATt2JCoPFvUcDAwPU1dVRX19/1DdKTdPM/sTFMioul8ssLzOVZbbiieNMJsPSpUspLy8/psdPl1gsxu9+t4tHHnHR3OzAanXS1xenvFwhGu1DVRV8PoNIpFBTzmJRqKuzMzAQJ53Ok81myWYzlJVBOJzBYimEQZfLidfrJh4vLP2WlzuIHdi/V17uJBp1HugLXEE+n2b//iGSSQ9OZxlNTXVkMgnyeUgkqgo1AustRMOF9JbMW1FQCNQV9gEW+/fuHyrM7lksYPMUWt5BYXYPFbK5QnFnpwPsjkIgTKQKs36qCh99v0a5upNEInFM4e9QE5XdcTgc5raCo3WtKYY/m81Ga2vrMYU/i8VywrrilCoJgKVLAqCYMgmA08MwDPNgyFTCoNVqxWKxjFluXbRokXno5Fhomma2pBsZGUHTNPPNfbL9iYut3TRNo6WlZdZOHI+MjLBr1y7Kytxs2lRFPK4RjQ6yYIGTvj6oqEgRDsew21UUJYWqGtjtOiMjKUChsrIwq1Uo65Igmy304s1mM7jddhIJ3dz36fdXmmEwEKhicFDDZlPw+azs3TuMzaaQy9XicjkJBu2EQlmCQR8DAy7cbo1czkI2C8FaCwNDFjTNYEGTQjIK0REoqz5Y8y9Yd3BZGMBfAeHowa8D/sJ1hQNLvx54/3kafnsh/DU3N09bIC+W3YlEIkQiEbNrTfEE+qGFytPpNG1tbVitVlpaWiYs/i3hb2ZJACxdEgDFlEkAnH6GYZgzg8UagpOh6zq9vb1mb+Hp6OIw0Zt7sT9xRUUFHo9n3JtxKpWivb0dRVFoaWmZlqXkqSgW3S62Env55TSbNsWoqIiTzaZR1ThebwWDg0N4vVbs9sIp02g0PebvCQTcDA+nqKhwoqoK0Wgaj8dJb2/U7MXr8zkZGSkUIvZ63VgslVRVGWSzOfbtS5FKpWloaCKT8VBXl6O3tzDj5fMFSaWgsjJDX58LwwCP30YiAQvqNPYfuF9VEIYONL0oc0Ny1G/sYvHnIr+30N5t9C/1a96jUV8+/eHvUMWuNcWfl0wmM6Y2pcPhYOfOnVgsFlpbWycMf7qum0vMo0n4O3EkAJYuCYBiyiQAnliTDYPxeJwPf/jDXHzxxaxduxa32z1mmXg63jQNwyAWi5knRHO5HDabbUxLumI5D7vdTnNz84ycMJ5IKBSis7OTyspKmpqaUFWVdFrnM58JUV6uEwqFCAZ1BgbCBIM2+vvDo4o7u4jH06RSOYJBDwMDCfPQhWEYBAJuEokcZWU2LBYVw4BUyiCbTZPJpFAUC5FIHlW14vOVMzCQxecrx+lcRGVlAlV1MDBgwem0kslU4fUOY7H4GB62Uu5TiKes+MtzWOwWBkMWDAN8tRBNFPb1VdcWlnod1sIJYI1C2DMMUFRI5Qrt3YpjvnS1xtJAB8lk8oSGv0MVD5EUf16KbdtUVaWuro6qqqpxPx8S/maHBMDSJQFQTJkEwJkzOgzm83nzTTIcDnPNNdeQz+d55JFHxpVYUVX1hITB4kxPOBwmm82iqiq6rpvlPGYr/PX397Nv374JD7/89KdRXnghhcMRRtPyqOoI2WyeQMDGwMDBEGixKNTWlhGLZRgZSZtt32pqCoGwyOm0AirpdOF0t9frZHhYI5PJADmSSQegUFNTictlIxrN4XbXkU5DIFCOrueIRDIYRg26DsE6lWxaY2REx+Jwks8rVFRBJAM+F5Q5oCdy8JRvub8Q9qCwx8/lKez5s1oLfYLPPl3jtPoOUqkkLS0teDyj6snMoEwmw9/+9jcMw8DpdBKPxwHGHCJxOBwkEolxBdVVVZ1wpllMHwmApUsCoJgyCYCzoxgGI5EIq1evZunSpXz/+9/H5XId8XGKophh0GKxTFsY7O3tpaenxwyBFotlTBeSE11I+NBx1NTU0NDQMO776+9P8e1vD6EoMDAQIRBQGBwsVFguhkCHw0JZmYVIpLAUXFZmw+22YbOp7N8fGxUSVVwuu7nvT1UVXC438XieWCyGy1UoCWOxFDp39Pcn8XgcZDLVOBx2Fi3yMjCQp7raQyjkwWrVcLkUYjGDqoCVoUhhebSuATIJGApBZcPBYs6BQKHkS1GgGgZHvY8vX6pz4RntZDIpmpubZzX8tbW1oSgKra2t2O32CQ+RvPjii+TzeS6//HLzQ4yqqrjd7hn5+SllEgBLlxSCFmKeKQa5QCDAnXfeySWXXGIWnz5S+ynDMA6cas1OWxgcHBykp6eHqqoqGhsbzS4kkUiE4eHho/Ynng6GYdDd3U1/fz/19fXU1dVN+P3U1LhoaEjR1+c4MPZC545YLMXAQJaGBj/RaNwMfwDJZA63205PTxyn04bX60DTdFTVQih0sB5gdbWX/v70gfqKeazWKhYssJPJ5IhE7FRVOSkv99DTo+FwhHjzzThWqw2Hw4Gm5amrS9PbWwjwhlJ4jWoCGj2dhVnbch8MxwqzfzYbRA4+NdVVBw99ACwI6qw+bfbDXzabpb29HWDMrPDo3s35fJ7BwUF0XeeRRx7hX//1Xzn77LO59NJLufrqq2ft9LgQpUBmAMWUyQzg3GIYxpj+xJPtRTqVMGgYBj09PfT29h52xq24B2x0IWGv12seIpmOrhKju4wsXLiQmpqaI95/9+4Yd965nQULysjlCjN3mUwYXc8RjaZRVYWqKieJRIZkMkdVVRlDxRMYFF6rigonw8Mp3G4bZWV27HYLuZxCOBwmnc4QCASIRnU0Tae6uopQSAMUrNZqqqqy5POwfz/kclmy2Qq83hA2m4dEogK3xwFWN8FKDcNQGAwVXqPqhQc7gNTVg2qF+iDUVYOrrHBCOJmGeFLjtIadOCyFZV+3233cr/FUZLNZ2traMAyD1tbWCYuM67o+Ztm3WL/yueee45e//CXnnHMOX/nKV2Z66CVHZgBLlwRAMWUSAOeu0WEwn8+P21t1OIWixge7kEwUBg3DoKuri8HBQRYsWEBtbe2kSsMUw2AikTC7ShTD4EQnQo9mql1G7r57O7t3x3E40iSTGSorHcRiSXK5g6+RxaIQCLjI5zUikTT5vG6Gv3D44PSbx2MnldIZHo6QzWZpbAySShVCm8tlJ50uwzDA73djs1kZGBjB56thZASqqtwoikF3dwhFqSAWy+HzG1gsKsm4g8rqcjJZBy6XQtpe6Opx2cVw1opCLcBD5fN5Ojo6SKfTsxr+crkcbW1taJrGsmXLJgx/xbZzh3bHmUvLvmvXruU3v/mN+XN18cUX8x//8R+zPKrpJwGwdEkAFFMmAXB+MAzDXCLO5XLHFQano9bg6K4SsVhhU5vH4zFrDU7mAImu6+zatYtoNHrMZW/+8pcw993XTnm5hXg8cuBwggWnUyESSWG3qzidKtFoxnwd/H4nLpftQD3AQmhxOq0YhkJ//zD5fB6/34fH4yWVKtRyLNYDVFWoqSmjtzdJWZmDRMKHoijU17vYvz+Gz+ckGvWj6zoVFRr792ewWDMkkg5UVWHxMgurV9v4+3eWYbdPHIzy+Tzt7e1kMpk5E/5aW1snLAN0uPCnKAoej2dOhD8oBMC3ve1trF+/fraHckJJACxdsgdQnDSO5RP7//zP/3DLLbeQTCZZuHAhDz/8MHV1dTM53BmjKAoWiwWLxYLD4Zh0GBxdoBoK5Ti6u7tJJBIsWbJkyrUG7XY7wWCQYDBILpczw2B3dzf79u07an9iTdPo6CiUNlm6dOkxfxBZvryCujoXvb0pgkE/AwPDpNMa6TTU1ZUTjyfN8AeFkiqGYbB/f7RQlsXnwOm0ousGu3f3k8/n8fl81NZWMjiYPvA9WonFdIJBSKXS9PYW/i6Px0MyqVBZmWNgQENRFJzOMqJRqK/P0NvrorzcRnXQT2+vQcOCOJe+fz+Kkub//k81T82O3k85Ovy1trbOWuHtXC5nFgCf7+FPiFIgM4BiyubaDOBkP7EbhkFzczPf//73ueCCC7j77rv53//9XzZt2jRDI507Ru8ZPFIYTKfTfOxjH+N973sf73nPe/D5fObs4HSV6CieDi12ISn2Jx7dkm502DmeFnN//OMgDz20G8OAykqNcDhBMGhncDCGqipUVrrI5TSi0Qxer2PMwRCbTcVut9DdPYimaSxYEKC6upxs1jgQFgsht7c3ga4bBINVDAxoGAa43TWUleUwjAxDQ4VDH253kLKyDIahEwq5MQxwuZ00L1W45WawWg1SqZQZlFOplLmf0uv1EgqFyOVys9p1JZ/P09bWRi6Xo7W1dcIT6UcKf263+4QcEDoea9eu5eWXX8bj8dDY2Mgdd9zBypUrZ3tY005mAEuXzACKkvP666/jcDi44IILALjlllvM2aip7EWbz4ozg06nc8zM4Og36WQyydq1a+nq6uL222+nrKzMvF8qlTKD4PGGwdGnQ0f3J+7r66OnpweHw2G2yjveZc6zz67i17/uIZPJYbOpBIM2BgYKpyw0zWBwMIHNZsHvd2GxqJSXO4hG09jtlgPhb+DAkq0Pp/Ng2AOoqHAxOFiodWe3WwmFCsHa63XhdGYZHByiqqqwdO5227Hbc4RCI/j9hdsq/BaWLlW48YZCTT9QKCsro6ysjPr6enM/5fDwMPv27QOgrKyMeDyO1Wqd8RqMxVB+tPCXTCbnVPhbvXo1O3bsmPDa1q1b+drXvkZdXR2qqvLEE09wySWX0NHRMWunqoWYbjIDKKZsLs4ATuYT++OPP87GjRt59tlnzduCwSCvv/46jY2NMzjiuasYBoeGhrjiiitQFIVNmzZRUVFxxMeN3jM4Xct5uq4zNDTEvn37zFlKh8NhzgxOtUtEV1eU//zP10in8we6fLiIxZJkMnmcTiuqqpBMHuzA4vU6cDgs9PT0E41m8fkq8PlcZLOGeYDEalWx2RzmPsBgsJKBgcK1wp6/BFarimFUo+tQW+smFCqckM7ngxgGvOtdNq66ysqRXr7icms2myUYDJJIJA6UoDHMAst+v/+Et+KbzPJzMfwd2ud6rs78HU5rays/+clPeOtb3zrbQ5lWMgNYumQGUMwb0/mJ/dDAMNmSKaVCVVUcDgeBQIBLL72UW2+9FavVOm4G51D5fN58o5+uMJjJZOjp6cFms7F06VKy2SzhcJihoSH6+/ux2WzmnsFj6RrR2OjlYx9bxT33vE4upxMKpbFYLNTWukgkMsRiWfO+hVIvGp2dvRgGVFVVUlNTWH5OJvPkcoVSLxUVbkKh9IHv30IsZhAIQCqVZGCgsETs95cTCkFZmU4ikSSfNwgEPAwOwhlnWLjqKsukwl8ul2PZsmXmjNvoJfTe3l7279+P0+k0XxuXyzWtHTWKezGnEv6AOR/+uru7aWhoAODVV19laGiIpUuXzvKohJg+MgMopmyuzQAe6nCf2F977TXWrl3Lm2++CUAsFiMQCBCLxUpuCfhY6bo+piXdZFksljEt6Sar2F/YZrON6y9c7E9c3BuXy+WwWq1maZny8vJJPdf27SHuu28r+bxOdbWDcLiwnOv3u1BVSCSyGIbO/v2FriEVFRW43fYxM4QWi0pNTTnptIaqKhgGOJ2OUfsA/QwMFMKz3x8kn9exWJKEw4XZy8rKajweO5/6lB2H4/AhrXjKNp/PH3a5FQrhLBqNEolEiEQiaJqG3W43X5vjba+maRrt7e1HLDljGIW9ixP1sXa73dNSB/JEeve7301/fz8WiwWXy8XXv/513vWud832sKadzACWLgmAYsrmWgA89BP7+9//fnbt2jVunLqu09zczMaNG81DIK+//jo//elPZ2PY89aJDoOxWIydO3fidDppbm4+YmAoHjAo1hrMZrNYLBZzKdTr9R7xubZu7efJJ/9GT0+U0bnI5bICBvF4BEVRUdUynM7CXsdU6mCw8ftdRCJZihPJPp+TkRHtwPeroqrl5HIGDocVl8tHPB6mstLPwIDlwKGTGj79aScVFUcPf5qm0dLSctTWf0W6rptBORKJmEG5+NpMNigXFWf+UqnDdxqZ7+GvlEgALF0SAMWUzbUAeKRP7A888AA9PT1mZ4EtW7awbt06UqkUCxYs4OGHH2bBggWzOfx5bXTJmOkIgyMjI+zatQu3283SpUuPaamwuOxYbEmXTqdRVXVMGJzo7xsZSfPb3+7hpZc6SafzuN12Mpks/f1DKIpKRYUPj8dBWZkNVVWIRjOk03m8XieJhIamHW4fYAUDAzqGYVBf76a/P4WmGbhcQdLpwj7Af/zHahoaDh/Cim3VjlRiZbKvTTEoRyIRMpmM2bv50PIyE9E0jZ07d5JIJGhpaZHwN4HizOp82VYiAbB0SQAUUzbXAuCJ9oUvfIEnnngCu92Ow+Hgzjvv5MILL5zwvoqisHz5cjPUfPvb32b16tUzOdxZUwyDxdnByVJVFZvNRjweZ+/evXi9XpYsWXLch0lSqZQ5M5hKpVBVdUxLukMDTzKZ46WXOvnDHzrZsWM/Fkuhn7Hb7Tjw9+UOfJ8QCJRhsahomkEsliOT0aiuLjf3AaqqgtPpw+MxSCZTaFoZqZRORYWbSMSNw6Fw6601LFt2+Nm86Qp/hyoGtUPLyxTD4KHt+nRdp6Ojg0QiQXNz84QleAzDIJ1Ok81mx10rhfAHEgDF/CEBUExZqQXAX/7yl1xwwQW4XC62bdvGBRdcQG9v74RvyIqiEIvFSr5khGEYZhCcTBj87W9/Szgc5rzzzqOxsRG73Y6qqtN2eKHYnzgSiZgt6SbqTxyPx2lvb2d4WGd42MmOHSGGh9Ok0we/B7fbTj6vk8nkD3yvUF/vJZHIY7GogEFZmZPe3iS6bhAI+BgcLPy6DQSqSaWsfPzjtSxaNL7YdVGxp66u69Ma/g732hTDYCKRADDb9Xm9Xrq6uojH41MKf2VlZSWzv1YCoJgvJACKKSu1ADhaoQZcBdu3bzf3HY4mAXC80WEwn8+Pe4N84YUXuPbaa/n85z/P+vXrzTfS4sxgcZl4usJg8TRxOBwmHi/U7SsvL8flcjE4OIjb7aa5uRmLxYJhGOzdO8LWrb38+c+9xONZ8nmDbPbgcndlZRnDwwc7iHi9DmIx3dwX6HJVkErpKAoEg/XcdlstNTWHD0XF8GcYBi0tLSe8pMuhz13cMxiNRs3bq6urqa2tHTcWCX8HSQAU84UEQDFlpRwAN27cyL333svWrVsnvK4oCm9961vJ5XJcdNFFfPWrX521/qxz0aFh8Omnn+bmm2/mG9/4BldfffVhQ56qqmZ5GYvFMm1hMJfLEQ6HGRwcJJVKAYUly8rKSioqKsa0pDMMg97eGH/96wB/+Us/u3aFD4S9vFkM2mJRsNudo/oCexkcLDy+tdXPjTcupqLi8Muho8Nfa2vrhC3xZoKu6+zcuZNoNIrb7SaVSqHrOi6Xa0yHlkwmQyaTGff4Ugt/IAFQzB8SAMWUnWwB8Gh1BhcuXAgUlilvuOEGfv3rX9Pa2jrh/bu6umhsbCSRSLBu3TrKy8u5//77T9jY5zPDMLjuuuv4wAc+wHvf+95Jv3EqimLODE5HGIxEIuzatQuPx0NlZaU5+1VsSVesp3fo7Fc8nqWtbYiOjmF27Qqzb1+UqqqD+wAB/P4qTjutgne8I8DixUcuwZLJZGhvb58T4W/37t2MjIyYPZeL5WWK7fo0TSMUCvGnP/2JD37wg+b/IwAul2vGu5LMBaMD4E9+8hPuuecetm/fjt1u5/zzz+eOO+7g9NNPn+VRHiQBsHRJABRTdrIFwMl46aWXuP7663n66adZsWLFpB6zZcsWbr75Zv7617+e4NHNf4ZhjOlPPFNhMBwOs3v3bnw+H4sXLzYPnmiaZu6Li0aj5uzX6DB46HOl0znC4Qy5nE42q5HPG5xyig+H4+gnmTOZDG1tbQCzGv4Mw2D37t1EIhGWLFkyYQcYXdcZHh7mlVde4Stf+Qp//etfOeuss3jf+97Hhz70IZYtWzbzA58Dij8Pd911F5/97Gepra2loaGBtrY2YrEYLpeLX/3qV5x33nmzPNICCYClSwKgmLJSC4Avv/wy1113HU8++SRnnnnmYe8XDodxOByUlZWh6zqf/OQnGR4e5kc/+tEMjnb+O94waLVasVqtRw2DQ0ND7Nmzh8rKSpqamg576nj07FckEkHXdZxOp7kUWlZWdlyzkMXwpygKLS0tsxr+9uzZQzgcZvHixfj9/gnvl8lkSKfT5mP6+vp4/vnn+cUvfsFf/vIXuru7S+LU76GKPwM2m40777yTT3ziE6iqSjKZ5Oabb+aRRx5h0aJF7NixY9K1HE8kCYClSwKgmLJSC4DNzc1Eo1Hq6urM23784x+zfPnyMXUGt2zZwi233HKgv2ueVatW8c1vfpPKyspZHP38ZhiG2Z84l8uZPYGPRlGUMS3pDg1ooVCIzs5OqqqqaGpqmnSA03V9TBgc3WljKv2JR4e/1tbWWVs6LYa/4eFhlixZMqnwN5rT6cThcJDJZGY8wD744IP813/9Fzt27OCee+5h/fr15jVd1/mnf/onnn32WRRF4ZOf/CS33nrrCRlH8d/9sssu48knnxxzLZvNsmjRIvr6+njwwQe54YYbTsgYjoUEwNIlAVBMWakFQDE3TDUMAuYysdVqZXBwkK6uLgKBAI2NjVOevdN1nXg8bp4ozufzZn/iYku6I/3d6XSa9vb2ORH+Ojs7GRoaYvHixYf9wJLNZs2DMqMVw99s2bZtG3a7nQ0bNnD22WePCYA/+tGPeOihh/jVr37FyMgIq1at4rnnnjshy9TFf+vnn3+e97znPeOu33777dxxxx1cffXVc6L7kATA0nV8FVaFEMeto6ODt7/97bS0tHD22Wezffv2Ce+3ceNGmpubWbJkCTfffPMxddw4mSiKgsViwel0Ul5ejsfjweFwTKpgdC6XI5lM8uqrr9LV1UV1dTULFy48rqXbYmHpRYsWsWLFClpbW/H7/UQiEdrb29m2bRudnZ2MjIyMC6vpdHrOzPzt3buXoaEhTjnllHkX/gBWrFjBqaeeOuHPwaOPPsq6deuwWCxUVlZy1VVXnfDwdeqppx7x9vb29hP6/EIcjQRAIWbZLbfcws0330x7ezuf+cxn+OhHPzruPnv27OH222/n97//PTt37qSvr4+NGzfOwmjnnkPDoNPpPGI7s5/97Ge8+93vJpFIUFFRQSwWI5FIkM1mj7t0h6IolJeX09jYyPLly1m2bBnV1dXEYjE6OjrYtm2bub8umUzS1taGxWKZ9fDX1dVFKBSiqamJqqqqCe93uPDncDhmPfwdTVdXF4sWLTK/bmpqoqur64Q+ZzAYnPD2mpoaoNDrWojZJAFQiFk0MDDAn//8Z6677joArrjiCvbs2UNnZ+eY+23evJnLL7+cmpoaFEVh3bp1bNq0aRZGPLdZLBYcDgcej4fy8vJxYfCJJ55g/fr1fOtb32LlypXmzF8+nyeVShGNRs0weCxLyxNRFAWPx0NDQwOnn346p512GsFgkEQiwa5du9i+fTuaplFTU3NMvY6nk2EY7Nu3j8HBQZqamqiurp7wfrlc7rDhb6YKVK9evZrq6uoJ/+zbt++ojx89yzsTNfoGi4UfDzEwMAAwYTcVIWZS6R3REmIO2bdvH/X19eZpSUVRaGxspKuri6amJvN+szGDMd+pqmrOTum6zhtvvMH69ev54Q9/OOHerKJ8Pm8ur48+QHI8PYkVRaGsrIyysjIqKyvNUi82m429e/fS1dWFz+fD7/fj8/lm5PRsMfwNDAywaNGiI4a/ZDI57vZiT+yZ8sorr0z5sY2NjXR2dnLWWWcBsHfvXhobG6draBPasWPHhF2CirVGW1paTujzC3E0EgCFmGWH7j873OzETM9gnExUVWXVqlX85S9/oampaUwXkiMZHQYtFsuYlnRTkUqlaGtrw2q10trais1mI5PJmAdI9uzZYy4jFw+RnIhOGoZh0N3dzcDAAI2NjQQCgQnvd6TwN1ENxLnqyiuv5Lvf/S4f/OAHGRkZ4dFHH+W55547oc95//33c/HFF4+5LZvNmls3jvQhRIiZIEvAQsyihQsX0t3dbYaM4qzMobMTxRmMopmYwTgZFYs82+123G632ft3MjNumqaRTqeJxWLE43EymcwxLRMXw5/NZjPDHxSWUWtrazn11FM544wzaGhoMA9lbNu2jba2NgYGBibsszsVhmGwf/9++vv7Wbhw4WH3quXz+XkV/h5++GEaGhp47LHHuP3222loaDBbNV5//fW0trbS0tLCWWedxac//enDHtKYLr/4xS/45je/aX5YS6VS/OM//iM9PT0sXLiQa6655oQ+vxBHI2VgxJRJGZjpccEFF7B27VrWrl3L5s2bufvuu3n11VfH3Gf37t2cd955bN26lWAwyAc+8AHWrFnDunXrZmnUJx/DMMzSMsdywnoyM4Ojw19LS8ukZvVyuZzZhSQWi2EYBm6326w1ONXl1/3799Pb20tDQwO1tbUT3iefz5NIJMbdbrPZcLlccy78zSUTdQJZuHAhbW1tRKNRnE4nzz//POeff/4sj7RAysCULgmAYsokAE6PtrY21q5dy9DQEF6vl4ceeoi3vOUt3HTTTVx22WVcdtllAHzve9/jrrvuQtd1LrzwQr7zne+ckOVBcTAMFpeKJ0tV1TEt6QCSySTt7e3mzN9U9vfl83kikQiRSISRkZGj9ic+nJ6eHnp6eiT8nUAT9QJ+8803sdlsvPOd7+SrX/0qZ5xxxiyP8iAJgKVLAqCYMgmA80M6neaaa65h+/btlJWVUVtbywMPPDDmkAnAiy++yJo1a8ZsTt+yZcucaFc1mwzDMIPgsYbBfD5PZ2cndrt9yuHvUJqmMTIyQjgcNmsLOp1OMwweLqT19vayf/9+FixYMKabzWgS/kqPBMDSJYdAhCgBN998M5dccgmKonDvvfdy880386tf/Wrc/U477TRef/31WRjh3FXsLWyz2Y4pDO7du5fPf/7zfPGLX+SUU04hn8+jKAqqqh5XkCoWM66srETXdTMMDgwM0Nvbi8PhMMNgsT9xMfzV19cfNvxpmjZh+LNarRL+hDgJSQAU4iTndDpZs2aN+fW5557LPffcM3sDmscOFwbz+fyYk9n9/f188IMfpKWlhcWLF6MoCplMhkwmg6qqZnkZi8Vy3F1IimFP13VisRjhcJhQKERfXx92ux273U48Hqe2tpb6+voJ/54jhb9iiBRCnFwkAApRYr71rW9x6aWXTnitra2NVatWYbFYuOGGG7j11ltneHTzx6FhUNM0crkc+/bt49JLL+XMM8/kO9/5zrgiz7quk81myWazY/6O6QiDPp8Pn8+HYRjEYjF6enqIx+MAhEIhNE3D7/eP6U9cDH+HlhaS8CfEyU0CoBAl5Otf/zodHR088MAD466tWrWK7u5ufD4f3d3drFmzhurqaq666qpZGOn8oigKVqvV3OO3Zs0avvrVr6Lr+hFrNhqGcULCoKIopNNp4vE4wWDQ7E0cDocZHBzEarVSUVGBx+PBarWOey4Jf0Kc/OQQiJgyOQQyv9x999389Kc/5Te/+Q0VFRVHvf+GDRvo6enh29/+9okf3Elq9MxgLpebdAHvYhgshspjDWKDg4Ps3buXYDDIwoULx5xMTSaTZuHpTZs28dhjj3HZZZfxoQ99iFNPPRWr1Yrb7ZbwVyLkEEjpkkLQQpSAb3zjG2zatIlf//rXhw1/vb29ZmHjWCzGM888w5lnnjmDozz5FGcGXS4X5eXleDweHA7HUTuJFGcGk8kksViMZDI56QAZCoUmDH/F8bjdburr62lsbOTGG2/ktttuo62tjYsuuoizzjqLO++887i/byHE3CcBUIiTXHd3N//yL/9CJBLhXe96FytXruScc84B4KabbuKpp54C4PHHH2f58uWsWLGCc889l4svvpgbbrhhNod+UlEUBYvFgtPpxOPxHFMYLLZki0ajRwyDoVCIzs5OAoHAuPBXpOu6uS8wEAhwww038Mgjj7Bnzx42bNgwayd+H3zwQZYvX47VauXee+8dc+1LX/oSwWCQlStXsnLlSq699toZH58QJxtZAhZTJkvAYrSmpiacTqdZlPjzn/88V1999bj7bdy4kTvvvBNd17nooou4//77p6U+3nw2epn4WNrLFfcMWq1WhoeH2bNnD9XV1SxatOiw4S+RSIx7DlVV8Xg8s7rsu23bNux2Oxs2bODss89m/fr15rUvfelLxONx7r777lkb38lKloBLV2n/1hVCTKvNmzdz+umnH/b6nj17uP3228e0tNu4cSO33HLLDI5y7rFYLObsoKZpZnkZTdOO+LhiaNy5cye6rlNZWUljY+Mxh7+5sOdvxYoV5niEECee/J8mhJgxmzdv5vLLL6empgZFUVi3bh2bNm2a7WHNKRaLBYfDgcfjoby8HKfTOa6UzGh//vOfueiii+jo6KCqqopYLEYikSCbzZrLxEcLf/MhdG3atIkVK1Zw4YUX8sILL8z2cISY92QGUAgxba699lp0Xeecc85hw4YNBAKBMde7urpYtGiR+XVTUxNdXV0zPcx5Q1VVHA4HDocDXdfNGb/izOD27du58sorufXWW1mzZo05i5fP58nn86RSKSwWy4TlaGY6/K1evZodO3ZMeG3r1q0sXLjwsI9dt24dX/ziF7HZbPzhD3/g8ssv57XXXhvzsySEODYSAIUQ0+Lll1+msbGRXC7Hv/7rv/KRj3yEZ599dtz9Ri81TrYsihgfBoeHh7n88stZt24dn/70pw/7uImWkWdj5u+VV16Z8mNra2vN/37HO97BmWeeyeuvvy4BUIjjIAFQCDEtGhsbgcLBhE984hO0tLRMeJ/Ozk7z671795qPE5OnqirV1dU899xznHHGGWNa0h1NsRTMfFj2Leru7qahoQGAjo4O3njjDZYvXz7LoxJifps/vwGEEHNWIpEgEomYX2/atGnCGoJXXHEFTzzxBP39/RiGwQMPPMA111wzgyM9uaxcuRJVVbHb7bjdbrxeLy6Xa9yparvdjs1mQ1EUPB7PnAx/Dz/8MA0NDTz22GPcfvvtNDQ0sHXrVgC++MUvcvrpp7Ny5UquueYa7rvvvgk/YAghJk/KwIgpkzIwomj37t1cccUVaJqGYRgsXryYb37zmzQ1NXHTTTdx2WWXcdlllwHwve99j7vuugtd17nwwgv5zne+g81mm+Xv4ORTrB+Yy+VwOp2oqophGHMy/InZI2VgSpcEQDFlEgCFEGJ+kwBYumQPoBBi3ohEIlxwwQXm18lkkt27dzMwMEBlZaV5+4svvsiaNWvGLBNu2bIFl8s1k8MVQog5SwKgEGLeqKio4I033jC/vvvuu3nppZfGhL+i0047jddff30GRyeEEPOHbAYRQsxbP/jBD/joRz8628MQQoh5RwKgEGJe2rJlC0NDQ7z//e+f8HpbWxurVq3irLPO4v7775/h0QkhxNwmS8BCiHnpwQcf5MMf/vC4kicAq1atoru7G5/PR3d3N2vWrKG6upqrrrpqFkYqhBBzj8wACjHP6bpOZWUlFouFcDg85tqrr76KoigoisLGjRvHPbapqQlFUcYUZ54PEokEjz76KDfeeOOE171er3lKvaGhgX/4h384rk4UQghxspEAKMQ8p6oq73jHO9B1nd///vdjrr388svmf7/00ktjrnV1dZmdOJqammZiqNPmscce44wzzmDZsmUTXu/t7UXXdQBisRjPPPPMhIWphRCiVEkAFOIk8M53vhMYH/JeeuklVFWltrZ2wmujHzufbNy4cdzhj5tuuomnnnoKgMcff5zly5ezYsUKzj33XC6++GJuuOGG2RiqEELMSRIAhTgJnH/++cDYAKjrOn/4wx9YsWIFf//3f09XV9eYpd75HABfeeWVcYHu+9//vtltZP369bz55pts27aNN998ky996UsoinLYv+/jH/+4uRz+f//3f+btAwMDvPe976W5uZnTTz993AzraM888wzLli1j6dKlXHHFFcTj8eP8LuePL3zhC5x66qmsWLGCs88+m9/97nfmNV3X+djHPsaSJUtYunSpHMgRYo6QACjESWDVqlV4PB62bt1KLBYD4I033mBkZIR3vvOdE84QFpeHi+GxlH3oQx/i97//PYsWLRpz++c+9znOPfdcOjo6+MEPfsC1115LPp8f9/h4PM5HP/pRfv7zn7Nz507q6ur42te+NlPDn3WrV6/mz3/+M9u2beN73/seV1xxBel0Gij0+N2+fTvt7e386U9/4t///d/529/+NssjFkJIABTiJGC1Wnn729+Opmn84Q9/AMbO8B0aAHt7e+no6KCuro7m5ubZGfQccv7559PQ0DDu9v/+7//mtttuA+Css86ipqZmwlnAX/7yl7ztbW8z9yTeeuutbNq06cQOeg655JJLzC4ry5cvR9M0QqEQAI8++ijr1q3DYrFQWVnJVVddxU9/+tPZHK4QAgmAQpw0Dg15L730EoqisHr1ahYvXkxDQ8OYa6MfI8YbGhpC13UCgYB5W1NTE11dXePu29XVNWb2sKmpif3795sHUUrJD37wA5YsWWIG6olem4leQyHEzJIAKMRJYvQ+QMMweOWVVzj99NOpqqoyr+/evZvu7m4JgJN06L5BwzAmfd+TyerVq6murp7wz759+8z7/fa3v+XLX/7yuBm+0a/NkV5DIcTMkQAoxEni7LPPxuVy8frrr/OnP/2J4eHhMQGv+N8vvvii7P+bhGJwHhwcNG8rls05VGNj45gDNp2dnSxYsABVPTl+xb7yyiuEQqEJ/yxcuBAofPC44YYbePrpp2ltbTUfe+hrc7jXUAgxs06O305CCOx2O+eccw65XI4NGzYAY2f4imFv8+bN7Nixg0AgwGmnnTYrY50vrrzySu677z4AXnvtNfr6+jjvvPPG3e+9730vr732mnm44f777+eaa66Z0bHOppdffpnrr7+eJ598khUrVoy5duWVV/Ld734XTdMYHh7m0Ucf5eqrr56lkQohipRIJCLz8WJKip0WxNzxpS99iS9/+csoioJhGPT39xMMBs3rtbW1DAwMYBgGV1xxBZs3b57F0c4dt912G08++SR9fX1UV1fj8XjYuXMn/f39XH/99ezZswe73c79999vhup/+7d/o76+nnXr1gHw1FNP8ZnPfIZ8Ps/y5ct56KGH8Hq9s/ltzZjm5mai0Sh1dXXmbT/+8Y/NAyEf//jHee655wD453/+Z9avXz9bQxWHGBkZme0hiFkiAVBMmQTAued3v/sdF110EQCnnnoq27dvH3P9qquu4rHHHgPgW9/6Fh/72MdmfIxCiLlDAmDpkiVgIU4if/d3f4fdbgcmPuAx0ZKwmFmHKzp944030traysqVKzn//PN54403Jnx8Z2cnVquVlStXmn927do1Q6MXQpwsZAZQTJnMAApx7F5++WUWL17MeeedxzPPPMPpp58OFJaQ16xZg9Vq5ZlnnuGTn/wk7e3t4x7f2dnJ2972NrPOnhDHQ2YAS5d1tgcghBCl5HAzr8U2dgDnnnsue/fuRdf1k+YksRBibpEAKKZMPjkKMXW6rhOLxSb8/+iuu+7i4osvNtv6jRaNRolGo6xatQpN03jf+97Hpz71KSwWy0wMWwhxkpCPlkIIMYc8+uij/PznP+eee+6Z8HptbS3bt2/nhRde4Mknn2TLli3ce++9MztIIcS8JwFQCCHmiJ/97GfcddddPPHEE2Na0I3mcDjMa36/n+uuu44//vGPMzlMIcRJQAKgEELMAU888QR33HEHP//5z83uGhMZHBwkl8sBkMlkePrppznjjDNmaphCiJOEnAIWQogZ9KlPfYpnn32W/v5+qqqqcLvdbN26lerqampqavD7/eZ9n3rqKSorK/na175GXV0dN954I0899RQbNmxAVVU0TWP16tXccccdOByOWfyuhBDzjQRAIYQQQogSI0vAQgghhBAlRgKgEEIIIUSJkQAohBBCCFFiJAAKIYQQQpQYCYBCCCGEECVGAqAQQgghRImRACiEEEIIUWIkAAohhBBClBgJgEIIIYQQJUYCoBBCCCFEiZEAKIQQQghRYiQACiGEEEKUGAmAQgghhBAlRgKgEEIIIUSJkQAohBBCCFFiJAAKIYQQQpQYCYBCCCGEECVGAqAQQgghRImRACiEEEIIUWIkAAohhBBClBgJgEIIIYQQJUYCoBBCCCFEiZEAKIQQQghRYiQACiGEEEKUGAmAQgghhBAlRgKgEEIIIUSJkQAohBBCCFFiJAAKIYQQQpQYCYBCCCGEECVGAqAQQgghRImRACiEEEIIUWIkAAohhBBClBgJgEIIIYQQJUYCoBBCCCFEiZEAKIQQQghRYiQACiGEEEKUGAmAQgghhBAlRgKgEEIIIUSJkQAohBBCCFFiJAAKIYQQQpQYCYBCCCGEECVGAqAQQgghRImRACiEEEIIUWIkAAohhBBClBgJgEIIIYQQJUYCoBBCCCFEiZEAKIQQQghRYv5//k0gpF2WT2sAAAAASUVORK5CYII=\",\n 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c2RGm+e+MQnIoRg27Zt3H333RQKBc4//3wuv/xytm3bRqlU4te//vWSrH3m+kwNDQ1x4MCBw55PJpOqBlBx3FECULFqadfltEVCJ4cOHaK/vx+YuSG2RUebsbGxYx5jaGiIm266iQcffJB77rmHa6+9lhtvvHHBheaFQgFN0+a8mfSaQqHAq171KiC8OUN4DeY639nPJRIJgMOiRG1B1ub73/8+pVKJf/mXf+Hqq6/mD/7gD7jooouOGF2aXZfY/t188pOfPCwyd8cdd/AP//AP8bphcb+7xXCkzuTZz8/3s3es/c6mnVL81re+dcxt22uYfS3aa2qvwTRNbrjhBn784x/z0EMP8f73v5/bb7990X52Qghe+MIX8s1vfpN6vc4tt9xCLpfrSisLIXjlK1/JD37wA0ZHR7n55pv5xS9+wZ/+6Z8edd/9/f0Ui8U5z/W8885j27ZtbNu2LRaEl19+OXfffTc/+tGP8H2fP/zDP1zUOcHcn6nx8XHWrVt32PPT09NzRioVipVECUDFquWss85izZo1fO1rX+t6/mc/+xl79uzhsssuA8L0mxCCb3zjG13b/cd//MeCj/e3f/u3FAoFHnzwQYA4WnGsaEAqleJxj3sct956a08jB3v37p3z+YcffhiYEVCXXnop9957b9f2tVrtMEuRdoq4HTlsM7trtZ3SM00zfm5qamreFiWXXnop2WyW0dHRwyJzF110EWeddRYQ/u40TePrX/961/tn/85Xmvl+9hbKJZdcwuWXX85HPvIRRkdHD3tdShmLw/YxvvrVr3Zt8+UvfxmAJzzhCYe9f3h4mJe97GU86UlPij/DAJZlLehzec0111CtVrn99tu59dZbufLKK7tS5J0UCgWuuuoqnve853Udcy7OOuss9u3bh+d5h73WjvZt27YtFnqXX345ExMTfOpTn2Ljxo2HeXEuhO9+97vUarX48a5du7jnnnsOq4s8dOgQzWZT+QAqjjuqCURxyvOrX/2KfD5/2PPPfvaz+au/+ituuOEGrr/+el74wheyf/9+3vve93LGGWdw3XXXAeFN5QUveAHve9/7CIKACy+8kB/+8IexWDlSF2ypVOJ5z3seL3jBC3jUox6FaZr853/+J8Vikac85SkAnHnmmRiGwRe+8AX6+vqwbZszzzwz7mDt5L3vfS/Pec5zeMYznsHrXvc61q9fz86dO3nggQf48Ic/vKhr88IXvpDBwUGuuuoqzjzzTJrNJj/+8Y/5x3/8Ry699FIe97jHAfDa176Wz3zmM1x11VW8/e1vj7uAZ9+0165dy2WXXcY73/lOgiAgnU7zpS996bDU3ZOf/GQMw+D666/nL/7iL5ienuamm25iYGBgzpv3bHK5HO9+97t585vfzOTkJE9/+tPJ5XIcOHCAu+++myc/+clcddVVnHXWWTz/+c/n/e9/P0EQxF3A3/nOdxZ0ne69997DjKB1XY87txeKruvz+uwthk996lNceeWVPPWpT+VVr3pVbAT9P//zP/zLv/wLhUKBK664gnPPPZfnP//5fOADH8DzPP7gD/6An//853z4wx/m+c9/ftyl++IXv5jzzjuPCy64gEKhwK9//Wu+//3vxzY3EDYxfec73+HpT386hUKBtWvXzhn5avOoRz2Kiy66iP/zf/4P+/fvP6wO8Q1veAOZTIZLL72UwcFBRkZGuOWWW+J/N0fiCU94AjfeeCO/+c1vuPDCC7teu/zyy/nkJz/JgQMH4lq/tjXTXXfdxYtf/OLD9nf++eezefPmru76I5FMJrnqqqt4/etfj+M43HjjjWSzWf78z/+8a7t7770XYNEiX6HoFUoAKk55PvvZz87Z8blz505e8YpXkEwm+djHPsa1115LOp3mGc94Bu9+97u7bDg++tGPkslk+Id/+Adc1+Xyyy/npptu4pprriGXy8153EQiwQUXXMDnP/959uzZg6ZpnHnmmfy///f/4pF1/f39fPjDH+ajH/0oz3nOc/B9n9tvv33OYvSLL76Yb3/727z//e/nrW99K47jsGnTpiWJhTe/+c3cdtttfPSjH+XQoUNIKdmyZQuve93r+Mu//MtY3A4MDPCNb3yDd7zjHbzmNa+hv7+fV77ylXiexxe/+MWufX7605/mjW98I294wxtIJBK85CUv4clPfnJXU8k555zDP/3TP3HjjTdy7bXXsmHDBl796lczOTnJv//7v89r7a985SvZsGEDH/vYx/jKV76C67qsW7eOJzzhCV0WI+3f3cc//vH4d/eZz3xmQQ0sc22bTqfjrtvFMN/P3kJZv349P/jBD7j55pv5xje+wcc+9jGCIGDLli0885nP7Ormvfnmm9m6dStf+MIXuOmmm1i7di033HBDV/ftE57wBL7xjW/wmc98hkajwcaNG/mLv/iLrhTwhz/8Yd72trfxohe9iFar1dVZfCSuueYa3v72t7Nx48bDxNAf/MEf8G//9m/ccsstlMtl1q5dywtf+MJj7vMJT3gC69at49vf/vZhAvAJT3gCuq4zMDDQZdh++eWX89BDD81Z/1ev1+fdfPOiF72IVCrFW9/6ViYnJ7n44ov5p3/6py4LGAij4RdeeOGcPo0KxUoiisXi4toSFYpVzsc+9jE+8IEPMDo6Gte+rTZuvPFG/v3f/31enoMKxUpw44038uUvf5n77rtvSdNiHnnkES655BK+//3v85jHPKYna2s2m5x99tm85z3v4WUve1lP9qlQLBYVAVQo5sG3v/1tHnzwQX7/938fIUScJv2zP/uzVSv+FIoTkXa5wm233TYvq6Yj8aMf/YinPOUpPRN/ELoCDA0Nce211/ZsnwrFYlECUKGYB5lMhttvv52///u/j9Ngb3rTm3jjG994vJemUCg6yOfzfOpTnzrm1JBj8fKXv5yXv/zlPVpViG3bfPKTn1z0HGiFopeoFLBCoVAoFArFKkPZwCgUCoVCoVCsMpQAVCgUCoVCoVhlKAGoUCgUCoVCscpQAlChUCgUCoVilaEEoEKhUCgUCsUqQ/WiKxbNXOPVFAqFQnHyUCqVjvcSFMcJFQFUKBQKhUKhWGUoAahQKBQKhUKxylACUKFQKBQKhWKVoQSgQqFQKBQKxSpDCUCFQqFQKBSKVYYSgAqFQqFQKBSrDCUAFQqFQqFQKFYZSgAqFAqFQqFQrDKUAFQoFAqFQqFYZSgBqFAoFAqFQrHKUAJQoVAoFAqFYpWhBKBCoVAoFArFKkMJQIVCoVAoFIpVhhKACoVCoVAoFKsMJQAVCoVCoVAoVhlKACoUCoVCoVCsMpQAVCgUCoVCoVhlKAGoUCgUCoVCscpQAlChUCgUCoVilaEEoEKhUCgUCsUqQwlAhUKhUCgUilWGEoAKhUKhUCgUqwwlABUKhUKhUChWGUoAKhQKhUKhUKwylABUKBQKhUKhWGUoAahQKBQKhUKxylACUKFQKBQKhWKVoQSgQqFQKBQKxSpDCUCFQqFQKBSKVYYSgAqFQqFQKBSrDCUAFQqFQqFQKFYZSgAqFAqFQqFQrDKUAFQoFCtCpVKhWq2u6DE9z6NSqRAEwYoeV6FQKE50lABUKBQrwsGDBzl48OCKHrNarfLQQw/h+/6KHlehUChOdJQAVCgUK4IQAinl8V6GQqFQKFACUKFQrCDHSwAq4alQKBTdKAGoUChWBCHE8V6CQqFQKCKUAFQoFCvC8UgBK9GpUCgUc6MEoEKhWDFOtRSw7/s0m02VYlYoFCcdSgAqFIoV4VSMxpVKJX7zm9+oLmOFQnHSoQSgQqFYEVQKWKFQKE4clABUKBQrhkqVKhQKxYmBEoAKhWJFOJ7ROCU8FQqFohslABUKxYpwPFLAUkoqDz9Mec+eZT2OSjUrFIqTDeN4L0ChUKweVkoASikZu/9+HvzqVzm4fz+1u+/m8TfcwMBZZ63I8XtF+3opgalQKHqNigAqFIqj4rouO3fupNFoLGk/KyViAs/j15/6FL+6+WYq+/fjVKt4zSY//ehHOfirX63IGnqF7/vcd999TE1NHe+lKBSKUwwlABUKxVEJgoCJiQkcx1nSflYiBSylZPSf/5nx3/wGAHtggMB1gVAY3nPzzey/555lXcNyoCKACoWi1ygBqFAojkpbfCxVvK2EANz39a8zPTKC77pIoFWrAeHaM4UChVyO0VtvJfC8ZV1Hr1DNKwqFYrlQAlChUByVXgnAXu3jSIzdeScH/vu/cbXway0xOIidSJApFOgfHsYyDNxyGadcZuy++5ZtHcuBigAqFIpeowSgQqE4Kr0SH0fbj5SS8fFxpqamcKOU7UIo/exn7PriF0HTqE1Pk0insTwPOT2NPz2NW6/TmJrCzGQA2PuDHyz6PBQKheJUQHUBKxSKo7LcKWDHcRgdHaVarcbPJZNJstksuVyOTCaDYRz5q6r8wx9y8HvfCx/09WHW6/QZBhPT0wCYhQJutYphGPhBgGaalHfupDQ6Sv7005d0TsuNSgErFIrlQglAhUJxVJYzBVwsFtm5cydCCM4++2xs26ZSqVCpVCgWi4yNjQGQSqXI5XJks1kymQy6rgNQ+v73mbz1VurJZLh/w2AAcG0bonV7jgOJBABuvU6yvx9naoq9P/jBCS8A2yxnCnh8fJxSqcSZZ565bMdQKBQnHkoAKhSKo9LLCGCbIAjYt28fhw4dIp/Ps3XrVkzTBGBgYICBgQEAWq0W5XKZSqXCxMQEBw8eRAhBWkqs++/H/dGP0IaG8IpFdNvGrFQIGg0aUcTQHhjAHxmJBaBhWWhAYf16xu69lzOf/3zsQmFJ57WcrEQE0HEc6vX6sh9HoVCcWCgBqFAojkqvU8CtVovR0VHq9TobN25keHj4iBEu27YZGhpiaGgIKSW1kRFK//VfNH/xCyYbDaTrUgUMKSlkszhTUxiFAq1ymVQ+T+C6pHWdtK6TWbMGUa8jp6YwEgnWDg6y7847Of15z1vSea0EyxkBVGlmhWJ1ogSgQqE4Jr2wcBFCEAQB27dvR9d1zj77bDJRU8aRaN1+O+799yNrNaRp4hw8iC0E2qZNiEOHcIVA6jqJcpnpsTG8apWmpmFqGlq9TqNWA98HKXFLJYxEAqTEazbJplIE999P8Oxno1nWos7pVBBPUkrVZaxQrEKUAFQoFMdkqQIwCAKmo6aMbDbL1q1bj9rYAeD89Ke0vvnN8IFp4goBQiAti+bEBAJIrF3LuvFx5PAwslLB1XWsXA727WM6m6VZLgPQMk28UgmzXie3di3++DhNIL1/P41f/5r0JZcs+tyWk5USmEoAKhSrDyUAFQrFMVmKAGynfGuRKfMZZ5xxTMHh795N8wtfAEIRFGSzyGgcmlsokAwCLMehXiwS+D5uMomoVOjbvJnGgQNQKGBaFkYmQ93zcCYm8CLz53KtRtayaB04gJ3JUPvpT09YAdhmuVPASgAqFKsPJQAVCsUxWawAnJ6eZufOnei6zrp16zhw4MAx3yPrdbwvfwlzsA+kxDcthONiDA8hdQN9YhIcB29oiKBcRgpBs1RiYM0aHM8LxUxfH8H0NHYigSsEGSHA93FdF9d1ado2zYkJPF0nu20b9ac+lcL69ce0nFlpViICuBLHKBaL+L4fN/coFIrjz4nzTadQKHqKlJI9e/bQ19dHNptd0r4WKgCDIGDv3r2MjY1RKBTYunUrxWIxXtfRIk7+bV9D7BxBB2QyRTA5ie4HSCBIZ8FxQNdplkrh/gYG6A8CmJqiEa3R03WS2Sy2lNR27MAsFEitWYM0DALfJ/B9tHwep1LB0DSKP/4xExdeCBzZcuZ4crJHANsG30oAKhQnDkoAKhSnKEIIxsfHSSQSKyoAW60WIyMjNBoNNm3axJo1axBCzEtkBA//juBH2wCQgC8M8IPw8eAQwdg4AN7AAHJ8nMTgIIHnIYtF3DVrkGNjSE3D0nX0Wg0vl6N9VGdqikxfH5aUUCphDgxQzmRI5vMkx8fpO//8uS1n0ulYEKbTaTTt8AFKJ3sKdbnXr9LMCsWJhxKACsUpTC+6dxeyn3bK1zAMzjnnHNLpdNc+4MgpR+m6+F/6wszjgTXIQ6ERNJkM3tg4wtARqRS6EBRMk8DzaESRxWazidA0smvX0ty/HyklToe/XWbNGoLxcfy+PnTAnZxkfSbFdDKDs3MnWrHI0PBwbDnTbDZjQXjo0CH279+PpmlkMplYEC53+nSlUsBKACoUqw8lABWKU5iVEoBBELBnzx7Gx8fp6+tjy5YtR6ylO6IAvPsHaIaEjWtAaOhNF3NtPwQBntAxg1ZY+5dMIA4dQkqJm0qFx0+nCVotBtNp6o0GAFpfH97kJABGMkkQ/exMTZEqFJClEn4qTf/0JJVEgtpPf0rhyivj800mkySTSYaHh5FSUq/XY0G4f/9+giCIo4Hj4+PkcjkSicSyCJ2T3Qew81opFIoTAyUAFYpTmLb3Xi/2cySh0Gw2GR0dpdFosHnzZoaGhuYULEeNADYbaHf9F7Sa4eNkHxRD2xi/fw0iigQGmRzuoXEEIPv68KLon8jn6ZuYQPo+TrkMQuBHk0X0RIJkLgdRF7EQAk/T0IGm41JwHAa2rqN4773kn/vcI649nU6TTqdZt24dQRBQq9U4dOgQxWKRPXv2AGCaZjzDOJvNYtv2vK7vkVARQIVCsVwoAahQnMIsdwRwamqKnTt3Ypom5557LqkoInekfRyRn2+bEX/ZPpgIxZ80TPyp6Gcp8XwtFH9S4kqJPdCHZejUx8aQnodXKEC9jtQ0/Hqd/oGBsGnkwAESGzfSnJyEIIijgK3pafxUEr3eIp8CZ2QEex4zcTVNI5vN4jgOxWKRCy64oCtCOBWJTdu2uwRhe9zdQllu8XQqCEDf9wmCYNHXWKFYbSgBqFCcwmiatiwCsDPl29/fz5YtW47ZLXvECKDnwd3fI3oRWn78kp8tQNT4EfQPISs1zKF+SNiY5TKiXMPtX4P0PCTExs+ZtWsJDh7EK5cJTBMRBPjj49iZDD7gVathFFAInFwOfWKC1JDA/5/7YB4CcDa6rpPP58nn89EpeVQqFSqVCuVymYmJCQCSyWQsCOdjOaMigPNnbGyMgwcPctFFFy3rcRSKUwUlABWKU5heRQBhRow0m01GRkZoNpts2bKFwcHBBd3cD1vPL38GldDShcIQTExBXx6ZTCL8AGPDIHguZr2KEE1krY4T5AnqNaQQtKaL4X77+hCNBsOFAo1GgyAIEJqGnJ6GaOScX6mQGR7GtC0amobM5WjUGySDAD8xgPHQL+A5L4QlihXDMOjr66Ovrw8Ax3FiQVgsFhkbC1Pa6XSabDZ7TMuZk90GRkq57DWAK3EMheJUQglAheIUptcp4MnJSXbt2jWvlO9c+zgMKeHu78BAHyQMaLqQDsArIaSBXgwbN8ivgWYoEoPCIMF4+LzXP0RwMBRTejLJYLWKbLVw6vXQeqa/HzkxQfsKpIeHYWwMc2iQ5OQEYt0w4+PT+JkMvqNhNCZh4iAMrVv0tZoLy7IYGBiIffBarda8LGdWglMlAhgEgaozVCgWgBKACsUpTK+aQACq1SpTU1PzTvnOtRaYFQHc8SB44+D7EORnIoF2EqYi8afpEM0RloBbb8U/O+UqyaF+bFOjuX8/AnCiSCCA7zjxoez+fkQUeXMcN/zyK5ZYM5yg6ibRJyax+kA8sr3nAnA2tm0zNDR0TMuZZDIJQKPRWLYO41NFAKoIoEKxMJQAVChOYXoRAWw0GtTrdYIgYOvWrQwMDCzpZt61nu0/C8UfgLBmnrczUAlFHLkBiLqA/b5B5KEJtEIOkcmQL5XQqlM4hTVxc0grmjks02k0KUn09ZErFLAMg3Z1oVcs4uey6LUqtjDJJH38gy2C5CD6yHZ4/NMWfX4L5WiWM9OR8B0dHUXX9a6Gkl4KwlNBAKoIoEKxMJQAVChOYZbaBDIxMcHu3bsByGQyDA4OLnpfh92cPRd+98vwZ92A6akj/FyEvjykbDQ3INFnglsiqAONsA7Qm4rSw319BMUiiUIeK5nE2buP5vQ0Wl8f8tAh9IEB/KkpEAIvmUSvVfFFkmRtjNYZG/AcH33nQ2FjynGaCdxpOZNOp/nd737HaaedRrPZpFKpsGfPHqSUPbOcOVXEmYoAKhQLQwlAheIUZrERQN/32b17N5OTkwwMDOB53pIjiYelgEceACdM55Lqg2rY7Uu2Hypl6OsH04TxgyBLoA2ilcJuWpnpg4liuNb8IPJANCLOtuhfN4Q5NU6tEdnKSEnQaKALAaUSWjpNUKvhTE1jaRp+uQEmWHlJc4+D1BzE7kfg9HOWdL69JJPJxPWDvu9TrVZ7ZjlzqqSAVyoCuGPHDgYGBsjlcst+LIViOVECUKE4wfA8j1arRSqVWvINbTECsNFoMDIyguM4bN26lcHBQUZGRvA8b8lrgQ4B+NufdxzUAV2DNf3gu5BoQL0BWhpkVMPYYQ8TBFq8r3ZNoLlxCLtSQTQa+Pk+gvEwfRrkcshaDWwb6fuIIEBYFtJx8IcGEZPjBOuSaC0Pa4MgmMqFaeB5CMDjMQqu15Yzp5LVzEp0Gk9OTpKJusoVipMZJQAVihOMSqXCyMgIF1544TF94o7FQppA2je33bt3Y1kW5557btyE0HM7mWY9jAACpLOQsCClg+FBuQgISOVhOmoKSWZhMkzdykQaJidBCIJsHithYyYTSF3iRc0frtDbFwCpaWG3cfv4jQbJTetpTRejZhCBb2bRitPoazyCobUwsh2ecVVPzrcXHE089cJyRkUA50f734BKNStOBZQAVChOMNo3l15072qahu/7x9yuM+U7ODjIpk2bDhMIvUoBA/C7X4Ghw4Y+kAKmxqIXOr6StI6mEC0BogKANFMwbCMSGkYA2mSU/pWJ8HVNw50Mo3/CskKx2E7XSUl63RrSk/tgMEFVSyNJ4zd9zMADPYuWqMPuBtQqoTg9yVio5cxSI7vzYaWicwvtTF8o7X+TSgAqTgWUAFQoTjCOOjN3Efs61n7q9Tqjo6M4jsNpp50WC4eF7mc+a4HovA79FgouNMfA7/C7q4STPNB0KLYbQcxQxPUXIJNATE2i+S7UDWiEN2Rpp5CT04DAz/eHncKWibl2ENttYvf1YZsmlqWTLI6HRs92lmx9HH/LOpoPHkL26wiZhPo4bBmCkQfh9y9d0jkvlV58Bo5lOeP7PhMTE7Rarbh+sBflB22klCsWAVxqxHw+xwAlABWnBkoAKhQnGO0bZS8igEdLAUsp4y7fRCLRlfKdaz89SwE3q7DvF0AAVhamq+ELqT6IInek+6A+AYkEDAxAZRKcIsghhO/ObFOLxsRZGaAOgBdAZvMa7OokTqVEqjJNUvgkhtZgmh1feU54XfSggbF1AN/xMRoe2BKyLuz53bwF4Mkyq3cuy5lf//rXcQfx/v37CYJgWSxnToUawPa/JWU3ozgVUAJQoTjBaN/EljMC6Ps+u3btYmpqisHBQTZv3nzUm2cvbnjtfej7fjPT2KGlgEgAyo6vI12HTYPQmIRWBZyoo7cZdQ1LCfXwOQkE0yXQBNrGITLFIlrJQSbTMFGZ2aVtwuQE2AloNUOxWTCgVMYaEnjVNTA9BsMABgS7w+Mcx5v9cjdoCCEQQpDJZNi4cSNBEFCr1eIIYS8sZ1ZKNK1EDeBKRQCDIMD3fQzDUGJTsWwoAahQnGD0sgZwLgFYr9cZGRnBdV1OP/10+vv7F7WfxawFQNtzf+di2q9CaQqyWehLwuRB8ASYqagRRITm0MVSKMhSeZgK08Uy04dmGmheCaFLcMPpH4GdmakbtCxEcTIUdJlsKAClhGQB6hMIq4Cer8K0B2YBWgHYu6C4H/o2LOm8e8FKRRg1TYsbRaA3ljPtz82pFAFc7uM0m022b9/OOeecozqOFcuGEoAKxQlGL2sAO42gpZSMj4+zZ88eEokEj370o0kkEvNeUy8EoPBaaAf+B0wdjDRUK4CAXD+YQHMCSMxE3cyZ1C5WCkQULdRssCwYLiACiTYVeQg23fhcg0o1PraezyHqUTRwchzSaajVwI3OKbDQgjHYOgw+UC2FqeDyjhNCAC4nR6vP64XlzEoJwFMtAnik46zU9VSc+igBqFCcYCxHBND3fXbu3Mn09DRDQ0Ns2rRpQTexXtUAJicfgcADIgFoOjDcB0EA5YlQ+DkdXam1xszPpahBJJ2CFIAHzUlELVqXbkGpCIBM55CTobiUQkOvFMO0chvLDgXgdBEyAhoeWMCQBjsdkC3QklDcCVueuOTzXiwr5dE3XxZjOdNOF58KEcCVsoFp/9ufq6tZSkmlEv4x076m2WxWCULFglECUKE4weh1F7Dv+2zfvn1BKd+59tOLCGBq7KH2A0gZoAfQGoOWHtY9oWPUp8MAoJGCYhkQkOkPZwYPp0AQRvEAUoNQDaNQJPNQCp+XZhKohFNABvvCsXOdN9PpScj3Q2kaUv1QLsMAYW1izoCWAJmB6R1LOudesZw396V06M7XcgZgamoKXddJp9PLIqBOpQhg27rpaBHA2T8rFAtFCUCF4gSjV13AUkpqtRq+72PbNmeddda8U75zrWnJAtB3SU4+gr9lE2QNKO0DBCT6cUoHKZcrOEYOq1LGNE2MXIaEH6AP9EPaDuvxGgLsjnnEbseaOtK/MvDQT1+DaBUxJ0sE+QFoyZmIomnBmiS0auDr4exfMwuNFgw5cCgX1iBW9oPXAmNxc3aXykrd4HslnOaynJmamuLAgQMUi0WmpqbQNI1MJtNzy5lTqQZwocdR0T/FYlACUKE4wehFF7DneezatYtSKZykcc455yzpptWTFPDYduSAgcYEiM2AQCKplhs0SyVM0ySTzeN7Do7jUCoVKSd9rMooZjmNpQks20IrR1Yxmg7F6GczCdPT0J+H/iT6oYOImkDqJnqtiqYLSKZg/RDogChC6yCclYQ97ZnBCahOQiGAwTxU62AEUNoDA2cu7dyXyIkaATwabcuZ/v5+Dhw4wFlnnYWmaXGEsNeWMysZATyekcbZ/w6V+FMsFiUAFYoTjKVGAGu1GiMjI/i+z+DgIJOTk0uOWPQkArj3R+hBM7R7adQJZEClXKFZKZFJpUhlcohGE/r7SQ/2k5/ah+v4NBjCG5+g5fu4zRzJehHTtDALa7GCcni9cvmwe7gxiZAzTSQynUOI/eECgiAcNZfWoBp5CdoGbG5BqRB2/hoBkIGcAwcrkBVhHeBxFoDLyXKbNHfWzaXTadLpNOvWreup5cxK1uZpmrYiAvBIx1FpX0WvUAJQoTjBaHuzLfSLXkrJ2NgYe/fuJZlMcvbZZ8edmku9yS9ZADamEeO/BSDQM7i1acrlMp7IUMgIbMuCZD8UBHgTIHw0oWHbNrY9CH7oi9YSWbxWK5xmcWgC3Xcx1uawnXFsr4Vh6Ih6R+OI50fXJnpsC/A7zkPaQAk298P/jEO/hCAJXgnW9IHmhwLwOHGiNYEshiNFzXppOXMqeQ1CeC2OJGZVBFDRK5QAVCh6gOd5jI+P09/fvyCT3COhadqCIoCe57Fz506KxSJr1qxh48aNXRGE4y4Ad/+I0LIZmq5GrVjE0A0GBtdiVIswNAgigPpkuH2znZaV4UxeQNd0Up4LuRzSSuAmEniV/TS9Ko3pGg1AmgnsehnLsjATSfSoKxiATDqcKIKApAWeA44bNpVoTSikwbTBATQH1mtw0DghGkGW6ya/EpYi8z3GUixnVjoCuNws5DhKACoWixKACkUPCIKAffv2kUqleiIAFyK4qtUqo6Oj+L7PGWecEVt0QO+miixJSAYB7L47jMJJiVOcJJ1IkMlkEelE2OARTEGjbeeSCH34EJDIwXTk35csQKUGG9cgNIk1OY6VSpFKDSH1MVzXpWnk8KsHqFaruIGBXQ3f63kufspGr9XCfaUKUB4LZw/ngEYTtphwIBFOGEkACR9ME+pj0KqGRtQrzMnWBDIXixWZC7GcSaVSXcdaLk4EAahSwIpeoQSgQtEDejm/F+YXAZRScujQoVh4nn322YeJz15ZyixJAI5vx6tOhClfI0MyYZHduD5Mr1YOhNtYg9CI7Fz0LBBFALUUUAlr+nJpsGvgjIHR0QnshWk5y7KwjCT09REEAS0ri+c0aTZ9an5AY+dDmKYR1g8aRSwZIACsHDRLsMaE3DBMT0C/AM+EPg/GZJgGHj7vsFM72QXaiRQBPBZHs5yZng6bgXbs2MHY2FicLu615cxKdBoDcXPMkdbQiYoAKhaLEoAKRQ/opXkzHDsC6HkeO3bsoFQqMTw8zIYNG+a8MS2HAFwotf/5byoNFy3XR+BLrEQDgjGgU8R17LfTCLpehzWDYFahPg5B1LxRLbYXBpV2V7AB5WL0o0EyaSJP30JlYoz0wADW1MEwSths0qjvw8+kSUgXPZMiIXyMoIDoK8FkABSg5UN2CorJIwrA2dfnZGMlawx7LZw6LWfq9Trbt29naGgIx3E4dOgQ+/fv77nlzEpFAI9WAzibk/Wzpzj+KAGoUPSAXqVa2wghjigmO1O+Z555JoVC4aj7gaUL08UIwCAIODDyC8zJn5HM6WTzGSYPHAKSIEyoTUU7N6De/tkMxZ2UUOgH3QubQrR82JgBYBegVgx/TvTNvDeRh0YR1g+AKWF6HAGYcgpTmiTXridZnEYi8TyfViqHVzxAdaJI0yjhWRrpXBU9uwbbMTCaDShIWN933BpBVqpD92Q/RpuBgQHS6TRSSur1es8tZ1QKWHEqoQSgQtED2p27vUwBz/6il1Jy8OBB9u3bRzqd5pxzzsGyrGOuq/3epbDQ/TiOw+jICOkdXySTTpBMJBBmH4KD4QZGAYimeZh9YXQPwMhBwQ9HvWlmOKkDQHSmtjvPOUqTGToUEmAD/hiYQ/EWvpVEOHWwDEgkEc0GpmFgSg/yefKGiWtLmkYS3y9RSTQRe8vYhQpmMonZX8M+WEOTcmZG8SnGyS4AZ3cBCyGWxXJmJQXgXN3OoFLAit6hBKBC0SMW2rl7NGaLSdd12bFjB+VymbVr17J+/fp53Yh6HZmcz37K5TKjo6Mk679jrT2NaSbDKF9jGohS280OqxbXC4VVYQA0DZqT4AHkZ7ZpdGxfr7UXE/68YQ3IaXDGQfrR9rV488BMAi74LqQT0Zg3Ca4Dff2I6hRW3wCWAfT1kRsYxGnU8KwWjbpJlXHwdKr3/5T00GZyudyyjTObzXJHe06VCOCx0sy9spw5Wm1eL1FdwIqVQAlAhaJHLFcEsFKpMDo6ipSSs846K7bJmO+aYGUigJ0Rymw6wZmNX6C3b6BmH7hhlC/Qs+BUo3nAubB2L20AFai3wu21RJTmFaAnoVoOtzdSUKuFAm7NGghqUcQvC/WoW1hPhN3C7WiQ70L7XtqqhJYzk9OQy0I+AVUJWhIa4yA1dF+Q3JSBmkO2rx8vMU1rvY9/6BDj40kOHDgQ15a1xcBypmpXQjgtJyvh0bfQYyzWciYIAgxj+W+bC/EBVCgWixKACkWP6HUE0Pd9Dhw4wL59+8hkMpx++unHTPnOtR9Y/hrAzqaUdevWsV78FlEXYPRBEEXlMoP4dUlgp0LB55fASkD1UPi6tQYIrT0wOjqBjQyIxszP/RZkAtAEFOvh81oSiASglQURvVc3MVrjYHZYuIgWPNqGoAiWA2II6jKyKcxCqwVDLWhkwJEYCQ8jb5NOaGxcc0FXbVmxWATg/vvvJ5fLxf8t9Pd0JFQEcGHHWGxUdr6WM5qm4fs+pVKp6w+AXrOQGkAVAVQsFiUAFYoeMVfd3lKo1Wozgmr9+kV90a9EBLBz9NyZZ55JQR+Hh78OBGHkzV4TeulJ0PQmhlcHLx1G6BpRjZ+U0JxJ2+K6Mz+3u4KzubA2sDkNngAp5t7e6xC76QKwv3vBeQtMDVp1aNVgkw//kwzX4JvQnILBAAb6oFoNfQK9NKR/d1ht2aFDh9izZw+Dg4OUy+U4lZhIJGIx2DYrXiwrcYM/GWcNd9LrKOORLGf27NlDq9Xi4Ycfjj8Ly2E5o1LAipVACUCFokf0KgLYjjwAC075zrUmWB4BKKVkYmKC3bt3x6PnbK0JD34aZHQdjHwo/uL3WMh2ZM8cDOv2AMxCZP5MZAQd1guCDloAG/vAr0RpXhGmjSulcBP0ma5gCdTKMwuP1hEvO5UBfwL8wfaJhcc7XcLuNDginAccpJAFFzFZC4WmY4C9K9yfmLkxt6/vxo0bgTASWi6XKZfLh5kVtwXhQoTCqRIBXO56yeWeBNK2nDl48CCFQiEW/JVKZVksZxbiA6hQLBYlABWKHrHUGkApJQcOHGD//v0YhoFlWUsSf+01tffdy/34vs/u3buZnJxkaGiITZs2oQkJv/s0uG0BpoUNHm3MPnTvEdDtUJE59Y4jdKRM9SwYPhQKYBpQ3R9G/KxBoG0WnQcRjY2z89BoW8HkoNk+vpipI2xTsCGohtvoUVdvYEFyHIaHodQIo4x+EuwmMplCBDo4PuBAsB/0jUe8ToZh0N/fT39/PzATOSqXy4yNjcX1g+26slwud0wrkpO9BvBkjAAe7Ti6rpNMJkkmkwwPD/fcckZKqVLAihVBCUCFokcsJQLoui6jo6NUKhXWrVuH4zg0OjtfF8lyCMBms8nIyAitVovTTjstTpOx/yvgHoJkZMEiTaiNh2JPCPC8mZuVNdDh/adBvQjJLCQS4eOED3Ic/H5iAed3nIPsvDl2REqEHdYV9kXpYs/HcxphWjiVgSASjb4bjnbza+AS2scMC9hfgT7A08GrE6wfRK8SNq0A+DuOKgBn02lW3CkUyuUye/fuPcyKpJf1g/NhpSKAyy1S2sdYCQE4W5j12nKm/R2iUsCK5UYJQIWiRyy2BrBtmwLwqEc9ilwux+7du3sSnel1E0i5XObQoUMYhsG5555LMpkMNyj9Esa/G/7sV8KuX28MbBGaOxtD4LXw0wU8y4akBYlBIAhFW+MQyEqo5VplQICwoD7dXkEoEiEUlLXSzOIa9fC5vgFICkg1wiYQYw00JjESZWQ6B0O5sKavjZ4KBaDTCgWg4cJQAYQHjo9ISeSghyyaCLcOgQnBDuDyRV/D2UKhs/N0rvpB3/cXdazFrG25WKkI4EoIofnU5i3Vcqb9b1WNglMsN0oAKlYtjUaDWq3G4ODgsTeeB+0OwfkipWT//v0cOHCAbDbL6aefHt8EemUp06sIYJv9+/fT19fH1q1bZ25QrTHY888zG2kW+HXiyJ1mRB59HjolhFgD7sGZ7WVypmZQSxB38xoF4q5gszDTMGLmoFGZ2T6RgCEBFKHZYdbsOfEhROCCVuk+mfb1bVYhAzgN2JKAAwlohulpgY7MGwhPgJsBY+fCLthR0DSty4rEdd1YDHbWDwoh2Ldv37L4D66USfNKRABXos5wMcdZqOVM+4+quY6j6v8UvUQJQMWqpZ2e6aUAdDu7UY+C4zjs2LGDSqXC+vXrWbduXddNslcdxb1oAmmvFWBwcJAtW7bMrFX6cOhLM6JLytCqxZmaeSySoVFzuCKEXyOu+TOHZqaACD3swJ058MzPsuOrSiRAq0N/P6Q0qEU2MmbfjEgUGjTCKKEAgmQW3ApkB6ES1RE2y1H2OACRAbcKgw04tBacGkgN4WoE/VW0Q4Brgb0fZGvWZJLeYJpmXD8opaTVarF7924qlcqi6wePxamSAl4JkdmrOsP5Ws7s3r2bQqFANps9quVML857ZGSE17zmNUxOTpLP5/nkJz/JOeecM+e2zWaTJz3pSSSTSe68884lH1tx/FACULFqaYusXt2g5hu1K5VK7NixAyFEnPJd7L7my2IFYGd6GqBQKHRfq+nvQ/O3YAJ2ErR+8B2Q6bBuzloDrfF4c18vYAZlYgHodFi/mP2xWXRo/hx1AksZizmSKUiYkNWAcZADHavt+Dqz8uBOxw+llQDqoHUIdN+FRBa8CgQJ0KpABobCXROkwXGRQyX8ch7dlYAEfxcYj1rglVwYQggSiQTpdJpms8n5559/xPrBthjMZrMLrh88VZpAViICuNDavPky23JmamqK0dFRLMtiYmKCgwcPxuUD2WwWx3Ho6+vrGnu3VG644QZe/vKXc9111/GNb3yD17/+9Xz3u9+dc9v3vOc9PPaxj+U3v/nNko+rOL4oAahYtbS/yHs13ulYTSCdKd9cLsdpp512xHmfvYoAtgvjF7qvrqke2SybNm1i+/bt3Rt5RZj6ZsebvHAqh3TDbxYrE6Z2jTXghaPYdG8fUo9uoOYg1CfaBwS3oytYZIFG+HyyHywTjAYEDWh1+Pq1Oixf3NbMz1r3dRVE0USnCMk8NKL36YlQAPpa6FnoWcjCGExoCM9GOKHw9AYK6GNRdDLYASyvAJzN7PrBzrqycrnM5GTY3NJZP5jNZo/5uVYRwIUdA5bPaqZN2zNyy5YtWJZFs9mM6wfHxsa45pprCIKApz3taTzpSU/iiiuuiKOJi2F8fJz777+fr3/96wA897nP5S1veQu7du1iy5YtXdv++Mc/ZmRkhD//8z9XAvAUQAlAxaplOQTgkYSW4ziMjo5SrVbZsGEDa9euPab1x3LNFT4Wnuexc+dOisVibELdrm3sOr/xr0DQIbqMwkwED0BLgxPV8GmAPQBpk0BPQXYIfB1yQ6HICwwIvDDCJ4BAh2QBggoYJjTHIACMQWhFotHIzQg5YUC9Qwx21P9J3UC4ZTBTgICEDe0G63bNpuuGUUxPRyR8gsE1CBeE7SL8BEHKJZASLdDA3zmz7xXy6ZvN7LqyueoHO42K2/WDR/rMnQo+gCe712Cb9r81TdMQQnRZzriuy2233cYPf/hD7r33Xt797nfz0pe+lFtvvZWrr756Ucfbt28f69ati4WnEIKNGzeyd+/eLgFYq9V4xzvewRe/+EVGRkaWfqKK444SgIpVS6cA7NX+5tpXZ8r37LPPjrsDj7WvXqWnFxJNrNfrPPLIIzNTPQoFYI5mktpDULln5o16rlv86dkZ8QehIbQ7gYaDJj3QhsCLGkEEYPXNpGz1PDSjfUnCqF2boPM8EkAk+owc0LaV0aEx8x7fzgAd9YTNCTDtMGLYrgNs1kPbGDe8+fpDNtruqGPYTaGbNRr9/aT9cmgF08FyR57ms/+56gfb0cG2UfFcvnQqAriwY8DyC8CjHUcIwbp167jmmmu45pprEEJQrVZJpVJLOubsazfX98Xf/u3f8r//9/9m/fr1SgCeIigBqFi19FoAzo60SSnZt28fBw8ePGbKd659tfex1BvbfFPA4+Pj3VM9OnzKDhOA9dshUwBphd2znZdQSpD6rMfQ7gqWkjDt2kbPhk0jcSNJxzUy+8LRb0TvbxZnXutM+XbV/+W66v/QBdEC2guCdB6KY+B7M3WA0oJWNKUk7RCIMHApXBMhp6kUUiSnLDRzPJwjrBVmX8Kes5gIY7t+MJFIsGbNGqSUsS9de5xZu34wkUgAYdR3ufwHVQ1g744z1+dh/fr1Szrehg0b2L9/P57nYRhG/L3Vnm7T5ic/+Qnf+c53+NCHPkSr1aJYLPK4xz2On/70p0s6vuL4oQSgYtXS/oLtlddaZwRwoSnfI62tV3WAR9vPnFM95jC7jdfj7ALn4ZkXk30QlIH+MJXrC6gfnBF01iC4k+0d4Wt9EDQ7dm7PbCssaE3OvNYpJI0+aLW7hPWOiR+A07G/WfV/elDlsK86rxSmmxu1mTpA0qFwDATCD/AHbTTPRLjh79TUTBrpFOlgHIKdoF041+XsOb34AyCTyZDJZOJ0frt+cHo6FMrbt28nmUx2zS/uRVkEqAjgYo7TTv/OZjk8AIeGhjj//PO55ZZbuO6667jtttvYvHnznPV/bbZt28bf/M3fqC7gkxwlABWrluVIAUspKRaL7NixA13XOeecc8hkMgveV6eB81JvxEerAeyc6rF169YjWuJ0CcDaD2a/GtrBMAXIMAJn2kAunLLhBzPTQBBofhWIDKRFCpyJmV3pBWLvP6GB0xHJCzpuvEYBiISiZobG0KYNmRzYRlhjiAxfG+0Qim36crClAW4CWTZgRCICM0wHB2l0t0F9PRg7UohIXFpSUEk6JOo2ur8DjAvnvFYnOp31g5lMhpGREbZs2UK1WmVqaopDhw4tqH7wWMzHPHmpnEoRQN/3V3wKyEc/+lFe+9rX8pGPfIRsNsvNN98MwOtf/3quuOIKnv3sZ/fkOIoTCyUAFauW5UgBAzzyyCPk83lOO+20uLB6sWtbzgjg9PQ0O3fuxDAMzjnnnGPWEQkhwC+D21H7p6XBnyQ2fTb6wC9GL06APQj+VNh4QRavmULzqoTJ1QD0dGQaTVTv1yHW9H5wJ+Z+rR0ZTOYgnYW+KuglYCoc+9bWzIk1yAEbKh2/Y12HgRKQA7MKa8rIygCiFb3PT4A/gbQS+Lksev0ASIERtAjMgGn7dAbdnUe9Vr1ipZpM+vv743F1x6ofzOVy2LY9b/GxUhHAxf5bW8gxYGXmDc93DnCvOOuss+a0ffn4xz8+5/aXX365iv6dAigBqFi19FIAtlotDhw4ADCnsfNC6dUINzi8CURKyd69ezl06BCFQoHTTjttXlFGIQSG82PQO1LmIgV0ePnN/kqRke+edEBOotsthNWEfAFEX9jUkVgDng+eFjVvSMKoYse5GzkQLqT7wDDCiGLBBFEOU8jNyCfQyIPXETWUHrqo4q9ZC5VoLcMDoI+F1i864NfwHqVh3hd6TOMKhA26l8LpF6TqINwMulUCmaFpaDT8OknZmz8cjsVyN2h0HmO+9YOWZcXNJLlc7qi1raoGcOHHma8AVGPgFEtBCUDFqqVXArCd8m1/GQ8ODvakcxd6HwHsrE3cuHEjw8PD816rED6W++OZ6JowwuhevEEYOZs5iWzYLBFHB/vR2IkkMlTWTJBj4f50IDkASZcwOpgOPf+wCDtMbHDLQCOyl+kQnV6HfyAdQkTKsLZPgE4R1m6FyQrkojUGLujR6hKSYDiFXq9CVPOneSZOooFn5xGuhbAq6PQhRIP9lsZWOT5zbsvE8bKZaTNX/WDbbqZSqXSNMTtS/eBKibOTZRLIfI4z37IPJQAVS0EJQMWqpV1ovVgBGAQB+/btiyNpAwMDjIyM9Ey0tY/Ri30FQUClUmFkZGRBdjSdJBKP4OYDfHsNwnHBM9C8TuuXHHidzR0dc30BkIhosEfYGVzt2NbGb43RbDUxDAPD7kdv1/gdtm2aOOooEuBViYVY0DHpw8xA0BmdHIet60GLjKT9eqwXtcDGO72I9mA2rvnTXAHJOtXCBnLR7GFbmtREGan1M86v0Lho/hdwkazETX6+x9B1nUKhENsDtceYlcvlrvrBTCYTRwhPlVnAR2vOWI7jzIWaBazoJUoAKlY1x5recSRarRajo6PU63U2bdrEmjVrqNVCsdGrtC307gu/0Wjw0EMPkc1mOf300+dtR9NJMvEgQq8R2GNgg5A5gqAPzTWhWUd40zPxMGFGtYERWiqKFopQAOr9EMykaptugmpxP1IGIMELAhJmE8uyMKwBLFlHtPfud3j6iQzQITrd0szPxkykULb/N+3NWALKJmADDsLXIeHirRnAPHgQAoHmuQjAyQiCevhVaQYBQhPYWJS4n4y2acHX8URiqT6AnWPMpJTx1IpyucyBAwfYt28fEP6bGBsbW3D94Hw5VUQmHL0JRKWAFb1ECUDFqmYxArDdPNHu8k2n0/G+oHdRu17sy/M8ms0mnuexdu1aNmzYsKibhpQtEvbDyHb+V2aQIjRR9nUQiUGQLsIfRLQAV0dzD83sQEtD0E7VtjuCQSJp1OuUK2USlk4224cUCdx6CcfVadQbOOUqFkVM08K0kthiCr09Tq7zfmjkQ3uXmFnXzs6DmIpLDMP/SRMKwLCu0R3SMA4JhGw3q4ApAiqpFBnAkHVAx6AJSEr23UguXvD1nC8rkQLulYiYPbUiCAJqtRojIyMEQcDu3bsB4vrBdoRwMX+MzGYlI4DLzUIaWpQAVCwFJQAVJw3tFOa5557bZVK8FBYiAIMgYO/evYyNjdHX18eWLVu6vqh73bgBSxMA9XqdkZERfN8nlUodZuy6EGTwAEJ4M923IgnMpGVDn+cW0mghdYkgTSCzCC8JLQ/NKRGawEAgbfCnCKSkUqlQb1pkU5BOZ8Mon17ASDokk0mkDHBbNm6rheM4FMsS059E03Us08TSBKYu0YQIm0HiBckOs+noJpmIJoeY2ZnXpBm+7Iem0sJo4fQPY3sO6BUIMuhanem0TtJNh7WEcgApSgjyOMZ+guQa4JJFX9tjcbJO6dA0jWw2i6Zp9Pf3s27dujhdXC6X4/rBVCrVNaFkMSJrpWoAV0oAHqkGUEUAFb1ECUDFSYMQAs/zembbAvMXgM1mk9HRURqNBps3b2ZoaOiwL98TKQLYOdUjm80uOZIkg3ujoF17PF1H44XUgalYZwmRAypIAdKqIKxBPOkgggKutHCrAW7gUCmV8H2ffGEdKSOq1ZMS/BlhKfQ8ll7BSqVJp9Lk6MOrSRzHoenoNJuhZ6BpmuiWRUK4GIaBMFJRE0kHVpQqjo2fgSC0fhF+PQpMVqgMD2Id0BC2QHeTSLsEWj9T5pkMeQ9gyQSOaJIgSZ0abt8vCfgjtGX4Oj3eTSC9OoYQYs76wXYzyVz1g7lcjlQqNS+RcypFABeSAlYoloISgIqThl5P7mjv81gia2pqil27dsV+ee2U75HW14sv6cXuKwgCdu3axeTkJIODg2zevJmdO3fiOM6x33wEpGwSyAdo1++1BV4bIfqQdHT/kux6XSJDVxe9iEiWkcJgggbkcvSnh7EQyKaDCNzQ4Lmzjq9zX1Kiyxq6bWPbNhltEL96CNd1cFoOjdJBmtJHaBpayiCpN7GsML0oNTPyCRQgvJnd+62oEzhAyDTIOl5K4tkJTED3DHxbYpHA0RqM6WeRkQ0cwIyKCaVRZ4qfMchli77GR+NEmDW8FI4UZbQsi8HBQQYHB49YP6jrele6uD26bjYrFQFciYibsoFRrBRKACpOGtppkZWKAAZBwJ49exgfH6evr4+tW7ce1Z7heEcAjzTVY76zgI+EDO4H2qJJcrjA655/LERHx67UgekwOigljpNAUgrrwLI6mtAJgnFISZAZhJcF30Z4Hrh1aFVnGkuMPLRmzKBFIDF0HUNPkkwPI5slXM/DcRwavk+1Hq1RQFPYWG4LyzQRVGbqAP0KSAFCIvwEUqtjSYNaQVCogeaF52YBLapU9SFkkIhsrIsIaeG7feziQQ4xziCPYoDTMZhbqJxorJRH37GOcaT6wbYg3LVrF9BdP5jL5eLZtadSBHAhx1ECULEUlABUnDSsZASwLaaazeYRU76z6bV1C8w/Ani0qR6zjaAXiiP/Bw0tjOIh6az96xJ4zBUd7EcyjoxsaBwnjWlq5HM5EAIRtH+XAkkVTA/MVlhTKDNoQQ1JAqQFXhb8qGFACvBAaH2gBaClEF6AhcRCI+Ol8Scs3EaFaqWCa7Qol8LIomlaGFo/Cb2FbugIkQUqaL5OYIIhoZqukm7l0b2wNtAg/P82gimtiWA9HnWcQKemT5PAp8ohqhxiH78gySAFNpJnA0kKMx3MC2S5BdqJIgBn064fzGazbNiwAc/z4vnFc9UPto+znJyINYAKxVJQAlBx0tDr0W3tfc4WlFNTU+zcuRPTNDn33HOPOSJtOdYnhJiXR+HsqR5bt249rINwKRHAgCZF4xGQ/ZS1FMnAIEWAhkQgDkv/CpLIruigj+95lEplpPRJJJMEvgjFnzRAFmfeqxVAzqR/hUiBqAJNEE2wJDPp4DzC70gVSz3alrAZxCyjb9TRq5uoNR4hk/dIpvpwHAfHcSnXHRreNJqmoaVNkskmuuFBAgxc0DRK2Q0MFB8CqaOJEpAFpoEkgioCC0eroAmXJBnqVLDI4tCgxW6K7EYiyDJEkj4KbCDPBsz2HOQTgJWqAVyqcDIMY876wXK5zORkaDe0Z88epqenF1w/OF9WYtyclFKlgBUrhhKAipOG5RKAruvG+22nfPv7+9myZcu8Hfk799er9R0rcjffqR5LMbtusZ0w1AaBUafBAEXLR5PrsaSN6YMpiwi8cK2iw/hZ6rRaB6lUyui6Rj6/mWZzgpbfPqc+oMNIGqt73bLV8cgGWY7tY0L/vvZxCF+L35gB2QIRQHYMd30GS6uHBtOGQSoFOQq4FR/Xcag7Hp5XwavuRXgOuilw0lkCExxzEOE3wWhiYuPikKKfBg2sKM0rhMRhmiRD+ICOjUsdDQOLBFXGqDLGOA9hkSRBhizryLOJLMNHbR45mWxgjrT/5ThGZ/2g4zj8+te/pr+/H8/z4vpBwzAOm1+8FI73uLm5PgtKACqWghKAipMGIcScEbul0N5fZ8p3y5Ytix7nttR0aydHE26VSoXR0VGAY071WEoEsMlvOnYEkjpgE4gWTeHT1HykzGPKNFZgYMoiumwgpKRSMWi2StiWTTabRWhZEJ3Rwlm1g52pZRLhGLn270DkgA5BGHSaQWch6Jw40n3zlLaBpxWYqWMEjQq2aWFbFhmzD1/zcZyAqu7htCYouQFSekyKJBtLHnrex9I1XAEmLg3Ap0oiSFKXDTT6qTMTkbTIoGMgkWiYBLgkyOJSoUqdKmMc4H4S5LHJkmcTeTaSpO+wdPHJ3ASyVKPphdDf308+nz9i/aBt213zixcazVuJFPBKzRtWKEAJQMVJhq7ryxIB3L59O5ZlLSjle6T9LWcEUErJwYMH2bdv37yneixWlAY0afFwxxNp0Bu0o28aWQKmEQI8UUVqA9QB6Q9Rm24S1ByG8oNkkm2/5rChQwJC6l2TQISWA9lRO0g2TPt2rGbmIugQlDrG8HY3pYQTPjowWvhWcpYvtA9mAbwieDV0WyOZ0NEzOaTWRATD1Lwy0y3BnppBPjiAG5i0UgaW5SDMNUgtAOEi3QySIO4rMUnj0cJlZgpJigISH4M0HjUEGjo2TUo0KVFiLxYpBDpZ1pJnI3k2nhIRQFheATh7Ru9c9YOd/oPj42HUOZVKdc0vPpboWkkBOFfmQUUAFb1GCUDFSUUvI4C+71Mul/E8j4GBATZv3rzglO9slpJuPda+PM9j586dFIvFBU31WGwEsMWDdEbNhLSATm+9jggekoAanutRKk8hpUZuXYammcYjheXbmLKGJJrEQR8cxTpGyA6rFimAYse2fYiOSCKd22J2CUmJBmYVDAfpDyL8jvXrVnh60onmFreiTuAmKdMCO42dHqZecPFaQ6ScvVTdcLKK4xh4VoGm2cK3XDyZxBQGJgUC3K7jJ0jT6Fi/SRIdDYGGQQKXBglytKLzb1FhgoexyVLqq5Bw1lAiRYZhdJY+NaOTlfIZXAmReSRxZhgGfX199PX1AeEYx7YgnJiY4ODBg2ia1uU/mEwmD1vzSgjA9neb6gJWrARKACpOKnoVYWs0GoyOjtJsNhFCsHXr1p58mS5XBLA91cPzPM4888y4GH4+LFYAdqZ/JRK0Vsd+dAJm6u40cjSaE1QqlbBgP78FYYQpUZ86jpGigUPd6qPZKpDWC1gE6MEUSImIomXtfRPMdBajFUDORAtDQRgvrKtxBC0HwcwMYmnkEV5oUu2nshiVDgEoZoQapIAWWmASAEZ0ngYSobkEySHKyRQ5klQ9l4bTImjq+K0WUoPxyQlSoh9S+7EsC03XsEmjYRHgQGQcY5HGp44fiWeJJEEBCEiQw6WORKJj0aKCY5RoGSVGqRLgk2aIPJvIsYEU/YvuLo6vzykYATwWduQj2fYfbDQacXRw//797N27d876wZXyGoT51wAqFEtBCUDFSUUvUsATExPs3r0by7IYHh6OJxD0guWoAZyYmGDXrl0kk0ke9ahHLbiYfTFRyTD9+7v4sUYWoe2nfWoaeQKmgHB4R7XcpOFUsO2w3k8XMxV+7ehguBgI9CY1o0jNACHXYPk5TBxM30IPptDo66oV7PqakhKCjlpBkQ/TwTHdEVypz0TMArM8MwYYkLJCoKfR/Vo8Ek5EDSp6lEYO6xJ1BFWagEOTwEhhG4SasW7TLJlkUn1Ug0ncalibaJLEtJsYtoZpmghNkKQfcCFuEtGwSNPqqB00sNHQowYSDSlLmEEah3DySom9lNiLTZYAP04Vh93FiytdONkF4LEigEdDCEEqlSKVSrF27VqCIIjtZiqVSlf9oOd5tFotPM9btm5glQJWrCRKACpOKpaSAvZ9n927dzM5ORmnfKenp3vaqdjLCKAQgkqlwsTERDzVYzE3ucWI0jqPEJBHMI0gQMMKGzLicwv/f+AHlMslHEcnm8uQSCQQYnZ0MBt6/LXf6WXi90vh4RoaLapgCIQcxg7ymEES069jeFOzmkPSCDrG0DFbDLe6Hvn6TLOIFC2CxCB6MxSXU9nTGE8lSbdc+hst0u4Y+GFUMDSLzhCIGjr9+DRIMUSdGhp1LNI4NBFoSBFg5D0KFDD8BF5L0nBq1N0SQcsPawBFgUZyL6ZlYRgGpkhjYAEBGgYBHhZpXBrI6BwkEsNPIUUYHfRw8HExsON08QS/o8QefFwS5MmzkRwbyLIOfR5f7ysVAVzO1OlCI4BHQ9O0OOoHdNUPjo+PMzk5yeTkJOl0Oo4Qzqd+cL4spAlEiT/FUlECUHFSsViB1Wg0GBkZwXGcrikZndYyS63/g97VADabTRqNBkEQdK13sWuC+d/sJZJJfkETH8hjksJCEARpAkqE6d8SrutRLpeRfoJCnxmPXeuMDoaPQ5vkziN0H28mLRuIAEefwtEDMEEL1qMhMAIdI/AxfYHhO4h2baLsTONqXdFAiY7UoseREAmSAXoTGvZ6DqTAFCalRJOqnWFTZRPp1sHwmskWGn2RMLNo4KFH6dkAH0EVmxwN6ePrNUKPQImmG5CqkkzppOQgwrPxmgF1r0StXoN6DT1IYibqmJaGaVkIXZCiDwgwSUXRPolNmrK+DyEETcoY2BiYUftIBpcmJgkcakgkdSapM8kUO3CpxVYzoRn13Oni1VADuBTa9YP5fJ7x8XE2bNiAYRgLrh+cL0erAVQpYEWvUQJQcVKh6zqe5x17wwgpJZOTk+zevRvbtjn33HNJJmeMeHstAHsRAWxP9QDIZrNLEn+wcAHYZD9NDkSPwoq0GmWqukngDJGVG2jVJ6jX9mCaGn359WAUO/YgO36SBF0ROw2hV2lH7gQpAmqxNDHIIensDk7iMYmntd+dJ0hk0KWJ4VuRILTR/SYiEGgdncXSyIEodngHQqBN41qD7MpnQcjYg88XTUayBc716iCrIBwsadIUHgbhTTmgRooBGjTQSNCKRKjupTDJoGHQpBifixA6mgma2SJDgqzMIFo2zVadulei6bSQEhLkqKf2YZkmhmliahYWCQSgBTaB3sIkhUcLiQ80o9rBHB6tOB3s0Yqig2H0tcReakywl3vRMedMF6/GGsDF0D4P27bp7+9naGjosPrBffv2xfWDnePqLMs6xt5nWEgN4MkcAezv7+dxj3sc//Vf/3XMbf/oj/6In/zkJ7Hht6J3KAGoOKlYSArY93127drF1NQUg4ODbNq06TCR12tzaU3TFiRQO5FSsm/fPg4ePEihUIhT00ulc0TdfKIk09w7e2XtPeEJh7HyBA2nSCK5hnR6CE/T0QmAEgIjihKGaGSQHQ0emsx3mUVrpCJvwfYRRFd8UHaldKPUshD4wgMtjWvOCD5NDmA7aRLNErpf6ar/62QsvxZPc6L9z/zeDZHg4fw6TvcOoonJji/HChITgcDAQSdLAw+Jiy9cPKOGhkaTIiYJdCwE4OHgRdFNicQWKVqJMlYCTHJxurjp1Kk3azQadTTfwrY0jISGZVr4oonl5dHQsUjh0ow8BWe6hn0cNMxYzLbTxRp6lFIOCHAZ5yEqHGSEH5BigDwbqRugu7k5r1MvONkjgG3mEmZHqx8sl8tMTYVR8Lb/YNuD8Gj1g+1/o/Pt7j9ZWeh3m4p+Lg9KACpOKuYbYavX64yOjuI4DqeddhoDAwNH3B/0VgAu5svKdV1GRka6pnrs2LEjnlKyFBYyV9ilTKWr+SOJSyX075OSwLFo6CWymSyJZAIwqVEGNDTWYJPBoI5gCoGLRhK/QwC25Z2UYWBOMuPZJxFd4lGQIKBCu21DI0fQER2c3fARiBY1u0bN0kl4GzCly2xJEIgEJS0NhAKw07JFQ6ehw27tTDb4oMfp5RYmGTxcPNI46FgkInHXwPDStChHoi+sC/RoEeBHli+hOOu0gtGxEDqIVItkSicpBzC9DM1mg6ZbplarUAUML03DmkZrOlimha4b2GQRUQOJSx2dBAEuXse1tMni0sAiDUgcmtgk4+hgnUlalCnlygQ5D8GOo5pRL5aViM6t5DGOJjKPVj/Y6T+YTqfj7dLpdNc+1Ri4w6nX68f0OlUsDiUAFScVx+oCllLGXb6JRIJHP/rRJBKJI26/HAJwofs60lSPXtUTts9xPgKwyC/p9PfTsQlo4DgOrVYLghSFgQKm2f7qmNmnj4MTTcKVpDFJYmOg0YdgGtCQ2kwtoCCJH5lDh8fK0en3p5HG7xA1nbc7iYSuukKTgGq4lRA0TIe6TFOYFYytifX4Ioj35XfUH2rRebeEz//oG9giW0jKgIlGjhoarSgi6UVnbvkFmhSxyOHRwMCOaviCWBCG4m8qWmUaHQuJRzMS1gC2yNIyy+gmpEmRlXloGhTLU3i+RqVSQfMNDNPEsMuYloVpmiRErsNguoVDnRR9NCkCghYVBDoGFgEeCXIE+ARRVDAQDpIwXdykxG5+jEU6tprJs3FJs4tXMgJ4oonMufwHO8XggQMHDqsf9Dxv3gJwNfDwww/z4IMPsm7duuO9lFMSJQAVJxVHSwF3pnyHhobYtGnTMdNCvRaACxFtUkoOHTrE3r17yWQynHHGGV1/6fbKUma+EUCfFiV2ItGiMW06rixRb9Sp1Wpo2GA3MM2wfqwzOghgkImjfeHED4NalKYUDISpSTmNH5kxC9KILgGm0XnlJB0dvEj8js5inXRXZ7EgBx3RQY0CLVGmqW1BdIjKKS2JRzO2Uw4rHBP4NCO/PvCoo4k0OwTonBEJOh+bNIIAiR+mdCkwpY/hGU2a1LAjwWqTBgQeLgKB02lwDbQoIQnQ0KMIoYXTdS4GujBwkw2CZgtDNxhIbcFpNWm6NRqNMrVGHdNLo6VKWKaFaYXRwaQoIAniCKAWRUnd6DqHUcEMPg4mCQwvjSfCOsIwOihwqDPJw5TYwwj1OF0czi5eG+9zPqxUelYIsSICcCl1wrZtMzQ0FNcP1uv1w+oH29dpYmLimPWDvTjfhx9+mJe85CVMTk6Sz+f55Cc/yTnnnNO1zc9//nPe+MY3AmFU83GPexwf/OAHF2RHdfPNN/N//+//7XruV7/6FRdccMER39NsNuOo6bOf/ex5H0sxf5QAVJxUtCOAs4vX20bJruty+umn09/fP6/9Ha8I4HymevQqAjhfAbiXbZQpI0hikcL0DYqV3bTcKqlkCt3PU/fG4+3b0cH4OLO+TjrTiBIfF4+a1qSqJ0nJIZIihQ5RdNDvso4RmNFjER0rP+v1RJcAnN1Z3I7x1bQWyBQ6LTzRT1WE22ok47VrmPg08akjsMPIHhY+DtBEoCPxaVFGw8QkhUTQ7Dh+gnQk9CQ+TijiMJD42LRr7GRod9NOgxMgEHF0UMeOo22tDtFo+hlco4owJEksknINppui0arRdKpUnSqiYmAIi2qygmWamJaFrWWiczWj6sUaSfI0KREKvRquXsUM0ng043WGwlXG3oN1JvFocpAHEAiyrCPHRgpsOma6eKUigCs1oq1X5yGEIJ1Ok06nWbduXVw/uGfPHlqtVtwElkgk4ujg7NrBXqzlVa96FS9/+cu57rrr+MY3vsHrX/96vvvd73Ztc95553HHHXdgmiZBEPCyl72Mz33uc7z61a+e93FKpRK7d+/uWnuz2ex6bi6y2SxXXnkl73znOxd2Yop5oQSg4qRidteulJLx8XH27NlDMpnkrLPOOmrK92j769X6jiW05jvVo9cRwKOdY5FRJnkQCKNijudwsLwX3/coZDeRThSoVaaQwUy8z+3y+jPxupo/ErhRXRyATgq3o55PYFKPt89hk8fEQaeCoIROFr/DSoZZI9AETiz5JES1gjOPvfYcXhFQ0QYZYA9lbU3H+23aY+1ER0TLxA7NnqPawACXJDnq0f7C51M0aWCSxgx0DEcQ4MUm0zomAj2uyfNxsMnRpBJG3bDwo87iTqGnodOiEnX6CgxSGI5PYPtRVDbcyhIpHKuKYUGGNH3BGtyWR6vVoNEq0Ww20d0kWqKMaZtxd3FC5AnwSZDHo4WHhx4k8PQ6PiY+LiYJJB4aZpwuBg0nEuNhungPTUrs4adYpOLawRwbDksXr1R69nhO6OgF7frBZDKJYRicfvrpcf1gsVhkbGyMRx55hC9+8Ys87WlP41nPehabNm1a0jHHxsb4xS9+wZe//GUAnvvc5/KWt7yFXbt2sWXLlni7ztnojuPQbDYXfB1e85rXcO211wLhZ+LCCy/k4osv5nOf+9yc2wshSCaTS3ZAUBwdJQAVJxWdgk1Kya5du5ienp53yvdo++vV+o62r/ZUj0QiwbnnnntUsbpSNYAeTfZwR/zYaTlUixUwAgqFPgxTx0fQ0FwawsZjAAsLSYWACgKBTqarQSNsTOis37MQ1GNHFiE7v3okPgEt6oRTN4ZIkEHHxqCEpDrLDNrA7xCXGtlZjSbZru5hX29S9zdSEZ2fjc5Unux41oiuSRWdZGSuUiJBniY1DFI0qCMBhwY+Pp5VwyFJ2NqRRkND4iFwCfBIUqBBKa4JlPixQDRJo6EjCL3+ZqKDoKPjWhUMXUcjgUUGgYbTca4maTytCUkfO6lhUcByczSdOk6rTqNeph7UMWQKPRnWDlqWha0nMbFANjGCcA3hXOK216GHQ50EeVpUsMiiRWsWkVAFcKgzzU6mGMXHJc1gnC7OMHzKRQBXYhawpoXTY/r7++NMRqvVwnEc1q5dy2c/+1n+8i//knPPPZcrr7yS973vfYs61p49e1i/fn0cWRRCsHHjRvbu3dslAAF27drFddddx44dO3jmM5/JK17xigUdK5/Pk8/n48cvfvGLOeuss9i8efOi1q7oDUoAKk4q2jU4tVqNPXv24HneglK+s5lPdGwhtAXg7BR1EATs3r17QVM9VqIGUBKwhx+HkZ6oLqlZAiMdkM32ITQNiywtSrF485FUKUf1c3lskrj4hF8nHgK9KzoIeld0EN/ApYgW9ehqWF3RQoFFPRJMYGMwiInExEWjiEEC2RUdtKFrlnA413fmAkBZy+J1/j46RF+A17HpjOWNGRlYCwQBjUggyXgLHRMhWnF3iobAx40bRQIC0gwQRGlglxoaJhI/bm5xqUXRwRIisnqRhNHAZlfXsIlHAz+KfJoksEjh4cQrkojQHiZqJkmmbTJyA9LRcZwmDadMxamgewk0s4Jp6zjCxdB0TPrwcbHIEuDhUe+oCwSHKjoGGgY+LnZkfB1E7SwyimhWGcfDYT+/RMNEt/LUMtAQW8gw2LPu4k5WIgK4ErWMEJ7LXDYxtm1zwQUX8KEPfQgIvUJ/+tOfMjY2tqTjzb5uR/q+2bJlC3fffTfVapXrr7+e22+/nauvvnrRx/3kJz+56PcqeocSgIqTivYX1sjIyKJSvnPtbynj5Y60vk4B2Gq1GBkZodFoLGiqx3LXAEoko9zFBA8TBAH1ko9Xs8jmLBJZIqNkPbI7iYSylOH4s0h0+Hj4iCh6Z2KRx8JGxNFBMMnixmJGIIJkV8WeTjpuwAgfd9cWgkaDEg1AkiRBAZ0MJg00JtFmj3/r2FebqkiRwoxTuz4zFjF+ZKwcehB27mtGGIb7LCDQ4mS0jqQuq/F7dey42UIiSZGn0TXn10JDQ8OOZFOdJFmakfgNqwcbGFg0qISTP7wUpjSjzt22LY3EwKbGVJxQt8igY0a+fzLyLLSRQhLYDQwbsmQZ8NfTajVDQdgq4UkPmjaHgnA2tmVZmIaNKdJIwCYXXZ/wnNxIuLZwo2kkNUySUR1laJDTbiYJ8KhrB6jnm/xWm8COuovDDuP1S+ou7uRUigAezZC+899vX18ff/Inf7LgueCdbNq0ib1798azjds+pBs3bjziezKZDFdffTW33nrrkgTg0dizZw9f/epXOXDgABdccAEvetGLlv26r1aUAFScNHiex/79+wEoFAqcdtppPfli6OX83s6UsqZpFItFduzYgWEYnHvuuV31NPPZ13LVAEokO7iLCX6H54Uj3XANskMpdNvAQ2CSxMDEo4zERQiB7obec20sMrSipgIQ+PjUKCGRaGSxSEIUswIXEEi9DrS7G7XDooXurGYQr6vW0O6atqGxDhOdBFbkPWhFY+c6GlCkQUWDDKnYZ9CnhY5ARv3Foeh0IluYUOK5VNCwCQgwGGC6I6WdIkOVMo4GrrQwKRB2Cod2Kzp6lNINMbABBzcSlRJJkrAmL0keD4cAHw0NlzoiWqNE4pp1AkSULjYQaDSYiqNp4SxhP+4k1jGxyAJB3MgRHq9AQy8hUmCndGwGKR9qoJmhfK3VK9QrLTRRx0hMY1k2lmWS0HMEeFHk1Y5Sw5m4mcSN/BAtMjhUsUgj0PFxEdLHN1oIsjjUKbGHCR5GEpBmkBwbKLCZDMML6i7uZKVqAJe707h9nJXyAVyzZg0XXXQRt9xyC9dddx233XYbmzdvPiz9u2PHDjZu3IhpmjiOw+2338555523pGP/0z/9E+95z3t4+9vf3tVMcs8993D11VdTrVbjP6JvvfVWvva1rykRuAwoAag4KajVanHjBMDQ0FDPvhCWSwDu3bs3nuqxdevWo04AmAshROyYv5Qv+9kRwACfXdxNhQN4Dcl0dQqLFNmBNJoRiUUCBBq1SDAZZNH9NAETgAaRjYlHixmxJaMUoRMfJxwjVyKMf2URgSAI3DgCaJDD60h1muRwO2oJDTKzHqdwO6J0OklaTNMCDDaQwIR4jF2IGwyABrKjkSRs2EjRnlLSFoAgsUjg0CSMrKVwcCl2GEabWF3izvLtSAyFJMhQp4JJCiP6ipU08TrEX1hbF+7DjY6vRcYwOlY859fTd2HGgrSOHQkvDTMS14IAN448htcoEaWPZZwubo+SE1GsUUQ1f741iTBMMtkMhWAA1/VwWy2abiVMF7sJsEtYloll2RimQVLL4+Nhk48MqJ3I/zAUoA41DBJRA4nE8FLYIkvbl7BNO118gPvRMMjFs4s3kqAw73TxSkUAV0KAtGsA50MvxOinPvUpXvrSl/KRj3yEbDbLzTffDMDrX/96rrjiCp797Gezbds2br755jhTcvnll/OWt7xlScf91re+RaVS4Y//+I+7nv/rv/5rKpUKj3vc47jooov4j//4D374wx/y1a9+lRe84AVLOqbicJQAVJzQSCkZGxtj7969pFIpzjjjDB588MGepWxheQTgyMgItVotnuqxmC/rzuaNpXzZd+7Ho8nDfIey3E+tVqPeaJDW+ykU+kALI0aSoGvUWLQXWnoVx3RxpYElkhjY+JSROAgENoUoGhhikaYVGRK3o4MtrUnTkDRkmhSZ6CZvAy0k4HdEFyUi7uYNH4PXtSbR9dijQRmbJGsxOBg/X4nmDnt4XZJCdAjCzk5gvetr0cMhjYwEYNicIWh/+gxp4ehTEM3WNbFpUSOMjDXx0DCiySAmqUjg6bH4C49nIwkio5wwymhHc34NP4Wph5NEdOz4dyLxovR72GgyM4JOi8Ro1HyBQMeMrWbC/uxcdO0aUUo//F25WgPsANMW6GQZ8DfRatVpOQ0arSKNRgvdT1BOlsN0sWlhGUkMYUaJ7dAMOxSw7RF0Dr5Rj9ZVjiejeDjomPF1CPAoc4Aq4+yKzag3zitdvFIRwJUQgAuJAPaCs88++zDbF4CPf/zj8c8ve9nLeNnLXtbT4/7ud79jcHCQDRs2xM/t3LmTe+65h7PPPpv/+q//QgjBS17yEi677DI+//nPKwG4DCgBqDhh6fTKGx4e7vqy6JVgg94KwEYjjMS0Wq2uqR6LYaEzfI+1nyYl9nMnpeAA1XIFx3UpJNZgZiROPJ9XkKI/SvnZcSQqwEWK8BoFUUKyFkXudNKYUR1fWB8Y+tv5UcoX2obLNlJE9iz4SAyqlAhNZPqwSNJsFUF4mKaBKXJdzSMm+a7HYfSwc3RcmJ52gTzD6BwCUtT1IOqebdFdMTX311/nNGKHJBXsqMYu7B0OcDDJhi0QQR2kHp2fHrVFzBjUWFFdnkCEgg6bepQit0hjYCEJusyiE1HtoAA8vY4mEmikAaKom0Mo4JrRtQ4NnnUsGhRpj4kTaICIUuYiOuMwhduuJ9QCA8vLQ7RtuGpIkqcVpYsTKYOkXIvmWrhOi4ZboVarUPdtND1sJjEjQRimi11s0kgkddlE99NxxNSlEV2HBA61uLM5wMPHiQWwQ40Se5ngd0hklC7eSIGNZGaZUa9UBHAlRq/NtwYQTu5RcJOTkzzqUY/qem7btm0AXHXVVfG5PfrRj+b0009nx44dK77G1YASgIoTkmq1yujoKL7vd3nlxWnME0wAdk71ANi6deuSxF97Xe19LwUhBK3EOCOJnyNcSaVUA9dkTX4dpFqxYAkbHJI0OlKuNjkEIopjhQ0GCZmjJTrr9HRa1KJuWgOTJCY2PpW4IaGd8mx/sesy0REtFLiyRalSot6qgBQYAaSMJokUmLaPoetxp2mb2Y8hgYiETQmHPGvwsCA+ToBOKo4yBh3vl3TOjIvEEQkOoBNEU4DDhhYNP9o2TYqqXqSpGbRIkCYdzf8VSDxMdJodtXo2mUjYiVggB1H1XruRw8COmm7aC9PRMbqes8jg0Yrm/BKL9FbcSR3g0YptW3QsTJKIqF4viKOZEt23cRIlnOhaWKTRsSNxGbbFaFgIIfCtOpoVnndfsAbHcXBaLRpuiUarjOGmIVHEtqx4VJ0RJGiJKgly+Lj4uOgYcR2pQxWTZGSerWOTQxIQOj3OGIHPpIt/hY5BlvUUonF1fuCja4uf0DEfViICKKU84nHm+g44mQWg7/vhaMkOfvKTnyCE4LLLLut6vq+vj9/85jcrubxVgxKAihOKtpDat28fqVSKs88+u6vTrdddu3Ds+cLHojNSOTAwwOTk5ILr/eaiFxY1EskBcT/Ta+4j4SVoVZvomsHg0CC+EYoKixQaFhI/6kgV0fNpXKqxQAwIMFoZpKQjOmhHHaoz9W36YdHBFAFeFJGC2fcyGQTUpn2aQYVMJoNhGARNi7o3RbnmISoGCWmRTNaxky6maaCLFF7HHGHQu/zxBFDGwyWHEJ1RQhsi8eHjdXQCd/gGUgNMqgwQdAynS5GKp3+Enn4zoixBkkZHFC9JigpVNJLY2BjocYSUuGvYjLtqIWzmqDEdyUMLixS65yKtmQtmkY2jhS1cJAKbTFSTl+v6XbSjaaHgS9CMJpWYpCJBrzNljGBHcdFw4glxulhDj+xemNX4k8XRKpAAKyEwKWB7WVpOk5ZTp94oUa810aUNpkOgBVS9KWw9iSZCKxmbbCRObVxq0WcsdF1s/7HQbnrxcdAw4nSxj0eF/VQ5hMePqA60yPjr6EP0tLu4k5UQgCvVaXwisHnzZkZHRykWixQKBXzf5/vf/z6JRIJLL720a9vp6el4nrKitygBqDihaNf7rV27lvXr18/5ZbhUwTabpQjKzqkeZ5xxBqlUisnJyRUxcD4WPg6j3Mkko+i+jVsRJBN5soUMvjYjXkJT4woBPjomBgk0zGhm7YwvniUzVOwD0Ug0DZtsNL83iAyEg2jUWLlj3yIyTHYBAyETiBZIGRmfuD7VKQdfr5PP58OZtpgEpo9FHzIIcBwHrykoelWCosQINFK6j51uYdsGuq6jUUB21QeCoJ+A7q7roCN16NHEQofIYkVgIPEiETtMie6uab+jESQUg+X4tc4IohbV/IXH86OpIUb0nIZFaMIcxA00MmruqMTiOzyeh2vUEcJCC4fARWIubMIJbVoysSD0aETdxhIdK6rRbKFhddVzhl28WRpMI6SGKdPY5CMbmhnDbSOqOQzT9eFjM7bsCdcAkCBLy6ggDEikTFJyPbg6rtOi2pgmoElpvIYwq5i2Ho+qS2h5fMI5xGEcdMYWB4i6ocOZJO0GGNCQuPh48TX2tDoNbYwRvt+VLs6zccGzi4/E8baaOdUigM985jP5xCc+wZ/92Z/xv/7X/+Kb3/wmY2NjXHnllV3z0EulEjt37uQxj3nMcVztqYsSgIoTisHBQZLJJLlc7ojb9DoCqGkarusee8NZTExMsHv3bmzbjqd6tPfTS/++xeyrQZGH+W9q/hTlcgnf8EjpeTJ9CYSQUUouNG3uTLWFkTpBM0oDtxsXQFLS2qazMooO1qN0Xdv3rl07mIhr0sJU54wvn4aJk6jRFBayaVCb8jANm/5CGmHIuFbQb0faNI1cYphWokRa9uN5Pn5Tp+pVKFXBKAoss4FpT2DaAtMyw+5pBFXCibZhGjrExemQAwKdRDxFJEx9ekCGfeQxcGKRkSTZEQUTXREx20t0dSYnSEbRtpAUaRpdTR9G3F0driCsJzRJRfuVWCTjiKaIrlu9I2VukY0semY8DMPOWy+upwvXkseJTJ3DJppmLOwApPCRuLjU8XFD70HsKK1f7kizh96DzbiZJFxDmM6d8R7UsUFIfKuBbkUNNbU8ub4sTrNFo1Wi0WxguGlEshQ1k5iYpoklsgQEkfegE9npGLGVTYsqBsnIwseIrptEcxv4yQYyup2F6eJWV7o4HwnChXQXd7KSEcAj1QDO5mQWgG9605v4z//8T773ve/x/e9/HykluVyOv/7rv+7a7rbbbiMIAi6//PLjtNJTGyUAFScUuq4fVfxBb5s2FrO/o0316OVkkcVGAKfZyQ624bUCytNNPD3A8NKIlIsUFn7UcRpOeqhgkYnqxVxAzur+lThRfZ+Ohe4msYMCrj4j/gAS5LpqB8NasraBsY/ExyZLTZsGBI1mk1a9RSKZJpW38bTQcsXCxo9Sgm1R47SFjxCYpoFlJrHQCQKJ6zr4jRy1RgMaRYQWYJoWljFMI+GE0cEgidTakzdaGMzUE4qOKSJhd6rBXjZQj8Rxjjwtiszc+nVSZAhww2kdvoUny7EPoIagxYw5dIJUl/gzseM0cnRSuLRioRm6/eWi6x16EOpBosvmJfRY1GNBqGFjk4aoonDmdzLTTNKKIofhrN+ABPnQDNurIO0gioCGqfBQ6E3HqwlH1WlhxJC29bQejYurxNfOIkPYItNhSeOncIwiIpHATmhY9GG5ORynTtOpRaPqGujSxmiPqjNNbCP8w0OL4o5uFN1sj6rzcPBwwrnGeoOkzJEghxfVGLY/wz4eJfZTm9Vd3K4fNJififzxTgEvRwfw8aSvr4+77rqLz3/+84yOjrJhwwauu+461q5d27Xdrl27ePazn32YXYyiNygBqDjpWI4U8Hz3d6ypHr1q3ICFi0mJZB/3sY/7aDYaVKtVDMtkOLeBickJzCCHgYUfRVZm5rmG0xxCESCiQnw/6pztqAEUHiBoiGkMjDg6KIB6R6dpOELN6YqSJegDfHTfRiJp1lvkUn3YGY14xlxUAxe2o6SxSKLRnsIRRPsuxLVgmiZI2jl828eWKYSXQbYatLwyB2p1vEYVXdOxXRvsgCCQaJpAJx2bS8uOaFAATHJaXFkokZRwyDBIE2iSpIkbJYwDIECgUzc9SlHELYNFCwMNCwsr6pc2kLHJtOhIq4NNuitaaJOm2uGLKPw0mBZmNHVD4pMk1/Ue0OI0e9hdnMIiGc3tDVPMRA0+biR2XQh/58LHkImoJq+BSSoWjSBxqUd2PmV0TEzCKS5hQnzm9xtGFctRF3ToRWiToiHHOj25w5S1WUZEo+rScgPC0WlFo+pqTpW6Z6FF6eK2IAzTxU5HurhGIhqhB5JAa9KkghVFUa3IYqgdCW0LaIcaRXZ3mVG3p5NkWHPEdHEQBF2pyeWgndVYDSlggFwux+te97qjbvPOd75zhVazOlECUHHSsRwp4PmIrPlM9TheEUCPFju4i1owQb3sUHUrZOw82UwOX2sSGC0crUKaIcCIoysutWjW70yUKowO5mhRxooiQB4OUhr45gGImgZk5FznR55uYaepjkO1qykkrAucwvcDStUimm+STw2SyYZp1YAgmiJRoTMVHRDQpEYoL1IY6LGACfcdxqjaU0owJcJMkGcjflDFdV0cx8H1PITnMTk5iWmaBEYe0/YwDJ1AtH9PggnWMtYlCdvXQ6cSCYgkFm5HBM8XM6UDiSi6F84ODo2yqx1CLc0AHh4mKcBFhy4hp2PF6c4ZBC29Hs0GgTT9BASYUQpeRMPlZiKIoQlMNY7W6dik0DDxOiKrbWNoqfm4ep1mFBV0aZKIOnHdKF3crgsMYt9BP5obnESPJrqE59G+lhITmzpTeHoDEWhzjqoL08Xgd4yqs4MNNFsNnFYzShc30d0kWpQuNi0LwzCwRdjwYpND+GXaAredNneoYkQNSlpXd7GMo4jQObv4F+iYZFkf1w8mO9LFK+U1CPNrAjnZxZ/ixEAJQMVJh67rKyoA2zMy5zPVo92lvJI1gHUm+R3foeFPUy6X8TyPgdR6kukkgnYXqMT007HAaHeIWmSReFH6zMHDxYqiOdCe6hBG7aTw0N0kpkxhxjfzcG0+LibJjtrBdFQ7KGgwjeu4lMtl9MBCECATDVrRpIgwOiijG3i4vgRZWp1CCx+XFgE+Fjl0tKhWcabDF0AnS4kWumYgbIFlW5SDcjiNIqXjOC1K1QpGo4Smadhmk6QVMGFeyD49G0m0atc+/Y6IXRItTnDamJRFZ3pX4HfIx+4OX40mDfwoRash0BEYZDExCPDQkF1NGElyTOqTcZrSJEGdcixeBAbJKO2qR79TmwytjmaSdrNII+7KTmKRIpxz3AIpaY+Ja19LnxbhpBA7jgi3I2lhlDE8S5dG1CxUjpo1Qk+/znSxlGE8V+LTiM6t3UwCQdfv2CZLS6sgkhI7qcfp4marRtOtU2+UkbU6RpBCb5tRW1bYnS4TkdDL4NCMyg+cuCvaoxVdm3oUWTaicoD2Z11E6eI91JlkNz/GIhOlizfiyiYpbf5jHBfD0WoATyUPwE5arRZf+cpXuOOOO3jkkUeoVqtkMhnOPPNMnvrUp3L11Vcvad6x4ugoAag46dA0Dcdxjr3hAvZ3JJHlui6jo6NUKhU2bNjA2rVrj/nlK4RYsQjgJI+wl/uoOlOUy0WkgKHcJqTdwImicAIN08uBITCiMWPtkWOdBsQGCQwsBEZ0I63FkSZJQCBcPLOOkEORiW8YHXRpRVMdZiJ4fiQ6XVnHqbvUSg4JM0+qYDFZGmsPqsAmH9ebQVhLFqb5XNqm0npsNeMT+sPVo6hlnQR9eJQIDZET0Up9kmS7bFl0UiSTgmQyATkN4Uhc16XZ8vmf6mYmzSam4SOMBMmEh2EYCEFUaxdG1ySyqxbP7EgXGhhdtX3JWandBCnqs15vUI6MT1okyVClih0JQg1iQSaiuJ6M/m9mn2nqHZHbBPnommYis2f/MCEdTuSozjR3+EkSfl8Y4aU9s0WPPicz0cjQysePvAdl1FiSidPFoZl1LU4Xh80kCQzPx5NO13Vri8b2GsKReYmooaXdFx3WlbbMCroJaRJkZQ5aOi2nQdMtU3GqaL4RClyvhGi5mKaJrWUjP8ZU1NlcjyLapfjzE47jy+FSi6xuQmNtDaMrijjFKFOMMj58kIq+Fo3fI8/GJc0uPhILTQGf7PzqV7/iFa94Bbt37z7s/O6//36+9rWv8eEPf5jPfe5zXHjhhcdnkac4SgAqTjqWqwlk9si1SqXC6OgoUsoFTfVYiQigJGA3P+Wg/DWNRoNqLayJGiyswTda8QyOsEZPxzcncLQWFlkSkblzEE3skEhssnG9n9c2fCYXpWfT0ZQGD8NN0RJlTEwcaugYaJiRnUcOogShxMORdarVKs1mk1x6EDsnCAIPzbMxZYYEaRpM0vYHFGjRNIuZZpIEeSSH+9C1xVaDMhoGFmkasakyNKmhRZE1AI8A2jdsEZCwCxi2ye8y52F5koxbxHEcxpoVCtU6mB6WZZEz8gS2j6ZrpKI0aptWx5qSmDSjiF9o4dI5rziM/rXR0A4TZU703lb0fyY2HibSy6AH6cgmphi/xybdJXDDaRoOTtxxLUkxgOzoyjYwIwk5Ez0XgUbTKCNI0fYeFGi4HfsO08XNSFq34t9B2ISRi4yym9Hc5PC82ubWvuYQTk4ZRItuN63/n71/j7Ukvcv70c/7vnVf133p6/RcGDvjCzjgkwSHoHMRBwL2SSwIjpLwjy2k2CLBCEUmSiQioigKSIkEkon5gUSkIP6KjIQJkc4xh5CEQwiXOL+ExHY845npme7p7t29916XWqvu73v+eN+qVWt3z7i7p8d2m/W1bPfau1ZVrVq1q556vt/neXrtYito8Vhz4tTOys2WboCYPWY+UijqKMOPbApM0FymLApm82PqumC+WODVMTJyYpIgQHmSSEydCKlluXMHjJfd/kgUEh9D4z5T053H2lkDFf4pxzzPDf5PJMqpi9vs4slDqYv79SAikMedAbx+/To/8AM/0HmnfvjDH+ad73wn58+f5+joiC9+8Yv8yq/8Ci+//DI/+IM/yO/+7u9y+fLlr/Vuf8PVDgDu6rGrt0IEApsYpn6qx3A45NlnnyUIggda36N4Yn89BrBkzUv8J070i6TpiqIoGIYThqMhhTM9VgSEDB0kWoIQaEw327cpm+FrJ90sO2hhWLKlBlb4+CYBcnw9ACon9qg6dqdVFjcUqCYkPVmRNzX7o4uoQd1tT8sCMOScdikVLTA5yzoVHVtlXI5ssgWe7Bp9chp8QioHgKzSdQOSKlUgmNBm+mZc5L9yhQrFOU8QezFxHGOMYVAaiuoGZVlyms7J/Qbf81AqhrjC8zxGIiFnjTAxupbUBCj2wZmRNOQOYttYtPUZk+i+Mji+6/fDrl1aqZJaDFiwRjlTaevAtwGYtoUbbx2XgAHrHrMqCfFIsA1t4/ZrSO0fgTN7No5lLV2Ci0/iVMPVGeaxF1VHRutt2LaLKyc+aVlj2y5eORPr1E0pjhzbJyk60QluO5qM005M4hNiI+02xywkoVJriDX1qmA4GDP2DsnLFXmVslovSdcQNEOWybUuu1gpa7tjz/uhO9+syrkdi7C5zTHG2eJIEmSzxNObc6+h5pSr5My4yu8RMnS5xU8weQB1cb9apfH9gLvHHQD+7M/+LLPZjL/6V/8qv/iLv0gc323e/RM/8RN87GMf4zd+4zf42Z/9Wf75P//nX4M9/cauHQDc1WNXb4UIBDZP4C+//DKnp6dcuHCBK1euPPDF9q1kAFOOeJ7PkjULlrMVplQcjJ/AGzQd+2WVoLFLdHCt1SYmMBENRTeEb7MoIootZmnYY85sMy5wgoFaFjR+RilSRhy4lmBCRU5FTsyUgjl1VXNncRsQnNu/iAoMXmc0nCN1QClSQsKeb12FpnapJLatV2wZSttoNpuUgbMcgQaoqNDOsy9m3IGn3DGUQgiEEXgMKZkx4zmuc6UXR7ZhXIQQrEPBXji1zFYj8cuCoixI8xnNWtCYIbfDBh1HNDQIFEc9S5wRARkNNjgOZ699iMJgE4E3zJbCc0IXt/0eG9h+l22/vKFhzdoBxIaAIT4BHlD2WFPlRDt9+W1A1AOdxjKrBmQVdyxwv13cslkFqWu9K0JiPALH8rXN2m3xBeDAjx0kUHVEo/LOcshuvSFn2al4fSIUQWdh0/c/FLCVXey7/OTazYPS7fuQMliiXFTd0IwRRWDnB+s5y2KJ0AJfxqh4aQUlvo8nInuOIN04w7rncWk2opxGYlRFyAFg5x/tzOuiO05HfJEZr1CyYsA5JlzhgLeRuPd8pXojq5lvtBbwb/3WbzEYDPjUpz51T/AHEEUR//Jf/kt++7d/m89+9rM7APgW1A4A7uqxq7eiBQw21eOVV17pUj0eNn7oUc0AtvvWXvyP+AIv8/+jKHIWS5ure+7gAjpYYxNbBwjX5mwzZwEHggylt6Bm7IbwE0BvzakFjhHp+/vF7GFokDQYUWAMBLpVDQvXapbETBAYmrXHaXqHQMXs7e2hPZsy24pELKYVBM0QD+mG8TeqzIKViwKbo9wcmQWjfs+exLbtWuZSdKkUwv08oXACFQsUT917Ar7Mn+c6AzctZ2tFQ+xAcVuCMSFralURxzGT+AIrI1nUFVVR4pcLytSCkpEWrNJVxzJlYtPujYnIWHUN4BEJK2oChoRIfIRTFbcq42020K8iqrhw35f1EWwBbklJ7QQZDT4RER420C3v2fK0KSMbNlBRkdt2qF9iiIi7dnHo1L++ZfB6RtCgOnWxIsbHCi/6DxAeEQ01hpqajFpl+HpI6z1YU1CSEzvluWUR886ayGYcD92n0A6AbR72JMoJWmzkoNQhXjGmGmYOCtulfBFRRmkXVec3MVXeUJQrsnxBlmUoHeAFKV6oCIIATylCMXbnjbXFqSmJzITaOwKxAbEBQxoqIiadyMTO1K7cOXWbmpxzvJP7raZp7hsAPu4M4M2bN/nmb/5mhsPhGy43HA557rnn+PznP/9V2rM/XbUDgLt67KptAZ+d2XvYai+6L7zwAlEUdakeb2Z9jwoACiGodcVL/CeOzBcoV5AuSsIwYW96QK023nINFcLdjNohfNt+y9GqQBh7rDyCLubNmi0PHLOz2gJ/dsi/Z+5sEryqxKCRTpRgbUAEhVmQrlZkWcbAnzKeTPGkR01GRWEH+llaUOEVFHJBxAUqMteqNi7uK+mpMq2KUzn/utDNJLaxYJtcXkWEbclCa2ZsPfgKJFl9ketmwstcYuUsbIpeS7MGgl77GGCG5hwhigk3CRkjWYsVgR+w50doJLrRLNMltc6o8op1tiapfUh0p1BFbbZjGSXbui2pqBBkWA2vZMyAgAqDR0LN2s5pqhohwu79Z4UHVlxiQUlO3olJJAMiQpSb6WzBn3Gt9D4L6Ztoq13sEeMRg7N8MW4OtK8urinxCLpttzYvxs0Dds6OdUITrLv8FYMgYexY6pFTEltI2bZgS9Ju7tDOBA7cO+XWQ0DjzLKbcImREzxnS3NWHe4RYpRGDEqigU9kDlFVQlnk5NWC1SpltRL4zQCVLDu7GSEFCXto0zimVHWftZ11tF6Lnpu1FZ2a3iPiXfwVJ5q5v3oQs+nHHQAOh0OOjo6+8oLYeNB7WW7t6s3XDgDu6rGrszN7b6a01ty6dQuwxqRve9vb3rTj/6OaAQQwfsErg99G6DXL5ZKiLBmN9kiGMQjdmTa3MV/tgL8FgRE5CwvydAJGELpki357UCI6oNcCRzujt2nBekQ0oqIJVlRCERJ15s6FXjNfzCmqkv34Et6gphYratr5tCkGbW04RApa4Oth1+LNXXpDazdjh/UzBJKGTUvQmhG37OVm/63kYknEIQWKHEFJwk2sce9SwKmAKeAhqDHO986wYcVC6AFAjeEVxmTd7/upJ4I1IJVkqELWas3BwQF1XePnmmW9JE1ThIbUO8UPAycoGVL2MpgTIjIHWrVzDlzaNSMZMWZA0+QIHbjjONxqF/s9AGZLdJF0umsXD8iAkDE+CgUULag3BqEltSgJ2Jgce4RkPQAVO760b9Nz1oy6cfm8DZUzwbZ50jPxMkq035MgYrh1Xkl8FGGXNlKSEZK4NBGNQVM4pi1j3nkP2sSPTRpKa3fU2tPQY8Tb86MFr4EYUAZrVKAZMGCop4jcJ6tSsnJOXiwxxhCLCevkOr7nof0cISJCx3S3x8JzIqua/vd6wDv5f+Fz79bm69Wfphbwt33bt/E7v/M7/Nqv/Ro/+IM/+LrLffrTn+b69et813d911dx7/701A4A7uqxq0cFAPupHgAXL158JHFPj4oBXHCD48M/IGhCijsZpSg5HF5CxpssCmvcO6Fi6dgGScXasTxt2oWhkSVS+xTMnN1LSIPGUN9DkWqj31rDYOk8/1rT41ZMknNCXTcs5nNM7XFh/DR+LDvAZhu88RYbIwlQdYQwdgLR5s/aFJJti5UhDRqfoLPmCB1j1CpRPQIUERWCBp9T1sCUNTUSmw1rP4MGN+cXIUgd+xfiUziVsD7DrFlwsYGZFRvDZ9P7d2ikhUQCAt9H+TVTpjY5IpcsmhlFkZNla8p6jUlMl39rPN1bp9naRoNmRcHKMyAVMMTHJ8CnZo2mxMPfYi0ThluAMCQh68BzQY3nGF4nJjEaUWdoaecV7XsGDqC1kW9tVF3pvj/ftT3LblbUOhqqzozaWvZAzoxGajw9wGeCQnU+ke262rSZzT6PAGMFKuTUFERMO+avIqMicyzcGl8PUFWGR+TGCdpzSDtOPMe4GMONUfm2CjmUCXkyJ0QSsoesQ3QOWWVbxWtjEFqQpzWNuWm/Z6UIhAWjxjXLNRVDLvAO3o/Hg3vXvdH17ButBfzRj36Uf//v/z0/8iM/wuc+9zn+9t/+2zzzzDPd719++WV+6Zd+iV/+5V9GCMHHPvaxr93OfgPXDgDu6rGr9iL5ZkBWP9Xj7W9/O88///wjndurquorL/g6ZTDc4k94hf9C5a8o6yVSKS6On0b5srsRblpzm0F05dpRmoqIsZvH0kgyamm5oJoCiedabqaLzbIzdBt2xtrEyJ65c4QqE8J6Qh7OKIuSxXKJEh6HB+fQXuakB8JZwrCVbNEKRpogo5QeNsN26tgbSensNiL2OgaqtaRJ2HMcmdeZKUObvuuxJkMgCKhZY0HICGnBnmjAtb/7Wm7l7JMBctiCgIEzQQbLGuZUTjgDeV+BK+ru3wMCMrc+KSVREmAYYQzouqEuC4qqYJWmZI1k4Z8SBiF+4DPyh5Ryc87ExJ2tjD32fpdGYlAM2HdxboqK1FnNbNhBu9y2UCok7gBiRkZoYtZBQ2SmeIwQ1E4Ys2kXR71Wuz1moWsX2yU8YgIiNCW44+ITbb53A40sEQjH1rXJJNIxc4vuOPsMegIT+7/t9956G9rzddCBxoolWpYYYTr2uIVjNUV3DKyIpG9UbsVGws0U9qc/Pc+nHKbEhMTmAib3mB2folXNKrVHwyMkCHO8UOL7PlIK9vgm3sZ3OQP0B68/TS3g7/3e7+XHf/zH+bmf+zl+4Rd+gV/4hV8gDEMODg44Pj6mKJz3pjH8vb/39/jLf/kvf433+BuzdgBwV49dtRfJh1EC3yvVo61HObf3sOtqqLjKf2ZurrFarS0rIDzO712mluvulm4FEvYGZm+4WefX17IglUuGqKhQJoDGpjTY+TlrigtQsnSt4Xk3R2VvmHLL3FkLjVE1hZxjVoZsXpME+4z3EmrZBytJN+/XggSfmIYSI3rMy5l2tEARMHJAz3ctPeUUzTP3vtY4OCZljXZAY8yUJWtKUhLHAiYYUkCLCoznjtuGSZE99e+KxjX3WvCxuYkPEZS0UWyySwbxEORiw8CJ3ro9lAOlNup47A/JfEFMhNEGv5DM6zlFWZDlGUVdYGLd+dcJJUA4FbAQqL5SGevwuOwAX8SYEVCiKKhYOzZwA9zu1S4uhb3JNqImZeXUxYqIIR4WEGddpJw9Xq2vYrsOOwfYtmHte+1xbgEYhHrUaxcbCjL8zpA8xndguw/+2k+V9XORkQRMAN15GwoT2OMkKhoHKX0Gbg40cg8MBQLPjUPYKp3YKOe08x5sP2OfsVZCUXsNdbhmONkjVAfowqMoVmTljHVhj8e4uUKi3002LhgMvIcCaE3TvG7C0DdaCxjgp37qp3jf+97Hz/3cz/FHf/RH5HnO9evXAXuN//Zv/3Z+/Md/nO/93u/9Gu/pN27tAOCuHrt6WAbw9VI92vU8SgbwYdaVM+d5Pkuq77BcLimLEr8Z2igkaTrRhrWp2LRCLVMzBTTCCSZsGsakA3q1t6Y2DT4Hzq9v4qxTqq04rNbw18KZ0rXkrK9eTY5WJXmuqeqayfCQYCSoRYZP4obh1dZNu4V17YyhQKKqmLCeOK6uBRcegqC7+dokiBECdSZFwgaorTh1c2GZmxObMWDKijURjYOFORBRywbZtDfQzY1U079JCwIiCrctm71rl/XZTAeG0HGaCT45IIzEZ0CNxCdEY7M0oHag0EITgYehRkiBjAUjRhhjMI2mLEuKsmC1WpEt1yzUHD+0fKXUomvl2mMgu9lBu+eCpTPxBlAMUQQETKlZW19GvK12ccyA1ZavXtyzz8nccQYYEhEgaNz84OY9QWeDsmEMG2o3v2fnRlUxxAysgKNNMgl7tjGNS3xpnNegFZPY1n3mLH/asg8Ws63XjTHIJkDgs2EH7fxgmxsdMnGAz57LFbmzP2pHJKz/pEdExtKp5KOurdvO9wlASR8TF4SxJGQfWYeE6wMGR89xe3mHm6/dQinFeDzu/nu/UWZ/mlrAbX3f930f3/d938dqteLFF19ktVoxGAx49tlnGQzuX0Czq4erHQDc1WNXD8MA9lM9nnvuOcbjcfe7+83cfZD9e9An9hmv8BK/y7qasVjM0cZwbvwEs+yISlXUjqcIXEyaOcOUFFtzdqob3LdMSY40PlKL3rxV5vzXPGyG6sjOVBFvWcHkLIgYU7JGNj6qSqibioPJJWTctiwtcLRpHLOOtbNzd2YLNEiUZRG9OZLImTtbP72ctLvhhwxcZJfl2+zwf0hJScHKMUYLAgIgoiCnZt5FwMVMycg6JXSi7fGqnacdwN1Neh+cYnbZO76mx3q1/7ZK6jFzIzkSBR77WybWUzxSdwwtuK3cTwUTPMvK0iBEQegZ1t6aKIkxWuOXPrNq3sUdFvOcoloT+K2YZMxabLaVkLDqAcSAiNTti0Ex5JAa49rFKySCvGe90zGNveqri1fURAxInRI7wENTdGkfm/f0mT5blb9GKpv9G7rIt4pV9zCj3Fxie75ZEciInHk3twcGCWfOo8DyfWJF468xMiLiAAsCIwoH4UP3ECTAsdLWALqhdqryxgFkn4oV1lKnwD4A2VlbT8eoqiBwJtft/CDAgfcMbxv/XxFj+ze/Wq2Yz+csFguuXr1qj0sUdWBwNBq9Lsj7Rm8Bf+ELX+Cll17i3Llz/IW/8Be2fjcYDHjPe96z9bM/+qM/4vbt2zz77LO88533b6ezq/uvHQDc1WNXZ42b36jaVI/r1693T5ZnUz2EEI/UuuVB1mUwvMbnuMYfk+c563mBJ4Yc7B1Q+gtEjlVrIvEIuxkmywSOsbFd2ya8Br2ltIyYUJg5jd4krW5HvxVOZTpx7OCQ2rWSrSffgqosWSxuoxWM4328GHzGWEhUOj+41H2mpmN0bCsuwXNSjIoVRlZgrH2MzYW1ljQeCo8YSUDOsjfDZpD4zttOEjOmcFm3BgFENEzIO+5t4JriMEZwCgRGo4GCBuOMRzL01gWw6oQiYcczGQxFBxUNttF4kReIOKAgFcp9F/SWgnWPbRuiWLnfWoc84cCh3fcBHoKEEIEnK2RUMogGDLFWGX7iUYuK9XpNuk5JWSFD6exKPCq5DWX7Vj7W5Fl084NWv2uTXIyx339sBlvm0z5Bxwa21Tjg20bVxQwp0EQMO4PrPhsIOEPvPs+qWDlzcuVYNtmNGdjq283onmH0mgUBibOa0Rhn/N0+aAWmFa+0oFsSYc28PUI372pVyO32rNLcw+Zfyy4qDvdX0ba7K1YYoanlyj2EDTDAHk/xTfylrkUuhGA4HDIcDnniiSeo65rlcsl8Pmc2m3F0dNQt0wLCJEk6MPeN7AO4Xq/5a3/tr3F8fMy//bf/9r7eo7Xmwx/+MBcvXuSP//iP75tJ3dX91w4A7uqxq/ttATdNs5Xq8cQTT7zuBfZRe/fdz7pqCr7M7zAzL3ceenE0YDqaUMkUiYenB9AIvLvSFixTp53ZiufYNNuCs3N7bZ5vwZxGrahVgTUfsdmoNsPXsjhhz56jci3AVpFZrySz1YJAxdSmQns52t242xg3e3OdOCWopOm1kivWKCaULJAoVJPg6YFLh9jMIhpn6ty2iwMGljFkw/607V5JhMceM1bAmgDlIIxmyQX2KAlZ0ip2pVOmagwBkgpD7f6tHWhYYwgxrp1oa4CyamljCNnjRRG47A/It+bhmt57vM7vz257U9uA0oaQrd2E4RqICMgR+MQkRqDKHG/okyQxGJClYlktbMu4yPFqn0W06HwHYy8hE1kHShQe67vaxSsHcQRlM6IyEZEbHTAut2S7XTzcAoR9gNiu2yNAMiFAIahQCAfi2/PVdyIOB3ao8RGsWWAnLhM8gi221W570lkZtWpkC+gafEYYXSLLklpaP8G2AoZdvrDdfkxA4s53axwu3CNHn7n1nHWLjc2zSSCeTtBqAWLoHpeWXOLP8jTv647zvcrzPPb29tjb28MYQ1EUHTt448YNrl+/jud5HRh8EAD4KOr555/nwx/+MEdHR0wmEz71qU/dxbT9x//4H/kn/+SfkKYpUko+8IEP8JM/+ZMPDEA//elPc/PmTX74h3+Y973vfff1nve97318+MMf5l/9q3/Fr/3ar/FDP/RDD7TNXX3l2gHAXT121X9ifr1ar9d8+ctfvu9Uj682A5hxypf4/5A3C9LTirKEyegc0VBRCwtlDBohBJW/dk55MRIPibc1nG8zGkIHnNqItwEKz7FywioRtCQgIe/NUVmQ5dEf7veJ7Qi/WVrFap4zCvYZjUcc3bqDNIEDl5MuN1ZTUbqfVU6QYq1NrGlzC+A0DVrmGDOgYN7tZ+Oas/0WovV6053th3CgImCPNRmGUwZMWJFRsiRhjzU1YzTXCZkyZMB1QGypdWMEFa0VjCLr2o+GIQE5inb+LwJyA5qLHJN004UjcNDbgrq8bw2D6ODTWcAXu/ZvWwlBLycEAjxybLjdDFjLkEpEjEgYCEkQGkahj6GgaRpUrpjXc2tXsl6TNwWmNaL2AxIVsqb/2WPW/Sg6rUhVToGPtf6OaTB4GCpWKNQWOwjcNU+YMOraxSU2rcQyyVOockTc5pP0j9GwxxiKTpFeOcFG6M71qgc8TTfjt3LbWlMZTePVSCICYgy1E3JsBB82U9h30Yjt9kedEn5jiB455rruTKlDRqxMiqpjZ06dcYX38iR//g3B39kSQhBFEVEUceHCBbTWpGnKYrFgsVhwcmL37fbt2zRN07WLX2+c5FEwgB/72Mf46Ec/yg/8wA/wmc98ho9//OP81m/91tYy0+mUX/7lX+aZZ54hz3O+//u/n09/+tP89b/+1x9oW//u3/27h7Jz+ZEf+RF++Zd/md/4jd/YAcC3oHYAcFePXQkhujSQe9Xx8TFXr14lDMP7TvV4lPnCX2kG8IQXeZHfoagyFosFGs25/SuYyCZ4WN87O7+2UNcwjWXSanI3HzVD4Xfsm51v6nu32ZttxhLLrsQEtaKslxSkbGw3bNpCG/dl22AToKFqCuaLGVVdcZBcRg4qyzj6a2oMEy5j0Pgk7obcttdaS5rKtWNtlJtNSLD5rRKfWq6dLcwaQ9S1eyOnArZsXdEBjZLUGQyPaRzkBKhZEjEgpyB2shKPHBiQERIxIdELFrIVZIgtKxibrNFn5EJOe/NwysCaJ7huYp4QfQCzgcyRxvGLtnTv3xGKsgeWojMAsK8cbo/bpgylqvGxmRwLjGtyJoSMGCiBN6iYotCmpq5qqqKiqCuWyyXGwFKleJGNOlOeoup9BjY+2L19F6QOkCrGRMQYCozL4w17iSvt8St6r+37PCoKMjJyr2Jg9shQ1mOPhsZ5+fU3HvbmB63gqKRxs6iW3Q6wSt6Nj6DlYRuM1DTChtuFDMhYd2ISK/jwt9rMAkXjmDy7v3a2to2+a7+RwGUjVyKj8dfUIuMp/hxX+HO82ZJSdswfQFmW/I//8T8IgoCTkxNu3bqFEILRaMR4PEYIQRiGHfB7swDw6OiIz33uc3z2s59ltVrxwQ9+kJ/4iZ/g6tWrPP30091y3/qt39r9O4oi3vOe9/Dyyy8/8Pb+5E/+hIsXL/Lcc8890Pve9ra3cfnyZf7kT/7kgbe5q69cOwC4q8ey7sWyaa159dVXuX37NgcHBzz11FP3bRT9qBlAY8xdUXUGzav8Icd8mWoNs2WK8iUXJ09Se/YmWrLuDJQbCgI9ompKbMSV6m5amtraqmBoKAmcr5nNT816IMNaIVfeCY2/RjFyTKJPyaIDf1adObTmzlXDfDFH1j4XJk+gIkPpUhaEUfh629zZWtKEtLN62imLWwYQcJ51CRIQ2sfTEa2iuXQtSfv5qy4iLHDWHAUrt1xB7ZZNmLB2AEE48UrJkpg9clYIB26f52meMP+dOYYhwkHVDegSbLfctAmQSHwT0OiAazLijlum7z8o+iBPb4O49Vb7VG3JIuoz3nyrXqvYxyOn7mBRbAKO5eYcSghJHTNV0BAQcUINjBkIj1Eg0EFGxRLTaGQhWdSLjh0MdEAapW520MczHqVXMWSTEbzusZENDUsyGscJhiRYR0LZMXN9sQjgAKJjGA0YDZXKES69w75njLXuFhgKJ0rpzw+aHmMoqLHnf+FYb8ta2+9kJey2hRDO/Nx6BbZKZDsCscJnTOufaQ20+21xaw6tqbE2Q9a2qDW/tg9zgifFowF/96r2OnX+/Hn29/fJ87xrF1+/fp1PfOITLBYL3v/+9/M93/M9fOd3fuebMq1/9dVXuXz5cmc7I4TgypUrXLt2bQsA9uvWrVt85jOf4d/8m3/zwNs7Pj7mW77lWx5qXy9evMj/+l//66Heu6s3rh0A3NVjWWcZu36qx9NPP83h4eEDPSU/agAI27YOFRlf5reZmWtdWzWORuyPDhBSIVy0lGX1dNfmqtUKU9l12Lk8G5NmEzA21h+F8zWzZtA+HokblpeULBACMAZNgzUzPsWygwOnxBTkzCjzguVyiScj9g8O0N4ajWV1AoaopqAWhVP54uYOq55Cs7Wk2YBUsFFk7TKNv6aUDSMuY2icICWjoSRkSubad4WbIYsYuyadBBpaBXBEjCZBO8hcIUhoyDBMgRMq5oTsMQFOiTGsEN1sIsBZzneuB3ypPHCfxJCEbaCd6VJD7PfZZ/E2580Qj7y31j4bqBBkW+KQgHWvvRrjU/W2EXyFFmMfdtrgvZAVHop9BsojTjRTQmqT0dQNMpMsmgXL1IKmWCcICVVdoTxFJKKt9nBC0s34CTdaMHOvJQMGRFRoJIEzgt42nzaA30RbxyAk3jKWtjIgD58Dxw6mbkxhAyo3c3rCzYOuO7W30AGqxqnjy62jEjJyCSKCwj04hAycsfQUm6ZTO+avdvtj/8427WKJ18RM5pd58txbA/5gM88spUQIQRzHxHHMxYsXqeuan/7pn+a3f/u3+S//5b/wyU9+kiAI+Nf/+l/zgQ984KG3efb6+EZdi8Viwd/8m3+TH/uxH+Pbvu3bHnhbYRh2iUsPWlmW3SXc29WjqR0A3NVjWf0WcJvqoZTine9850P5Rz1qEQhsAGDKba7ye6TNMbPFKU3dsJecJxhsYtPADrxrp5q0cVo1gR6RyhMayo7xa9up9qZXO7XuaAOwqNA0jsVICRhRaw/qyrEk7Q1YoKmtFYbJydOSbNmQhPsM9kK03IAVj4iCJY2fOp+0xA3jF87LzYpO7ND+jH47OmKMpt7MGhpB0Azo23pYk98ptkUbOybUI+hF2rXHqKQGElaUKDyOsV57mitMOSVC41Nx6pzwPi8v8GQ9w3NQtnH/D/30X1srvclvTQRdQzABarHxBcwdkAao5ea88RFbE3N9wDfAZ9UDSPIMwKu2wJPZeq9VLRdb7+2zhwrJ2r1uXMN2QQMkhGLEwBcov2SfgKapKKuSMiuhhjRNSdOUlbdGRVZdrJTaYjztNjZsujVukazIMChCJgwIaMixfK/GM4rayxGi9ZI03QNLWzFD1iw60Yz1UzQE7INjfBV+x+gBrs1rGcOGitrP0WhKSgIG3X72U0Y2gqilO9Y5AoXCJiR7DKid+KncamlrRuUTyNUTvJXVXnvu1bEQQvCud72Ld73rXfzoj/4odV3zxS9+kWefffaht/fkk09y7do16toC39Yg/8qVK3ctu1wu+dCHPsT73/9+fvRHf/Shtnfx4kVefvlliqJ4IDVvURS8/PLL99yvXb35evPBp7va1degWgbw+vXrvPDCCwyHQ9797nc/tHnoW8UA3uZ/83l+ndPyVY5ntzCV5Pz4GZJBvNV+jZz3WUnqAJomZg+EQGgPg0G6FI+CJQ0lBQu0a/+2+anS2cXYFq9lPUpSjGgwskYbTcgYnwSfAZqaWttZxDRfMBoNSfYVyIaAAQEjZ6SbWuZMWKCn8Mg4cQpfn4AxCfsb0QnWisMnIuOUgiWaioAhXjVws1atcXEIBBQsyJlTOS+4gKFjaEx3nDSSGqvAbWgcE2hvmiENX2Kfmn2Mu4kPqChEQJNPafm+ordtKwBxoMTArWYzLxr38NlAbIDLiE2z0jeGSm3Om/4s4BAfvdVu3pRlFDcAzwK4vrAi2AKEFlyZ3utwC0wlBFvQKukpmS1E97iN5IQxtbrAOHqSYbKPwTAej9mP9qmpSNOUk5MTFreXHKfHlEWJMcbt3/asXxtVZxnZijU1CxpyYgR7KD0B7Xf7mTDcEpTYc2hb5CGRlJSkrEipURxQE+EzReIjUVTOp68tv04cQBSUZBRkFOTgZvtCRsRMtuYAgc4epmDlGEfpVOIjAkaA5Dzv5Fz2Z1Hy4TPH76f6DODZOsvMeZ7Hd37nd3Lp0qWH3t758+d573vfy6/+6q8C8Bu/8Rs89dRTd7V/0zTlQx/6EN/1Xd/F3//7f/+ht/cd3/Ed5HnOZz7zmQd636//+q+TZRnf8R3f8dDb3tXr144B3NVjW4vFgrqut1I9HraklN3T8JstKaWb9/sv3DGfJ89t/qvnBRxOL1GrVZeZGzBE4m1FrknHSuTMqNUKLSunWlRn1LrW1qLPIoYM0Wg8l6Zg2cExubwN0qZ51OTETClYIquA+emaioZz4ycgak1w7fye9WWboYgssKxWKBltzWxZZ8HKsTI281c4v7Y+C+O5ubwmSKllSMKUkJHjOvtGxsMtM2qfBEVABZ3hsY+kQaFpGIKDFWsEE24RcsjKSUusLvW4OMcFcwIiQGOIkBSuSR7gUdIQmojKbG7AQe9eHKA7vm3T8ITECCoExliz6kr4CMbkKAyStWNHbXO0RjlYaHMrajyXNJEQsNzy4pP0G2ZnFadnm3VnmbXmzOsWiBogpcYQMhMRqT5gT51jMHIqWF1QVRVyLVkUdn5QIBjKETq26mLPU8QkW2kkASFFL5kmpyYVFWvlETPBY+AYRWvBYt8TbQHAmMEZgYkkI+selGzjeYJNJampWCJ1SO3Pwc2LGoxL+bDnSc4K35mh++4cbpxNTUE/XUW5edqqm/3b52m+ie/kmr7+pubt7qfacZb7AYCPygPwF3/xF/nIRz7CP/2n/5TRaMQv/MIvAPDxj3+c97///XzgAx/g//g//g/+63/9r6xWK37zN38TgO///u/nE5/4xANt64d+6If4lV/5Ff7xP/7H/KW/9Jfui9F79dVX+amf+imEEPytv/W3HvwD7uor1g4A7uqxqzRNWa3sxftsqsfD1hupih+0aplxcvEPKSQUi4pypRnGBwwmCbXc3OCUYyAaKqyXX4LvrChaM1wQqHKTntBWzJ5rE7ezV4aYadd+rbrlphg0AQMwSzCCSNgs4LIsWSyOUFJxOLmI5yuki4hrXLBZy+jZdAQDytDInIDz2Hgvm/lanYErpcsCtu3iEFBOdOJYRGMVxJkzBha0wDHasI2uJB45GR6By5C1JsAJQ1IaCuYETLB5yBMKKk55gglzZpQgfDIiTJ0Q+IYS4QCgvel6DgDWvfav/Rh99q5/bvTEINqnqaYcmacIhNpq0x6gWTp2coDghDaFRHGAxyklNlxuSO3mG0MacHkn3TbEtrBEIrp2rz2PJOmZ1/32sHfmddsuNsZQS0MqjGtNJyRyTBwaZFjhGZ+mqSnKkiovyVc5qxUoqRh4JUQG3w+QUrhjuNlGTMTC7XMt7NmUuWi+hBDfPZy0Fiz2qG63nEOSLYAYErHqsXiSmFpXyNqgiByQnnQPIvZ7E91cYkVJRUnEkJyVe/iS1OQI9/9tDdjnOf4f9qHrARI6HrbeiAE8W48KAL7jHe/g93//95nP51s//+QnP9n9+xOf+MQDg7171fve9z6+//u/n1//9V/nu7/7u/mZn/kZPvjBD97z82qt+cxnPsM//If/kKOjIz74wQ/yF//iX3zT+7Cru2sHAHf12JQxhqOjI65du4ZSCt/3Hwn4g0fXAl7wGs8H/2+qcMZyYde5N72Alwg0BQEDZ19B56EHrZef3wGi1ualaQLW6gaGTWvbDre3dhhWyNHO47XOHgIPv5etW8kC0XgEZgBGUKwrFus5kRowno4xsnSgMcN3Hmw2Js6ycTYiLsOoCiM8SlaEDClZOZsOy0a26uKWHayx1jZWwSkJGODVBb7c5A9DCwGtzYeNfkuQ+BiEE4VYwGCTQKzvXEnKiAlLMkbAMTBBcxvJHcYcMHPHJAFfM1tcYnxwRAl4PTDdKkoXOtn6LuseAOy3Y0tqpJHU2XleMTnHas0BihGmg8ESszXvlyDo32brM63h046zs+xaTkDEkKrSBPUQQjCsEYgtNbBdd9hL+rj7dXyGXTzLNibG74DrmhpJTEqDFFNGns++pymTJQNdUFUlOjek9RK9sH8voQrJwgw/9O0Mm6BjA9sP2H94WVOQMGCNQDEmIcADyp4/pYe3URPTvn8b+IQkLORNKt8+HkXsUSLwmVC7B5CQwZboxEb4rVwL3j6MRYxclOGYNiH6XfxllGuja63f8uSNN5oBfCtMoL8W9alPfYobN27wB3/wB/zwD/8wh4eHvO997+Ppp58mSRLW6zVXr17lD//wD7l9+zbGGL7927+dT33qU1/rXf+GrR0A3NVjUWdTPZqm6VjAR1FvFgAaDLf4X1zjD6krjapitFdwYe8pm1Xqbl6ly1PNmbss3hBN5drAfSsMO2Zf+nNMoR1w9LDueH2FpM1EbQGhVeuOnMHGZjnfJCBTchaslyvyImfsX2A8GWFE7YQV22rd1iS3jYgLGCD1Am0at5xN8WjbyiEjZ/xsI+IsIxd37W2bPpGD1FRqyZBzeAQ0zmik3V8BLvHBgr6EEYVrBdqmbUXIRVbgch0mVBzhM3I/iRhQc8weDXMS3VB4hi+dHvId09ugjMsXab87iTCCo2ZbaZiJzfmQueMTYBB1xPX0Aqn28PysZyO4WX6EINvaxgYMKgRpr5U/wiPtqYojFAU1mTHMfMVIxuQofEYMsUyZj6B0gFCzfd7e/fre7eBu38TdDeV2uTklDREpMZEckYSCMGyIzIymqSnLEpkJluslrO3f0VCNKOOyY3d845O5fQUr+Gnbxw0NC9YEhFSERITO8c9Q9zz/YsfatWUj5ByD2HrjoVh1YC9gyIiaCo+ENufXstZ9pXAbISc6m5o/y/sJew9cX2sG8HGPgWsrjmN+8zd/k5/5mZ/hl37pl7h9+za/+Zu/uW2V5T7rcDjkox/9KP/gH/wDfN9/vVXu6k3WDgDu6uu+sizjhRdeoKqqLtXj2rVrj6xlC28OAGpqXuI/ccd8iXWWWWCqBPvD8yhf4jOhokBTufkky2jVbgpNoBxwsmrJxqkoS1bdTVMbjRYrNHXnu2cTWFO3vC2r8p27m9ymrZwyQ8uGxXxO3dQcDC4jk419i3DRb1atq7q5q4jxVq4worGMFPYWW7Ky8W6EHYArWHbmHgbtVMtl19ZtVI7nUhjsjdeKMkLGtGa8Vt28cuubA5KIPVbAkhLFkiVDNDYGr+IZDlmwYIUhJKDiS+zzzawsLPUUWiuybALDmQNJ9thWGHwT00+t9QVdWshImA6XB+ke/7NMEAhGqmHVuxf37V9C6HFwZiuNY4hi2Vv27EW4LyQxGDLRniOGGbBEoEnwGDABSho8rLLVQ93VHn691waD1IJMFD1wtq0uFoiu/Zy7b8xDocWEoecx9Gq8pCQ0IVVZUZYlVVaRVZtRhyZrQILylLU4OZNGEhE70QbkFFQoaho8hsQECDa5vJvj69rDDhudnScEQ0HeGYkrhgxIMJRIp5K3ghJ7Drb1JH+WCRe3tvXVAIBvNAN4th5XAAhWwPKTP/mT/NiP/Rif/exn+YM/+ANu3LhBmqYMh0MuXbrE+973Pr7ne76HyWTytd7db/jaAcBdfV1XP9Xj3e9+d5fq8SiTO9r1PQwALFjyIv+BTM9ZLFYUVcYgHJOtM2qVUbqbuUeMR4TEw3eMhM+Axmk023UFDGko8IiJiKjRiCqkMuvuwl+T4xNSMHPAbUjrYFb0LFgsrLOxcaVeIxsfUUdcPDikCVbdclY5HPUi4gwhIwQeDUU3p6XwEfhUKqVwbey2Xdy2rRuqrh1cuZt6hY2c01jDEK9JQJUEjNzQv3aff+Gsbew6NF5nB6OYkLIkYNqxXlOUM0LOOWLCnH32uc0YQUFBjSJrpiSklEoRCMMrp4fsD2bUoqG9/BUY1Jn271DQQZQEzRoYLEfM0yEitKhj4BlWHQCBvAcq+yzcyAlO2jp70c3PgMGWDTRA1EhKYWiNMyxb6GbrMNT4zgZnSMSYfSz0sSKa+u52b/+1gbDZbjkO7tFeXp153QLIBRUxARmGSBwSh4JBWFKOUuImIstysnXGrJxhKoOUEs/30b6GcAN21BkzipCIhhUNmpTcAcaKiCnWprvqxCLGWHB+ViRzNr/YYFh2s6UeIQNs9nCOdssNOeApvo2zpbXuDJPfqmrbzPcCd98oLeB+jcdjPvShD/GhD33oa70rf6prBwB39XVZXynV41HatvTXdza9441qzjVe4P9LUa+YLxboRnNu8CRhFJCtr6KNm5Fi5IQNxsk9bFvVoFEuCaOhdkbOtq1asaZygAk5I3RZuLaFPOraVm3yh09EycKlbfiOMbFt1TzLXbtcMd4f0ASWWfRJEE5Z3FcSW//AgsbdGBW+Uxc3GHHc3WqtWrcvTrFzWB3T1yU/bD5XTUGtVkTNFNBE1qiFmrJjGzfRdhCxR41g7falZOZSQAq0Y/sqMnzGFAhqLjEk70DKuhkScAKEDELNnSzmwnyPYnrKBgBqyno7LjAQpjsiHprhasDz16dc2N/cjH2v6dq/Q2FYiY3n3LoH6iLoT8RtAb4IufV6cIYd9PX25Jt3lxp489pCGZ8FPjBhiCTC4Pcydc+2fxFv/Dd09i/hLFjz3INAyw6OSEiRDJVH4udU5U0GFwIwNu7MZHBcHENq2aDYj6nCCs/zEM5iqNw6Wu1+CAr3mwEDcjQxgROANBQ9vlUgOZtfHBF355AtycIx1pKYmJh38H9DcvcM3lerBfx6qUXfKC3gXX391Q4A7urrrowxfOlLX2K1Wr1uqker2n0QwPZG1V7g72d9BsNN/gfX+WOyYsViuUQIwYXpkzT+msJkaFWjdEDMnmv1thdx4Tz/+kpF5dS6Bs+JLRQeipCVPMWohtws8IjwiLp2qU2+leCAIW5mzgcaajCaYmFYrSrCcEyhU7QsXWScce9LMTR4RC5uTrp923jvWbXuzO2sQTYRMXtUrJxxdd8E+pQWNig8QsZuxlF1bVevGVAFKWUHJgQxtt1jbTosXxYxdXnGMHAMoGV6VviEVJRMGTKnYormNoo77PEkr1AjCWlY6JCxtu3DOGxY5opXXrrIE392QagEBYZoOSHPBzDctBmF7N101yEvvDq2IKT3c90zgA7YAMYRgnJr/q+fDSzIegAvRlJumUNvVyXoQRLDaiuBRGzNDkoEy16rNMOQYoAQRcQEK2Zp28XCQKkaKzPmbnPpfvt3s87Na+O4xn6Vrp2/pKISmkwleGLK2A9IghJ/2JA2C9sqLiuqZcUyWyKEJAh8xt6YMiyRyh4JD491b34QrP+gxrCiYKlqQhMQMSJyn0zBVpKIR7AF/trxiLY0mn2eIHEJNmfLGPNVAYD3u40dANzVo6odANzV110JITh37hxPPvnk6xo73ytu7c1Uf31vdCFuKPkyv8OpeYnVek2+aIiDKfvTPXLlPO8ECK3AyE6cIZBuxs+CrrbsLB9b2brW80+iaRDC3QhNAi71o62AoWMRfWeguyZiTElKoxuWiwVFVbI/vghhRXknwDcDoOklbNjR+IocRUjOzLWpY9f6VWcSO8TW5/IJ8YgQKPezzUC+R7ilVg4Yo1As5Qrj2FGFj8HrPr+FKwMkEeueKrRgzoAhGQWamojAMaRtEkWFzarQpIyRFEypWHg+Og8wvsH3W6DlUd+8QPLEKTKP+eKL50HA9FlN6kydWwXwqPB45fqAVsSy7gkmCnXvyLeYTcKIhat173dqC/D1BQkCswXofCPIPUPLTQ7xWPd+P8Rj2Xs9wmfeA2gjfGuDg/UFbAi40WsXx80K6hLhvPnupS5+vfav/SwheW97If62ubWRFKqmFIZTKqe2liTqAqPY0MQpclyTVTlVVVKWJbN0TuWXeJ5HEARMvKntr4t2mzFZ729AaUXlWWOgVsjk4RGw5x6EMhSy8/azn2NI1mO8FR7P8vo5tV+tGcDX28Y3Ygt4V18ftQOAu/q6rIODgzf8fQv63goA+HqVccrzfJaVPmG5XFKWJePhHvEwphBLvA4MgRCn1GQupcO2VUtSJ64wztw4QlNQ9MQerbVKP/3CK4YIIzHOK27DtrXt4k1bWbuZwdn8NbTRXBg9iYkK6tqgvZySlAF7NK7lqmmoyLcAoaZxbeWQgjk+g04YIk1OrVbg2BKDtXqpKbC5rgkSSUO9Fall28oZJbVlIY1PxB4Numv7tss1VJSs8AlQxDbzFeHkMvuk+BRY8FxxkyF7biYsQlHxv7nAc1yjoeJIadbpiNG4QMVuHtOHL728z3tHJS++et62UQ3oWwFctoCnFJqwkcz/54B8ZM+NUEHZikGEdjN/9tj3LV9ED9SNkVtqYLYA3zZjN8RjtQVUlD0ujvHxz/CDZ6PkztZdat/e6xxNLTyO/CFwwBjphBd2/EC4//SrHwVn90dtNVoDvC1GMDK+TbLpPo8FmKlrSMeMqUXDINgjCSrMIKccr6kqKybJ1zmZvgEKAj/AD3yrCO3dtVSjaPx+IorNL67dcbWiK03EPoKamrRT+7b1DO8mYHsEYOs4fpVawPcLAHcM4K4eVe0A4K4ey2ovlk3TPBKbgK8EAE94iRf5HXQF6UlFDRxMLqGiBu1uKK3nXc4SIQ2ejp0li+hm4KD1/AvIneefR9AJRNacbt12I8bMw1cp8PFQHXBsyNl4/ikCbDpHWRQsFguUDLgwuYLne9RALWpEo1Am7BjImgJFgOcoFjurmKFcKoIFcML5AEYWvEqNVyUEDLDJCUVn4GtbnYaiayvHTjgitxhOhKDOJLfT1wjCAN8LCIX1R7Tt53aG0Bo+x5wjRbKkdFBrnzUNPoqCZ7nAKSklEdBQkBKQsUeIzU4+XiYMRguMYwCVByC4+uIFCrG56c5TxaWVz2xYURiD+uIQ2UsGScINszf2rOefMZAYQ9370jLTEBkPrw7RjUcjh5RGUWpYBo3jVWEiNJVqCDAoNIKGCI/SGXH30zxsu3VbCdtnFr9SO/her1Nh84yNgAWGBRLDkJARY6AgR1Bh3Pv6Jtf3av/2vRIB6jOvz/JYCkVGycwtN2IPo0YMlGYQ5QTGsKhtu7gsS+pZzdJL8ZRHEPgEQUghraK8rbNm0j4+a1ZU7jPYkQyNzxBDjkTzFO/gjeqr5QN4vw+yOwC4q0dVOwC4q8ey7oexe5j1nVUWGzTX+GNe479R5DnL5RLpKy6Mr2C8Cs+JI2rnf9d57wlDJTIEIwonzrAqWetcl/cAoUZjvfyWyJ44w7YcT91+GBQBYNycXev5ZxXAuZmzXq9YrdfE3pDRZEgjN1KEyEwQTYEwmyguq9YtOqsMwAE73QE3GyU36lhJI2oaVSA4pGLp9lVSkhGS9D6XcLOMgWsr+/hEGGAcxczFEVlesc5WCCEZqBoRl0TBgEhOqBzANSQsSF2726qRW1VuzZrbDMg5YMwpExpuUaLQ3NRjLnCLdJWSrwWXgoTMtXdbTFdX0rYXe3XrNZ9Lb6+prg44OvU4GG/UwL7X84/zm64RGbtzMGwUQZpwsw4ptL2ZH8QlM/f7qTSse63ioZDO9tp6EQ6QlHhAQowF6LJaEZmEGMGavHs4GOJvAb6z7eDBfbSHVyLrvfZYdGp0Q47PMQHgM0IxoqEhwzi2+l7t3/5rD9WtXwjh5gm3xR395QEH91u5is9IRPj+gNivqAcrIu0zK2eWHcwLmoWm9mtMaciyjIE/JPfyLeayH1cHlhXtm1S/m/fg8cYPkDsGcFffqLUDgLt6LKvfAn4UdS9AWZPzIv+RzJyQpSVpviTyY/bG57pIN5sr6uGROHHGyFqXaB+pvS5KzcZeaQdqqp7nXY1NrHW2FhjaeDVNZcUZZYJfD9F+veX55zmTZa1rlsuUei2YJheJxj61yHpt5TGZmKGDNaXwCIlclFztIt42LeQNgLMmuwlTNNplAq8wArx60DGarXo4YEhNQcSEhtq1v4e9tnLt2roDRJhxPrwIRpGWS5rCsKpnNMuaJUs8z2PgHeCHA3Lf2t/Y1I89UjKbAes8AKfADEnMRSJmQMKEhmXj4c+PyYzHhfMXSFTMzM3sVe7+uSxBBNvMlDaC8uWI23ecRUkfG/Svlj0BiCwE4fGEG4uIwwgKt5wUhmVPZRsp04Mj27+bIFjTb2VKbiM49UNqEXKJmIXz/Qtp8KjAKb3h7nbw2df3nCLbAhZn272bny/RSELm7uFkjEDRIFl0/nxn278xAanZfNp72cv0AWF45v0KuWVfAwMG0mcQJQyjgsosUbXH0fwWBkOappRVRRPVBH5g5wf9CZncgFwfn5zN34VE8iRP3evI9A6R+aqIQJqmeV2rmd0M4K7eqtoBwF09lvV6jN2bXV8LAFfc4Xk+S9bMWSwXVFXJNLrIcDhCi6pjozaed5ubXZvaIbTv5tQ2KRmtIrSgImRIQ4lPjEdMzboDdbYEtcxBQCmXRMSEjGnQLglhTtNoZ+7ccDh9AhEXLinCKj9t+/V0c383ds7QRs7ZH9goOZulumkrCwIGZL3WrcQjqEdUlCgCGiqEG7nvt5WtCXSMQBC6KDl7S5fdciUrlPCJwggRSiZMyZuUvFxTZz6z/BiT38E3Q0yk8EMf/Bm+HFBRM0FxSkNMzQyPGQPOc8cC2TrnhUVFtPC49MSQgYxIFxXVqMRXmkLL9lAwFrA464xSbW72vQ4wde/flbTn3fjU4/atPVKXIyzUZmUTZfoW2hQ9wDcVkrQPDmGLq6rOmEGnaCoEdwBQREhqAsaI7gy07Vpzz3Zw//XW7x2btK0u3n5tbW0qt19wB02KR8GEIYrETXz2q0FvAKYQdwHQs3YyAf5Wi/vueLuIVZcFoojFeQLfIOo18RCSJKQsK4oqpygLsjxjrdcQCoLAAsJEJVRi87kuc5mwc1i8dz1IRu+bqR0DuKuvRe0A4K4ey3qrWsBaa+7wAlf5z+TVgsVigTGGc+MnEWHpmC+Qzlijod4SbLQsmvEKKmnHzVuLFysEWd3FtlkWzapkGyqXnFFRkxEx4dR/GZxgI2dJyICCBaYIWJ4sMNLjwt4ldFCwaSvXSBqXOqIIiZHlGr9JtiLipGsrt4BQ4Xft1r7615rmCmrvlNpUNASETBCIu5JIbIpHXwBikzNajzUrOhlQUmF6ZtFKhUzi81RxSWy8LlmizhuWWY6RCyKVU8UBA9/HeHZizrrj1Sw5T1guODl5DfxDRsNnGIaQFQ03r8cETxQMQs1svbnRnukAA1D3nilqSUeftQpgiaEQmsFrA156JaAMsw5GFL3lQ7U5N0MBqdhkjQRie74vZRsM9r0AJ07r3NYekgUNINwZFDBDIZ3Vy5CaitKxu+ae7eAFpbU8cq/P/j7tAboh/ta84QDP+RwKB0wVBQEeh4wRBFRnYtu4R/v3rH3M9nxjPw3FrmMb9Hgo5mSceopCxAzkPlFUk0QFlUnxasWyskKt1SpltYSFWuCHVl3s+wHPyG/iK9VXEwDuZgB39dWuHQDc1WNZb0UL2KC57v0BK66SZxmreUkgh+zvHVL6m5zeDTu2ySoNGDgzEtvyFUKAtgxYXwCh8PFdu1jidZFUipDSAbOaHInv5vEEqorBWI2tRFGQkmUZq/Q2QZiwPzlAKGu4rF1slhVx2JuwQaNFg/FKSpkyYt+ZRVtI2beWUY7N09SO4Rwg8anJacg7NifozK21OybKtbVrN+do66yqGay5s81jkJTUCFpT6awD2IkYU4Q5fujjDQeUVcRpqanyjOK4YhW+ZsUgUUYZXsbzC/5HMeXp05cI/CnT6R7F7YipOSYNNFnhM6kUJmhg7REHkJUgGu4y38t6ZFZ7ZHxlutCwkQb95RFHqSLxC3LR+h4alj2ypuwBwInSvZwVQ9YDgFPklop4gKBAdASaMts3/LO3/1btq4FTbI7LDA9FwhSDpCHApqPcre29G1ydhSHeXWydOmN07TlzHjjBMCFmjkLqAFNLEjFi1XvoONv+Pds+VsgtwQncS4DiAKPz7Sxo4/YUUkw58EPG/hidpNS6xCs85vWMqqzI85yoijla3qacVEwmE6IoesMUjq8nBnBXu3pUtQOAu3osq71YP6oWcC1zTi/8ESuvolnWZHlOHA+YjiZUcrkF3Az1FoumCLbSNBQhXj1CCLYSNqyJs+4BQkPEFANbkWt9KxQjDY2fofQYjwChJfN0xrpIGQV7xOMA7dpaDUUn4vBcXnDJ2lm8pBjXtqzICPGoWSHwOjsYgdz6XNoZr9iIOJu56zcGXfpbXoYGQ8SgUzVvMohjKtZbZtERU9bMO8hhmdQxNTmmBypy5vjsUxFxLAoIGlRwwGpY4zUBQbGGYsFRsaZJT7jhLTgxikN1yGQackdJaunhVYLSs0AsWgXUgf13CwCzCvpdQAWsC9sZlQIyFwM8CJwps4HkhuKGGyQMfdNqahgFdCBPCsOyx2L1jaVHQpKJDePVN5E2GCd/ce/ThoXUHXsaAvOtzGHBoredEDpVbQPMEMyQGBJiBi7tuUL2GLfVGTaun1tsMHe9zs+wddUZtq5xgwQrYVh5AXsMnURK4FOhqOhno4T4WwxgTPCG84IRQQcYraJ+GzBqrO9gjQYSYjkmiAV7JFQsaeqGZxbPoFaK1157jWvXruH7PpPJhMlkwmg06ubxvloM4Ov5AN4L/O0YwF09qtoBwF09liWE6NJA3mwtucUL4rM03ppyEVCbhsnokCjxqIW9EbXMmgVqmsBFs4GhJNsCLx4+2l/TmIYhB52qt2XW2gqdQrhlxxQBIQMaanTHPRlUlVCJFaKpWSzmlHXFYfwEyTB201cWPsRMHVizbVkLuCbW80+Mkc2MxmgXuWabh4aKjNK1pBcEzvOvYE1I0msD2xg3IQS1nyJJekxmmx7Sh3WqaytbmxurFu6DP9uATsicFjZwx6lyjfOUDN8dY5ugUrFAUKuSm8ll3p4kHOo13qkk1TCl4ct6xKWTV0m9IaosSeeC4rBBCANLD3lgj79yV760AhmAdjs1kLBoTYejvgLY7sbkFUntGDltNFVTIBwbHfSuplNP93jfbcHHQJgO3lgPwZ4yGLnFrg0ay7G20GCE5LS3/ABJcSZKrv962ls+wxDiceQ4V6klUWXPu9oBqLNm0wP8LQAY421F1/lIt3zLgrbzhe3cn00vabDsoDXtDvBJGKAR5JRn5gfP+hfKu7KCfQsATbtPwZb5y1nA2AB3qNyapky8gG/Zfw/BfoDWmuVyyWKxYD6fc+eOnbIcDodMJhOCwA4J7JJAdvWNWDsAuKvHtqSUb4oBNBiO+AJX+T3KqqDxChpyzk+eQgYGn9ABrNSBq411S8GyA03W/NhzvnqhBU3CgDY01PjQRaRZkOMhEGScIno3N5+4S9NoBSZKhByrF2iagNnsFK3hwvhJTFh0c1QegfM1q7H2MTZyLepUuLatbGRD2OwDtuVasqLvIQiWsZRIPCIaChflVlNT2kgtdRtT4QykMxQ+OcueWXSNRjv1c3ucQVO4tBGJzwCQNDQd4yjctkP2AEnRzQauGbPHgoKSJZIxGmtD/Xk9YXJnRdMsGE2f4DDe4+UmYi+tqHTDokp56bWKhczwKsHihk90zp4vonflG0mYt3N7vfMjCjYAUHiG88eCoxuCvcvQ1A3z+ZxkqBkNbYxdXyTi99q/UwkrY4gaSVR7aAQjBEYLPGmopSYGEIZINUihMbIhb4yDRpuM4WyLbdsGj5yZJbx7+Zads7UQgkBF1MT4LozPo0bRdDN4AXJLnBKynV2c4DHfArAe8xZAGhhotQXO7HyhlYysEIQMqImZuNlBzeoN27/Ahv0zm9nbfmP8rMAkIugYRo1hyIiADbBrmb8nn3ySwvlozudzbty40T1gvvbaa+zv7zOZTF5XrfuwZYx53RnAXft3V29l7QDgrh7bklI+NAOoqbnKf2ZurpGvC5brGRgYhYeYwN7i7BC9IGKCQfeUvMbN9lnWqyKjTcGoyaztitY0TUrI0IEce4OyZtGCghUesQOZ9hZZ0J8zlO4GfoKRDUXa4MkhB/v7lF7aTXJZvz7VeQMCnTij7t26bVycTynTLsGizf+lm0esXTSd6UQcrbm1QiFR+HpIpnM8Zwxt5wc3ZtGNmx1s28r2vzWVA3TGTR7WlGhqF48HJRkeA9YOckXEFA5MahZOJa079W9QF8wXGUU94psONGUYEVIiVUIYnOeJuMEfjDg6Djj0b7BQOTeuZyQXlszmEHqKpolRytsSgvRG8zqWEMBPBddesN/OvKqYzS1gDvcPEJ6HMabXPLdJIp4RTCtJUCgWVczMCGIJa68T33IubLjtvg9pDGEgOj4srD1u1hOeyIcMjUciGmpZE0o7kTkVagt8TVHMeq8nKNItZa01bmmhkqcNqYJ9rAjnGJjhUzFhhGBATU2NcbODcHe7tz7zehuOmnvMF54FZ/YznGAAjwmH1NQMMEgyfOozdjH+1ryg1JCpmrD3LWZnGMWK7Zb105zj9SoMQ86dO8e5c+fQWnN0dMS1a9coioKXXnoJgMFgwHg8ZjKZMBgM3jQj90Zt5l0LeFdvZe0A4K4e23rYFnDBkuf5LKm5TZqm5HnGKDikXFcY0bhJPMuNyTNzcR4BisBpWkNq8i7+rZ2LK1hiZN15iISMqVkj8DDormXbuBuroUE7gAk4iFRSmRWrlZsr9DymBwMqseqSQ0BSsbbiDFc+CRWrrtWsCF2rtsJIm5cLVrRSkVP3mpGWfTNoCmp3FOxy1sOwpqBSa2QdOZYwcNNkrc3NRuzROJsb62VoLWEqMjxipxA13bHyiJGEtHm77bJhBwIbhg7ceJRUlWZ1ch28fUbnDlBewoQNM5RVA0bxHXJPo2XMQbjP6ErMXHtEQnKM4XS1YnWSo5TErzzKQYTvB1S9SA/jCJlEw+0XLC9rdMGt0wWe53H5cEzm2YVGPh3nOWoMau5RLRS3jGAygsrdx0f+RlgijHHAx9ahb0FYW4lDo5mQlI1gX3rMjIImxMMwkoahtCm/uajuUoecvbhHyC0j6oERrHvvmTpwLYAUgyTglhMoTYGIioK+r55084Otrx5bdjPGGHKlu5A1+0CzDcbuBpCGVTcTGbOHh6AhoKJhtSUYMcYQNN7Wxz7b/vXwtgChQnKFQ+6npJREkd375557DqBjB4+Ojrhx4wZKKSaTSQcIHyaV6Ks1Z7irXZ2tHQDc1WNbD9MCrljzRX6TdXPKfLGgqWv2kgsEA0mm50jh4bHftVT7YocNaNr8zFq86K0ZwIAha7FGy3LLKNkybJHzw9tO2AALhkJG1GSoJmQ5y8irCmUivBFoJxxonEF0wbwDabaVLLasWwzGRdNZdlAgUU1MxB4li26f21nBwrWfAdcEHtO4OLV2uUCPWHs3t4yrY/ac8CR2IBC3vtbmJnWs6ZTamWDXZDSUBIwpHKsKOTHW56+homJNxD4ZNSVzYibM8mPSuUT4PtPJGCMNL3KBP2NusBL2xp8Zj0lt0B7EoaFeBahIUymP/WLCxcOIpgTlbGbmq5xVXSIEFKWPUSFBEFAJhTEQvwp3GkGeZwg9IxgmjEcjBpHcpIF4UBiYplCngjuuTRhImOkN41e0I43AgW+40wOA9RlfwD44nAjhxB12RQPgBgahJSAZCp+l0AyFnQAtTclM9qPftsUjNmVmu3Va3jV7hzvfLDDdJ+YYnyEwRDsPxrRbwxC/SxMBGDSK0x5bdXaeMEQ6e5k+gOwDRMPSGS3ZW9WYCkVAgqQgY44RZnNwuTuvOMbfisC7xD7BA9z2+uDM8zwODw85PDzEGMNqtWI+nzOfzzk5sfOuSZJssYP3A+p2DOCuvla1A4C7emzrYVrAPgnPLN7PF6//MUlym9FFRRmeoLGeaNpofJJO0dpaoXgEltk7A5pa0QVYgBUyQVN1dikbcGXBkFW7mg44WsbOKmVbb8Cmbjhe3EGbhsPpFU6Xt/H1wHnnrVzkXCvisHYvEWMy5s5UOqB2CSVbgFBoMMIBQuPm9tp5xNmWQUjQzSPijsEQj4BTcbR1PK2gZLb5TvAIndZU4Lu5RIHPqJszbA2nY5cy0reIKVnhMcSwx8yJBwznyU2NWmak+ZKD4Bx6OiIWkhmaTEfUIqamJKTB+Jo6C2EEg6TBpD7slxRAfsNn8Gca7lQ+gzgkDEOMGRLHNWVZsl6V1JkF+FkgOah90tsh67BinWU8cTmmGI/tmeGunsaAnwHHcLsWnJ/SgbxxCHfcYY3OgEHdmxMcScPcmA6PjSWses82Xg/oGAxCbhN+gbAKZGtDozhkQG0MCRpBjW8aTmVfHOJxTWz2ZYAk7beH2aiJ7TkAC8cOroAUwYCIgoAp4FPBGTWx6P7Hln+GooydfUx3DPA384NYwLjaAoyqB4IjhPbJjWFkxkgqtDMS6tdZP8E3av/eq14PnAkhGA6HDIdDnnjiCaqq2hKS3Lx5E6UU4/G4A4StoOT1tnE/M4A78LerR1k7ALirx7aUUg/EABpjuHXrFteuXWM6epJnL//f8XyPjFNOeZms/hzGFFtiD6ATbFjQlCDxAUHeA03tDGDHtkmDLEMS9h0L2C7Xii62EzZixnb2rjDMlzMUHuenl9BegVnXlGJJjXTZv5t5xNaBL2fh5v5y13YNXRt1hDWuyfF0TCVT12oWTthhWVFrGxM5y5ZtEYedR9SsOUHLCtH4ztRjGzhKPBQ+Wc/xLmSEwKd0wHfTXI864Ojh4zOgpMSQkLJ2uRB26UBXXFutyfOMafQO9kcr7gjBgIYZkrGouc4ePreYUJP6mvlsQDRq8CJNfscnuGBzdPNTj4FfIoA4hGVmb6p7vo8OfebVAG00ui6oZMmt/5qTJClr7eMphR8H5M57rhQW/J1fwrwQVO5U3DRJwTj1MMDIg9ydVoHYbv/G0nTtWGXYmu0bC5yS1y5wgNgCZxt20FaC4BiNMbB0Z2ZIQGAMsRswMFSbHcOKPcQWGLPt4G4bHfja/L4VnBxjY9ZqfBIShmggZybSDrBY775tMHZ2nvDso5x/Zl7wLGCMjceRr1DSJs/s42Go8SkoSZ1f4QYQSiRXOOBBqgVgXwl4+b7PwcEBBwcHGGNYr9cdO3j16lW7v3HcgcHhcHhXmtH9sIU7ALirR1k7ALirx7aklJRl+ZUXxF5kr169ysnJCRcvXuSJJ57oLqYJ+yTsk83HVKw53A845Sopt8+0gYVjrAonjgjwiGk1sf12sSRAi7QDjh4RHjGtD2ALmqzoAnIzY51lrFYpidxjb3qAURUNFmxIHSDwtrZh28keEolxkXM+kWMFbYvWztiFSCRCSPwmcobSckvs0VC6eUQLEEKG2MyLiqaX7GGXERzPb+GHgjBICOUA23jcNpW2wo2Cxu1zQIDHgAbdta/bbXsMAI/8jPp3rguOZ6+RNyGj0Qji86xYELNGUwI+ERVfNgOeMwlSVtzxGmanI65cukMRNmRlSKDtjdz3Bb5DYX3bFp9NcpkUkskkxjsNeKnRSL8mCAJ003BnOSddrfB9jzLwOZcGnJ76VFP7aXwFyxarmY26GKDqpYRMfcMROKUsNMLO4EkjLBjUkM8yhpUgEdLyawYQhrrnKWgwnPGJJmQ7V3gfyR00mYE5krFQZCiCuiCsBQmK094s31lrGvsdbTNRZ9m8IR6n1KyAFZIJI05pSJqCiCGSYstexkdszQ9aZnE7fu6sP2HJ9sOebvdRCOz5pzihwUpLIs4h8SgxrGiouMQeIQ82o9faszwI8BJCMBgMGAwGXL58mbquO3bw5OSEW7duIaVkNBoxmUw64He/LeBd7epR1Q4A7uqxrfsVgeR5zpe//GWKouDZZ59lf3//nstJKRFlwDneyTneiaZmwWuc8jIzrtIqftv0i4bKpX9YcGRBk23L5hyjVWVRhRBI1441aBRezxtwRWNKlsslRVEwDg+JRopS2HapR4BfjRC+sK1lV604o+4JQNrIudavkDMiDq0ySlUxYoSmccBQUbJ2mb2bz1aQup8Vjh2MaSgZBDHN8JiyzMiWDYIlkRzjxQY/8Im9kRO7QMVyq70rCchdG922qkNKciRxp/6NGbBy+27qlNPFCqM1T44vkYcKn5rnGfN2E1CJOYYERYVAoOsJfrBGC5BK4RU+ZaDJBOylEikNKhKYhQQfVA8LNDVI14EzQLyo+MJ/W2K0Zu/8AZ4MMQbkYQNliSozzAspL5wq9saazEsIgoDp1Gcm7I18Gm2MoQNh27++gWltE0hGlceqhiQU3G53xBiCoOHWPKVpPK5M97heKgR2Ru680tTSMJUGhCFQmmNTb1rHiC2fwBDByRltLkAjBEslafCZugcTyxU3+NRbauKz6mF1hoG0ZtHb4MwANTBTHuCzR0RNQ4JGUhDSbLV7R2fmB8/Gz4VINz+4AYztvKDgboFJAyyRrNzs4ADJFS7yoPUg/nyvV57nsb+/z/7+PsYYsixjPp+zWCx49dVXO5B38+ZN9vb2GI1G3Tbfihbw888/z4c//GHu3LnDdDrlk5/8JO985zu3lrl69Sp/5+/8Hf7kT/6EZ599lv/wH/7Dm97urr7+agcAd/XY1v3MAM7nc1588UU8z+Nd73oXcRzf9/okHlOeYspTjpG4w4yrzBw7GDHesm7JWRIxoWRpLVaqGoxPIGLK3nLWG9DO4jWNZnVSUleSg8ll5GBbdOETU/tHaKWASW8ecRPDtskWnnXbAEnMXo/Zc6KQauBYxLPziA0BCSUrNHprbtECwzUxE0q1Zn94EYMma+aQB6TVMfmqwqxAqhlDtY+MKyJ/iC8iaioE3lZbuXbBagZFmxJi1b8rRow4rhYs58cMmgnewYTEU+SApETgc5Mxe2bGSNAZCa/rhEkwAyCJG4o0ohlZW2Ev9UgSjVgrymMJFzcqX4B1BZG2/oL5yyU3T04An+ne1FKFNSQxZFIRRTFP1DHXlWEyKRkMM+ZlSZ7lqKZmFcUEYch+ZMGP0HDeQLqGeQ4rH1aBvKcwZKJqXpzNwcB0b0oSC/IWBBjD3EDeCGgEyhgCT1AaxUhCLDSB0iRKW7WvsLFy/VbzOSE5Fu15A7GGO26uLkMAiiE+AYYQTUVNgN7yApwgHdNma4q3JTCJkO61nWkUwNLp0pdIIGbswgjDM8rits7Gz7Vxc22N8LljnEuiEL18YlvbDKNlRJ9ietd2vlI9CgDYLyEESZKQJAmXLl2iaRpee+01bt261c0PCiE6djCOY5vZ7E6WRwEAP/axj/HRj36Uj3zkI3z605/m4x//OL/1W7+1tcx4POYnf/InWSwW/PRP//Sb3uauvj5rBwB39djWG6mAjTHcuHGD1157jclkwjd90zd9RQPXNwKUAsGQcww5xxX+PAVLZrzCjKvMuY6dd9sYLzcqp1FrPHPeGSpPXDs565arKtsaQsDh4WVMkHfefNqZOucs7EW/zSRF9trKCdbFrjWV3twcQoZdEgcYAgb4VUPjlxjCbslNKsjmc7aqXkXY5fpGPRFH+xkTtYcZNCQ8RalX5NUak/mk9Ql6rhFiQRAEJN4BMqoIpfUGLEiJmJI7QNhQkDCkILeuf/ltihONihV7ezGpUjSsMCQOjCbkNNTmHEPRcJMShWGNYlSD8Qxh3DCbh6g9uw0994inDdpXpC8JuGjbrm1lFcTX4eZLBcv5goNDxd50ipSK1J1icWTn+y6soVrbm3EQhMTTkP3G0DQNfpxjipo0TbleV8SlR1HEnO4HpDKwN/eQDlCNPNsmFkBVVRytjxGez2Rvwr4vOTZN910denC7RwgdeMKqiAUsDERCcr0BGokHnJMGIw0jCSkaJbZnC40x7gawORAHKI67ZSQRAWsMI6x194pyy0rGnl3blSCt16Sx6z47P5ggmXeMoo9yEpIEDRTkrLfUwnC3Xcz2lsVd+cQDfGY9+5cnGRI/xO1Oa/2Wzt0ppTqrmfe85z0URdGxg9euXeP09JR/9I/+Ed/93d/NX/krf4Vv+ZZveVPbOzo64nOf+xyf/exnAfjBH/xB/u7f/btcvXqVp59+ultub2+P7/iO7+B3f/d339T2dvX1XTsAuKvHtl6vBVzXNS+//DKz2YxLly5x+fLl+7qIP4iqOGTEBb6ZC3wzDRVzrnXsYEWOZwKk8SlZIFE0lC5POEIgaFaKeXoH3w/Zmx6glW171i5qTqBonL+epyv3/qjnSShcqxeXyBHgu1aedfLbeBdKfNvcC61lh8cY32ZPnFExe3gEPVWv6RjH2tnBCKwDotcmnrR7IyWT8BwmhJFJWNcLqqKhzAXHxQ1Yge/5+EHA2D8HvgVQGwucFGUC1kvFaVrjxwnjyUWEPGFCwJySER4pDSMgFZrr+jxvN6+A8JhQk/sezWrEYKJRoWZ9IyTGoJShnCn8CyUrBdlKcC6F9WjzfR5qePWFjDRNLWidjMgqySCBlcMZXgB7Go7+N0yubN671BY4J6FHEQ2ZRHBQN9w+rVjmJVWZsyADIfEDH+UF6CBASkXk2/cXZYFenJIPQqaTMVJIpGo6Y+pIwEmvHTiVhjs91fBE0ANutl18KqA0AhqF0IJDKdHKUAlNKmomjWGuBBP3ngC2Wr/WukcyBzdoYA2SGgxjxw421CzuoRZu3y8EVGcgYnjGj3DiZvcWjh2ckNBgiJwVkOrmAbfnBbskEGG+ImD8M4x5mHrUDODrbUMIgZSSOI6J45iLFy/SNA0vvvgi3/3d383v/d7v8S/+xb/g8PCQD37wg/z8z//8QwHTV199lcuXL3cPw0IIrly5wrVr17YA4K7+dNQOAO7qsa0WsPVbJFmW8cILL1DXNW9/+9uZTqcPvL4HLYXPPt/EPt+EQbPkFjea/81C/7fuJhWQWCNlsyZNU7I8ZxhOGY+nKKmosGIIn4SGsjN3LliC0GBs5m7ImIrMJXdUnehCU6HxnGta3WUVaxrnqZe1ugREZyBdIVBuHlF03nttWVVwTeZ+pvCcCpmtdq5HgEF0DCECYn/KwA/Rw5qsCSnLkiqvKZYNN9XLFvwEMQN/DxMalBny2vKUfG1oJmO8JKEQIa9yiQto4JQETQqM0KQoBJLSJCAMQ2pOlebOfMJksmQdNBRGcLj2MHGDmXt4Da7xDP4rkH9zS6waTl9ISdOcKIoYjYZklT2foqQXBycg/bw9iEuHN0ZJa70Co8iufLgAqRXKV4z8iL3EcNRYmxlfZ9xcWmYq8BVlHVAB63XGhUFANJkghGAsDOvGMMQ6PA6kYWUEWkCjDc720Z63xqDlhg8zxjBUomfOAwdScQPjaE9FYhR57TFGk2jBSmiGQm3NC+4jXUpze54bUqDEikkg4DwhPtqxgxVjZMf2GWPNpvv2MhKY9cCand076z+oOKYGfAwTLiEJqYCSgowhAcserBzgd+k2dj/Flp+gAN72dQ4A72UBo5TiypUrfOITn+ATn/gEWZbxR3/0R7z00ktvipU8+96d0ORPb+0A4K4e22ovzO0F9PT0lJdeeokgCHjXu97VtVYeZH0PGy3XlkAy5hLSjFi+NuRt0ysU3m1OeZm5vs58MaeuKvbjC6hBQyVaraMhcq1XMA6cCUJGLNUS3dSdqXTIiIbKgcXWMHnbVLrsRBwZPhEBManOMdq2XDdzhtotv3ICAOsNCA589m7O1nw6pfVCtIa8gVP69tW/CRUVxv3MUz6DeA8TQ2Zm1GVIUZbkZUaWWnvs3EvRaAbTIUE8JqfGkGOIuYliwhU8bmGIXMNQEYmKq+aAibmN5ZkMyyrmsFkw9xuEgDD1qGONDkCspJ0jVFDegUEJpW/IjufceLliOEiI44RBLDrQp8LNIchfhKqA4RBS97M4puNagwbMTTip4PAAWgzi+QJf+vi+z/nhAE83lGVJ0mTcWmd4WrNfNyjfIzgt0CrECwUL41MAsYQbHh3gO+dpjgpDIKy34FhqagyBMDTCEIqG416m3Qg4MT0DQmPwhOQUiRCKqvY5D1ZcIiQrWdMIc9dk3h6KOz2gNURw1NnTeAgUEYIxmpIagyFAbsG7KR4nPXA2xmPZYxCt/+C2GfQMQ47CGsHEJLhUEm2BdCjUFgAc4jPvtX8vkzB4QPVvt3VjvioA8PW20QdncRzzPd/zPQ98XevXk08+ybVr16jrGs9FGF6/fp0rV6585Tfv6huudgBwV49ttU/NTdNw48aNTkX3zDPP3POJ+n7Wd5ZRfNhqL+i+HrLHRSbZ2/nSl7+A8W8yeVKxTl7bMqkNmTgPwY2nnk3iqBDuJvqVTKUDBhSsMD1T6VbEUbJGmYBSrAkYYdwsXn85m9qx6rahnDegBXzKCVk2ZWcUZ2xsbgJAsj4DHH1iZ6Kt8URAEk4pwwxhBszKE6plSlTGrH1YpSl1eoc8CQiCGt+LqQSkxASMGdC4XOEIRcUts8fUDEBYGJ3EhnodkQ+twbReegR7mtSDei4QApIEZAH+iw2zZI5+LWc82u9urHEEKyeuNgqo4VwFd9yoZJRsYt+0Aho4bGBxBFVtma+FwzTGwEJv/r0SIKViEkR4y5zBvKbxBgwGhtdWDU2TEogFtwchfhAQBgHDgdeBMYVhLqxpeQUEBl7TYLT9/obCKo8jKUikwUMjPVj1QNY5IXllvaKsKgZJwsBYL7/GrYPG55KQaKEJpGYhaoZCdmIR92kw7pxp66ADiJYdlHVEKQX7BKwoaTBkZ1qzZ/9Kx2cA4lmBiQCuu7NqLcaYBkoREyApHb+rz2zj7V2T+8Hrq8EANk1zXwDwUdT58+d573vfy6/+6q/ykY98hF/7tV/jqaee2rV//5TWDgDu6rGt9qL54osvkqYpV65c4cKFCw8N3vrWC48KABpjOmYyDEP+L8/8PwnDEE1Dyk1OeJkVt0m5Rd8s2ifqkjgQBlXFJOy71mu7b4KQ4RlTaUnAHoYGhU/jbqYRY1L/NUxdd7N7ERNsBrFtK2tqB/6sYrmhpKbolM1tW9naxgy2gGNFjiKkYOZaxQPnHbcNHO3kWAl4lGVJurRmwZPzIQdij1m1oCxyipVmtV4jGlgPYoaezxfDA94lZ6zJ8YHagb6smeB7NqHEixrmixg9ykkiTTHz8C9k5BLETQkXIBxAkde88h8XTL6rwKwOiaJNSoPsoZISa+FSXQfjFhEBHbu3MnC+htk1KCb2aIx7beFJZAUaYHOAVQ37a831m3NSr2I0mpJEMf4Q9htodMOYjNfKmtVqRblassg9/DAgCAIuJx533L4ZY/DUxr9QGINUoI1gbWBVwzlPclIbhPEYSRgLw53VgiJbEQ8GjOMEI9gycTlEckM7xlDbROxAGfaEZCU0pTAcnmEDQ9iynwEL7k58jwoPUFzE+kxG1GRUBPewkznrP3j2r3DUB4jGIIXiNRSQ4JGwh6amRqI7IPhn3iQAfJiHyQfdxv0CwEchSPnFX/xFPvKRj/DP/tk/Yzwe8/M///MAfPzjH+f9738/H/jAByiKgve+970URcFiseDd7343f+Nv/A1+6qd+6k1vf1dfP7UDgLt6bKs1gc6yjOeee47x+OHmfNrqt5Tf7FN/+/7bt29zcnJyFzMpUYx5gjFPYH0DZ5xylTmvsOa0a/faZX1qWfbSSGKkU/L2kzgUHgL/jIhj6IBgAa7xK+4l4kASMbaWKURuzlAREHfLtfYxAQkNNaFjKGsnVmmBo2UXF/hMnZ+gVVsUpASMyLGpHsvlkkRMCfcCd7xWDNWQKqrZ11OO6jUmU8zLitPVEbdUTSUlh0nOyK85VuBhWFQRE6WJ0Iig4fprMeNLOYNY08x9okqQA9VtibpsaFTFtaM5TSU5vHXIsdxuD7YQxBhYa5gcO9Dnqm0uBh4Mc7j1Mpw7oANmceTMoIEotAzgREOSwfUTzWIxZ+jljMf7hGHIwLeMoQCUVDTRkEkCRmsmsuB6WVMUJfV6zf9egxdYMPhE7HFiNufpOU9w22wA1AVPdCDNCPuBnk+XrPOcwWDMuThhDJQGSjS1sIKQO712scEwEnBkDBgwSA4RaGEYS8mSBiPEXXYzEwQvK9E1Xm0WsSBz7GBEwBgoqFlTooEJ3pag5GwcnfUb7AFEAz1PbGqgIeC228YYwVNIRg/Z/gV7LfD9h3///W7jfkHmowCA73jHO/j93//97vV8bh8gP/nJT3Y/C8OQz3/+8296W7v6+q4dANzVY1nHx8ddxNLTTz/9psEfbAPAN1vtOk5OTrh8+TKXLl163Yt3a70Ss8dlvo2KjBlXOeUqa45Ziwwtym5pkFSs0NROWZwgMFTk1GeSODQllQOTygSYUm7Z1djlzog4sEbTCt+5t9myANM7A04t29dG4VWsEHioHnC07V9BxAhjBOv1itV6RRiGDEceoRiQUWBoiNBUGLRMCYMQESj2GVPXmnWRsKwqghNQ4jqzaIwnYxbemHPZiEnSsPI1xkiGuYeJGtaeb42fgTAU1KuM03RGQ8TFi2OWNxVyuB1Dljk/4jCCxMDt5+H8s3RRt0uHSfY13HzZcaDh5veVpFthreHcCo5mUE1qZrMFxmimlw5IlUWVg3CT+7sf0pn3BEqyCmLGAQy0YUDJoijIs5xqvuK2As/3COOIQRRQRx4jBIUwJMCx0R2FFhrDnXTOuiwYjUaEYUgsBDccsAPJoRAoDFMhWBiNFoLzgs43EMAzkAlYG6wXIR7nhaARhsCxgxJDSWtdZHdg/wxjCLaVq/GQeEywkg9rDW5DAMddsoetswBRaUPqS0L3+ixAXGAeWvzR1ldLBPLVagHvalf92gHAXT1WpbXm2rVrHB0dMZ1Omc1mj6xF86gAYFEUvPDCC4Cdubl8+fIDvd8n3kojeWH9f3Ij+yLBgUG4RJG2LEDTFA6UWTCmAENJ2ok9AKQJaNScnACBIGCIRFGTO4aw3b5VIvfj30LG2BnBjb1MOx/YB4QeCZLQiUssEhJ4+ITkZsFyuaTMavbiQ9QIPBHTEKKZcuqmyiKGGG4xdvYvEyRzT3DJizgmIYwvMtYvcVp5NMsZt2SDOWrYfyplFh6iTYK/8GliTaYgOhEYaahVRv7aDH8UMp1OGFSS2R04H8GRm/kTwMph7SSC+nn7s9rdn4cjKwDZk6B7I5HrNkZOwtzNAV4QcOcGaA2eqHj12L5hf2/KWtlLb39GEMBIiBoYNRBIOM1h3UAhBesoRKsQhmMueg3XiopVWVIsSgbrNZnybBpLECIiRaw8QglSNyzmx5S6YjSZEkUB54Tgdg9cBNh2dgaWWUNyUYIWhiGSpfMjHAm1ZRUTIzgyhsaxg0NgKiSZbNDOpiaCLXWxnWWV5O7ctLInyas0QEjiEqytF+YmhO7s49NQi96ZdzdADJG8h9c3fr+feqt9AMHOAL6eR+lb0QLe1a7a2gHAXT02VVUVX/7yl1mtVjz11FPs7e0xm80eCWMHjwYALhYLXnzxxQ6UJkny5vYJj1HzBKsTxbc+/a2sudMZUKfcJma65eW3EXYs8Eiw4WkFioCVuoGpN+khAunm+Aw+iVP/ii2ACThxycKBSbusR2Rtbci6FrRlAEt0F08niFz6wkqfsJwvqOqa0WREFPnAhGNyl+5RUzNwToOCY67wFCsMBRGaOYKIGggoVYTvDxnHIeFwRFokNCeCJn+J03JOPdfcXArid8C6CQluKfS5BatyxVjE+IdTFq8JfCemXJyASCwRNkg2Ct9oCTePLUhbOnYvTqCRkH4Z4kN3fAJIXRrbdABLAXsZmMaCv6oqaZpjZBAymUw4N1Jdu3gvgpmB0EXEpYVlIDMDo5FtQbfLHbn3KGAuFFGoiMKIA19zsykxZUlZlojsDq+uPHzfKo8H+YpjYDw5JFYB57VVC+8LnDQHQiFYbvkMwi00xggw4BvFOSkojcEHKmEFKHVvflBgo+euGY1oBKZWjKRi0ihmsrGJJ9xtLyMxLuXDLrAGYhS3EC6NRxCiyXvKXoNhJXrKZuAsh/ZtJAR3/fTB6mvNAJ6tHQDc1aOsHQDc1WNRaZry5S9/GYDnnnuO0WjUAbWvBwBojOH27du88sorjEYj3va2t/Hf//t/fyT7JqW0dhRIhpxnyPleGsmrzHiZOdfdgH2b2CGoyahZEzKmJsc3I6ra+dt1CSCt+nfdU/8GzgOwQaC24u6srMN3KSMCjwCPGIFi7cBkWy1wrOqK5WyFqEL2DiZEwQFrcjQLxkxJyTFoJvjMqKlZkzPhSwy5TIhmBQywbokBnqi4WV9kEpySixLfn7B3PmI/eorTSCHOKfJrNeXymJMTRfFqjTeoGe6NiI/HrKW7ibrRrryAc/twO7cze2kOex40x/b38RBy9zUKD/wbUJTg7PwYjzbzf5GA7AiOczg4hGKds1guOXcuYDqaIoTEeEDT+uSBWsPJCszQtoIFMI621cNpa+IIHARw1O4PhrUy+MrHD3wuigFzPWJYlRR5gZcuuONJpJCURUGC4SjyaBBgBMLAOXcXCIQgNYZYCBZuts9u39rDvNa2dI1kgiARhkwYG1UnhMshNp3fZIMFZdcNmEYxFoIYgxEahOnA2z7SJpq46gtKNIITbAv5lJABgiEGn4arYtXhP5/teUGAP8/gdf+m7re+lj6AsGsB7+qtrR0A3NXXfbXAKkkS3va2txEEdnaqfRp+vTi4B632Qv+g69Na88orr3Dnzh3Onz/Pk08+2Tn7PyoAeC97GptG8m4u8O57ppFIZybTpoLUck0tckw+pQkaQjlxs4SaqJsLtIbQDRWh8xYMGFnTZdaEJB3ABKgp8YjJOUGh8Bk6LkdSsKAsKxaLBVIIJuem+N6QqqfQbEhRBDRoFAWgaGgYIUiBGYdMWBIhKMkxJBhR8mJzyHv1ilJUgIFQc3w6QF0pmBzEpGXIIPK4oxr82CDXirRZM7tWEF4IqcqI3PdpjUhWMyC0CuBQwuoLEB/Y45yM2XCaM1gcw3SfTmrTzv8dKFjc2swQ3liuWSxXxFGAGNsWuhRwWsOeAT2HowAqd48ve+ROFGysZPYjOO2UvjY6rq1zoemYQYxBKJBC4hufsk6pfcVoMKBuGuoiZ16sKFJJENhUlieigDtms+EhgsYYpkJZ4EnDVEhubzF2gBDcANACz0j2AaTBw1BiWC6XjMqaxWhE6M6WpdYoIZkZiYdkKgQKzUJuZhXBRsX1FcUThGsh25avfRzwKUxE0khGhPg0W/YxTxNy4U2IPzaH9GvnA3gv8LdjAHf1KGsHAHf1dVt9YHXu3DmefPLJrQulEOJ14+Aeph6GAayqihdeeIH1es0zzzzD4eHh1v49in0TPSbm9W4AZ9NIUo6Y8yonvEibPBt5CYVouLN8DQEEQUDoJwyCCcKzymALHD2nErbAsSR1Io/IWcWMXfu36AlKrMVHyYKACTkpeu2RzlNU7HE4ukylagrXMo4YkDvrmQEJC0pnXr1HgWaAIXWK4lOe5IAZdzAWGIoSBdypDlDhTSY01H7D0XzA+csVKmxYmob1F5aEgcfBE/uEXkAa1Ry/UKKOM9Z1wdUbAnHqE4YhdR1wYaJojCC6CfM11K57r0KggX0PblsTOoKYTvSxNnBOwvJVyIYABsWS+bIgjmOevDDg1CGcQw/KBZymtl3cgr/Is8Cw/XbnvdNGemzYPw9KDSMDUoMWgj0jaLBpIctaEFUl89mcA08wOjyH70k0hnA0ZNZUzMqCoioJFnOeXws85REEAePQp/QDSmCp7QYPUawwHEpJjSE1DUMpmW0ZhMNcQGEERgvq+YJhvibe36foKWgPxUYIUgMn2hAJyaoRlh0UoNDcFjUI+7doxU1n/QYlxxhqYOZ5+ChAEhIwcLODf443N3rR1tfaB/Bs7QDgrh5l7QDgrr5uK01TTk5O7gJW/ZJSPnIG8H5B22q16sQe73jHOxgOh3et71ExgHD/7SCBZMRFRlzkCn+BnLlNIgmvIw9fpW5iyqKkygzLfMlCzvA9jyAIGfh7hH5II2ra4DiF3zGAYHGPxCdk4KxiBpSskEgkCZlZsl6vWK/XBIOQc8MnMVLRUCCw+b81KRFDcjIKFsSMySgZIhwPWAARDQXX2WNMDaQMaUiFYk/WnNYRF3zFSNTc8HwMEKYBmSq4c5oxvuVx+X1jgjsR+jWonwpIkoBL3pDRUzVH1ypEmpMuVqxWK6oK9gY+i1cT9g89Uu3OB8+CLXkCDhd1ohDfh6SGoxfh3AXIakOaLomjlMFgShIneCH4FUxzMDnMnN4miOj8BMcR5O403usxfpG05tLnG9t2LryNxcy5GO5UbboH1KFgllnftrHnUe5NOTXSspOe5noDEOIHIVciQzWqmVQlWZmxXK+ZrQylUviBTxAEXApC7rjPmWoDxrAvPLSGcwIyYSi1Rkhrs2O0Zr5YEJYlYv+QLAzxtWEsJJEw1ki685G2P5+5lvHSGGotqIV91BgJgRQGiea45/Vi84pNtw4BTJDcQVMBKYKLRLznEQBAY8yOAdzVN3TtAOCuvm5rPB7znve85w19uB4VyGrXBfcHAI+Pj3n55ZfvakufXd+jmOHpM4APUxETLvGtXOJbqUXB3HuVuXeN08FVymZNWZaUZUGxaFir60glCILAZfUeICR3qX/1Xepf603Y6IblcklR5iTxiMngHLlYuf2I0UDlFMcNK0KmDjyW4LhLQ+TAZkhJRYjmS2bERTICYWPghrLiWPrIdIo/rGhExDAyZDc1N8d3kPKQg71zDJVh5UE5t+BN+dCcQPh2jzjy2H8y5vimoSxLpCm49j8rjJkjo5q1TAiCgFUVcFgr2k6pMbBw7N+BgpsvOc9kqVksFpRVxd7FPTwV0zSgSuA23K4hmGzWkfZOs6J3X5c+RDWMa9ugvpHCAhgE9j3O0o9CsmEGfXgts/OGge9z/nDEzAkgVCuYcOVjLVJyFPgxeBEXR4Z1XaGKgrLIadI1z/sKz7dgMPB9zvuB9fqzWiAibOJIgCDSmldOZ4R1Tbi3h3R/D5UQGOA1bTBIxkIQYPAF3DJNNwco3UigO6wcG8PESJbAyAhCYSiEJjCGU9keQ8PAcCahBH6ABHWXZvjBq70OvJUA0BjzujOAu/m/Xb3VtQOAu/q6rq9kwvooW8BCiK/Ytm2zM2/evMnBwQFPP/30694gHhU4bQHgo1iXR8gBb+eAt9s0EnWT0/gqaXzEcnyTqgwtGCxLyqVg7r+IH3gEYcjQn+KpmJrM8S22fBJKKhqdsZjPqaqGw8EVokHMmkWnEq7IMBhCzpGhWFLjEXOHoRvu96i5yZgRS2pr/4JhguFIKGjOo9UxEBFIu/1FOmF/eMPtyZLrr6wIv83n8GBMqBTMGzIJRQX7ORQj4Bh8x7Z5oT2+YRhyfhDiLQxNXTOcZixnDetsQXOkufGFiAvv9GxWytRjpQUHPjQuE65pGq7esZ/9/MGYSoXEAvaAG273pkOYue0OY1i5rzP0YOYI1z0DZgH5ys4djqeb767vFzgJNgkjxsA8X7FYrgmjkKemQ067ow4HgeaoW9Yw8eC4hy0ueHAbwPepfZ/JcMRcN4zKkqDIqecp0jS8EAb4wf+fvT+PlWTv0zqxzy/2iIzIPfOcU9utW1V3ecdvQ78D3bQ1dEMj1I1b3WIEjdHMCIYxszDTuC2kEQapkbAthLBkyZZMw9iSh01GII8MGJg2MsKyEPaoLRvoxt3vrapb69lyj1xij/j5j19mnqy6VfdW1T1Vd3nzkUp1MjMyMjIyMuOJ7/f7PI+FZdn4lomhw1xAVRaEYYhbFLTbLTzTIkMyl5KO0NZ+hGprQinpC41TKTGlTkOo0QGBZLKTX+xKwUqoSdFw7VXYRmeOpCkFiIq53Hw3Lsje97C4fQmzf/DuCODrvMa+ArjHZWJPAPf4RuMyW8Cb9b2MaBVFwaeffsp8Pn+l2LnLmgF83RbwK6/3+TQSMWNqP2JqP6SQCYtiRJ4J0ixjvpgT5QWaV2BZFp4VYBvKAHrFnLIomIchlZT0mocIqyRlho2LhknCCh2XEovFmt5VQEqERYMVkhIbg+sEa3WyS0GIvrZ/0ZlUdTraEEOwNcYudR2ZGKzyCD1NELUO1yqdZU0iEkiGGkkHDAPsFeAr/z5jXdCsdgov8Ux5HhqGiVMzaQF+sySdpIy1nPEiIikSNKtCCIezuw7BdYuyLMnzCZmu0Ww26bZNZAWzcyhbF+u3bNjYLda8CzJXt8EroVqArl+QM9dUnoKbI2zXL9C2UMkcUmKmS07jBNd18Go+paYMm03AFZK4Ym0VDoEGg6raqmc7GsoPcH3bBeXPp2mYjkNl2xw1G5wWGV6aYiYJ1XJJrAtGro1lmiRJQq0ssTttIsMgWpclewgqoCM0QqmSRvo7gpJcCEZS0hE6I1nRqAS2gFSWlELuSDrYKowlkEoJUuBlAgyddmUQi5KCip8VlzP7BxcE8G2Srs1v16u0gPfkb4/Lxp4A7vGNxmW2gD9vfUmScPfuXYqi4IMPPqDR+OJ80a9jBfClr/F8GomImZmPmJqPCGvH6IXFIh+RZiXRasVytcSRdTQvx9BsojDH0B1a7S6lsaEArJNJEhzaLMko1npaD0m6ft0AnTEFGQknBFyhjcZsPQ3oocLXbIRWkhQH1M2UqciVzbRV8vihpGzOaXRaVFVAcVbgHFbEBqQjgdZRSl4Rgt5R7VYxBCxI1xtas5WCV6wDLBZr2W9NaiznHp0uZM06dpHj+RHDezlVFTM7VQyy06vQ6y0sw0Rfwdk6ziPeOYevdj6+ZH0ub0mQIUzX3fRem23WnO9CvOMDuJkLNIQiiVJKisUCEc25UWvgGD5GBqMMJEoY4rmCRb5uBQvJzFRKYluAp1dASUesjcNlpYysd46LQ10wpNp6Cuq+j1WWTLIMM0kwoiUNWZKaFqs4xrJsTMukKzSGzyV/XJFK6RwA8/X8XkcoQQdCEKLSPWrCIJeSDsokOpUlibZraQ4tKbivCUxNR1YS0Pj3NY+GuLxq3buoAH7ea+xbwHu8bewJ4B7faOi6TlEUX7zgK+JFpG02m/HgwQNM0+Q73/kOjuO88rou40f8bVUAPw+7aSQlBQvjhKnxkJn7iLRaIVOTVTEliRMkCRhg6x2iIiTQ2khNzQiqODqTiDkWDuVaBJIxx6ZJSoFqdhpUlARonGJyQJ+Mc3Q8UlIEPrmW8f2kx/eMp0ghaYqM01XM+NzhO0c60nMRI0E6M6hdTYk0SFNoF6D7UDwBcaBMm5NHEusjWK1FFDUdVmtSVqur9A3PllQLJYTx2zAVAtuysCML35PUmhGPwgghIBOSdDqGpc7DwEU3bYKawTxTrVjPgWWuCm2OCVUOrSUsVlDshFVMdw7l3blA3WTrHdgzIU8qzodz4jih6HRAd4kzaLuwiQP2dRjvOKx0TLat4ESCLySjavOooKcLRoWkpoErwBaShApNQiXUnJ6vCUJdxzRNwihC0w1ErUlR5BhRAvMVUkge1rx1u9hCaBp9oXGyGR5EzQ+2hUYKGFIq8YeU+EIQrpcZSmU2bQodp4JAQESFJiuOoyVlUeC5quL3k5rNj2qbULjLwbskgK+SZrSvAO5x2dgTwD2+0bjsCuDuTKGUkrOzM46Pj2k0Gty6deu1Yucuqz19mRnFbwIdgyY3aHJDJTBoI6bOAx4vfp2EEyzTxtICVkVIuSiYE2KaJoHRp7R1ClOV0woSavgsSBUpWgfQKX/BJikVHpIlMKdGixoNYLKeA5xRoAmY5C0qfUIyOWMha3RabaxEMA0ShJAYrkCbaeSA64K3JlmFDm68FoPogvqoYtgGLxAsBxfv1wsgSiVmVLDK1U+ksS5EHlhwOlZxf0UUYlk1Ws0Avy2ZPcrQ7YThKkHKGE2URHkNy7LoNA2iQscR0JZwfKKIWa8Fw/U4ZcuH6fojdk0I16JZTah2cb+A1RKWTsXTqRKb3Dqos7AVg3Q0mOzoIRzzItXEEvKZub+WIRmuFbigWsGjSrWCV+uOcCgkZSUQUlAXkpomyCSILCOcz/GFoNZuU2iCAAeCgEZZcJ4nyDRDzEIKwNF1HtaUoMZcR575QuN0TfQE0JQqVm7Ohd2RIaUyp95stARXaiznS4wo4mqzTm7bfCwMfk5cLvmDd0MA9y3gPb5K7AngHt9oXKYIBC4IZVmWPHr0iMlkwtHREVeuXHntH2BN08jz/IsX/AK8ixbwq0Ig8KoOg4cL7Mlv4Yeu/ThWN2EkPsVGoywr0jSliixG+SlEYNOAWoFt2WAsCESDJTEZC1xaxOQEW/uXDLAoyXjKFd7jHDDxKZkJk5ZecDey8bMQQ9i0WldplCb5TEA3IfAqZK4TnevQADcAZiCPVJ3Rmqn34TRBRtCkwm5rnN7fiRQzoW9UzJ6qkT0hFHms25CFkjiOWS5XNDo+dS+g5wsGT0HTXLqHLlUsKfIcq5YQrnKSJAGZ40mbcOViHliI9U9vtXPeN0y2Mthg3f71gUYJJ0PVTraNirNJSFkU1OsNKtdi02lt2BeJJY4Gox3+0DBVRU19hpJUK9loLixgyUWpUJcSocttroYUYAmNk0qppefzBR1N46DdpNJ0FlJJgrqaYKJpeFYNajXKsiTIcs7yDG21gvmCVNNwbYsTx8G0LDRNUy8rYCPlcYUgkCrReigvEkMcCbN5yDzLCJpNEtflptT4o8JBewvkaN8C3uPbjj0B3OMbjbchAimKgu9///skScKtW7dot9tvtK63YQT9VWM3j3l33xzxQyQsmOgPGHuPmHsDnKpDnuWkWUK51AjlAmFIbDPDtnqUdoSj5cSwngg0yIiRWKRkzGkQUwdiVD3PRCuWjMOKRtGgfcNiXug4bsV0adJcGkivJFrpLM80tDqYNcjGqMpWAfYIpKbujyeCxamkvaxoBBqzudrXmi45v1vSamtkS4GUEOWgnUpSe85qldE7cMDz6diCaqlas1JCmK1JsmtR2BYtE9rkjMKM8SpHyAWfngl03cB1TM5XNrphghTPiDxEBe0VjBfgtNS6y6okXY2p0Gg0m7Q9c+sJyHMJIXVLtXkBGlJSlNBlnderQ1Zo+KzJnSZJpUaOJJMS34DJzmfeE4JhJcmylPl8Qc008Ft1RoJtv/kAtZ+aQmMuK0rgUDcYujp1V1UH8zzDTzNGeY49m1FJyC0Tz3YYOTaGroMQFFISC1ii2s5NwKwkw3nIPM8JggDHcbgqNP4zw8V9S5Wx11XovglehwDuK4B7XDb2BHCPbzQuuwVcVRWr1QrTNPn444/xvDdXFX5VRtBvCxshTFmWLzS+dgi4wm/hiO8y4C5PtH+J5iyxHAszqLNMBcdFQZZmyDgiEi5HRorlGCTOEltvk1KtY+AkDUq+T8AVWVCKlCTWyMIJhnEF03wftGOVP2JXxBJaxxb61ZIlSgTRTQBHzdLVFor8VEswO2svvyW4DkzOASr8Gng+RIMKWYFhrecDG+CVFZ8+WSD6Mb7foH/FpRAwuQvOkXr/jeZFfFujrlI+5BJMx0Q3TZpN6DYrjlc5WZpiihXTWYIQgnZgMI8cupaJm+mcLS/av9MSyrJgFob4PjQaTQzdwLVhsYmLs2EiwZZQL6FKICjU3KHtCQZrYYkG6I7YJpA0DMlA7MwJGhWjosITSijiiopcSPQ4JlwtcW2bw4bPYoeLtBAMkMj1iJ+QgiNNo0BVMBdUCODAcphYNg2gKkvKLMNIEsLVCnuxINd1pGNjWDYLy0RoGpUQxJXkfB6yyHMOgoCO4+ILwX9sONTeIinazwDu8W3HngDu8Y3GZbaAh8Mhy+USXdf5zne+84UehF+EyzaC/ipbwPP5nPv372+FMLb98pkrgcYBH9HjDud8n3v8JjORgQNHtFn4GUVRIhKXHm5a3wAAn/1JREFUp3mGHoK/HGFpksiz8U0faZq4oqDCoSh7ROld0mWG4ddoaIGSk4ZNWrWC1FS2v2lo4F4tkIDvgzmHpKfm5+yRyvk1PQgKSWYIpUBuCpIz9RmtVmAYkvm6lJauBSKeXXDvV5cIPcV2W9i2jakr8hfUYbFu2zo1mMdgCjASGI5U91LsaIZKXcO2bGzLptMOkFFBlmWYxZL8OOLTlclBVxAbHrZl0W0YDLKceRjimGA0O+iaIgubuDhPgptDkMI8hcCD0XqbhITZziHYsS9awVJKhMa2FbwxjJaoOcBkbTY9jhKWywjX8bhdr6mqIRAj0ZHMeVah29U0TtbefQAOGm1diZs3gg9L0/Bcj7nrUpOSPMshTamShNUqQgMKx8G2LGZZyqooqNfrVLaNIQR/3HAILlHx+yK8CxuYfQt4j68SewK4xzcamyrb5+XkfhGqquLJkycMh0Mcx0HTtC9N/na37TLWA1/dCWE0GvHo0SN83+f27dsYxqv9bGjoHPFvsSDnAb8BqCYvgGHoHPg1dDz0hs4i7eEmj4mjiKw4YWIFBEZG6vR5sqjw0oqjuk3k1zCSitiomDxtcv3WjIlWoWtK/CEmihw5PhQDRQAroByCdwhaCeaiYuTp67aqgB36YlkXfy+WAkHB+HhIUZi8/2GThWbj2TD8FMoS3AYsYrV8VELLgHQEg0iRP8eGWaz+tgyYru1lEDArBF3bROQmqVYjc0p8LaPQYlYrFU8nqopZqaHrOjeuNBij3l/LAb0AL4ZlAif2JrgPSp3tXGDHhdHmDUlY7rzdtnlhLQPQMSU7WhjamuTxasUqinBdl1v1GufI7bpdBBJBXYAulH+gI+XW50+9psQ3BGc797Uk2AhWysgQIQSBZWHYFqvApyhLsixDJAnpfI4BBLpOluccCY3/wq1Tf8vkD9TvghDirYtANgb0z2PfAt7jbWNPAPf4RmPTOnlZnNIXIc9zPv30U5bLJe+99x5xHLNYLL74ia+Ay54BfNcVQCklJycnnJ6e0u12uXHjxhudDO/wXSacEzIhY4VLg5gCQQaYFFoJbp3KvcORnBFnOYvMYpmuWKQzjFJjXr3Hd7Q5y0pS13MelwaaEKSTGrQK6rWKvNSZPzAwr0iEK4gkeBFkJmiailfLdcinIGoSxxdMZsokWjkJScJ19a/eFEyWJVr5lGjh0Ww2sQODZaTsSM7XLdW1rzOOA04MwzPoH0K0frxeh2RNEBsBDDPVfj70IF3BbAmNGsQ56JpOM3BJbZeuVxFFEfNSGQSWZcnxeERp2BxoFlpiMSrUz3fPh+G6FWzrqmW86ek+QwbtC5NpKaXKM17f9jXJYMe3r6VJHi4XxHFMzfM49L1nkjp0CbomWErJat367WmCUEjaQmAgWFUljq5U3NvnAYWA6fq2iaApBRqSyVrwYRgGvqYzSVIiQ8fzPMqypL1Y8ZOjYz7VNOr1Oo1Gg0ajcSkXa88jjmNOTk6+1AjIq+B1frf2BHCPy8aeAO7xjcauRcrrEsAoirh37x5VVfHhhx8SBAFPnz691GzhyyKAmqYxGAwoy5Jms/nKXoRviqqqePDgAdPplKtXr3J4ePjGJyANnR/ix/h/8k8oKXCRxCj7F50GJRAgOcfiSPQx7HMOdI+neUhQFKS1gPnM4Xw0ZuqNqVcpkdanZwvmUwuzUWK7FaOFTr7Q6CaSwoRlCc6pMoHWMxAzycoSpAuNbpmRNW3SlaDZFowGklZLMl0zE00vqPIJaVTguh10XScpoeeAXHv1WTaEMVgatIGT8wvF8AbxzsdfadDTIZ4rEjRbE0PbASL1d91XtjBJmqJVCzTXoxsEWFWJUSQMznMepEuMQIJhYVsWiWsiMREI6u5Fi9czlC3Mxtw61xRx8yuoaVDk4EooJZgmmOXFhi9XU5w4ot+o4zkuhpBUEiIkEklT0xjvVKhaGkxQ7eOJlEhZ0dN0FlVFW6ghw6gqMHWN3csrDVgKdTwgoQ44ZcXZdMoKSbPZxDRNrguN/7x9CJ2IMAwJw5CHDx8CUKvVtmTQ87wvTZTiOOb73/8+pmly586dL7WuL0JVVS+9qNq3gPd429gTwD2+0dj8eJZl+VqVgOl0yoMHD7Btm48++mg703aZopLNDOCXaU9v8P777zMajTg+Pubp06c4jkOz2aTZbFKr1S61OpDnOffu3SOKIm7fvk2r1friJ30BatT5mO/xb/hVMhaY+ORU1NGZUmJSAgZzHGq5TTQeIE2L6+0GA6tG2woYp9+hYRxTpQXRImK2XDKb+lyrKtIDm1wa+DWBNReMO6q4VYzBvg25AdpMEnUEhiYQsxKjtm6abuxP1vwnTTPG0xlFknH9+iGTiY7lgKXD+T1oXFXL1duwAsQYFg11n23DNFKrrXkwT8DVoWHA5BTyQr1OtH4tKWGeXeynQpNEUcQqirjad9G8OkEuEFJjkJsEdbjilgzzjDTLSOMVD0YgNB3LsrAsE2nYCCHwLUgraJZgVRDOVaUzFCBqgtmOYfRIKMNqicRaTTnJS4Kgy8pyqImK0+LiO3GkSXIkXQSlAClLlrJC7hyDfU1nuFYIp1KiS6ihU1aCrmCtOK5AE8SbJwlBVlachDMSJAeNBm3Toic0/kPDwRMCfB/f97l69Sp5nm/J4NnZGScnJ5imuSWD9Xr9tS8Kd8nfhx9++Faqi7t4HQK4rwDucdnYE8A9vtHYbQG/Cnbbmq1Wi5s3bz5zkrhMW5nd2b0v++PdarVotVqUZcl8Pmc2mzEajTg7O8MwjC0ZrNfrX2pmKY5j7t69S1VVL1T6fhlc5RZDThhwTIDOhAqTHNAoSQCfMF3y6dTifb1SlU59nRxh5KSZz/XyCqvWmEOrjeGWrFaCxaMVM2fMfJJhFjqzmQ1NB8fVMAswlzCXUIWCoF+htTVkpmH6inSGC4GmVcxmkjhOmM9DjmoVmlNHW6dL1FsQPlFVv/k6ts2yYP5E+ffN1zN/jTYM1nN+dRfcFEZDqHcV+QNoN2G47h23Apit7zcMydPJiiiO6bgODc1neC5IJDR3nIhcR8fRXRzHpVOrOE1ysixDKxOOpwmaWNCTGnPPocBhLHR6AUSV2kZv3SbeHJKuBctKkb/lPKSscur1BrZt42vymUg3X8AAyebrZkiJbWhoUlBHzQKaSM6qC1/BbYLI2itnKVUSiIGGVQlqAlZUiKribDYjl+qzrwyDQAj+I8PBecH3xzRNut0u3W6XqqpYLpdbQjgajRQJ9n2azSaNRuMLq+ZRFPHJJ59gmiYfffTRK8+6fhmUZfnK39c9AdzjsrEngHt8o7FbAfwilGXJgwcPmM1mL21rXoaoZIPd2b3LGiTXdX1LBqWULJdLZrPZlhBq6/mozUnvdSoYu0rf3aroZUEguMYPM6CkYITAJmeFxCcjR0YJs9US3eriNm5S6BkZKZIauabKZGHsUnccMAuGnkU9cOg4PpKIqlZR5gnDMCf4jRmaZSFyB+epTeKb2KYgyEvyukYxNCBUpDPPBUeHFednM0xzxLVrJlmmqp7LpcrJ1cqSPDPoHMI4Bs+SDB9BnguafYjWcRWxBN8AO4XJANIcEM9W+dKdfWLaQKTIF8UMI0q4ZtaxRY3hTC1jmTBbP19KnvELlLqGZdlYls2h65PFBWfjjELGnMcREGHoOrmwwLTVfJ2t5iMBLKHi4qSsCOdzgjKibHWwLAspJaZRsS1ESYmubUcKkVLSMlBZvqhc5TaCQQWaEDQQmEgMIRmsU0YAbEBbW/2wVgt7UjKdTvCrikanzUrXeX/t8/ci8vc8Nsd9vV7n+vXrJEmyJYNPnz7lyZMn2La9rQ4GQfDMd3JD/izL4sMPP3wn5A/2LeA9vlrsCeAe32i8akxakiTcu3ePLMu4c+cOzWbzc9d3GQTwbUe4CSEIgoAgCLh+/TpxHG/J4GY+alMB+aK5wY3SNwgCbt269dZOgB18TgmwsWiTsmJJIHXOoyVlmGAHAd1awD3N5D05IREZPpKlKHFFRWYJxk87tK8tqIRFvSbJVxrRQ4+jWxWlE1DMS2qzmKyRMhqsyD9Zod006JgmyUDDOPCYzXVIU+p+RJqWPPh0zmIRUavVODpqMRxCrWawWml0uysWCxW3pllgZJK6XXK2UvsoXlfwap4ifqNzsHuQrklbpwXj5GKZML3w3QtTsERFOZsSpSnSbJMKh3qD7Vxg04fB2talWVMRcQC6BtNcGSWLFSwSwao0sRyTXqMGeUWWZbgyZhipeDpdE2SlCZaDZZo0PI3zqiIMQyhytG4H3bQA6JrymTm/vs4zCt++LhjvVAdrwFwqolcBk6riQFcKYE1AUwgsKZFCMKyqbQnSrSoG4YRCE5itNgvD4AOh8Z/qDvYbfgcdx8FxHA4ODrZV8zAMmU6nDAaDLWFsNBpYlsWnn36KbdvvlPzB588u71vAe7xt7AngHt9ovEoLeFPZMgyD73znO7iu+9Jld0nbl63avWv7Ftd1cV2Xo6Mj8jzfksHPmxuUUnJ8fMzZ2dmXUvq+KjQE12hwnzEFLQKZEc8mREVOO+gx930MUVBisyoO0I2n1EXJSmi0zJxTqSFii8bMhRo4bsVwqSFzgTXRGDrKYiYIfKyaT9GuqJoZNS0i1iMGxzmWDJmHAd1Gha7DYDAly9J1woTNfK6m4TzPwjQLomhFHNeQEpaJpK6VlFLtI89X3n9NB1zg7Fy9T2kCm6rdzu6s1WAVq7nApg2rqOTR0zmGKLDaHZx11XW2UybMd55v2UCuCmd9HaIFzCKo2WoeUb2gqhJqQsOxHTp1h6qQ5HlOjZjzvKRM5mhIkkRHywrqecVRt01eWhQ5ZCWktoYmVVSbrcHMkngCjLVBtJSSrqZRikrN8wnl8bfBoS627eMKWEhJTQjmUmIIQV0IRFFwNhuT6xrNZhNd17ktNP5Tw31j8vc8nq+ax3FMGIbMZjMePXoEXFxMJUly6TO1n4eyLF+ZcO4J4B6XjT0B3OMbjc9rAUspGQwGPHnyhHq9/kqVrcus2r3tCuDnwTRNer0evV7vpXODjUaDJElYrVZcu3aNg4ODd3KS2RDAqEo5nXq08iF+4wDXNZkDMSngsdQEjfQA01kCJo6RIzObli95MnDwrmVUdkWFsljJZwIOIQiUACQ9UySoc9XBzR2y65LpLMMs5+RFQRQvefo4IQgyNM3FNC3abYvJRBF2IQySZEQQ1IgiCJrgGRXnxxVeR1141JpKVTt8DGlPvT+vBuOVqvJ5LowjpXZtOiAyqMWwXIHZKnl0FlJWFe+/32RWKPLXDmCyriqaxrPt37iCHrAKIffWXWyg5qqoO4CGd2ESvUkSEUJgWRa+Z+GUYCY5aRzxcJShtk6QjpdIW7WTD2oGE3lxLNQsyVAKMqm+V7opmJQCFRis0TMqprIiWCeIWEKykhUqGkQgJASaYLa+GCqARZ4zns0oNZPDeoO6pnOI4I8Zb175+yIIIfA8D8/zqNfrW8GH67qMRiPOz8+3342NkORtVQQXiwWr1Yp+v//Cx/cVwD3eNvYEcI9vNDYmqs+TrKqqePToEePxmIODA65du/ZKP6DfFgK4ixfNDU4mE0aj0bbVvVwutye+t618vE6DslRtxyyv0W/dwbdLYnJsIKWiScVMyxnEXb5rJqBDsZ4D1N2Kcm4QnJlMr+ZIJHZNMJlDdwWyBqslRE/Afg9MH9JjEIGgqCzc1KXVtimSFCFShPBI0xVJEqNpLlHkoesGZbkgSQo8TxGzwCs5OS7x64JVoqqEIoXBiaDVg+maqPlNNROoa9CqKSHILFR+g+ehWsZzch6czAFoNptEOz/FmoliSEAzULYwpoCOAbOh8hLUNUh2OofJxZ9Y1sXzWy6MUXYvQQHzMSQlFIVAFgm6blCv1wm0nEFRkCUJURRTxRWZ42JZFnXbYrRDBtuGip3b3tYlI6lEH6FUldBSSAop0NHxpRKPJFKiozijnhdMZzNKQ6fRaJBqGj4a/6FpvzXyt4vVasUnn3yC67p88MEHGIax/W5sZgfH4zGgxigajcZ2jOIyiNhiseDu3bvUajWuXLnymce/6t+MPX4wsCeAe3yjsfHI2/3BzLKM+/fvE0UR77//Pp1O55XXd5mk7esQ4fY8xNpoNwxDDMPg+vXrpGn6RnODbwotLqnCJdKsuNppcGLa3CRFEFIHhoBPyUzodOyce8tDasGEWJTYoiJeZ/QWkU4Ql2BLCgSFhOoMeG8d/2ZDp4DMUpm4wRw0XaBrEpEfs4p9Wq0Wuq5z5UqD09MlUSSQco5tR3zyiY1tWxRFl3o9I0kqhNCo1TWWE8mVTsnJiYqh010gV3FzSQ49GxZzOBuoxBCE2gaAoshZVmOEMGk0m/TbOuM1edQ1mK7bv1KqH+huAZMZVC1I1ss1fRivSZ5jQlioiqOUsNg53OwKWjFMIhWPl1TK5idazKh8g0ZDtV091ySoVNXJkRnjMqfIM5IkRTdylo6LZVs4pk2mXxAgA0ksdqrvUmLrMF/fLAFbCI6lBCkQUlArc1aTMZ5poDcaSE3jhqbxn1v2Kwk+viyWyyV3797FcRw+/PDD7RjJ7kzttWvXSNN0SwZPT085Pj7GsqxnqoNvMi6xef1arcadO3c+sw4plRXQZpt0Xb+UmeQ99ngeewK4xzceu9Yty+WS+/fvA/Dxxx9Tq9Vea12vayvzRdt1Weu6LGzmIS3L4s6dO1ul7+vMDV7G6x90PLSehqUrdfCYgAYxJgXKICQHLCwz5yQJaIddyuaItpFzWmnYppo3i3/TJLhZMU4BoQiYHisyFLShnELUV2pcewCmlzAZhmhFztFRgEVKOMsIQ4Nr1wIgYzhM8f0Oq1WObQtms5A8PyPLrmBZNlHqcdAqKUu17boBk3XF76AJx2dK39A/gsGaCXXbMFpBnmfE0YzStmg2G2iajtyp5DV9NS9nSMgzODtR7WwpYZ7v7EidbZUvqEGyqRh6igD2KkgWcLZS6xNAoUGeZITzOT0fymZLxR5qMFkfokIIAs8mrdRxYcmCucwgzcgWS5xywsixsS1L5Rm7OuN17K8QcGCIZ2xjWgKGOwrgrMjIZlNiRylypRDc1jT+E9NWPn9vGRvytan8fZ5PoG3b9Pt9+v0+VVWxWCy2s4PD4RAhxDOJJK+iml8ul3zyySd4nsedO3c+8/ob8rf5Pdv4iL7LucQ9fnCwJ4B7fOOh6zpVVTEej3n48CGe53H79m0sy3rtdb2NFvDXxc5hOBzy+PFjgiDg9u3bnzn5vMrc4JfxG9x8PkEQ8Duu3uS/1T8hIUHgsKKg4Co9jgGDdD0HGBspEp9FYeHPGmhBDrlN3a8YT3XkSlAfapSWIkJFAquHoPuge7AaqRZs4UC5iIimM6Rw0IWPLUZYbgtmGUFQsVzOiaIcISBJbHzfpttt0estiOMW5+eSNJ0zGc55MtPpHNYoCpfukUEqBVUIkaOIkKYpccYGcQFpmjKfzzk40CitplKi2iAKJejIMijmEK6f1++CXP/dDmC6JnmauJgLBMjXvMBEtXnjBQwL6ASwWB96pg7nq5T5Yo5hGAS9BvO1OqVpXaSHWCgBSK8CSjB0Ha1ySTQXLEmlZRRZThRm2NWMU8vEskwsy+bINYhMaGk6mpBooqTUQFAhEWRZhj6bsHJUFU0IQVMI/phl47+DbN9d8vVF5O95aJq2JXrP28w8efKEx48fby+WGo0Gvu9/hrCtVqvPJZ8bgUpRFM/cX5blG0dd7rHH52FPAPf4xkMIwXw+5/z8nE6nw3vvvffGStZv4wzgrtK31+tx48aNL6wmXKbfoJSS09NTTk5Otp+P1FSdr6CiAcyQFBikNLHIyTZzgELQMUpmUjA8rXFHW6pqklNRodNswHwkcI/A8WGyUlW/bg1SAxYZmI8Bd8l0NceTFrnTxqhWWKaBqEK63ZLhcLE1Rm63XdbjX1hWzmiU0et1SFOTdrfk9CTB8zLOBymySrHcksWkRqNtslzYCE3Q7cFg7Q3YbUO4jNCSGVcCm5rdIkk1khSsLgwHarlm/YL8Pe8daFiwicxoBRftX8tQ1c1+DqMJjBuQrR8TBrCuGtoi5iRcYlkWB+06YSW2EXEp0C5By8HQ4Gy9DZoA4Qo2Dd6eKxjiENgOEklD5JymyoR6vlhQRTmZZWPZFqZp0nc1ZqUEqWPkGdZ0jO5atOtNVoAjBP+F5dB6B+RvM3P3JuTveQghtor7w8NDiqLY2sxsLpZ0Xd9+P+r1Onme88knn3ym7bzBhvzlef6Z16vVanvyt8dbwZ4A7vGNRlEUZFlGWZZcv36dfr//pVol37YZwLIsefjwIdPp9I2Vvl/Gb7CqKh4/fsxoNOLKlSscHR1tX/8KdR4zw6Nkho5NwSe0uS1nZCIlWN8fWCmTwqDlCe4/9WkeFCwNlUlreYLZDA6nULRVrm2jCeYCxjUQmmS1nJOmCzS3gUxqdOsF0jFIEpv5/Jxez2TdxARASnW71ZKcnc2REsLQwLIqNCpsy6F/GHA+0mnVlzx5AlmeYVcLZhMTy7LQayZt14ZcZ3S6YjiJcJyAK4c+w7l6Hd+D4Xz7soqwref/Og22c4FCwGTHFkYYQAFtTQkujs8UN6x7MC8unrOpGCZJTFpOsW2fIAioe4K4gkYJXgHnEVTrCmCwE/zScdQ8ptonEIv1bgICXRAKi1rNolar0RIFJ3mKyDKiVURTpnwaGVi2jSYELBdUNaW8DRHoEv5j0+LKW7Qc2mBXcPGituuXhWEYtNtt2u02UkpWq9W2OvjgwYPtcqZpcvXq1RfO/H0e+XuXvoR7/GBhf2Tt8Y1FHMfcu3ePqqqo1WocHBx86XV+myqAm0zfOI4vLdMXXuw3GIbhZ+YG6/U6p6enLJdLbt68SbfbfWY912nwmBnKMM+lIEPiMCl7eNoAoak5QGlmQA3LlRBpuGcm0bUM34F0TYzKBdBQ/MSuw/wUWk7FRJ+zkhF+1CCpBZgOmFVC5TiMzwVBYDMcpvR6DqNRgmXpTKcV/b6K8KgqnUbDZj6HdnvKZNIEICs0eu0CgU0QWAhdQq2gJjLqzpKzByln1Yp2t2ISaWtTYp/R/IJ8Wy6wrhLWa6p6uXm02uEonTqMMkXCagboCTghTDJo7HykjgvzTcRcTSl14zgiXi0wm4r8GQiMDIKlMqG2Gxfkr2bCvLzYhkJnG/vRsmC2M8lg67Bc39aRrAyBazi4roMpKzKZYKUpaZIiqhJ7HemXpCmWZfEHHJsP30FV622Tv+exiZ/b5BXP53Pu3buHEIKyLLdRc7uJJHme78nfHl8J9kfXHt9IzGYzPv30UyzLIgiCS5uze5mtzJvieYXyu8Im01dKyUcfffTaYphXxYvmBsMwZDgccnZ2BrD1UnveXPsaDQBSUgQuKQUeFaFeMJ3d4FrjCWiSpZZjiYqloSGR5EIQPDXQ2xWDRMfQoTKgPAbPgaKARVqx+vUpRTvFqrfInzjUOyW2oVPGBqaVrT3hfJbLlNEop9WysW2NOF5xfp7g+yqA13FcbHtOWWpUFZimQBMV42FGvalMxbuHgqQyqeUmhl+jWZXMF3OiXDGyJEmYL2OS0sO2LXodk/FCbNvOpgNiTQZrLkzWQhbPUL56PQGLpUoSOV8LS1wLZsmO+ndHjKtbktUsIlpFXO24yFpAqxQkERwv2bZ/Vztfm5qt1NMArgGTnbxgzWBLBn0dRpWqPlpAXYeoElvi6OiCpWZjFpJxEdNxdFaOR5ZlLBcLPk5Tjsqck3XF2HXdtyJw2JCvd0X+nkccx9uEkY8++ghN0z6TV5ymKb/8y7/MT/3UT/HTP/3TW5N6z/P25G+Pt479EbbHNwpSSs7Ozjg+PqbZbPL+++/z+PFjkiT54ie/InZVxZexrvF4jKappIM3Eaa8LsIw3JLjDz744J28JlzMDTqOo6LFUB53y+WSe/fufWZusGE6+NgsSddzgNCk5ERoOAYch9fx6zNWOnTtnJPEJrAgzCA/0blqKEZSr8M8hPwUetdhlJeEYUhQZhz6LUapi+NDQEFu64RnDnp0ihA+s9nF7GKalqxWEVlW0Wx6rN8Cul5ycrKg31cVzFZbMh6lSiEcKiZlajB8KvDrMF2omUnfjynNFn7DxnNSwjjDlCtkPGc2BtNycRybWs1GFjo9B2SlfPzkCqIENAeO14RPSkUUNwh8iNdzgQ3/ov0rpeTpdEkUJXQ9l45Z43wkGEjoNy7UxIENy+pi3btk0LdVrjGAp0FVQL9S5No0YFkq78EciGxBsaZ/dR1GhUaSxCyXKX03IK/5tDSwLeiIkv+gWBLPZpyfn3NycrKtiDWbTYIguBSitiF/vu+/0GrlbSNJkm21bzdebpNXfO3atW2LWErJL/3SL/En/sSf4Cd+4if4uZ/7OX7hF37hnW7vHj+Y2BPAPb4x2J1nOzo64sqVK1ufrMussl1m1e7GjRuMRqOtUtDzPJrN5pYoXXblYzgc8ujRI+r1+guVvm8buzYzu+QzSRKm0+ln5gbrhzDzS3xDzfsp+xcT28k4nwV4ky5ua4ppZpDYeJ5kmQnqPpw91fD6Su2bT6HegHyYM6nmuLWSVqfD6qkJrqTWEaRzgd4uyDKDhmvQvrJgOvWp1WqkaURZZmSZ+twtS1l6NBqSwWCGEGI9B1hS5JKy1Gh3DJKR4PAw4/iRqSLT6hVPn4bkRcHB1RaB61KkUOBiSRep12k1c45HOVmYUhRLav6cTDrYlkW7ZTFaGds+rOPCcqH+bgUXEXFSwmJHLOo4qv0rkYgipEpjrup1ylWNcy7avDujhHiuInIAdXvHP1BCIqFXQpEqBfFgkz8sUKW/Ndr2xZwggKFDEsUsl0scx6HW8lgCkVT//keBxRWzA50OVVVtK2IbcdGutcqbXjDN53Pu3r1LEARfGfn7/ve/j67rfPjhhy8USKVpihCCW7du8Vf+yl9BSsknn3zCP/tn/4xf+7Vf21u+7PFOsCeAe3wjkKYp9+7dI03Tz8yzXWbFbrO+yyKAm+Hwoii2J7qzszNOTk6wbXtLBr+sz5eUkqdPn3J+fv7KSt/Lxq7Ny/Pk03Ecjo6OtnODm30hjjOmvSmJiFm5bSqrBNNlaWRoQjKVOtn9DtffC5FI0rUJtFOD+RKaM5irTi2anfHgQUirXtF5v8PsxKCK4LBTUVSC5YmBpy+wnAa27TMeDyiKR7RaTZIkI03VMSSEYDKBbjdD0yAMdRoNh/kcms2Q2Uy1rrPcoNdNKQuBQCBEyYPjGVQV33k/YDhzSELo9mC6bu+6jmCRXYgn2s2C43GOSFMWyxVoIXGpfPZaDYvRwmTDBg2bi4SPAGbrSp6UisiZSORsSpZECKND5rh0GzBafzXsdazc5rCIdz4714ZFDm3AKeF0DIv1uv0dYUjb/TxhCDxdRaxWK1zX5WbTY8RFWfHHbcGH5sUxuakI1+v1rbXKZp708ePHPH78GNd1txXjV/mOhGHIvXv3vjLyl6bplvx99NFHLyR/SZKQpukz9wkh+OEf/mF+9Ed/9F1t6h577AngHl9/LBYL7t+/j6ZpfPzxx3ie98zjlz1n9zbm9gzDoNPp0FlXPjYee+PxeJs/+qYee2VZ8uDBA2az2aUooV8Xu235V7HhMU2TbrdLt9vlWlnwm+ljKB6QJDFRHCPLgkXNJ8BkJgPqhuDhp0167YqRKbF0SbomRUUGTgpLUs7CKWg2TVnHsHRKCa0eVAtJ4kqSRMMyJUE9ZbVyiGM4OKhxfDzGcXT6fYvJJKfZ9NC0hLOzkCBQAb+W5dDthlSVQVUJbFtgGgWD85Rm06esSjRnjIvEFm2ywkQIRbZWO/P9fgCjdUvXc2EWG7iOgeu4tJsVp2GOmaYkScJSn7PILJXhW7MYLizEer+aa1sYW1M+ftmq4vTJnDwraB11cda94sqAjY9Lo6YsYwA8S7WMhQCjAi0BdwmTHHqNLad7tjIIZBrbWcC2BdP1ghJJGi9ZRQme5xF4LgtxQf4aAv6A+/nHpOM4HB4ePmOtMpvNGAwGnJ6ebqMKN9+R56vbG/K3qX5/VeRP07TPrfw9T/5ACave1ajGHntssCeAe3ztcXZ2huu63Lp164U/ql/nFvDL1r8hexvbiE17dOOxtznRNRqNzx0Gz7KMe/fukSQJd+7codlsvrXtfhGklDx69OiFNi+vAkc3OHAPWKFzm4RhXqAnGg/ynCg6Y7zKMCudOHVhYNE8EJg1GIZqFi0BZr8eUwZTMsOj065jGhrluVq/GcDyqYanV9DRcXWHqpyTph2CwGY6TTEMjSQpSZIYwxBUVcRolNJq1bZzgIaRc3q6otNRhLDZlJyfJ3iexmhcMg9ndPoVWtaj1tEJ10KLbl9ZvUgJ/Q7kpTJoRoJdU559VbXmVJpG07cpXBvLkoRxjqNlZFlKmS1IE52abeI6NuXKoZYZLGMoWhUPTkKKouDm9TpLocifob/YMBrAd0CW4KeQpxfef1LCfGcW0LUuxCWeCbMdYYhYC0MkElYLztOYnufRcWu4miSt1PsUAv5dX+Bpr35cPG+tstsqHo/HW7Xt5juSJAn379//ysmfEIKPPvrohWQuXRP757Enf3t8VdgTwD2+9rh169Y28/dF2BC2y8rLfJfK3V3biGvXrm3bYNPplAcPHmw9+DaEcfdEEUUR9+7de+tK35ehLEvu37/PYrF4oc3Lq+KHMPnrMuAOJqa5xLVM2gTIeol/7LBIMhajJY+qguqBzeHHBmXp0Ql0Hh9H5LMlfdPBaDZouoJwCskD6N2AVQVRItDTilpbYzUyWc4r+n2VOLxcpvT7AYOBYnrttslgoJqjluWu74OzM2XYN5vp1Gr52n5Gw7YlsyczDg8SsvwKuq7j1GERKiNlBPR8lQu8ipSwA6DXg5M1SZVSmUWPRxf7xGsIjMLC0Cxavk+O8rucRRmFM+fxSYxhGHiuwb3jjLKsaDQaWJ617e02/Yv2r7lDBuuAWEEcqkX7LbaG0U0Xwh1hyGKHDPqWmuMD8ARQQK+QTCZzknSF7Teh8pgswPAE+bpKe8WEf/uLU9Jeipdl9M5mM54+fcqTJ0+Ai+i2dz36kGUZn3zyCQAffvjha5E/x3H25G+Prwx7ArjH1x5fJGTYze+9DNHDV2XdspswcHR0pE74azK4EZHUajWazSaGYWzjp96l0neD5yuPjUbjjdf1HUxMNE5kjVpUI6sNsURFZujc7JicxT59vWIQZuirlJN/EyNbEbKSRCuDowMLmTVpC4HuQjaCZgeMBWQOuDVwdY08LFgsFGmSMsI01bEyHmtYloHvC87P43WUm2A8hn4/Q0oNKQXNpsdyWaHrIbNZkzRNOTkpOOiXtFp9RiMd04LxAgJX/TtRTjj0Dtamz4DrqOzgDXrti7bw5vZwcXG7UYfhwsDzDALfA6NOYGakSYouFhSliSYEZZFwNpOYlqWsjHbav01PVRrLJSQZhMaOFczOoW7bO89xLsggUpHFbg5lCqYJ56WqzKVJSr3TxHPUaEbHhR0uy88EF1XDy8BuRu9kMtkq3suy5O7du+i6vvXZ+6IK+pdFlmV8//vf316EvSgPOMuyl5K/V8kP3mOPt4U9AdzjG49NZbAsy0shgLquv9CY9V3DsqztiW5XRHJycoKUEk3TCIKALMswTfOdVT52PQZfNJP5urCF4GMMfp0cWfmk0xsc+hFDq8CyM4htnJqGm7n0rrucDyvs5YiBL0FAVMUsRiVHCx1x3UFKG6epMX0C9RxEF+KhYDmR9Hsp4LJaFkTRCUdHLmUpME2X2WzBJhGk3fYRImYwWGLbXUBiWTZBMMUwbJIkJcuWtFoueR6QJGo0odkHIeH8KZSH6v3pOoTrqpyUYHsQr9Rtz7kQiIDKM94lf80ABvMLAtVpwGChYZom0WpFXJkEvk9RFNhmxGieIgQ4jklq2DQdi0DqlHMYr1+z34JkPYYW7CiBpbwwdwawTJAFtCW4BZwOVEVQSqjVJcvFgiRJea9bI1qTPykh1dgOEfYN+G07quHLxKZK3mq1eP/99xFCEEXRVkgymUwAnmkVX6byfhPvVlUVH3/88UvJXxzHn7l/T/72+DpgTwD3+MbjshM3vqoK4OdhMxMVRRFSym3+7mQyYTAYYJrmtk0cBMFbm4F6mc3Ll8VvweLXyXGdlHFYYzxt4FQZeS9CF5LQUHLTzKiYzxcEacH1Xgtx02Exy3GmGWkZkd+PSaROqVsUCwfXsvA9neFK4HkCTcRU1IgiaLdrzOcxSbKkLDf2Lxq+b1CWMdNpRqfjMx5LpARNi5lOE2xbZ7FIuXbdIok96g2D5ULDMCqypSCcCVo9ZdIM0OnBIATPhnYDMgn9QD2mWaoyJ4GyAt2GuICiUqQvry7In6nDOIKiLAhnIc2ggFp3e9ETNHxkXGIVCVoZMT2OeZwYeK6O8G0sy0Y3dFJ5QYBcB5ZrZXHduRB8GJVKHKktYJqB0VTkDsC3K07DBWmaEdQD7JpDtKka2hDukMj/QbBuhV8yptMpn3766dYLdHO812o1arUaV69eJcuyZy6anj59im3b2/la3/ff+HuS5znf//73Kcvycyt/LyJ/tm3vyd8eXwvsCeAe33jstoAvA19HAvi80ncTe7cZkN9k8w6Hw62IpNVqbVM4LgMbmxff97l9+/altta+g4GGIDRyLK1iioYTWqRTi6NGxYklCOySh6dzqArsTpf4sUXvDiwMi6vXLFZzn/JxwVEv4um8xKhCRsuKJ/cdagcWTt0kiR0MM0TXNYTwWK2WdLs+o3UPNs8rTNNgMNgoNR2krDg6EpyeLkiSmNFI5+BAxzJ90kTgOhbRStLppJyfq3KX7gK5In2U4ElIZjCoIFsXl/s9OFt7qkgJvS4Mx4oMWgb0WpAW4HmAUAbRyzQjjSY0bJ2jXhuEDlIJPpYrgb4yyCufetPHlSW6meG7EWeriNUqwrMly9LFtm1M09yqqUGRwaIAP4Myh7PFxbbNt3OBkjSekeal8uvzbKY7DkzmJlYZ6OjwO75ccfiF2CV/m/ngF8GyrG1KzUZ5H4Yh0+mUwWCw/Z5s/r1IYPYibCp/G/K3m329u8zLyN+Llt9jj68CewK4xzceuy3gy1rf14kAfp7S9/kB+V3D5U8//fRzRSSvite1eXkT1ITGHQw+kTk9N+N45VD3YTATpKFGS88ZJ1MqafD+jRbzpYlvQXYG0gSrBrMZdA8NiOq81wWzVXJ2L0OIFCMbcTKwiUeCen1MtxswHltYlsFoVNDr+QyHSzzPYDRSagmVCyzp9RLK0mK5XGHb0G471Go587naB1Gk0WyukFLtW8eF8Rx66+rX+XoOsH9FtXMB6oFSMm/Q717MCApUNvDx6CKXtxnAySAjnM/RdZvbN+sMxhfjDq0GzNct3mZd5fxqmo7tuDgNl44jyfOMwIk5WamZNMesmFeqFdkxTVjoxPO1MKTJM/ODYaWOgzAM0Z2cer2JbdnULUjWXxVLQFVCv4A8h9/dWhtHXyI2M3/tdnvb9n0V7Crvb9y4QRzH21bxxpi8Vqttq4Mvi6crioJPPvmEPM8/l/xFUfSZ+y3L2lf+9vhaYU8A9/jG49tcAYyiiLt37wJ84bzdrojkypUrW7XkdDrdGutuRCSbJJIvgpSSx48fMxwOn0lfeRv4LZh8Qk5mZ8iVzdIUSCTDPGf6GyHdA6FIrKbDErw6nJ/CYQfiNa81PBiOoLkA/ZqOabkcfexSLD18M2YYF2iazvHxUxzHRAiHNM0Yjy1qNXNtKaRKdM1mjSxbMJkkzOcGSZJydHQDyHDdGouFwPc1HCdhOMy3n02jC14Cw2Nw1ibVlg2j9ayf0JSf86alWvdhtDP357swW12QP8uA2TIlnM8xDJNOp84ivSDgTR8mO8sbJheqXl9F5wkhsCwbK7Bp25KyLPCtmHiVEg8iBhXkNUctY1osiotTg22DzCvC+RybFL3RwjItZQQNdCuQKegaDNa8RwA/euHVfil4U/L3PFQGtIfneVy5cmVrTB6G4dakfTNS0Wg0tr6cz5O/TW7vLj6P/L2N5J899vgy2BPAPb7x+LbOAG6qeI7jcOfOndeu3u2qJTcikul0yunpKcfHxziOs62KvChloSxLPv30U+bz+ZeyeXlV/BAm/w0QiYquXTJODVwt5Wm4oB0IyqzDtUow0gEJ2UbJOgfRUi3ScG1ubPuwfAC2JZGWIDw2se0F773vs5h5FIXEtufk+YIsW6HrykLINE3qdQtd11mtFiyXObpeEccVjUYdx5GMRjmmqSo5QVByelrRaBjM5xqeVzIfacSxoHsAo3VVrtGBeawqe5YFywSC9fZrQiludV39E7q6f5mq+UBdjxkOlliWRb1ep9UQW8IoJUjtgvy59lrssb7Dstnawni2qgy2TIGFSZaZJCXYXkndTzhZFqxWK6Q1Z16YWLYig/PSIAyVz+B715rMdIuGBC+H89GFVUwzuPgsP/ah+Wod1VfCeDzmwYMHdDodbt68ealEateYfBNPt6kObkYqfN8nSRKKouDjjz9+IfkrimJP/vb4RmFPAPf4xuPb2AIeDAY8fvyYRqPBrVu3vrS6+WVJJKPRiLOzs8+ISDaWGpdh8/KqCITGTQweyALHSYnDnDyZYVk1+p2A0VCjCMFrg6hDuFBm0LGA7D40rqkZulYHwhmkIVw9qjiPNTRDEAQuebIgSQK63TpSWsznQ65fb3ByMmK5zLfq74MDn8VCJ01TPK9Jq1Xn+nWL09Nk3RoWdDoRYWgCGo5jkyQVtVrKcKgqgeX6I+s0IF1CEgIu5LoidoAiiTut4FYDppOL243aitEk5KjlUq8H6Kb4jCp4ssM5Av9CYSwETFNwdfA0cHSQC1UR9NeCD2V5o4Ndo6GDrCoaXsrxsiBNEkS+5EmpgwDf8bArg/oSwgTs5kXGsKPDtLjYrv9++7KOirdL/p7HbjydlHI7UnF2drb9TXj48OG2Vex5HkIIikKR5+dhmuae/O3xtcWeAO7xjYemacr37BIrgFJKqqp654kCUkqePHnCYDCg3+9z/fr1Sz95PJ9Esql4TKfTbcUDVKvsgw8+IAiCL1jj5eG3JS5TmXBWhCRLDavu03IC5pUACUtA3ofG+4rMNBqq5etrIIdKDGF4kI6gcwjhTKPhFIhDg2xuM5stODoKKUuf4TCm0wmYTJbcuHHIaDSnqiS6XjKd5mRZhOua5Dk4zpj5vIuUJo2GT5apx6OohZRqDtC2l4h1CofnK4La8wEB81CRo6B5Qfg6HTUHuPl0+20YzC72hWOuuP8kwnECTOkTJ4IyAa1QlT7HVmrdnruTzpFDz1SzeLal/AXjHBIBpgfZJtXDheWaONY9WKyVwELTqCyXIICy8tCKEXlR0cwhnYb8m1GEZZqYpkXoW2xOIXUPkvW6HQ2+d0nXC++S/D0P1Ta3CMMQIQQffvjhtl28G0/n+z6PHj3ie9/73jMXaqZpvnSWcI89vg7YE8A9vhXQNO1SK4Cg5u9eJYD+srBpuYZhyI0bN+j3+2/9NZ8XkYzHYx49egSolvonn3xCEAS0Wi2azeYrKyXfFN8zdf73/7Jimeq833EI/YBWpohM01ezcb4G5UDFpwlHkR+vCcOn0LEgbgECpAvLU4FeVrhBwXCmzJSrKtmeqIvCQcoFo1FOp1NnPA6p1WrbSL4rV7qMRnMWi4zhcIamadi2TpJE9PtdFgtoNHQ0LWY2K0lTtX8aLWACowEYdfXeav4F+TNNZffiWqoFHHgXZtASicGKp4MI1/Wo+TUMTaAbsBGWrmLlITiYXuy7XvPitkC1wTeK404DRjtexLOdOFrXg8X6tmfBIgdZleTTKbaoqBs90AyOrhZUq5wsS5HZnKdDHcPQsSyLhW0hNeVF+duaYF3CddNoNOLhw4d0u13ee++9d06kdqvgH3zwAb7vA9DpdLYXTtPplH/9r/81P//zP0+32+VnfuZn+Omf/ml+8id/knq9vid/e3ytsSeAe3wrcJltW9d10XWd3/zN38S27a1o4m2SwV2l7wcffPBOWq7PYzKZ8OjRo63NS1mWW3uZR48e8ejRo9cWkbwOyrLk+OEDPsgF369dAeHSOpdUax5sesAKvAAGZ3ClC+N13uwKNQ+nG1A+hnYLJiE4HpiWwdnjjE5HYhge02lBWZ7S7/ucnxfbKuBkktHr2ZyeDmg0lOAkikosy+Pq1aucnJSUZcp8PmKxyEgSkyxLCQKLycSh3baZzQT1es7ozCDPBd2jC4GH54MrgRKECcOZut91YVRAUYJlSIScs4pXXOv72G6NKBEEAUx20kL6rWcNo1uB8hrcHJ69NgzWHUkpId3x5msHMMkuHlvsXDcFLlhpwfDpBFlA2elhaMba4NnAdQ1c16XbKDmOcrIsQ2YRjycxmqaEJt/pVpSl/6XGFr4O5O/evXtEUcSHH364JX8bbIQkVVXx4z/+43zyySf8i3/xL/in//Sf8qf+1J8C2F5I7bHH1xViNpvJL15sjz0+i6+CpLwMv/7rv06j0eD69euXsr6qqlgsFltLlaIo3prZ8q7S94MPPvjSyRqvi1exeSmKYksG5/M5VVVtRSStVms7C/WmKIqCu3fvEscx/o07/K9GdSTgTEEzJIYPOYL4DJoGhMfQtyBvgoxgNod6DKsFMIVWDQpLWZOMn0iIEjqdlDyvMZlM6PdjomiBYaR4XkCWzZnNQs7PJxiGQb3eoN9vMBplSCnxvB5Q0WpJTk7AsgSzWUCtNmI2s8gynX4/QEqPZhNmM/XdaBxCuITDNpxPFeHy/Yv0DYBmF2ZLVfmLVwsqGaGbdVzXQ0rodyBKlU+fEMowehZfzN/pmqooJvnFbc2BfP0arboyc96g3YTJupIYeDAX0NTBKiDKCx6fqTLlnZt1poWqaPoOLHfKBUHzom3c8yUnaQ5xSiNa8e+3n6Jpgnq9vlXSvo6AaTgc8ujRI3q9Hjdu3Hjn5K+qKu7evctqtXrpCERZlkowI589fRqGIsgnJydcu3btrWzfL/7iL/IP/sE/4NGjR/zar/0a3/3udwE1N/xH/+gf5f79+9i2zV/9q3+V3/k7f+cXri8Mwy9cZo9vJ/YVwD2+FbjMFvBmfRuD2BfNyem6viWD9Xr9jasdX1bp+2XxqjYvhmFslZJlWW7J8ctEJK9DjtM05e7duxRFsa22fCeG31it/fImgiMJgxo0ahBGSkk7L6H8BLp3FAF06mrWrn8Ag2PwUnCvQ1EI+j2D1UKi6xPqdYssk6xWIf2+xenpGa5bMZlM1yrgOs2my3CYIQS02z5SliyXExaLDiBpNhsEgSDP65RlnSzLyHOTKDqjKBzm84J212SxtKkbFpUUW9sXqwasq3m9vvL/k1Iyn89xrBWa3sJxlMr0oHsxF7hKwFGaEyiUP59lgblWFZeVIn2dFgx24uWEAawJoKXDNAZfV7N6joA8VMIQz855MpsjBNTrTTJxcUx7LizXBNO1YF6oNrOUoJeCw9RivLL4sfcCfuj95jOVYwDP87bHx+fNxX0dyN+9e/femPxtLoTeFvkD+Pmf/3n+9J/+058hd3/mz/wZfuzHfoxf+ZVf4Vd/9Vf5+Z//ee7fv/9Ws5D3+GZjf2Ts8a3A21TuPj8nF8fxtjI4Ho+3ysFWq/Va4fPn5+c8efJkG2d1GTnGr4PdmcP33nuPXq/3Ss/bJb+7s1CbJBJd17cqyUaj8bnva1P9FELw8ccfb9vKv6elCGC2HjlcFdCcgtYAorX4YwjNGiwegVe7SKtYVutKWxdO/g0cXi9JC53VqqDRqPC8lNNTpQoejRKkLHj8eEqt5nLtWpvJJKGqDIQoME2B6wpOT8f4vsNi7XmiaYLRaEq320bTNA4PVR6vrneQ0sc0czRtwfhJwhiB3bbRDZtuz2Q81xAoxS9AN6iYTqe0azFO0MEwXbJMJYCczy5EIqYBhgHLdfVutlRpIU8HzxpGJxG0DGUpY1mQpdARSnnsGnA2U5m/iQ5YKnauKAuW0RghLPXZ+TqLnTjsnfFBAlcJProSZAyn84vX/96Byrk9PDzk8PDwmQzr8/PzZzz2nr9Y2Cjf35b46YtQVRX3799nsVi8NvnTdf1LV8FfFT/xEz/xwvv/7t/9uzx48ACAH/mRH+Hg4IB//s//Ob/7d//ut75Ne3wzsSeAe3wroAx83751y66J7NWrV59J3njw4MEzyRutVuuFooldpe/BwQHXrl175ye7PM+fGXB/03b+Ljm+fv36NmFhOp0ymUwQ4qIV+LyIZJMrbNs2H3zwwTOPfbcGHRNGuao4hRm4IbQdRe6qdaHUXCeA9AqQTWW8PJtAtw/jsTJFLuIKzQLPF1hWjeFwzNGRRlF4nJ+PMQzVznZdn/E449o1nzjOse0M09Q5OVHlOsepsViA7wsmk5CyhOXSRAiJbafMZiolZDp1eP99jenUx/cLgnbEybggX6ZYTo5pOKSJQ+XbDM4FYRhi2zl6rUcZ2xBDw4f5AloeWKaq7mn6xdwgQLv+rIrY0FUFcLUReEhlC7NYE0bLgHl60TpuN9ScYFHkzOczDN+k2WygaTqOA4s163MtCHP1OpYAPQVzBsMC+o2L1/ctuPOc+fPz9kMvii3cGC2r1vxXR/42npd37tyhXq9/ZpnPI3/vUiz2IozHY6qqeuYi7ubNmzx+/Pgr26Y9vv7YE8A9vhXYOPW/aziOw9HREUdHR2RZtiU/u8kbrVaLVquFbdtfidL3ecRxzN27d5FSfmG6yOvg+YSFNE0/IyLxfZ9ms4kQgqdPnxIEAbdv3/5MlVAT8Lub8N8MFYlJJhDU4eQRdLowK9c+d2vvuVTA7Dfg6hVIDKjWpKl3BUZPDGxnRrvrcX5iIqWgqjIWiyVJssJ1fW7e7BCGSxoNjeNjNRMlpVy3Ygs0TTCdQrtdYZqC5VIQBDaLBfR6C6bTGgCGYdFs5kiZUZYWhmFieg0ONGjWch6d5YRZim2H3HtgIAQIIam16lQoc2nHVtYteQmztdCj01KtYM+GmqPIXpQpMiyE+r8RwHhHGNJvXUTPATTqzwpH5hnkRU44C+k2ofJbawukZ2cUfQ+sEswMVhGc6Bekb2e0kN/aV5/by7DrsXf9+nWSJNkSwSxTa1qtVpydndFsNt+Zf96G/IVh+FLPy6qqvrbkb4Pnt+H5bd1jj+exJ4B7fCug6/r2JPJVwbKsZ5I3NuTn+PiYp0+f4jgORVFQluVnMn3fFRaLBffu3cM0TT744IO3mk1q2zYHBwccHBxs/dM2+0NKuW2bJUnywvbZv9OEfzCCfF0YLExAgrkAO1BGxNOp8tMbjxQxPDtTCRVGB3xfskgFUgrqgcN4sMQ0HWq1Go8eHRNFJ9y82ca2bXQdOh2D8/MLM99eL2A0UhcVrVaArhcMBjNctwfINdlNyDKJOvQERQHL5ZxmU1WQmmtDZD0FGZh4nonrevjtnOg4pJKSZqvgfLhC12Mcx8K2LNLCQiC2IpBNpS9OFeErSmXxYpsqXcRxINzxIa45z5K/mvNs3Fy3CafzTGX7GgaNbp0wU63Yhrc2izagJtczgmvPwH4L4nVl0DaVuGTzqf3bB69+bGxiC+fzOVmW0ev18DyPMAy3STW2bW9HCXzffyuenFJKHjx4QBiG3L59+6Xkb7lcfq3JX6fTAdQM5aYK+OjRI27cuPFVbtYeX3PsCeAe3wp8HdI7dvG8aGIwGHBycrI9iTx58oTFYvHW7WV2MZlMePDgwdbm5V0Oh5umSafT2VZ9NrOSw+HwGRFJq9XanuxrOvyYD/+PhWpDTjM1AxfmoD0Ga3Nucy7+l4DbguED6PcksSGpXdFYzmySZMXR0YLT04zlMuPWrevM5zGdTs5otEJKaLU8NE0ShgnzeYXranieQIiMwSCh1fKZTtVnqOuSk5MZ/b46+XY6BqvVkrKE2Uzt25onOT1RHn4bG5dGs+DhyRzHqrhxtQmGQd3PSOIEyZLFVLWuLdshqNtMFg5K+QGOpVq4G3+/zf9P1nOAmqb8Em1LVQuzQglEHBdWa2GIlLBIUsJQZQsf9OrMU42aAZ4Otq5I3yoFXGW+vTk8k52vWOBBur7mcgz4uPN6x8TZ2RlPnz59Zgyi1+ttFfibavpgMNjOlW7+XcaxuyF/s9mMW7duvfCC7GWVP03Tvjbkb4M/9If+EH/5L/9l/vyf//P86q/+KmdnZ6+kAt7jBxd7ArjHtwKXrQK+TCwWC05PT3Fdl9u3b2/nBsfjMefn5y8kP5eJV7F5eduoqopHjx4xHo+5du0ah4eH223bnOyfF5G0Wi3+LVnn1ysdY90GbjZgNFY5s+kZ6ALGqbJBmUTQbMJwCkEDRiOBLQr8Ixgda9TrJsfHI6SMODhok+c6QsSEoaBWs1itMmZrh+R+32U6TYnjEs/zGa1dlDXNBSTttsb5+QwQLBY6ui7R9ZwkUXOAk4mg10s4P7MVsewptW9ZFiznQ2rCQK86lJXObALg0G47jBcNBAVxmgIRJ58WwIq6b9Btm3ieyyrVt63fdkMZSIvtflb/n44u9n2noaLxXEMJSUwt5vR8Rse0COp1AlPjbAYrCakO0lLtc1BClNVulvBOxa/c4T4/1APjNQ6pDfk7PDzk6tWrzxCpXQX+jRs3iKJoWz2eTFROXhAE2+rgm/hRbsjfZDLh9u3btFqtzyyzIX/PX1h+1eTvF37hF/j7f//vc3Z2xu/9vb8X3/e5d+8ef+kv/SX+yB/5I3zwwQdYlsXf/Jt/c68A3uNzsfcB3OON8XXyATw9PeX8/Jwf/uEf/qo3ZQspJYPB4KVK3+cVtFmWXZq9zO5rvIrNy9tEWZZbdeX7779Pu/3ioFgp5TMK6ziOKaTGf3X3Ju0PLeZLl76pMzqFngXDYzisQeaBIZU5dNuDyTG0azB5DL1WyfC0pN2uGI/HjMdLjo5KOh2P09OKfj9nOJxRq+kkyZKqkrRaJpO1UZ6uCwzDI00rLMsgz+v0eiVgMBhAo+EShjVarTnLZYM8h263sSYNOZOJj5QQHECaZmTLMbPIpt5o0O3pTNftVNMEzYJ0XdELarDKIc8L0jSjzBM0IyNKdQzTIPAsjnomQrdYxKo1LOXaIHp2sU99VyWObAhdWSQsl3PQber1Op4jSCRbi5r+joG0ECj/xfV1Vb8Fg832Gqolvzl5/Ce/FX770asdD59H/r4IWZZtyeB8Pl/PaTpbxbnv+1+4PiklDx8+ZDwec+vWrRcej19E/t71BdTbxN4H8AcX+8uDPb4V+Lq1gHeJ18uUvs8raKMo2ra9voy9zAZvavNymXhebfwideUGzyus0zRlOp1y/Tjj3m9kVN6ClWWjxw7DykLTDRY5LL8PvQNo2xDGSiwyDlUc23ylUVUZ4/EZWWbT6bRotUqGwzH9vkqskBKWy5KDg4CqihmN4u02dToBg4FiZa2WT54nDIcRhqH6nZZl0+2uKEuNPAcQlKUkDJd0Ous5wA6UZcrg0ZzWgUGj0UQIjXKH2zeaKiZOStXmzaWq5um6Qa1m0Kx7jOclupWRZhlFvuA372sITceybJp1i4OeSZoJLFO1hg0dpLggf2maoFVThOERBPX1/oZ4udn/EO5ExLUbMN4Zq413vl4ND5YF1CUkK/ih7qsdD5v5vje9GLEsi16vR6/XoyxL5vM5YRhu/SgNw3imVfz8BZSUcluJftnFiJTyB4b87fGDjT0B3ONbgQ0BlFJ+5XM5m4rXfD5/ZeIlhKBWq1Gr1T7XXuZVM3kvy+blyyBJEu7evUtVVW+kNrZtm8PDQ37f9+C//n+XtK2ERxRYImQ4NugCg4XHlbbJcGDS9wTlOXh9MDxFfpaDAk2bsAgdDg4q6nWP6TShqgSgM5sdc+2ay3g8ZzDI0TRot5VRcZZJkqSk29XRNNWqjqKSbrfBaKTImmkWnJws6ffVZ9xu6ywWS6SE6dRQYhdi7v3mCtfTEXoTDY1OX7Wsu4GyeZnMwaggL6BWVx5/NUc95njKqy/wdFaaS6/tEiV1hJmRpRlpmpCs5vyrgYVtW1iWTbth0qxrpIWaGZyEMVo1JZY+QRAghKBRg9HyoqXbacIoutj/1Q7P8R1YZMoGpqGDHkEyU/6AP3JTCUK+CLvk7+rVq691LLwIuq5vFfYb0jabzQjDkPF4/Iwl0yaN5PHjx4xGI27evLkVTuziZeRv8/3ck789vk3YE8A9vhXYXOlXVfXODZV3kWUZd+/eJcuyL0W8XmYv87ydysZeZhdvy+bldbBarbh79y6GYfDxxx9/KbXxDx2qz7cUNe7oUN4I4EmOVkbE45in6YJsbJLGFnZlcXZmUUeQlSmynFBi4/sBQRCRZQuKwqDTqTMchgSBxdOnSw4OHJIkp6pgMokxTYFl6azWA3D9foMoUorgPDeRsuLwUOPkRLXPZjMd05QYRk6ei+0cYKMx5t69AstyuHk7YDTXsAw1u6gnypalcQDJuvLW76pZQVCzd7Xas/N89ZqatesEAt+xCSObTsPnbFTguilpmhHHKRoFJyMPy7KUgKEI8QOXa+2ASgqiRCWEiJ1M4HhnhNa1VWJIzVRpIa6hSF+4hIUFyc411o/c/OLP8OTkhJOTE65cucKVK1fe5DD4XAgh8H0f3/e5du3aMxZET5484fHjxxiGQVEUHB0dfS75e36WeLPuPfnb49uGPQHc41uBzY9zWZZfGQFcrVbcu3dvm2rhuu6lrHfXXmZjpzKdTrf2Mq7rbisheZ5z//79d2Lz8jKEYcj9+/dxXZc7d+58YbXyixDYcKcDd0cgQ/ANDcuyQdgcHUpWUU7HiTk9ydCtBeHQQgYwfijp93WCThOtkuS5y2y24MqVnNVKCQd03Qdizs8zDg7qnJ+HGIaG65rM54odeZ61tYOp1RyWy4puN6Kq1OfbbHqEoaTRCJlOm+v1WnjemMFgjGEc4vsBSSmo15TKdnCu3lu3D+P1zJ1jw2Qnwq3VuIiBA9UaznKY79i99NpQVoKrPZNlbLKModMsOB5myCpluViiaxWOrRNGFvH6+3HQFpxP1CyfbSoPwSiDJqplXNMgDZUwJNFgvpMtXPdV2xfAs+C/9wV87m2Tvxdh14KoKIqtybOmaZyenjIajbYikk0aycvI377yt8e3FXsCuMe3Apsf6K9qDnA6nfLgwYNLIz0vg2maz9jLbAbiz87OODk5ARRhvH79+jvPFQaVSPDw4UPq9Tq3bt26NDL+Ww8VAfQacPopHPVhWEGzI8hzC6Nm0U4kRV7QbC0Yj0v8ZslgoJOmx/SvNBkPbep1kyTJMIw5vm8xm2V0OnUmkwWDgSKBZZkzHiu1gxIZeESRmgOs122qasZ8XlGWikSapku7vaCq9O0c4GKx4OnTU65c6ZOmAY2WwDFheArWmgNJeWGkXJZQb0CUqNk9Q4dKV/Y3cbaOgdMvYuBAtWyHO/P7UsKVLpTS4Frf4GQoKYqcZt1gERvkScRqFdGqSz499rBtCzDRNcEgVN6CAJ6j4uI2aDdhuEM6Fztzgt+7rrbrRZBScnJywunpKVevXuXo6BVVIpcIKSWnp6fM53Nu3LhBr9d7Jo1kNBohhOD+/ftIKfnJn/zJ7UXThvx9lR2FPfZ4m9gTwD2+Fdj8SE8mE3q93lsjYM9DSsn5+TlPnz6l1Wpx8+bNd3bC0HWddrtNq9Xa2rxs0kbu3r371u1ldrFrNdPtdnnvvfcudRbztx7B/+nXVd6vAJI56HMwr6r0iVUGvT48fpghRYrj+vS6LlWW4Tg5937zDM+ziGPBcJjQasU0mxa2DXluY5pLWi2N6XROnlfU6zaOY6BpBlVV0e2qi4uzswlSQq/XYjhUpEvXU4bDiF6vi5RgWREnJ3Mcx0NKpY6ouRWnJ5ryA1yTtt6Byjju+cq/7/x8szOh0YFwN8WjoRS3nqPmBHXtWWNnKeGgBWcTkEiiVUQcr3jvyMbz67QKwWwp0URKlGakSUIcxwgE/Y5BXLpYponQNFwXop1K5GJHCNIKlB/jBj/y/os/r68L+Ts+Pub8/Jzr169vU3eejy4cDof8vb/39/jlX/5lyrLkp37qp/h9v+/38ft//+/fk789vtXY28Ds8cb4OtnA7AovgGcEE2+rErar9H0TS4vL3oaNshJ4qb1Mq9XaZq9e5jZsso3fptXM/+yfwskcWrlKAGnp6v+uA5ORpCxD8sWKRjugGficDARtU7CaSZJkQqOxZDKxCMMTGo2MxWLB4aFGFOX0+zUWi4g8v6ggB4FFHFcUhfqJ7HbrjEYZUkp8v0cUVRwd6RwflwghMM0DomiOECFp2uLq1TazWYNeb8lsViPPBb0jVbVzbPBrMJwAEuo9WKwFGL3+s5W9fvfZVnAjgOUKat5aMVyqCuFgtiF/K6Io4vqhS1IG2+fZpoqDQ0CSSSbzgpodczoptq3PTlMnFS62baFpOt0m7Iii6bYuhCKBA//LP6jI6y42xOvs7OwZz8d3jePjY05PT1+6DVJKoijaRkjmec6/+lf/il/5lV/hn/yTf8LP/uzP8hf+wl9415v9zrG3gfnBxZ4A7vHG+DoRwA3yPN8KJhaLBVLKz+TxXgZ2ve02raV3jVexedmc5Db7JEmSrdHuRh35Zcxiq6riwYMHTKfTt55t/Pf+f/Dffh+6FoyeQt2B8BwMXRKdz5nPM947dImmPrYl8ZwK3dMYPhB0uxGLRYLrrtA0h8FgSlGckKYJQswpConvKzPm5bLA80yqSiNNFTFqtTwmE0X0ms0aReEgxALLqjMeS5rNGicnkqp6guPcwDA8er0WUFIUGdNpAymhfgA6oFVKBQzQ6V3MAXouJFIpdwG6bSUK2dDpmqu8Andjr/ttZQbd8CWr1ZzROKbf80kKf7uMJiAIlIhjg0YN0BR5LMuccJlSyITRQiKEsqBp90wK4aDrOrYpyDQwNfAM+LHb8Ad++7Of0deF/G3mDl+V/O3C930lOnrH88Q3b97EcZytsfWf/bN/lj/8h//wW3/dPQH8wcWeAO7xxvg6EsBdFEWxFUyEYYiUKr91Uxl8U5FGmqbcu3ePLMu4ffv253rbvS3s2ry8LMbqRYjjeEsGoyh6bXuZXRRFwb1791itVty6deuFaQqXiYdT+Iv/d6WglUPlk9cyKh4+XGATIZIWtu3QMCCcQq9ZMRlJ2i1FmKbTOZ3OCk0rGQ6h1ysZDMZ4XslweEqaput2r4lhWESRwLIsNE3DdT2iqMQ0oderc3a2wLJ00rRBVUlMU2e5HNFotElTRcS73Qbj8Zxut8lwaOE3VLLG4BS6V2A0U63behcWMRhCtYWLQnnyabqyYpGo+bxyHQGX7LRge20YhACS5WJJHCe8d9UDrUbgqXbxfKWEG9Md8udYar3pzrr6nXUVUZYImeEYK2arYpvb3GxaRKWHxETTBP+L/wB6Oz8BUkqePn26bbkeHLxGOPAlYmM387LW88ZwPM/zzzxWq9W+svSMmzdv8g//4T/ku9/97jt93T0B/MHFfgZwj28tDMOg0+nQ6XS2gonpdLo9QTiOs60Muq77Sm3Ljb2JpmmXqvR9HSRJwieffIKUko8++oharfbKz3VdF9d1t/Yymzbxq9jL7GLX7ubDDz8kCIKXLntZeK8JTRdmMXQ7MBhUnIcz8qzErbe5dmQzOIFy3fFfJYKykJRlQVlq+L5OVbmMRuccHWksly66rhHHcOvWDc7OxjSbOsPhjOl0gZQVtZpBq+WvSZ7AdW2Oj0Plo9eoc3YmCcOQWk0ihEu93mY4hFZLZzpVSRVhqPwAA6/i9FRH12G6nt/r9KAS0HUBAadnF+83aF20hTUBtUARwKav/AENY2MZI1kuFsRxyvvXPFaZOh7iVFUO23VF9npNZS2TFS8WlJxPFfEUQsexXQrpYnuSLMtAphyPMooyRNMEv/2OxKgMylKl1XzdyN+VK1e+UeRvjz2+CuwrgHu8Mb7uFcCXoaqqrXp2NptRliW2bW+rYC/L+XxXSt/Pw2Kx4N69e5du87JrL7OJ2PI8b0sGHcfZ7pONzyDABx988E5J8P/tX8I/ewy5LHn4GyFlWXG7FxCGNkKAX8FiDv06DI6h26gYnVcEwZRazef0NMbzJth2wWo1o9cLyLIUISSmmXB8PNu+z7Is8X2Nk5PFulUoaTabFIWBZdk4To/ZbEqtlpBlLSzLxjQP0PWKeh0GA41m01nPAS4IwxpZptE7VMTNdxWJm60TQBoHMF8Tvn5/U9lTj/W6Kilkg3ZdmUdLKamKGVUZcdBvMotr7ArhN8ttoGtKyCE0ZfcSrtQ8YrITFyelSgHZrRh2WzBcKpV1lqX8sX/nKU17iRCCer1OVVXbcYi3OQbwedhEzL3MaPrrTv5u3rxJo6FiBH/H7/gd/MW/+BffyWjJvgL4g4s9AdzjjfGuCWCWZeR5jmmamKZ5KUKDzYlrUwkrigLLsp5RzwLPKH3ff//9r8QXbDKZ8ODBA2q1Gnfu3HlrJ6zdamkYhlRVtSXItm3z5MkTbNvehs6/S2Q5/E//dwWT5RhHq2i3utiGSbkEWQAliBwyCdkcNCFZjCoODmIWiyVFYdFqFQwGY1qthDBcUa9LZjPFvOp1C02TzGYJzaZLGGZIqY6Tet3k+DgiyzIaDQ8wiKIlBwfXSBKbdrvGculj21OyrE2WQb/fRMqSNE2Zz1WLvHGg2thFoqLrhFjPAa4rcp4HSXUxB9jrPCsKafiwWEFZSeaLOWmacfumzyJx0YR63DRAA86mF8/ThDKSnu0QO9tUZtOGodrMYaRI43BXgVyDWXYxh/hD78Gf/FlViZ7NZpyfn29JVa1W22ZZ7140vG2cn5/z5MmTl4qxpJQkSaKqmc/h60D+AB4/fsyNGzfI85xf+qVf4td+7df4x//4H7/1190TwB9c7AngHm+Md00AV6vVM0PbhmFcKhmUUsV9bWbk8jzHMAwMwyBJkpdm+r5t7FrNtNttbt68+c4IaFVVzOdzZrMZk8mEqqoQQtDpdGi329tYsXeFKIr4P/7jE/6v/7LNtYM68cxASugEMDpRZKrnw/AYOt2KuBA4ZkW0qEiSc65ckcxmNeJ4gO/DfD7EMDRqtZL5XDEwKSVXrnhUlYqCWy4LDEOnLE1sWyBEyWKxZDRKcF2TOK6haTpXrhygaSW27TKbBUgJ/X6d0WhGr9diMHDwairqbXAC/atKBbxb/atZKsM4L9X9hgFpBbqu1LZCQF7BdC6ZhSFplnPnps882anCCrU/xnMVJ1dzVdtXAyY71jGmAbYDy534t0ZNzRo6Fggd8hyEBdOdZf70H4DbR88q0K9du4ZhGMxmM+bz+faiYUMGfd9/a8fJYDDg8ePHL/1+fh758zzvK6nkfxFOT0/58MMPWSwWX7zwl8SeAP7g4qu/7Nljj1eAlPIzir2iKCiKgjiOt2TQMIw3Jkebdla9Xuf69evM53MePXpEkihT4NFoRFEUb8VK5WXYtVj5KqxmNE2j2WyS5zmj0Qjf93FdlzAMGY1Gb9Ve5nls2t8/+pHNvUmD6VKndwDDMxgv4OCKEljMEjAtGI8FQRBh2g7xQKPd9pjNImx7iOf5jEYLer0Go9GcONap1WxWq5R+3+HsTLXBFQTNps9kEjOflwiRMp1KWq0Wh4c9Tk5SkiRhtZownaa02y2iCHo9m8lkRlXBYqEi2RqNitMTHU2D2frc3u0pstU0VGv2ZMcPMGgpgQiouT3bhWVcEYZzLD3lzp06leZil0odjIBOHcbrc/oqUUbSjZpq93YaqhK4iMCyn00V8Wy1bJZfkMJ+GwbrKqJrww/d+iz521Wgd7vdZy4axuMx5+fnGIaxVZ7X6/VLU9cOh0MeP35Mv9//RpO/1WpFnudbMdff/tt/m+9973tf7Ubt8a3HngDu8Y3Ai+Z2drEhg8C2amea5hsTkizLePLkCWVZ8sEHH2AYxrZNPB6P0XWdRqOxJT5vwy6iLEsePHjAbDZ7qc3L28auoW+v1+PGjRsIIbY2Grv7ZNdeptlsXuo+mU6nfPrpp/i+z507d/h3dZ3/+ldgsIB+D4ZD9XezBbMpas7uscB1LUanKzodGyE8oihCjSzOsW1YrSxAkucqPuzKFX09B3jx/vv9OsNhQlEULJdzdN1Z+0sazGbQatnU6zYnJ5Jm06UsG2haTBRNmUwcWi2P6TTlypWMeajEMu2eqtAFHlQ5TEeq4tfccSzpHzw7B1ivwyismM9DiqLg+lGTQWgjUCKPwFU+f1GqKoVSqqpho3YhOhmHag4w8JSKut9UxDFOAaHI3wadBpzP2O4LTcDP//jLyd8Gm4uGZrO5zdfdzNuOx+PthdZmmTclYaPRiEePHtHv97l+/foLL4zSNP3akz9QLew/+Af/IGVZIqXk1q1b/I2/8Te+6s3a41uOfQt4jzfGu2wBV1VFURTkef5C766XQdf1bZv4Vcngcrnk3r17aJr2GZHDpqIwnU6ZTqfEcfxWiE+e59y7d484jl/L5uUyIaXk0aNHjEYjrl69yuHh4Uurj5dpL/M8RqMRDx8+fGb+sqrgf/434HSyVsgCi4VSx2oxpAl4ErJEUhYL2u2KOPYoigmelzOdntHtQllquK4gSRZMpyuqStJsOhiGYDxe0e/XGQwS8jxnPp/T6fiUpfKJ6/XqCCEYjUIajQ7TaUmzWWO18rGsKWnqM59n1Go2WZaRpglFcQXbtji4bmHoBnkM8zVha7UvUjZqHsQ7c4D9LpxNlHipLEtuXquzSiw2P95SqlnBjVBE06AdqBm/yWJN8Lggf7tzgIau7tM1QMA8Vu3hXWEIwH/2s/C9Dy6OiZs3b9Ltdl/rs9zMDc5mM5ZLtRFvMje4OSZ2L0pe9Fppmn7mftd1v5KYxK8r9i3gH1zsCeAeb4yvSgUspSTP87dCBjdCC8/zXknpu0sGN8SnXq9vic+bDJcnScLdu3e31cfXsXm5LOyaTL/uiT5N0y0Z3Jzkfd/f7pPXUS5vlJ0vOtH/f+/CX/2/KEPomg7lEqoCDA3iSJGgyRPo91NGo4RGY4Ft1zk7m+P7U7IsQ4gVaVpwcGAxGIRcSB3g4MCjLCvyvGA0mqDrFs1mF9vWEEKSpgZhmOH7NouFixDQ73dIkiWapjOb+UgJh4dNzs4m+L7NyYmBJMWvlyxmDlduGCxiF00zaB0JZkto1cD1lUdfXoBhwjwpiVZThCg5OmphmBZZrlq5efEs+QNFKJu+qvxJoO6pGDkh4Gx8sZyhq/t3W8GWqQioYSgCuojhex/Af/T7vhz5ex4b5fnrzg2Ox2MePHjwuZGDaZpuRzd2sSd/n8WeAP7gYk8A93hjfB1sYL4sGTQMY+tjtsmyfVOhxYuIz6YK1mq1XqkK9rZsXl4HRVFw9+5d4jjm9u3bX+pz3iSzbE7yn2cvs4tdX7mXxctJCX/t/wz/r/+Put1tKPGHENB0AB10JGkiWcyXHBzEFEXOaCQ5OKgYDgd0Ogaj0QQhBO22RRguKYqSgwOP8/MleZ4xn89xXZt2u02aqpJYv99gMFDHW6/XZjhU97fbDpNJQq/XYTjUCQJBkgjyvML3+ywWGkdHOY8eVaRpBnZCmgkazYqg6SBzB7dmMcvEtvXq1Quens6RsqLTbuL6xraiJ4ErHSgq9fc8UgKSlv+s4GO38mcaihxWldqHnxGG2M96BF7rw3/5hyVnpw8Zj8e8//77dDqdNz0kXojducGNGv9Fc4OTyYRPP/2UTqfDzZs39+TvErAngD+42BPAPd4YXwcCuIuNUGRDCF8VQgiGwyHT6fTSsmzzPN9WBjdKvk0VrNVqvfBE9K5sXj4PaZpy9+5diqK49Orji+xlHMfZVnw2/otSSh4+VGTji0yFyxL+6t+Gf/2b6nbXh9G5ysudnUGrVVKWoBspeZazXJ5x7ZrBcGgj5ZA8L+h2YTRSn1G9buD7kuPjkCzL1+TP4vCwz3KpjinPs0gSk6oC09Qpyzr1usR1JaenAinBdfuApNVS5s+eZ7Baden3l6Spy3xu0OrAZCkxjRRkwvmwpKoqGgcVaelhWRaHh4L7TxdKKdxo0O8bz/j6dZswnF3ULaWEg3VhLisU4XtR21fXwPdU5a/pq0pglIBuPlsNbNfhT/97ktn47ZG/5/H83GCSJAghcF2XKIpoNpvcvn37tcif4zhfycXUNwF7AviDiz0B3OON8XUjgLt4VTJYFAW/+Iu/yLVr1/iTf/JPbufVNm3iy1DcFkWxrQxuqmCbuaeNt95XZfOyiyiKuHv3LkIIPvzww20m6dvAiyo+pmnSbDaJoojVavXKZCPP4X/7t+A37yu7FLuEaAUtFxUF155hOx5npymtVkiWxQixwvcDzs6mmKYAVjQaGvP5kiwrgIKiiACdTqdLGKrhPCkl7XaL6VQdUwcHDaqqYDCY0273mU5LGg2PKKphmhOE6LBaqYphUQjiOCSKDpSVziHKamZZEVeaIo61jFGUk2UZppGTCUFZCZoNjytXHMahvp37e97kGZRh82jnPstUs4Ab0+csvyB/4fOE0FWVxJoDWalay//lvydJ5g+YTCbvhPy9CEmScHp6ynh80bt+0dxglmXEcfyZ5+/J3+djTwB/cLEngHu8Mb7OBHAXu2SwKIqtvcdyueSP//E/zv379/lbf+tv8fHHHz/zPE3T3goZ3E3cqKoKwzAoiuJzZ5reNubzOffv398aPL9LheTGf3EymTAej5FSomnatlr6KvYyaQr/678Gnz5RZseLocq/nZ3BwUHCYrFASgvfLxmNhnQ6OfP5kkajIstKDEOSZQnLZUocxyyXKxzH5ubNA+bzlFrNxDR1LMskzwVSVlSVZLUSJElBEHjM55s5wBZJskRKwWLRBBRRHA6ndDoNhkMf3YBOr2JwKulfEQwm6v21jxSpa7g5s8WIKNUoSxNNT4krHdO0qHkm/Z6N0A0MXbV8V5FSAO8aRj9v/CxRZNC11ezgfKXa5RvyF+5U/mwT/if/Q4nIFPm7desW7Xb78j7018BsNuP+/fs0m01u3LjxwrnBsiwBPtMW3pO/L8aeAP7gYk8A93hjfFMI4C42ZHC1WvG7ftfvotVq8df/+l//QpWtEGJLBnVdvxSSlmUZ9+7d24pHpJTblmir1cLzvHdCBjet5yAIuH379luxtPki5HnOJ598QpqmXLt2jSzLtu2/jcq61WrRaDReun3LFfxv/gokOZi65OkptFzBai4pinMODiTjsQuMsKyK1WpIrWaQZXPyXM3wuW7OkydDPM/j6tUO0+lFRanRcFgsCqq1NLfbbTAapeu/e4xGFbqu2sjTaUq/32UwMPE8KEtJmla0WodMpwZHRzknJxpCQNDRWKwENV/i1QSzUU64mJAYpnq/mk6tWTCa5mRZSlmkGLaklDaWbWFbNoddnTgVeK4SbqxicJ0L+xf4bOXPtS/Uv6c7whDLhP/xz0uM4qsnf2EYcu/ePRqNBrdu3XrmQmBTRZ5MJvydv/N3+HN/7s/x3e9+l5/7uZ/jZ37mZ/jhH/7hrySr+5uGPQH8wcWeAO7xxvgmEsBd/KN/9I/4Pb/n9wCKgFwY/34+LoMMPm/zUq/Xmc/nW1+9siyxLGtbBXtZPvGXxSZC66tsPadpyieffEJVVXzwwQd4nrd9LI7j7T7ZVVm/zEPuv/vv4P/w10DTJJ1uTqmZjI/FOot3SRDEGEaNwWBGt1swGoU0mwbT6YTVakUcxxwc1Dk8DBgOY/JcVZY8zyTP2RLFRsNlNpMIwboqGNDpCHS95PxczeJ53iFlKel0Sk5PNSxLJ8sO6XYjqspgMjEI6rDIdDxHUnP//+3de3icdZ3//+d9zzkzmckkmcmhaRraJilIaalyWKWIIK5UwS8ih70ALchCf1Bd1/XsspcHtLDLuqiAeGlRFKwsReQggidOallhLdWlNUkPaZrmOMlM5ny67/v3x3TuJk3apmmaQ+f9uK5eF5l7pvPJNGRe8zm839DbV5gh9i/QwVaFqloIBmDgwLKuYRT2NfYP5cwadxXuHCMpBw67A4fDjtVqo8qvkNcLHT0y2cKhjomWfYt7A62WQgs5qwX+4WIDu7abSCTC4sWL8fv9J/Tf/3CK4c/r9bJkyZIJfzZzuRzJZBLDMNi/fz8vvPACzz33HC+88AIXXHABv/jFL2Zh5POLBMDSJQFQTNl8D4CjGYaBpmnmnsETGQaPVual2J+4uG+wuD+uWEZlOtqvFd8w+/r6Zq3FHRzcd6iqKi0tLUdcrptMeRnDgG9+C3b8DYLBHPF4DofdgaEbRCKDVFenUBSDvr4cXq9KIjGIYSjY7Qn27+9jwQI/imIhm82jKIpZE9AwFEKhwuECRYGyMg+JROEUcG1tBZlMlnA4SUVFFZGIRnm5i1TKi8sVQVF8RKMQCPjI5ewkEkPk80EMQyFYr5DJKqRjOmlNY2g4irscHFWVqKqK3Q66FTDAYS8cbomlCwc8Mjnwl+uFMJjNks1k0XUNf4VOKufG7rBjt9mxWhR87kJZHDg4K3jowRCbFf6/y3Wcxp5ZD3/RaJSOjo5Jhb9D2e128xR5S0vLTAwXgI6ODj7ykY8QCoWoqKjghz/8IaeddtqMPf9USQAsXRIAxZSdTAFwtNFhMJ/Po+v60R9EIQyObkk3UaAqFpm2Wq2TKvNiGAbxeNw8UVzsT1xcJi4vLz/mWTtd1+ns7GR4ePiop2xPpGLJm6nsOyyWlymesi6Wl/H7/WRzFfz7f7hQLQa5dBK/3yCRcGGzRVCUDNFoH/X1NrJZO4aRpbOzG6s1Q0ODm2g0Rz6vmc/jcFiw2SzE41kURcHjseP1ukgm8wdmBxVSKRVNMygvdxGNOlAUqKmpIp1OkM3qJJMVB26rZHg4gs9XRihU6AhSU68w2Gfg8ebYvTeG1Wpl8Vu8DMcsVJeD1QGhSOFAhrsM0sbB4tAeF+gKOB2Fmn3ZvAFk2T9YmB3UNL0wE+qzkc4XSqCoqorVUjhAYhiFvYDp7MHw59ALtR9nM/zFYjE6OjrMri/HGv4mW0x6ul144YV8+MMfZu3atWzevJn//M//ZMuWLTM+jmMlAbB0SQAUU3ayBsDRDMNA13VzZnCyYRAwZwaLYbDYzmyqZV6K5TGKS6KZTOaYe/FqmsauXbuIxWKccsops76xfzpK3hQP1kQiEbO8zJ9eq+aPW6qpr1MYiRhUVydQVS8DAxF8viipVApNi5BOx9G0GIpSOCxgtapUVjoYHk5htSpYLCqJxMFWYoGAh4GBg3s2q6t9DA0VrldXVxEKFQ6UlJc7D+wDrGRgwIrDUag9mUxqVFVVMzRko6ZGo6/PQi6XBWuEZNpFIFBORcBCPFw4tJE+kIkNo9A+rjh7Z7EU6vUlR1U8CVYV+vb63AeuZbLkchn6hvLkc3lQwGG3UlVhJ513YlELJ4qrvHDd3x8Mf0uWLJmVzjNwMPy53W6am5sn/Hku7uE91GyGv4GBAVpaWgiFQlitVgzDoK6ujldffZWmpqYZH8+xkABYuqQXsBBHoCgKFosFi8WCw+E4pjA4ugRNJBJhcHBwTDuzqYzF4/Hg8XhoaGgw98eFw+ExvXgPd1gil8vR0dFBOp2mubkZr9d7zGOYDsVODhUVFeM29k+F1WqlqqqKqqoq82CAzxfmjW0xenoNUok8DoeGzZYFLFitHjKZOKqqMTKSpKqqCp/PSjSaJp/XGRhI4fXacbkKS8HFrOH3lzE4mDIDRnW1xwx/DoeNoSGN6urCEvHgYOFwSCplxWo1qKrS6ekBVVUIh634/Wl0XSWbzZNOj4DNRU3AS2UF9O4vPF9gIaQP7P0LBOBAqcLCPkDv2M4fAT/0hwv1AEcSQBIqfXaSGTtLGgFDIzySQtMy9AwkwUhitVpxl9m54p0Kdm0/I9HorIa/eDxuhr/DzfwdLvzZbLZZC38A+/bto76+3vwgoygKjY2NdHV1zfkAKEqXBEAhJml0GHQ6nWP2DB4pDH73u9/liSee4Mc//jFVVVWk02lzqXiqb1iKolBWVnbgtOqCMYcldu/ejaIoY8JgsbuHrussW7ZszEGLmVRs7XaiSt6oqmoeEFl/m8a37s3jLR8hFEqSTndTU2Nh714fhhEjk9FoaKglnc4Tjyv4/WWEw0n8fifRaIpotPBvarGoBAJlGIaCz2cnHs+hqioH6nvjcKhUVbmIRpOEQlkqKqoADbfbQSJhUF4eJZUqhO3KyrIDzxchk/ERjcapXWDH6fSSz2hEYoVfyTY7DI2a7YunwWUHpw1cLsjkCzN9eb2wNzA0MrqJ3YF6gAcC4nAUVNWCz+shl/cQqNKIJzOER7K87617IZlkBMzWhYZhzHiQisfjtLe3my0YJzrpfaTw53K5Zi38FY3vVCOLa2JukwAoxBSNDoOjZwaLNckMw2DDhg088MADPPDAAwQCAeDgzGAqlTKD4PGEQSi0uXK5XNTX15PJZMyZwT179gAHw+uhp2xnyuhDJ7W1tSxYsOCEv2Gf/haVJadkiScqyefKqK9XGR4ewmbrwWJxkclkSSQM8vk0druDkRGVhgYvfX0jaNrBQO/x2AmFkuTzB2/z+z2MjKQwDA2LxU5fXwxdL+wDDIc1FAXcbhc2W4xkUiObLXyvFosNTQujqgZDQ3HsdjtVlV7CQ3nc5QqRWOF+/mDh5G9VeaEsS2+o8Ly6o3AIpLhN0eWEaLwQDl3OwkEPqw36wgdfB0UphMXi8nEibcFqKeOzH3Fi1waIRsHr9RKPx/nb3/5m7jEttmA70SfDE4kEHR0duFwumpub52X4W7hwId3d3WYLO8Mw2LdvH42NjbM6LiGORAKgENNAVVUcDseYZeLPfe5zbN68meeff55TTz11wsfl83ny+fyYMGi1Wo/rTdfhcFBbW0ttbS2hUIi9e/eiKAr5fJ4dO3aY/YkrKipmpDeqYRjs3buXUChEQ0MDtbW1J/w5obDceuZKld+9EMftsaGqHmKxPnw+A7dbweEoIx7PYLXqaNowmYzBzp1WysocBIMeEoksVqtKPJ41A6FhGASDhRIxRTabk2SysNTvcJShKBpWK+TzKSKRDIFABYODoCgGqVSccDiB1argdLppbHQRGtDRdbDaCsFHUQ1Awa1BpB8Uz8HvqXxUpw9FAZsNUtlCIEymIVgJvYOFIFjhAbsVFEuhzl8xI1ktsO7/6di1ncRiMZqbm/H5fOP2mIZCIXNbQUVFBT6fb9rbEyaTSdrb23E6nbS0tEwY/jRNmzD8Wa3WORH+AILBIGeeeSYPP/wwa9eu5fHHH6epqUmWf8WcJodAxJSVwiGQ47F9+3a8Xi+BQGDMzOBkTEcYLIY/r9fL4sWL0XV93MlZt9tt1ho8ER0TdF1nz549hMNhmpqaqK6unvbnOJJYTOeznw3h8+ns3JnBbg9RVVVOJjNIdbWdwcEwuq5RXg49PcPkcoXSLjabjWDQg91up6zMTiqVIxrNUFNTzsDAwfAXDHoZGCjsA3Q6beRybqqqCt93KFT4d6uoCDIyYlBXl2fXrjyxWJyKikX4/WV4PDn6+x0oKqgOG4oBwYBBT1/hscEFMHBgKbfCD+H0wSAXqILBUTN91RUwGB27FFzlh6FoYRbR5y489qoLdRz6TuLxOEuXLp1wL6hhGKTTafPnpViDsby83JwdPN4PD8lkkra2NhwOBy0tLROGy2L4O3Q51Wq1zlih9Mlqa2tj7dq1DA0N4fV6eeihh3jLW94y28M6KjkEUrokAIopkwB4bHRdH9OSbrIsFsuYlnRHYxgGfX197N+//7B77SbqT1wso+L3+6elD7CmaezcWQgas3m44L77BnjppUEqKzX8/hqGh2NUVaUZGooSCFgYHCxMqXm9drLZNOFwnLIyg8HBGIZRCBsOh52FC/0oihVVVchmdfJ5HcOwHyj/o+B2OxkaSpNO5/D7qwiHNZxOG5mMD78/RjKpsm9fmmDQh822AFUdxGarJhZTqQqoxJMW7Goei8NCZKTQJcTuLxRyBiivLJwMdljBboc8hQMhea0wqxdOFHr+cuD2QNXYgyKqCrd8QKeMI4e/iRQ7sxRrMI7uZz26H+9kpVIp2trasNvtJ0X4m88kAJYuCYBiyiQATt2JCoPFvUcDAwPU1dVRX19/1DdKTdPM/sTFMioul8ssLzOVZbbiieNMJsPSpUspLy8/psdPl1gsxu9+t4tHHnHR3OzAanXS1xenvFwhGu1DVRV8PoNIpFBTzmJRqKuzMzAQJ53Ok81myWYzlJVBOJzBYimEQZfLidfrJh4vLP2WlzuIHdi/V17uJBp1HugLXEE+n2b//iGSSQ9OZxlNTXVkMgnyeUgkqgo1AustRMOF9JbMW1FQCNQV9gEW+/fuHyrM7lksYPMUWt5BYXYPFbK5QnFnpwPsjkIgTKQKs36qCh99v0a5upNEInFM4e9QE5XdcTgc5raCo3WtKYY/m81Ga2vrMYU/i8VywrrilCoJgKVLAqCYMgmA08MwDPNgyFTCoNVqxWKxjFluXbRokXno5Fhomma2pBsZGUHTNPPNfbL9iYut3TRNo6WlZdZOHI+MjLBr1y7Kytxs2lRFPK4RjQ6yYIGTvj6oqEgRDsew21UUJYWqGtjtOiMjKUChsrIwq1Uo65Igmy304s1mM7jddhIJ3dz36fdXmmEwEKhicFDDZlPw+azs3TuMzaaQy9XicjkJBu2EQlmCQR8DAy7cbo1czkI2C8FaCwNDFjTNYEGTQjIK0REoqz5Y8y9Yd3BZGMBfAeHowa8D/sJ1hQNLvx54/3kafnsh/DU3N09bIC+W3YlEIkQiEbNrTfEE+qGFytPpNG1tbVitVlpaWiYs/i3hb2ZJACxdEgDFlEkAnH6GYZgzg8UagpOh6zq9vb1mb+Hp6OIw0Zt7sT9xRUUFHo9n3JtxKpWivb0dRVFoaWmZlqXkqSgW3S62Env55TSbNsWoqIiTzaZR1ThebwWDg0N4vVbs9sIp02g0PebvCQTcDA+nqKhwoqoK0Wgaj8dJb2/U7MXr8zkZGSkUIvZ63VgslVRVGWSzOfbtS5FKpWloaCKT8VBXl6O3tzDj5fMFSaWgsjJDX58LwwCP30YiAQvqNPYfuF9VEIYONL0oc0Ny1G/sYvHnIr+30N5t9C/1a96jUV8+/eHvUMWuNcWfl0wmM6Y2pcPhYOfOnVgsFlpbWycMf7qum0vMo0n4O3EkAJYuCYBiyiQAnliTDYPxeJwPf/jDXHzxxaxduxa32z1mmXg63jQNwyAWi5knRHO5HDabbUxLumI5D7vdTnNz84ycMJ5IKBSis7OTyspKmpqaUFWVdFrnM58JUV6uEwqFCAZ1BgbCBIM2+vvDo4o7u4jH06RSOYJBDwMDCfPQhWEYBAJuEokcZWU2LBYVw4BUyiCbTZPJpFAUC5FIHlW14vOVMzCQxecrx+lcRGVlAlV1MDBgwem0kslU4fUOY7H4GB62Uu5TiKes+MtzWOwWBkMWDAN8tRBNFPb1VdcWlnod1sIJYI1C2DMMUFRI5Qrt3YpjvnS1xtJAB8lk8oSGv0MVD5EUf16KbdtUVaWuro6qqqpxPx8S/maHBMDSJQFQTJkEwJkzOgzm83nzTTIcDnPNNdeQz+d55JFHxpVYUVX1hITB4kxPOBwmm82iqiq6rpvlPGYr/PX397Nv374JD7/89KdRXnghhcMRRtPyqOoI2WyeQMDGwMDBEGixKNTWlhGLZRgZSZtt32pqCoGwyOm0AirpdOF0t9frZHhYI5PJADmSSQegUFNTictlIxrN4XbXkU5DIFCOrueIRDIYRg26DsE6lWxaY2REx+Jwks8rVFRBJAM+F5Q5oCdy8JRvub8Q9qCwx8/lKez5s1oLfYLPPl3jtPoOUqkkLS0teDyj6snMoEwmw9/+9jcMw8DpdBKPxwHGHCJxOBwkEolxBdVVVZ1wpllMHwmApUsCoJgyCYCzoxgGI5EIq1evZunSpXz/+9/H5XId8XGKophh0GKxTFsY7O3tpaenxwyBFotlTBeSE11I+NBx1NTU0NDQMO776+9P8e1vD6EoMDAQIRBQGBwsVFguhkCHw0JZmYVIpLAUXFZmw+22YbOp7N8fGxUSVVwuu7nvT1UVXC438XieWCyGy1UoCWOxFDp39Pcn8XgcZDLVOBx2Fi3yMjCQp7raQyjkwWrVcLkUYjGDqoCVoUhhebSuATIJGApBZcPBYs6BQKHkS1GgGgZHvY8vX6pz4RntZDIpmpubZzX8tbW1oSgKra2t2O32CQ+RvPjii+TzeS6//HLzQ4yqqrjd7hn5+SllEgBLlxSCFmKeKQa5QCDAnXfeySWXXGIWnz5S+ynDMA6cas1OWxgcHBykp6eHqqoqGhsbzS4kkUiE4eHho/Ynng6GYdDd3U1/fz/19fXU1dVN+P3U1LhoaEjR1+c4MPZC545YLMXAQJaGBj/RaNwMfwDJZA63205PTxyn04bX60DTdFTVQih0sB5gdbWX/v70gfqKeazWKhYssJPJ5IhE7FRVOSkv99DTo+FwhHjzzThWqw2Hw4Gm5amrS9PbWwjwhlJ4jWoCGj2dhVnbch8MxwqzfzYbRA4+NdVVBw99ACwI6qw+bfbDXzabpb29HWDMrPDo3s35fJ7BwUF0XeeRRx7hX//1Xzn77LO59NJLufrqq2ft9LgQpUBmAMWUyQzg3GIYxpj+xJPtRTqVMGgYBj09PfT29h52xq24B2x0IWGv12seIpmOrhKju4wsXLiQmpqaI95/9+4Yd965nQULysjlCjN3mUwYXc8RjaZRVYWqKieJRIZkMkdVVRlDxRMYFF6rigonw8Mp3G4bZWV27HYLuZxCOBwmnc4QCASIRnU0Tae6uopQSAMUrNZqqqqy5POwfz/kclmy2Qq83hA2m4dEogK3xwFWN8FKDcNQGAwVXqPqhQc7gNTVg2qF+iDUVYOrrHBCOJmGeFLjtIadOCyFZV+3233cr/FUZLNZ2traMAyD1tbWCYuM67o+Ztm3WL/yueee45e//CXnnHMOX/nKV2Z66CVHZgBLlwRAMWUSAOeu0WEwn8+P21t1OIWixge7kEwUBg3DoKuri8HBQRYsWEBtbe2kSsMUw2AikTC7ShTD4EQnQo9mql1G7r57O7t3x3E40iSTGSorHcRiSXK5g6+RxaIQCLjI5zUikTT5vG6Gv3D44PSbx2MnldIZHo6QzWZpbAySShVCm8tlJ50uwzDA73djs1kZGBjB56thZASqqtwoikF3dwhFqSAWy+HzG1gsKsm4g8rqcjJZBy6XQtpe6Opx2cVw1opCLcBD5fN5Ojo6SKfTsxr+crkcbW1taJrGsmXLJgx/xbZzh3bHmUvLvmvXruU3v/mN+XN18cUX8x//8R+zPKrpJwGwdEkAFFMmAXB+MAzDXCLO5XLHFQano9bg6K4SsVhhU5vH4zFrDU7mAImu6+zatYtoNHrMZW/+8pcw993XTnm5hXg8cuBwggWnUyESSWG3qzidKtFoxnwd/H4nLpftQD3AQmhxOq0YhkJ//zD5fB6/34fH4yWVKtRyLNYDVFWoqSmjtzdJWZmDRMKHoijU17vYvz+Gz+ckGvWj6zoVFRr792ewWDMkkg5UVWHxMgurV9v4+3eWYbdPHIzy+Tzt7e1kMpk5E/5aW1snLAN0uPCnKAoej2dOhD8oBMC3ve1trF+/fraHckJJACxdsgdQnDSO5RP7//zP/3DLLbeQTCZZuHAhDz/8MHV1dTM53BmjKAoWiwWLxYLD4Zh0GBxdoBoK5Ti6u7tJJBIsWbJkyrUG7XY7wWCQYDBILpczw2B3dzf79u07an9iTdPo6CiUNlm6dOkxfxBZvryCujoXvb0pgkE/AwPDpNMa6TTU1ZUTjyfN8AeFkiqGYbB/f7RQlsXnwOm0ousGu3f3k8/n8fl81NZWMjiYPvA9WonFdIJBSKXS9PYW/i6Px0MyqVBZmWNgQENRFJzOMqJRqK/P0NvrorzcRnXQT2+vQcOCOJe+fz+Kkub//k81T82O3k85Ovy1trbOWuHtXC5nFgCf7+FPiFIgM4BiyubaDOBkP7EbhkFzczPf//73ueCCC7j77rv53//9XzZt2jRDI507Ru8ZPFIYTKfTfOxjH+N973sf73nPe/D5fObs4HSV6CieDi12ISn2Jx7dkm502DmeFnN//OMgDz20G8OAykqNcDhBMGhncDCGqipUVrrI5TSi0Qxer2PMwRCbTcVut9DdPYimaSxYEKC6upxs1jgQFgsht7c3ga4bBINVDAxoGAa43TWUleUwjAxDQ4VDH253kLKyDIahEwq5MQxwuZ00L1W45WawWg1SqZQZlFOplLmf0uv1EgqFyOVys9p1JZ/P09bWRi6Xo7W1dcIT6UcKf263+4QcEDoea9eu5eWXX8bj8dDY2Mgdd9zBypUrZ3tY005mAEuXzACKkvP666/jcDi44IILALjlllvM2aip7EWbz4ozg06nc8zM4Og36WQyydq1a+nq6uL222+nrKzMvF8qlTKD4PGGwdGnQ0f3J+7r66OnpweHw2G2yjveZc6zz67i17/uIZPJYbOpBIM2BgYKpyw0zWBwMIHNZsHvd2GxqJSXO4hG09jtlgPhb+DAkq0Pp/Ng2AOoqHAxOFiodWe3WwmFCsHa63XhdGYZHByiqqqwdO5227Hbc4RCI/j9hdsq/BaWLlW48YZCTT9QKCsro6ysjPr6enM/5fDwMPv27QOgrKyMeDyO1Wqd8RqMxVB+tPCXTCbnVPhbvXo1O3bsmPDa1q1b+drXvkZdXR2qqvLEE09wySWX0NHRMWunqoWYbjIDKKZsLs4ATuYT++OPP87GjRt59tlnzduCwSCvv/46jY2NMzjiuasYBoeGhrjiiitQFIVNmzZRUVFxxMeN3jM4Xct5uq4zNDTEvn37zFlKh8NhzgxOtUtEV1eU//zP10in8we6fLiIxZJkMnmcTiuqqpBMHuzA4vU6cDgs9PT0E41m8fkq8PlcZLOGeYDEalWx2RzmPsBgsJKBgcK1wp6/BFarimFUo+tQW+smFCqckM7ngxgGvOtdNq66ysqRXr7icms2myUYDJJIJA6UoDHMAst+v/+Et+KbzPJzMfwd2ud6rs78HU5rays/+clPeOtb3zrbQ5lWMgNYumQGUMwb0/mJ/dDAMNmSKaVCVVUcDgeBQIBLL72UW2+9FavVOm4G51D5fN58o5+uMJjJZOjp6cFms7F06VKy2SzhcJihoSH6+/ux2WzmnsFj6RrR2OjlYx9bxT33vE4upxMKpbFYLNTWukgkMsRiWfO+hVIvGp2dvRgGVFVVUlNTWH5OJvPkcoVSLxUVbkKh9IHv30IsZhAIQCqVZGCgsETs95cTCkFZmU4ikSSfNwgEPAwOwhlnWLjqKsukwl8ul2PZsmXmjNvoJfTe3l7279+P0+k0XxuXyzWtHTWKezGnEv6AOR/+uru7aWhoAODVV19laGiIpUuXzvKohJg+MgMopmyuzQAe6nCf2F977TXWrl3Lm2++CUAsFiMQCBCLxUpuCfhY6bo+piXdZFksljEt6Sar2F/YZrON6y9c7E9c3BuXy+WwWq1maZny8vJJPdf27SHuu28r+bxOdbWDcLiwnOv3u1BVSCSyGIbO/v2FriEVFRW43fYxM4QWi0pNTTnptIaqKhgGOJ2OUfsA/QwMFMKz3x8kn9exWJKEw4XZy8rKajweO5/6lB2H4/AhrXjKNp/PH3a5FQrhLBqNEolEiEQiaJqG3W43X5vjba+maRrt7e1HLDljGIW9ixP1sXa73dNSB/JEeve7301/fz8WiwWXy8XXv/513vWud832sKadzACWLgmAYsrmWgA89BP7+9//fnbt2jVunLqu09zczMaNG81DIK+//jo//elPZ2PY89aJDoOxWIydO3fidDppbm4+YmAoHjAo1hrMZrNYLBZzKdTr9R7xubZu7efJJ/9GT0+U0bnI5bICBvF4BEVRUdUynM7CXsdU6mCw8ftdRCJZihPJPp+TkRHtwPeroqrl5HIGDocVl8tHPB6mstLPwIDlwKGTGj79aScVFUcPf5qm0dLSctTWf0W6rptBORKJmEG5+NpMNigXFWf+UqnDdxqZ7+GvlEgALF0SAMWUzbUAeKRP7A888AA9PT1mZ4EtW7awbt06UqkUCxYs4OGHH2bBggWzOfx5bXTJmOkIgyMjI+zatQu3283SpUuPaamwuOxYbEmXTqdRVXVMGJzo7xsZSfPb3+7hpZc6SafzuN12Mpks/f1DKIpKRYUPj8dBWZkNVVWIRjOk03m8XieJhIamHW4fYAUDAzqGYVBf76a/P4WmGbhcQdLpwj7Af/zHahoaDh/Cim3VjlRiZbKvTTEoRyIRMpmM2bv50PIyE9E0jZ07d5JIJGhpaZHwN4HizOp82VYiAbB0SQAUUzbXAuCJ9oUvfIEnnngCu92Ow+Hgzjvv5MILL5zwvoqisHz5cjPUfPvb32b16tUzOdxZUwyDxdnByVJVFZvNRjweZ+/evXi9XpYsWXLch0lSqZQ5M5hKpVBVdUxLukMDTzKZ46WXOvnDHzrZsWM/Fkuhn7Hb7Tjw9+UOfJ8QCJRhsahomkEsliOT0aiuLjf3AaqqgtPpw+MxSCZTaFoZqZRORYWbSMSNw6Fw6601LFt2+Nm86Qp/hyoGtUPLyxTD4KHt+nRdp6Ojg0QiQXNz84QleAzDIJ1Ok81mx10rhfAHEgDF/CEBUExZqQXAX/7yl1xwwQW4XC62bdvGBRdcQG9v74RvyIqiEIvFSr5khGEYZhCcTBj87W9/Szgc5rzzzqOxsRG73Y6qqtN2eKHYnzgSiZgt6SbqTxyPx2lvb2d4WGd42MmOHSGGh9Ok0we/B7fbTj6vk8nkD3yvUF/vJZHIY7GogEFZmZPe3iS6bhAI+BgcLPy6DQSqSaWsfPzjtSxaNL7YdVGxp66u69Ma/g732hTDYCKRADDb9Xm9Xrq6uojH41MKf2VlZSWzv1YCoJgvJACKKSu1ADhaoQZcBdu3bzf3HY4mAXC80WEwn8+Pe4N84YUXuPbaa/n85z/P+vXrzTfS4sxgcZl4usJg8TRxOBwmHi/U7SsvL8flcjE4OIjb7aa5uRmLxYJhGOzdO8LWrb38+c+9xONZ8nmDbPbgcndlZRnDwwc7iHi9DmIx3dwX6HJVkErpKAoEg/XcdlstNTWHD0XF8GcYBi0tLSe8pMuhz13cMxiNRs3bq6urqa2tHTcWCX8HSQAU84UEQDFlpRwAN27cyL333svWrVsnvK4oCm9961vJ5XJcdNFFfPWrX521/qxz0aFh8Omnn+bmm2/mG9/4BldfffVhQ56qqmZ5GYvFMm1hMJfLEQ6HGRwcJJVKAYUly8rKSioqKsa0pDMMg97eGH/96wB/+Us/u3aFD4S9vFkM2mJRsNudo/oCexkcLDy+tdXPjTcupqLi8Muho8Nfa2vrhC3xZoKu6+zcuZNoNIrb7SaVSqHrOi6Xa0yHlkwmQyaTGff4Ugt/IAFQzB8SAMWUnWwB8Gh1BhcuXAgUlilvuOEGfv3rX9Pa2jrh/bu6umhsbCSRSLBu3TrKy8u5//77T9jY5zPDMLjuuuv4wAc+wHvf+95Jv3EqimLODE5HGIxEIuzatQuPx0NlZaU5+1VsSVesp3fo7Fc8nqWtbYiOjmF27Qqzb1+UqqqD+wAB/P4qTjutgne8I8DixUcuwZLJZGhvb58T4W/37t2MjIyYPZeL5WWK7fo0TSMUCvGnP/2JD37wg+b/IwAul2vGu5LMBaMD4E9+8hPuuecetm/fjt1u5/zzz+eOO+7g9NNPn+VRHiQBsHRJABRTdrIFwMl46aWXuP7663n66adZsWLFpB6zZcsWbr75Zv7617+e4NHNf4ZhjOlPPFNhMBwOs3v3bnw+H4sXLzYPnmiaZu6Li0aj5uzX6DB46HOl0znC4Qy5nE42q5HPG5xyig+H4+gnmTOZDG1tbQCzGv4Mw2D37t1EIhGWLFkyYQcYXdcZHh7mlVde4Stf+Qp//etfOeuss3jf+97Hhz70IZYtWzbzA58Dij8Pd911F5/97Gepra2loaGBtrY2YrEYLpeLX/3qV5x33nmzPNICCYClSwKgmLJSC4Avv/wy1113HU8++SRnnnnmYe8XDodxOByUlZWh6zqf/OQnGR4e5kc/+tEMjnb+O94waLVasVqtRw2DQ0ND7Nmzh8rKSpqamg576nj07FckEkHXdZxOp7kUWlZWdlyzkMXwpygKLS0tsxr+9uzZQzgcZvHixfj9/gnvl8lkSKfT5mP6+vp4/vnn+cUvfsFf/vIXuru7S+LU76GKPwM2m40777yTT3ziE6iqSjKZ5Oabb+aRRx5h0aJF7NixY9K1HE8kCYClSwKgmLJSC4DNzc1Eo1Hq6urM23784x+zfPnyMXUGt2zZwi233HKgv2ueVatW8c1vfpPKyspZHP38ZhiG2Z84l8uZPYGPRlGUMS3pDg1ooVCIzs5OqqqqaGpqmnSA03V9TBgc3WljKv2JR4e/1tbWWVs6LYa/4eFhlixZMqnwN5rT6cThcJDJZGY8wD744IP813/9Fzt27OCee+5h/fr15jVd1/mnf/onnn32WRRF4ZOf/CS33nrrCRlH8d/9sssu48knnxxzLZvNsmjRIvr6+njwwQe54YYbTsgYjoUEwNIlAVBMWakFQDE3TDUMAuYysdVqZXBwkK6uLgKBAI2NjVOevdN1nXg8bp4ozufzZn/iYku6I/3d6XSa9vb2ORH+Ojs7GRoaYvHixYf9wJLNZs2DMqMVw99s2bZtG3a7nQ0bNnD22WePCYA/+tGPeOihh/jVr37FyMgIq1at4rnnnjshy9TFf+vnn3+e97znPeOu33777dxxxx1cffXVc6L7kATA0nV8FVaFEMeto6ODt7/97bS0tHD22Wezffv2Ce+3ceNGmpubWbJkCTfffPMxddw4mSiKgsViwel0Ul5ejsfjweFwTKpgdC6XI5lM8uqrr9LV1UV1dTULFy48rqXbYmHpRYsWsWLFClpbW/H7/UQiEdrb29m2bRudnZ2MjIyMC6vpdHrOzPzt3buXoaEhTjnllHkX/gBWrFjBqaeeOuHPwaOPPsq6deuwWCxUVlZy1VVXnfDwdeqppx7x9vb29hP6/EIcjQRAIWbZLbfcws0330x7ezuf+cxn+OhHPzruPnv27OH222/n97//PTt37qSvr4+NGzfOwmjnnkPDoNPpPGI7s5/97Ge8+93vJpFIUFFRQSwWI5FIkM1mj7t0h6IolJeX09jYyPLly1m2bBnV1dXEYjE6OjrYtm2bub8umUzS1taGxWKZ9fDX1dVFKBSiqamJqqqqCe93uPDncDhmPfwdTVdXF4sWLTK/bmpqoqur64Q+ZzAYnPD2mpoaoNDrWojZJAFQiFk0MDDAn//8Z6677joArrjiCvbs2UNnZ+eY+23evJnLL7+cmpoaFEVh3bp1bNq0aRZGPLdZLBYcDgcej4fy8vJxYfCJJ55g/fr1fOtb32LlypXmzF8+nyeVShGNRs0weCxLyxNRFAWPx0NDQwOnn346p512GsFgkEQiwa5du9i+fTuaplFTU3NMvY6nk2EY7Nu3j8HBQZqamqiurp7wfrlc7rDhb6YKVK9evZrq6uoJ/+zbt++ojx89yzsTNfoGi4UfDzEwMAAwYTcVIWZS6R3REmIO2bdvH/X19eZpSUVRaGxspKuri6amJvN+szGDMd+pqmrOTum6zhtvvMH69ev54Q9/OOHerKJ8Pm8ur48+QHI8PYkVRaGsrIyysjIqKyvNUi82m429e/fS1dWFz+fD7/fj8/lm5PRsMfwNDAywaNGiI4a/ZDI57vZiT+yZ8sorr0z5sY2NjXR2dnLWWWcBsHfvXhobG6draBPasWPHhF2CirVGW1paTujzC3E0EgCFmGWH7j873OzETM9gnExUVWXVqlX85S9/oampaUwXkiMZHQYtFsuYlnRTkUqlaGtrw2q10trais1mI5PJmAdI9uzZYy4jFw+RnIhOGoZh0N3dzcDAAI2NjQQCgQnvd6TwN1ENxLnqyiuv5Lvf/S4f/OAHGRkZ4dFHH+W55547oc95//33c/HFF4+5LZvNmls3jvQhRIiZIEvAQsyihQsX0t3dbYaM4qzMobMTxRmMopmYwTgZFYs82+123G632ft3MjNumqaRTqeJxWLE43EymcwxLRMXw5/NZjPDHxSWUWtrazn11FM544wzaGhoMA9lbNu2jba2NgYGBibsszsVhmGwf/9++vv7Wbhw4WH3quXz+XkV/h5++GEaGhp47LHHuP3222loaDBbNV5//fW0trbS0tLCWWedxac//enDHtKYLr/4xS/45je/aX5YS6VS/OM//iM9PT0sXLiQa6655oQ+vxBHI2VgxJRJGZjpccEFF7B27VrWrl3L5s2bufvuu3n11VfH3Gf37t2cd955bN26lWAwyAc+8AHWrFnDunXrZmnUJx/DMMzSMsdywnoyM4Ojw19LS8ukZvVyuZzZhSQWi2EYBm6326w1ONXl1/3799Pb20tDQwO1tbUT3iefz5NIJMbdbrPZcLlccy78zSUTdQJZuHAhbW1tRKNRnE4nzz//POeff/4sj7RAysCULgmAYsokAE6PtrY21q5dy9DQEF6vl4ceeoi3vOUt3HTTTVx22WVcdtllAHzve9/jrrvuQtd1LrzwQr7zne+ckOVBcTAMFpeKJ0tV1TEt6QCSySTt7e3mzN9U9vfl83kikQiRSISRkZGj9ic+nJ6eHnp6eiT8nUAT9QJ+8803sdlsvPOd7+SrX/0qZ5xxxiyP8iAJgKVLAqCYMgmA80M6neaaa65h+/btlJWVUVtbywMPPDDmkAnAiy++yJo1a8ZsTt+yZcucaFc1mwzDMIPgsYbBfD5PZ2cndrt9yuHvUJqmMTIyQjgcNmsLOp1OMwweLqT19vayf/9+FixYMKabzWgS/kqPBMDSJYdAhCgBN998M5dccgmKonDvvfdy880386tf/Wrc/U477TRef/31WRjh3FXsLWyz2Y4pDO7du5fPf/7zfPGLX+SUU04hn8+jKAqqqh5XkCoWM66srETXdTMMDgwM0Nvbi8PhMMNgsT9xMfzV19cfNvxpmjZh+LNarRL+hDgJSQAU4iTndDpZs2aN+fW5557LPffcM3sDmscOFwbz+fyYk9n9/f188IMfpKWlhcWLF6MoCplMhkwmg6qqZnkZi8Vy3F1IimFP13VisRjhcJhQKERfXx92ux273U48Hqe2tpb6+voJ/54jhb9iiBRCnFwkAApRYr71rW9x6aWXTnitra2NVatWYbFYuOGGG7j11ltneHTzx6FhUNM0crkc+/bt49JLL+XMM8/kO9/5zrgiz7quk81myWazY/6O6QiDPp8Pn8+HYRjEYjF6enqIx+MAhEIhNE3D7/eP6U9cDH+HlhaS8CfEyU0CoBAl5Otf/zodHR088MAD466tWrWK7u5ufD4f3d3drFmzhurqaq666qpZGOn8oigKVqvV3OO3Zs0avvrVr6Lr+hFrNhqGcULCoKIopNNp4vE4wWDQ7E0cDocZHBzEarVSUVGBx+PBarWOey4Jf0Kc/OQQiJgyOQQyv9x999389Kc/5Te/+Q0VFRVHvf+GDRvo6enh29/+9okf3Elq9MxgLpebdAHvYhgshspjDWKDg4Ps3buXYDDIwoULx5xMTSaTZuHpTZs28dhjj3HZZZfxoQ99iFNPPRWr1Yrb7ZbwVyLkEEjpkkLQQpSAb3zjG2zatIlf//rXhw1/vb29ZmHjWCzGM888w5lnnjmDozz5FGcGXS4X5eXleDweHA7HUTuJFGcGk8kksViMZDI56QAZCoUmDH/F8bjdburr62lsbOTGG2/ktttuo62tjYsuuoizzjqLO++887i/byHE3CcBUIiTXHd3N//yL/9CJBLhXe96FytXruScc84B4KabbuKpp54C4PHHH2f58uWsWLGCc889l4svvpgbbrhhNod+UlEUBYvFgtPpxOPxHFMYLLZki0ajRwyDoVCIzs5OAoHAuPBXpOu6uS8wEAhwww038Mgjj7Bnzx42bNgwayd+H3zwQZYvX47VauXee+8dc+1LX/oSwWCQlStXsnLlSq699toZH58QJxtZAhZTJkvAYrSmpiacTqdZlPjzn/88V1999bj7bdy4kTvvvBNd17nooou4//77p6U+3nw2epn4WNrLFfcMWq1WhoeH2bNnD9XV1SxatOiw4S+RSIx7DlVV8Xg8s7rsu23bNux2Oxs2bODss89m/fr15rUvfelLxONx7r777lkb38lKloBLV2n/1hVCTKvNmzdz+umnH/b6nj17uP3228e0tNu4cSO33HLLDI5y7rFYLObsoKZpZnkZTdOO+LhiaNy5cye6rlNZWUljY+Mxh7+5sOdvxYoV5niEECee/J8mhJgxmzdv5vLLL6empgZFUVi3bh2bNm2a7WHNKRaLBYfDgcfjoby8HKfTOa6UzGh//vOfueiii+jo6KCqqopYLEYikSCbzZrLxEcLf/MhdG3atIkVK1Zw4YUX8sILL8z2cISY92QGUAgxba699lp0Xeecc85hw4YNBAKBMde7urpYtGiR+XVTUxNdXV0zPcx5Q1VVHA4HDocDXdfNGb/izOD27du58sorufXWW1mzZo05i5fP58nn86RSKSwWy4TlaGY6/K1evZodO3ZMeG3r1q0sXLjwsI9dt24dX/ziF7HZbPzhD3/g8ssv57XXXhvzsySEODYSAIUQ0+Lll1+msbGRXC7Hv/7rv/KRj3yEZ599dtz9Ri81TrYsihgfBoeHh7n88stZt24dn/70pw/7uImWkWdj5u+VV16Z8mNra2vN/37HO97BmWeeyeuvvy4BUIjjIAFQCDEtGhsbgcLBhE984hO0tLRMeJ/Ozk7z671795qPE5OnqirV1dU899xznHHGGWNa0h1NsRTMfFj2Leru7qahoQGAjo4O3njjDZYvXz7LoxJifps/vwGEEHNWIpEgEomYX2/atGnCGoJXXHEFTzzxBP39/RiGwQMPPMA111wzgyM9uaxcuRJVVbHb7bjdbrxeLy6Xa9yparvdjs1mQ1EUPB7PnAx/Dz/8MA0NDTz22GPcfvvtNDQ0sHXrVgC++MUvcvrpp7Ny5UquueYa7rvvvgk/YAghJk/KwIgpkzIwomj37t1cccUVaJqGYRgsXryYb37zmzQ1NXHTTTdx2WWXcdlllwHwve99j7vuugtd17nwwgv5zne+g81mm+Xv4ORTrB+Yy+VwOp2oqophGHMy/InZI2VgSpcEQDFlEgCFEGJ+kwBYumQPoBBi3ohEIlxwwQXm18lkkt27dzMwMEBlZaV5+4svvsiaNWvGLBNu2bIFl8s1k8MVQog5SwKgEGLeqKio4I033jC/vvvuu3nppZfGhL+i0047jddff30GRyeEEPOHbAYRQsxbP/jBD/joRz8628MQQoh5RwKgEGJe2rJlC0NDQ7z//e+f8HpbWxurVq3irLPO4v7775/h0QkhxNwmS8BCiHnpwQcf5MMf/vC4kicAq1atoru7G5/PR3d3N2vWrKG6upqrrrpqFkYqhBBzj8wACjHP6bpOZWUlFouFcDg85tqrr76KoigoisLGjRvHPbapqQlFUcYUZ54PEokEjz76KDfeeOOE171er3lKvaGhgX/4h384rk4UQghxspEAKMQ8p6oq73jHO9B1nd///vdjrr388svmf7/00ktjrnV1dZmdOJqammZiqNPmscce44wzzmDZsmUTXu/t7UXXdQBisRjPPPPMhIWphRCiVEkAFOIk8M53vhMYH/JeeuklVFWltrZ2wmujHzufbNy4cdzhj5tuuomnnnoKgMcff5zly5ezYsUKzj33XC6++GJuuOGG2RiqEELMSRIAhTgJnH/++cDYAKjrOn/4wx9YsWIFf//3f09XV9eYpd75HABfeeWVcYHu+9//vtltZP369bz55pts27aNN998ky996UsoinLYv+/jH/+4uRz+f//3f+btAwMDvPe976W5uZnTTz993AzraM888wzLli1j6dKlXHHFFcTj8eP8LuePL3zhC5x66qmsWLGCs88+m9/97nfmNV3X+djHPsaSJUtYunSpHMgRYo6QACjESWDVqlV4PB62bt1KLBYD4I033mBkZIR3vvOdE84QFpeHi+GxlH3oQx/i97//PYsWLRpz++c+9znOPfdcOjo6+MEPfsC1115LPp8f9/h4PM5HP/pRfv7zn7Nz507q6ur42te+NlPDn3WrV6/mz3/+M9u2beN73/seV1xxBel0Gij0+N2+fTvt7e386U9/4t///d/529/+NssjFkJIABTiJGC1Wnn729+Opmn84Q9/AMbO8B0aAHt7e+no6KCuro7m5ubZGfQccv7559PQ0DDu9v/+7//mtttuA+Css86ipqZmwlnAX/7yl7ztbW8z9yTeeuutbNq06cQOeg655JJLzC4ry5cvR9M0QqEQAI8++ijr1q3DYrFQWVnJVVddxU9/+tPZHK4QAgmAQpw0Dg15L730EoqisHr1ahYvXkxDQ8OYa6MfI8YbGhpC13UCgYB5W1NTE11dXePu29XVNWb2sKmpif3795sHUUrJD37wA5YsWWIG6olem4leQyHEzJIAKMRJYvQ+QMMweOWVVzj99NOpqqoyr+/evZvu7m4JgJN06L5BwzAmfd+TyerVq6murp7wz759+8z7/fa3v+XLX/7yuBm+0a/NkV5DIcTMkQAoxEni7LPPxuVy8frrr/OnP/2J4eHhMQGv+N8vvvii7P+bhGJwHhwcNG8rls05VGNj45gDNp2dnSxYsABVPTl+xb7yyiuEQqEJ/yxcuBAofPC44YYbePrpp2ltbTUfe+hrc7jXUAgxs06O305CCOx2O+eccw65XI4NGzYAY2f4imFv8+bN7Nixg0AgwGmnnTYrY50vrrzySu677z4AXnvtNfr6+jjvvPPG3e+9730vr732mnm44f777+eaa66Z0bHOppdffpnrr7+eJ598khUrVoy5duWVV/Ld734XTdMYHh7m0Ucf5eqrr56lkQohipRIJCLz8WJKip0WxNzxpS99iS9/+csoioJhGPT39xMMBs3rtbW1DAwMYBgGV1xxBZs3b57F0c4dt912G08++SR9fX1UV1fj8XjYuXMn/f39XH/99ezZswe73c79999vhup/+7d/o76+nnXr1gHw1FNP8ZnPfIZ8Ps/y5ct56KGH8Hq9s/ltzZjm5mai0Sh1dXXmbT/+8Y/NAyEf//jHee655wD453/+Z9avXz9bQxWHGBkZme0hiFkiAVBMmQTAued3v/sdF110EQCnnnoq27dvH3P9qquu4rHHHgPgW9/6Fh/72MdmfIxCiLlDAmDpkiVgIU4if/d3f4fdbgcmPuAx0ZKwmFmHKzp944030traysqVKzn//PN54403Jnx8Z2cnVquVlStXmn927do1Q6MXQpwsZAZQTJnMAApx7F5++WUWL17MeeedxzPPPMPpp58OFJaQ16xZg9Vq5ZlnnuGTn/wk7e3t4x7f2dnJ2972NrPOnhDHQ2YAS5d1tgcghBCl5HAzr8U2dgDnnnsue/fuRdf1k+YksRBibpEAKKZMPjkKMXW6rhOLxSb8/+iuu+7i4osvNtv6jRaNRolGo6xatQpN03jf+97Hpz71KSwWy0wMWwhxkpCPlkIIMYc8+uij/PznP+eee+6Z8HptbS3bt2/nhRde4Mknn2TLli3ce++9MztIIcS8JwFQCCHmiJ/97GfcddddPPHEE2Na0I3mcDjMa36/n+uuu44//vGPMzlMIcRJQAKgEELMAU888QR33HEHP//5z83uGhMZHBwkl8sBkMlkePrppznjjDNmaphCiJOEnAIWQogZ9KlPfYpnn32W/v5+qqqqcLvdbN26lerqampqavD7/eZ9n3rqKSorK/na175GXV0dN954I0899RQbNmxAVVU0TWP16tXccccdOByOWfyuhBDzjQRAIYQQQogSI0vAQgghhBAlRgKgEEIIIUSJkQAohBBCCFFiJAAKIYQQQpQYCYBCCCGEECVGAqAQQgghRImRACiEEEIIUWIkAAohhBBClBgJgEIIIYQQJUYCoBBCCCFEiZEAKIQQQghRYiQACiGEEEKUGAmAQgghhBAlRgKgEEIIIUSJkQAohBBCCFFiJAAKIYQQQpQYCYBCCCGEECVGAqAQQgghRImRACiEEEIIUWIkAAohhBBClBgJgEIIIYQQJUYCoBBCCCFEiZEAKIQQQghRYiQACiGEEEKUGAmAQgghhBAlRgKgEEIIIUSJkQAohBBCCFFiJAAKIYQQQpQYCYBCCCGEECVGAqAQQgghRImRACiEEEIIUWIkAAohhBBClBgJgEIIIYQQJUYCoBBCCCFEiZEAKIQQQghRYiQACiGEEEKUGAmAQgghhBAlRgKgEEIIIUSJkQAohBBCCFFiJAAKIYQQQpQYCYBCCCGEECVGAqAQQgghRImRACiEEEIIUWIkAAohhBBClBgJgEIIIYQQJUYCoBBCCCFEiZEAKIQQQghRYv5//k0gpF2WT2sAAAAASUVORK5CYII=' width=640.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"9b53d2621a7e4781894109f7e6b65468\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close('all')\\n\",\n    \"plt_logistic_squared_error(x_train,y_train)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"While this produces a pretty interesting plot, the surface above not nearly as smooth as the 'soup bowl' from linear regression!    \\n\",\n    \"\\n\",\n    \"Logistic regression requires a cost function more suitable to its non-linear nature. This starts with a Loss function. This is described below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Logistic Loss Function\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_LogisticLoss_a.png\\\"     style=\\\" width:250px; padding: 2px; \\\" >\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_LogisticLoss_b.png\\\"     style=\\\" width:250px; padding: 2px; \\\" >\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_LogisticLoss_c.png\\\"     style=\\\" width:250px; padding: 2px; \\\" > \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Logistic Regression uses a loss function more suited to the task of categorization where the target is 0 or 1 rather than any number. \\n\",\n    \"\\n\",\n    \">**Definition Note:**   In this course, these definitions are used:  \\n\",\n    \"**Loss** is a measure of the difference of a single example to its target value while the  \\n\",\n    \"**Cost** is a measure of the losses over the training set\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"This is defined: \\n\",\n    \"* $loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)})$ is the cost for a single data point, which is:\\n\",\n    \"\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"  loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = \\\\begin{cases}\\n\",\n    \"    - \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) & \\\\text{if $y^{(i)}=1$}\\\\\\\\\\n\",\n    \"    - \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) & \\\\text{if $y^{(i)}=0$}\\n\",\n    \"  \\\\end{cases}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)})$ is the model's prediction, while $y^{(i)}$ is the target value.\\n\",\n    \"\\n\",\n    \"*  $f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = g(\\\\mathbf{w} \\\\cdot\\\\mathbf{x}^{(i)}+b)$ where function $g$ is the sigmoid function.\\n\",\n    \"\\n\",\n    \"The defining feature of this loss function is the fact that it uses two separate curves. One for the case when the target is zero or ($y=0$) and another for when the target is one ($y=1$). Combined, these curves provide the behavior useful for a loss function, namely, being zero when the prediction matches the target and rapidly increasing in value as the prediction differs from the target. Consider the curves below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=600.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"89fa8263d4574b58bb48a78098e2c94a\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_two_logistic_loss_curves()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Combined, the curves are similar to the quadratic curve of the squared error loss. Note, the x-axis is $f_{\\\\mathbf{w},b}$ which is the output of a sigmoid. The sigmoid output is strictly between 0 and 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The loss function above can be rewritten to be easier to implement.\\n\",\n    \"    $$loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)$$\\n\",\n    \"  \\n\",\n    \"This is a rather formidable-looking equation. It is less daunting when you consider $y^{(i)}$ can have only two values, 0 and 1. One can then consider the equation in two pieces:  \\n\",\n    \"when $ y^{(i)} = 0$, the left-hand term is eliminated:\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), 0) &= (-(0) \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - 0\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\\\\\\\n\",\n    \"&= -\\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)\\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \"and when $ y^{(i)} = 1$, the right-hand term is eliminated:\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"  loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), 1) &=  (-(1) \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - 1\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)\\\\\\\\\\n\",\n    \"  &=  -\\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)\\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"OK, with this new logistic loss function, a cost function can be produced that incorporates the loss from all the examples. This will be the topic of the next lab. For now, let's take a look at the cost vs parameters curve for the simple example we considered above:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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' width=900.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"69578fe1a0534bfc81d02c4ee11c4e8d\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close('all')\\n\",\n    \"cst = plt_logistic_cost(x_train,y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"This curve is well suited to gradient descent! It does not have plateaus, local minima, or discontinuities. Note, it is not a bowl as in the case of squared error. Both the cost and the log of the cost are plotted to illuminate the fact that the curve, when the cost is small, has a slope and continues to decline. Reminder: you can rotate the above plots using your mouse.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulation!  \\n\",\n    \"You have:\\n\",\n    \" - determined a squared error loss function is not suitable for classification tasks\\n\",\n    \" - developed and examined the logistic loss function which **is** suitable for classification tasks.\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"toc-showcode\": true\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/C1_W3_Lab05_Cost_Function_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Cost Function for Logistic Regression\\n\",\n    \"\\n\",\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- examine the implementation and utilize the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_common import  plot_data, sigmoid, dlc\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Dataset \\n\",\n    \"Let's start with the same dataset as was used in the decision boundary lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])  #(m,n)\\n\",\n    \"y_train = np.array([0, 0, 0, 1, 1, 1])                                           #(m,)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"We will use a helper function to plot this data. The data points with label $y=1$ are shown as red crosses, while the data points with label $y=0$ are shown as blue circles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=400.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"8b8e9aa822714ef6b137dc661735ee88\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig,ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"plot_data(X_train, y_train, ax)\\n\",\n    \"\\n\",\n    \"# Set both axes to be from 0-4\\n\",\n    \"ax.axis([0, 4, 0, 3.5])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Cost function\\n\",\n    \"\\n\",\n    \"In a previous lab, you developed the *logistic loss* function. Recall, loss is defined to apply to one example. Here you combine the losses to form the **cost**, which includes all the examples.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the cost function is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m} \\\\sum_{i=0}^{m-1} \\\\left[ loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) \\\\right] \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"where\\n\",\n    \"* $loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)})$ is the cost for a single data point, which is:\\n\",\n    \"\\n\",\n    \"    $$loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\tag{2}$$\\n\",\n    \"    \\n\",\n    \"*  where m is the number of training examples in the data set and:\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"  f_{\\\\mathbf{w},b}(\\\\mathbf{x^{(i)}}) &= g(z^{(i)})\\\\tag{3} \\\\\\\\\\n\",\n    \"  z^{(i)} &= \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)}+ b\\\\tag{4} \\\\\\\\\\n\",\n    \"  g(z^{(i)}) &= \\\\frac{1}{1+e^{-z^{(i)}}}\\\\tag{5} \\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name='ex-02'></a>\\n\",\n    \"#### Code Description\\n\",\n    \"\\n\",\n    \"The algorithm for `compute_cost_logistic` loops over all the examples calculating the loss for each example and accumulating the total.\\n\",\n    \"\\n\",\n    \"Note that the variables X and y are not scalar values but matrices of shape ($m, n$) and ($𝑚$,) respectively, where  $𝑛$ is the number of features and $𝑚$ is the number of training examples.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_logistic(X, y, w, b):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes cost\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n)): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)) : target values\\n\",\n    \"      w (ndarray (n,)) : model parameters  \\n\",\n    \"      b (scalar)       : model parameter\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      cost (scalar): cost\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m = X.shape[0]\\n\",\n    \"    cost = 0.0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_i = np.dot(X[i],w) + b\\n\",\n    \"        f_wb_i = sigmoid(z_i)\\n\",\n    \"        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)\\n\",\n    \"             \\n\",\n    \"    cost = cost / m\\n\",\n    \"    return cost\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Check the implementation of the cost function using the cell below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0.36686678640551745\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w_tmp = np.array([1,1])\\n\",\n    \"b_tmp = -3\\n\",\n    \"print(compute_cost_logistic(X_train, y_train, w_tmp, b_tmp))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected output**: 0.3668667864055175\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Example\\n\",\n    \"Now, let's see what the cost function output is for a different value of $w$. \\n\",\n    \"\\n\",\n    \"* In a previous lab, you plotted the decision boundary for  $b = -3, w_0 = 1, w_1 = 1$. That is, you had `b = -3, w = np.array([1,1])`.\\n\",\n    \"\\n\",\n    \"* Let's say you want to see if $b = -4, w_0 = 1, w_1 = 1$, or `b = -4, w = np.array([1,1])` provides a better model.\\n\",\n    \"\\n\",\n    \"Let's first plot the decision boundary for these two different $b$ values to see which one fits the data better.\\n\",\n    \"\\n\",\n    \"* For $b = -3, w_0 = 1, w_1 = 1$, we'll plot $-3 + x_0+x_1 = 0$ (shown in blue)\\n\",\n    \"* For $b = -4, w_0 = 1, w_1 = 1$, we'll plot $-4 + x_0+x_1 = 0$ (shown in magenta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=400.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"c2b2b69dcbcc4b899752bb860409dc0f\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"# Choose values between 0 and 6\\n\",\n    \"x0 = np.arange(0,6)\\n\",\n    \"\\n\",\n    \"# Plot the two decision boundaries\\n\",\n    \"x1 = 3 - x0\\n\",\n    \"x1_other = 4 - x0\\n\",\n    \"\\n\",\n    \"fig,ax = plt.subplots(1, 1, figsize=(4,4))\\n\",\n    \"# Plot the decision boundary\\n\",\n    \"ax.plot(x0,x1, c=dlc[\\\"dlblue\\\"], label=\\\"$b$=-3\\\")\\n\",\n    \"ax.plot(x0,x1_other, c=dlc[\\\"dlmagenta\\\"], label=\\\"$b$=-4\\\")\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"\\n\",\n    \"# Plot the original data\\n\",\n    \"plot_data(X_train,y_train,ax)\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.title(\\\"Decision Boundary\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"You can see from this plot that `b = -4, w = np.array([1,1])` is a worse model for the training data. Let's see if the cost function implementation reflects this.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cost for b = -3 :  0.36686678640551745\\n\",\n      \"Cost for b = -4 :  0.5036808636748461\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w_array1 = np.array([1,1])\\n\",\n    \"b_1 = -3\\n\",\n    \"w_array2 = np.array([1,1])\\n\",\n    \"b_2 = -4\\n\",\n    \"\\n\",\n    \"print(\\\"Cost for b = -3 : \\\", compute_cost_logistic(X_train, y_train, w_array1, b_1))\\n\",\n    \"print(\\\"Cost for b = -4 : \\\", compute_cost_logistic(X_train, y_train, w_array2, b_2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected output**\\n\",\n    \"\\n\",\n    \"Cost for b = -3 :  0.3668667864055175\\n\",\n    \"\\n\",\n    \"Cost for b = -4 :  0.5036808636748461\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"You can see the cost function behaves as expected and the cost for `b = -4, w = np.array([1,1])` is indeed higher than the cost for `b = -3, w = np.array([1,1])`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you examined and utilized the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/C1_W3_Lab06_Gradient_Descent_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab: Gradient Descent for Logistic Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- update gradient descent for logistic regression.\\n\",\n    \"- explore gradient descent on a familiar data set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import copy, math\\n\",\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_common import  dlc, plot_data, plt_tumor_data, sigmoid, compute_cost_logistic\\n\",\n    \"from plt_quad_logistic import plt_quad_logistic, plt_prob\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Data set \\n\",\n    \"Let's start with the same two feature data set used in the decision boundary lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])\\n\",\n    \"y_train = np.array([0, 0, 0, 1, 1, 1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"As before, we'll use a helper function to plot this data. The data points with label $y=1$ are shown as red crosses, while the data points with label $y=0$ are shown as blue circles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=400.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"43d7811257e14d9dbb8b37981066b70f\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig,ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"plot_data(X_train, y_train, ax)\\n\",\n    \"\\n\",\n    \"ax.axis([0, 4, 0, 3.5])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Logistic Gradient Descent\\n\",\n    \"<img align=\\\"right\\\" src=\\\"./images/C1_W3_Logistic_gradient_descent.png\\\"     style=\\\" width:400px; padding: 10px; \\\" >\\n\",\n    \"\\n\",\n    \"Recall the gradient descent algorithm utilizes the gradient calculation:\\n\",\n    \"$$\\\\begin{align*}\\n\",\n    \"&\\\\text{repeat until convergence:} \\\\; \\\\lbrace \\\\\\\\\\n\",\n    \"&  \\\\; \\\\; \\\\;w_j = w_j -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} \\\\tag{1}  \\\\; & \\\\text{for j := 0..n-1} \\\\\\\\ \\n\",\n    \"&  \\\\; \\\\; \\\\;  \\\\; \\\\;b = b -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} \\\\\\\\\\n\",\n    \"&\\\\rbrace\\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"Where each iteration performs simultaneous updates on $w_j$ for all $j$, where\\n\",\n    \"$$\\\\begin{align*}\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})x_{j}^{(i)} \\\\tag{2} \\\\\\\\\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)}) \\\\tag{3} \\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"* m is the number of training examples in the data set      \\n\",\n    \"* $f_{\\\\mathbf{w},b}(x^{(i)})$ is the model's prediction, while $y^{(i)}$ is the target\\n\",\n    \"* For a logistic regression model  \\n\",\n    \"    $z = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$  \\n\",\n    \"    $f_{\\\\mathbf{w},b}(x) = g(z)$  \\n\",\n    \"    where $g(z)$ is the sigmoid function:  \\n\",\n    \"    $g(z) = \\\\frac{1}{1+e^{-z}}$   \\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Gradient Descent Implementation\\n\",\n    \"The gradient descent algorithm implementation has two components: \\n\",\n    \"- The loop implementing equation (1) above. This is `gradient_descent` below and is generally provided to you in optional and practice labs.\\n\",\n    \"- The calculation of the current gradient, equations (2,3) above. This is `compute_gradient_logistic` below. You will be asked to implement this week's practice lab.\\n\",\n    \"\\n\",\n    \"#### Calculating the Gradient, Code Description\\n\",\n    \"Implements equation (2),(3) above for all $w_j$ and $b$.\\n\",\n    \"There are many ways to implement this. Outlined below is this:\\n\",\n    \"- initialize variables to accumulate `dj_dw` and `dj_db`\\n\",\n    \"- for each example\\n\",\n    \"    - calculate the error for that example $g(\\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b) - \\\\mathbf{y}^{(i)}$\\n\",\n    \"    - for each input value $x_{j}^{(i)}$ in this example,  \\n\",\n    \"        - multiply the error by the input  $x_{j}^{(i)}$, and add to the corresponding element of `dj_dw`. (equation 2 above)\\n\",\n    \"    - add the error to `dj_db` (equation 3 above)\\n\",\n    \"\\n\",\n    \"- divide `dj_db` and `dj_dw` by total number of examples (m)\\n\",\n    \"- note that $\\\\mathbf{x}^{(i)}$ in numpy `X[i,:]` or `X[i]`  and $x_{j}^{(i)}$ is `X[i,j]`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_gradient_logistic(X, y, w, b): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"    Returns\\n\",\n    \"      dj_dw (ndarray (n,)): The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db (scalar)      : The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m,n = X.shape\\n\",\n    \"    dj_dw = np.zeros((n,))                           #(n,)\\n\",\n    \"    dj_db = 0.\\n\",\n    \"\\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb_i = sigmoid(np.dot(X[i],w) + b)          #(n,)(n,)=scalar\\n\",\n    \"        err_i  = f_wb_i  - y[i]                       #scalar\\n\",\n    \"        for j in range(n):\\n\",\n    \"            dj_dw[j] = dj_dw[j] + err_i * X[i,j]      #scalar\\n\",\n    \"        dj_db = dj_db + err_i\\n\",\n    \"    dj_dw = dj_dw/m                                   #(n,)\\n\",\n    \"    dj_db = dj_db/m                                   #scalar\\n\",\n    \"        \\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"outputs\": [],\n   \"source\": [],\n   \"metadata\": {\n    \"collapsed\": false,\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   }\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Check the implementation of the gradient function using the cell below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dj_db: 0.49861806546328574\\n\",\n      \"dj_dw: [0.498333393278696, 0.49883942983996693]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_tmp = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])\\n\",\n    \"y_tmp = np.array([0, 0, 0, 1, 1, 1])\\n\",\n    \"w_tmp = np.array([2.,3.])\\n\",\n    \"b_tmp = 1.\\n\",\n    \"dj_db_tmp, dj_dw_tmp = compute_gradient_logistic(X_tmp, y_tmp, w_tmp, b_tmp)\\n\",\n    \"print(f\\\"dj_db: {dj_db_tmp}\\\" )\\n\",\n    \"print(f\\\"dj_dw: {dj_dw_tmp.tolist()}\\\" )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected output**\\n\",\n    \"``` \\n\",\n    \"dj_db: 0.49861806546328574\\n\",\n    \"dj_dw: [0.498333393278696, 0.49883942983996693]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#### Gradient Descent Code \\n\",\n    \"The code implementing equation (1) above is implemented below. Take a moment to locate and compare the functions in the routine to the equations above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def gradient_descent(X, y, w_in, b_in, alpha, num_iters): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Performs batch gradient descent\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n)   : Data, m examples with n features\\n\",\n    \"      y (ndarray (m,))   : target values\\n\",\n    \"      w_in (ndarray (n,)): Initial values of model parameters  \\n\",\n    \"      b_in (scalar)      : Initial values of model parameter\\n\",\n    \"      alpha (float)      : Learning rate\\n\",\n    \"      num_iters (scalar) : number of iterations to run gradient descent\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      w (ndarray (n,))   : Updated values of parameters\\n\",\n    \"      b (scalar)         : Updated value of parameter \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # An array to store cost J and w's at each iteration primarily for graphing later\\n\",\n    \"    J_history = []\\n\",\n    \"    w = copy.deepcopy(w_in)  #avoid modifying global w within function\\n\",\n    \"    b = b_in\\n\",\n    \"    \\n\",\n    \"    for i in range(num_iters):\\n\",\n    \"        # Calculate the gradient and update the parameters\\n\",\n    \"        dj_db, dj_dw = compute_gradient_logistic(X, y, w, b)   \\n\",\n    \"\\n\",\n    \"        # Update Parameters using w, b, alpha and gradient\\n\",\n    \"        w = w - alpha * dj_dw               \\n\",\n    \"        b = b - alpha * dj_db               \\n\",\n    \"      \\n\",\n    \"        # Save cost J at each iteration\\n\",\n    \"        if i<100000:      # prevent resource exhaustion \\n\",\n    \"            J_history.append( compute_cost_logistic(X, y, w, b) )\\n\",\n    \"\\n\",\n    \"        # Print cost every at intervals 10 times or as many iterations if < 10\\n\",\n    \"        if i% math.ceil(num_iters / 10) == 0:\\n\",\n    \"            print(f\\\"Iteration {i:4d}: Cost {J_history[-1]}   \\\")\\n\",\n    \"        \\n\",\n    \"    return w, b, J_history         #return final w,b and J history for graphing\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's run gradient descent on our data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration    0: Cost 0.684610468560574   \\n\",\n      \"Iteration 1000: Cost 0.1590977666870457   \\n\",\n      \"Iteration 2000: Cost 0.08460064176930078   \\n\",\n      \"Iteration 3000: Cost 0.05705327279402531   \\n\",\n      \"Iteration 4000: Cost 0.04290759421682   \\n\",\n      \"Iteration 5000: Cost 0.03433847729884557   \\n\",\n      \"Iteration 6000: Cost 0.02860379802212006   \\n\",\n      \"Iteration 7000: Cost 0.02450156960879306   \\n\",\n      \"Iteration 8000: Cost 0.02142370332569295   \\n\",\n      \"Iteration 9000: Cost 0.019030137124109114   \\n\",\n      \"\\n\",\n      \"updated parameters: w:[5.28 5.08], b:-14.222409982019837\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w_tmp  = np.zeros_like(X_train[0])\\n\",\n    \"b_tmp  = 0.\\n\",\n    \"alph = 0.1\\n\",\n    \"iters = 10000\\n\",\n    \"\\n\",\n    \"w_out, b_out, _ = gradient_descent(X_train, y_train, w_tmp, b_tmp, alph, iters) \\n\",\n    \"print(f\\\"\\\\nupdated parameters: w:{w_out}, b:{b_out}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#### Let's plot the results of gradient descent:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=500.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"ec8ee91db69c429d905158037c7587d0\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig,ax = plt.subplots(1,1,figsize=(5,4))\\n\",\n    \"# plot the probability \\n\",\n    \"plt_prob(ax, w_out, b_out)\\n\",\n    \"\\n\",\n    \"# Plot the original data\\n\",\n    \"ax.set_ylabel(r'$x_1$')\\n\",\n    \"ax.set_xlabel(r'$x_0$')   \\n\",\n    \"ax.axis([0, 4, 0, 3.5])\\n\",\n    \"plot_data(X_train,y_train,ax)\\n\",\n    \"\\n\",\n    \"# Plot the decision boundary\\n\",\n    \"x0 = -b_out/w_out[0]\\n\",\n    \"x1 = -b_out/w_out[1]\\n\",\n    \"ax.plot([0,x0],[x1,0], c=dlc[\\\"dlblue\\\"], lw=1)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"In the plot above:\\n\",\n    \" - the shading reflects the probability y=1 (result prior to decision boundary)\\n\",\n    \" - the decision boundary is the line at which the probability = 0.5\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Another Data set\\n\",\n    \"Let's return to a one-variable data set. With just two parameters, $w$, $b$, it is possible to plot the cost function using a contour plot to get a better idea of what gradient descent is up to.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_train = np.array([0., 1, 2, 3, 4, 5])\\n\",\n    \"y_train = np.array([0,  0, 0, 1, 1, 1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"As before, we'll use a helper function to plot this data. The data points with label $y=1$ are shown as red crosses, while the data points with label $y=0$ are shown as blue circles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=400.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"c721569b6edc46c3b99487769597fe1c\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig,ax = plt.subplots(1,1,figsize=(4,3))\\n\",\n    \"plt_tumor_data(x_train, y_train, ax)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"In the plot below, try:\\n\",\n    \"- changing $w$ and $b$ by clicking within the contour plot on the upper right.\\n\",\n    \"    - changes may take a second or two\\n\",\n    \"    - note the changing value of cost on the upper left plot.\\n\",\n    \"    - note the cost is accumulated by a loss on each example (vertical dotted lines)\\n\",\n    \"- run gradient descent by clicking the orange button.\\n\",\n    \"    - note the steadily decreasing cost (contour and cost plot are in log(cost) \\n\",\n    \"    - clicking in the contour plot will reset the model for a new run\\n\",\n    \"- to reset the plot, rerun the cell\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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zbVCkKrVq2oVasWM2fO1Hpuk5KSWLNmDS1atACgSZMmHDhwgO3bt/Ppp5+W6DkiIiJUEyZ5UaZMGRYsWKBq5+Tk0KZNGxo2bMimTZsYNWqUwffipk2bjHq9Fi5cSGpqKps3b8bb2xuQ3j+1atXC2tpadbw1a9Zw9+5dduzYobqJa9OmDdHR0UyfPp1+/fppTRB06tSJqVOnGrzGhQsXEhoaysGDB7V8GzRXgJo2baoaP0ifh0aNGhEUFMSlS5eKzO8BwMbGhnLlyhEVFaXqGzx4sOpvuVxO48aNcXR0ZPz48Xz11VeYmpqqwuWfDZ0H6Ny5s1a7UaNGVK5cmY4dOzJnzhycnZ2LbPwCgcD476o5c+bg4ODA5s2bVXnljRs3Jjg4WEugX7lyhfT0dK37AZC+zwEqVKiQ55iMvW85c+YMrVq10vrt1ZwAMPRdc/bsWTw9PXFxcdHqb9asmVao/ZEjRxg8eDALFy7kzp07VKlShSNHjqgiDfPC3t6edevWYWoqBctWrlyZdu3asW7dOq3f/Vq1agHSb76mQC9Xrhze3t6cPXtWCHSBoJgRIe6C54YydFs5G6xE6Qx67NgxQPpRKF++vEqcg2Re0qlTJ4PH9/f3p0yZMnz00Uds2LChUGFYp0+fJjk5OdfqorGcPHkSFxcXXFxcCAkJYe/evaxcuVL1Y5eWlsaxY8fo3LmzapU7OzsbuVxOy5YtVc9VREQEERERucRCx44d9U4wvPXWW1rt/fv3U6FCBRo2bKg6T3Z2Nq+//jpZWVmcOXMGgNq1a7N27Vq+//57Lly4oBUKrHz8ypUrjBs3jkOHDuVyxNXF8ePHadeunVb4tqOjI+3bt1e93kpsbW1VwhkkX4BKlSqpbqZK8hz5Zdu2bbz55pv4+flRrlw5XFxcuHv3Lv/991+e+xr7ep05c4b69eurxDlIQvXZSZPjx4/j5eWVa4WlZ8+ePHnyhJs3b2r1P/v+0cXBgwepU6eOQZGdlZXFjz/+SMOGDfHy8sLFxUUVzWDM85Bf5HK5VuRAdHQ0n376KbVq1cLNzQ0XFxeGDx9OQkKCUa7vSUlJTJ06lTp16uDu7o6LiwsdOnRALpdz9+7dIh+/QPCqY+x31ZkzZ2jTpo2W6ZuHhwcNGjTQ2i8yMhKQhGVhxgR537fUrl2bffv2MW3aNE6cOEFmZqbR54iMjNQ5xubNm/PgwQNCQ0N5+vQpN27coFevXlSqVInDhw8Dkmg3ZvUcpMlXpTgHadLR29tb9ZuiRDkWXb4q5cqVeyn9VgSC0o5YQRc8N5ShXu7u7lr9yrby8aioKFxdXXPt7+bmZvD4ZcqUYceOHcyePZtPP/2UpKQkqlevzueff55L4OZFbGwsQIENWwIDA1X5rjdu3ODrr79m4MCBHD9+HBcXF+Li4sjJyWH27NnMnj1b5zFkMplqRfDZ58PMzEzvTcezYdgxMTGEh4fnmp1XorzWWbNm4e7uzurVq5k2bRply5ald+/eTJo0CVtbW/r06UNGRgarVq3i119/xcLCgjZt2vDNN99orVpoEhcXl+v1Buk1fzYX+Vl/AQBLS0vS09N1Hrs4z+Ht7W102Z29e/cyYMAAevTowUcffYSrqytmZmZ88MEHeZ4HjH+9oqKiqF69eq7Hn/2cGHp+lI9rYkwYf2xsbK5VqWeZOnUqixcv5pNPPlGtXstkMt544w2jnof8kJaWxtOnT1XXJJfL6dOnDw8fPuSzzz6jWrVq2Nracu7cOT799FOjzj969Gj279/P+PHjCQ4OxsHBgYcPH9KvX78iH79AIDD+u8rQPYKmeFT6YTxrlKqc1AwPD89z5dnY+5ZPPvkEa2trNm7cyPfff4+9vT2dOnVi2rRpeU4Q6DNzbdasGSYmJhw5cgRHR0ecnJwICgqiefPmHDlyhO7du3P58mVVyHxe6LqHcnV1VU1kKLGxsQHQ6bdhY2MjfDgEghJACHTBc0Np0hIdHa3lCq0UocqQUXd391yresr98qJWrVqsWrWK7OxsLly4wA8//MCgQYM4evQoNWrUMHqsyh/UyMjIfO2nxN7entq1awNSCLOvry+dOnXiu+++Y86cOZQpUwZTU1OGDBlCnz59dB7D1NRUdRPw7IpfTk4OT58+1bmf5ioiSM+rr68vy5cv17m9j4+PasyTJ09m8uTJPHjwgG3btjFlyhQsLS2ZMmUKJiYmDBo0iEGDBhEfH8+BAwf48ssvGTx4MPv379d57LJly2qFHSuJiorSMu0pDMVxjpYtW7Jq1SqioqJ03kBqsmXLFipWrMgvv/yi1W+sOZqxr5e7u7vOleBnPydly5bVuWL97OdOybPvH12UK1cu103ds2zevJnevXurwkIB7t+/n+exC8L+/fvJycmhUaNGqvOcO3eOn3/+md69e6u2u3LlilHHS09PZ+fOnYwfP54PPvhA1Z+YmFi0AxcIBCqM/a4y9rtPuf2z370tW7Zk2rRp7NmzR+vzrW9MymMbum+xsLBg7NixjB07lqioKP766y8mTpxIWlpangZuzs7OuczalOcODAxUCXSlYG/evDnjx4/n2LFj5OTkaEWFGULXPVRMTEwuU1HlpIOuiYW4uDhq1qxp1PkEAkHRIULcBc8NZT7qs67ZyvrRTZo0AaRcrYcPH2q5cMvlcrZv3270uczNzalfvz4TJ05EJpOpHLSVs9R5zQA3aNAAe3t7vSIpv7Ro0YK33nqLlStXEhERgZ2dHY0bN+bq1asEBwdTu3btXP9Amun39vbOZZC3c+dOvQZez9K6dWvVOXWdR9ePsI+PDx988AE1atTgxo0buR53cnKia9eudOnSRefjSpo2bcrff/9NUlKSqi8pKYm9e/dq5ScXhuI4x4gRIzA1NeWTTz7JFfoP0kTO+fPnAcmB/dn0g4MHD+ZKudD3XjT29apfvz5nzpzRCs9PS0vj77//1jpe06ZNiYiI4OTJk1r9mzZtwtXVlYCAgPw8FYCU43/u3DmDgjc1NVXLeAjQ8gMoKmJiYpg8eTIeHh5069ZNdW5A6/xyuZy1a9fm2t/S0jLXa5CZmUlOTk6xjF8gEEgY+11Vv3599u3bp5Vm9fjxY06dOqW1X5UqVQDJMFKTevXq0bx5c3744Qfu3buXaxxyuZw9e/aoxgR537doojQCbdmypdbvo67vGuU4IyIidP6mK1fLjxw5ohLizZs358mTJyxZsoTy5ctr5YkbYvv27VqVQE6ePElERESunHjlZMGz0QU5OTlEREQYle8uEAiKFrGCLig0//zzT65VxjJlyvDaa6/RvXt3vvvuO7Kzs2nYsCGnT59m9uzZdO/eXRUy++677zJv3jz69evHl19+iYuLCytXriQhIcHg6t7evXtZvnw5HTt2xNfXl9TUVJYsWaJVk1z5A79gwQLatGmDmZmZSgxr4uDgwFdffcVnn31Gv3796NGjBw4ODly5cgUrKyuGDRuW7+dlwoQJ7N69m3nz5jF79my++eYbOnbsSNeuXenXrx/u7u48ffqUS5cuYWJiwqRJkzA1NWX8+PGMGTOGDz74gC5duhAaGsrcuXNxdHTUyifTR8+ePVmzZg2dO3dm1KhRBAUFkZmZyf3799mzZw/r16/HysqKNm3a0L59e2rUqIGdnR3Hjh3j6tWrqhX+Dz/8EHt7exo0aKDKqd6wYQOtWrXSe+5x48bx119/0blzZz788ENMTEz48ccfSUtL47PPPsv3c1hS56hSpQqLFi1i+PDhtGnThkGDBuHv709KSgpHjx5l+fLlfPfdd9SpU4c2bdqwc+dOPvvsMzp27MjNmzf5/vvvc6VL6HsvGvt6jRo1il9//ZVu3boxfvx4LC0tWbhwIVZWVlrvi3fffZeff/6Zfv36MWnSJLy8vNi4cSMHDx5k3rx5eVYz0MXIkSPZtGkTXbp04dNPP6VGjRrExsaye/dufvjhBxwcHGjTpg3r1q2jWrVqBAQEcODAAXbt2lWo1+HRo0ecOXMGmUxGXFwcZ8+eZcWKFcjlctavX68KzaxatSo+Pj5MnToVU1NTbGxsWLFihc5Ii4CAAP7++2/eeOMNnJyc8PDwwNPTk4YNG/LTTz/h6uqKp6cnW7du5ezZs4Uav0Ag0I+x31Wffvop27Zto1u3bowePZrMzExmz56Nm5ub1ndfhQoVqFChAufOncuVQ75kyRI6d+7M66+/zrBhw2jUqBHm5ubcvHmTFStW4OTkRPv27alevbpR9y19+vQhMDCQ4OBgnJycuHz5Mvv379fysdH3XdOkSRNmzJjB1atXc5mRNm/enEWLFhEZGanKNXdzcyMgIIB///1XZwReUFAQPj4+ub5vk5OTVS7yT548YerUqVSqVCnXMc6ePYuFhUUu4X79+nVSUlKKbOJbIBAYjxDogkKjSxQFBgZy9OhRFi9ejJ+fH6tXr2bOnDl4eHgwduxYLWdwS0tLtm7dymeffcbHH3+MnZ0d3bt3p169esydO1fveStVqoSNjQ2zZ88mKioKe3t76tSpw9atW1U5Z+3atWPIkCH8+uuvzJo1C7lcrjf0eOjQobi7uzN//nyGDh2KhYUFVatWZdy4cQV6XmrUqEHXrl1ZtWoVn3zyCSEhIRw4cICZM2cyfvx4EhMTcXFxoVatWgwZMkS1X//+/UlOTmbRokVs3LiR6tWr88svv6jKP+WFhYUFW7ZsYe7cuaxYsYKwsDBsbW3x9/fnzTffVK0SNmnShK1btzJv3jyys7Px8/Pj22+/Zfjw4QA0bNiQNWvWsGHDBhITE/Hw8KBnz55aIczPEhgYyM6dO5k2bRojR45ELpdTr149du3alSusrqAUxzkAunXrRkBAAPPnz2fGjBnExMRgY2OjqomrNA1SllBbvXo1K1eupEaNGvzyyy/MmjVL63j63ovGvl7lypVj27ZtfP755wwfPhxnZ2cGDRrE06dPVcZGAHZ2duzatYuvvvqKr7/+muTkZCpXrsySJUty3bQai5OTE3/99RfTp09n3rx5xMbG4ubmRvPmzVUVCWbNmoWJiYnqxrZp06Zs2bKlUO7ta9euZe3atZibm+Po6EjVqlUZOnQogwYN0srZt7S0ZN26dYwbN45Ro0ZhZ2fHO++8w6BBg1Svk5LZs2czfvx4evfuTUZGhsqt+ZdffmHcuHFMmDABU1NT2rZty++//85rr71W4PELBAL9GPtdVa1aNTZu3MikSZMYNGgQnp6ejB07ln/++SfXMbt27cqff/6Z6/vXy8uLAwcOsHjxYrZt28b8+fORyWT4+vry5ptvarmxG3Pf0qRJE7Zt28ayZctIS0ujfPnyjBkzRqs6iL7vmiZNmuDp6cnevXtzCfQmTZqoPGeqVaum6m/evDm3bt2iWbNmua45NTVVZyrWRx99xL179xg5ciSpqak0b96cWbNm5YoU+uuvv2jfvr2WCZ+y393dXec5BQLB88UkPj5eXtKDEAh00atXL9LS0vIV6v6ycv78eV5//XV2796tM8RO8GqizEesXbu2Vqk3gUAgeJlJTk6mTp06DBkyRGuR4P79+9SrV4+dO3dq1RkvbcyYMYM//viDc+fOGeUDoo///vuPevXqaZWpzQ+RkZEEBgayZcsWVXlPJQ0bNuTtt9/myy+/LPD4BAJBwRAr6IJSwYIFC7C3t8ff35+kpCS2bdvGX3/9xYYNG0p6aMVOaGgoy5Yto3Hjxjg4OHDr1i3mzp1L3bp1S/UNh+D5M336dPz9/alQoQKxsbGsWrWK27dvs3jx4pIemkAgEDw3xo0bR8OGDfHw8ODx48f8/PPPpKen56rPXbFiRfr27cu8efNK9e/lyJEjWbZsGdu3b8931RlNjh07RqtWrQokzgHmz59PkyZNconzXbt2ER0dzejRows8NoFAUHCEQBeUCqysrFi4cCEPHz5ELpdTrVo1VqxYQdu2bUt6aMWOjY0N169fZ926dSQkJODs7Mybb77J1KlTCzXTLng5mDlzJo8fP8bMzIzg4GC2bNlCrVq1SnpYAoFA8NxIT09n8uTJxMTEYG1tTcOGDdm1axeenp65tp04cSK///47qampucK2SwtlypRhyZIlucpe5pcBAwYwYMCAAu/v4eGhlTevJD09naVLl+osVSoQCJ4/IsRdIBAIBAKBQCAQCASCUoAosyYQCAQCgaBArFq1iiZNmlCuXDmWLl2qc5s7d+7g6ekpclkFAoFAIDACIdAFAoFAIBAUiJCQEH7//Xe6d++u8/GcnBzGjh1Lx44di3lkAoFAIBC8mIgcdIFAIBAIBAVCWdpQsx61JnPnzqVdu3YkJyeTkpJSnEMTCAQCgeCFRKygCwQCgUAgKHKuXr3K/v37GTlyZEkPRSAQCASCFwaxgi4QCAQCgUAn7du359atWzofO3z4MOXLl9f5WFZWFh9++CELFy7EzMzseQ5RIBAIBIKXCiHQBQKBQCAQ6GTPnj0F2u/x48fcv3+fHj16AJCQkABAfHw8CxYsKLLxCQQCgUDwsiEE+iuMnZ2d3rxBgUAgEBQNMpnslcu/rlChAvfu3VO1Z8yYQUpKCtOnT89z35L4bYpau5bY3bsBsChbFr/p0zEvU6ZYx1Ci3FkPoTulvy2doNF0sHIqyRGpWHUb/nog/e1sDd80AAfLoj1HzIMk9i27hCxHqjzcqEtlKtfPXWM9X1wAdiv+NgHeAarn/zDynBwezptH8oULAJjZ2uIzcSLWvr7GHyQrGc59B0mhUtuyDNSdCPZeBnfLlsH8K3A+RmpbmMKHtSDERff2sZHJ7P/1Chlp2QCU9bCj9aAgrO0ttDeMB1YDCYp2WaAv8MxH7umOHURv2KBqu3bvjkuXLrlPnBQKZ6ZDTrrUdmsIQaOgpO5xE/6D679Dcpi6z9wOqvQC71ZgYpJrF7lcTtLJk0StWkV2YqKq36ZSJTz+9z+sfXx0nysdOAScB5SFs02ARkBzwEL3bgCZadlkZKfl58oExYQQ6K8wpqamQqALBAKBoMBs2LCBKVOmEB8fz+7du5k3bx7r1q0jODi4wMcs7t+mhKNHeaIQASbm5nhPmoRl2bLFdv4SJ/I43Fkn/W1iBvW/BBvnkh2Tgn8fwSbFPI+5QhyWsS7ac6TEp/PPssukJWUBUKNZeao29C7cQW8CW1ALpo5AzfwfRi6T8XDuXBKPHAHA1Noan3HjsK1Y0fiDZCbB6a8g4a7UtnKC+hPBXnd6ipKMHPjmPFx4IrUtTeGzOvrFeUxYInsWX1CJc9cKjrQZEoy13TMKMQb4DbU4dwH6kUucR2/cSNSqVaq2+3vv4da1a+4TJ0fAqa8gU3FAt3oQPApMS0DiZCbCjeUQvk+7v0IbqD4QLB117/b4MY8WLybp/HlVn5mNDe79++PcoQMm+r4PrwPbgCSNPm+kySDDcy+EXo7h7K7/aDOyhuENBSWCEOgCgUAgEAgKRK9evejVq1ee233xxRfFMJr8k/7gAQ9//FHV9hwyBLvqBVjmfFFJCoeLc9XtGoPBuXTcsN9LhPmX1e1hNaCKU9GeIzsrh33LrpCalAmAZyUnGr1TpXAHDQfWoRbnTRX/8olcLifip59IUIpzS0t8v/oK24AA4w+SmQQnv4RExSyHlRM0npGnOE/Phmnn4PJTxW5m8FU9qFVO9/aP78azd8lFsjJyAHCvWIa2w4KxsnlGnEcBvwLJirYrMARw0N4sev16otasUbU9BgzAVVcpx5THcHKiWpw714C6nxe/OJfL4MFfcHMVZGmoZceKEDgCnHV/p8izs3mybRvRa9ciy8xU79a4MV5Dh2Lhomc2JAHYgSTQlVgCbwCNAQO2HykJGRzfdIvQyzFY2QoZWFoRr4xAIBAIBIJXjpyUFB588w2ydCks1qlVK5w7dCjhURUjWSlw9ht1WLBXC/B7u2THpCApE745B5kyqd2mPLStULTnkMvlHF57g5hwKZzYoaw1rQcFYWZeiOiNp8AKIEvRDgIK8JaSy+VE/vILcf/8A4CJmRk+X3yBvaKsoVFkJirE+X2pbVUWGn2TpzhPy4avz8D1OKltYw5f14MaeoIqHt2O5a+ll8nOksS5ZyUn2g4LwcLqGZUYiSTOUxVtD2AwYK+9WdTatUSvW6dqewwciGu3brlPnB4rifN0xSyCoz/U/wrMrAxeX5ETdwuuLlZHKACY20LAe+DbAUx1q+XUW7eIWLCA9NBQVZ+Fiwtew4fj2LCh7nPJgVPAXiBTo78q0BkpVUAPcrmcm8ciOL3jLpnp2UZdmqDkEAJdoEImkxEVFUV2tvjgCrQxNzfH3d1dpEQIBIKXArlczsO5c8l49AgAG39/vEeNwkRHbuhLiVwOl+ZBSoTUdvCD4DE6c2OLG5kcvr8E0YrU2AAnGFGz6Id2ef8D7p6PAsDC0ow3hwZjU5jk9mTgd9QCtCLQAykfOJ9Er1nD0x07ADAxMaHCuHE41Ktn/AFyiXNnaPwt2BsO3U/Jgsln4Fa81LazgKn1oaqT7u3Dbzzln2WXyc6WZlLKVytHm/8FYW75jCiNQAprV6Y7ewODAFvtzZ4V557/+5/unPOsVDj9NaRFS237CtBoGljYGby+IiUjAW6uhPC/tfu9W0H1QWCtWy3npKQQtWoVsbt3I5dLYRYmJiY4v/UW7v36YWZjo/t8j4E/gQcafXbAW0AtDL7P4h6ncGT9DaLuJ6j6bOwtadK9kNEigueGEOgCFVFRUTg4OGBvb5/XhrB3LwwYoH+bFSugXTtwdy/aQQpKhOTkZKKiovD0LKRpjkAgEJQCYv74g8RTpwAws7fHZ8IETK2KeeWtJLm7GR6flP62sIN6E4t/5VEP6+/AOYUpWRlL+Lw2WBRxpb7w6085s+M/Vfu192rg7JXHvY8hMoFVQKyi7Qa8R4HusmO2bNEyRvP+8EPKNM1HjHxmIpyYqDaEM1KcJ2XCpNNwV+FP5mAB0xuCv+60acKuxrD/tyvkKIz1fANdaD0wCDOLZybyHwDLkczMACoAAwENHSqXy4leu5bo9etVfZ5DhuDSuXPuE+dkwbkZ6skHGzfJ1FBPfneRI5dB2B64tUqKQlHi4AeBw6GcbrMBuVxO4vHjRC5dSlZsrKrfxt8fr9Gjsa2iRyxnAQeBw4BMo78uUnSGHj0PUgrHpX1hXPonVPU6AQQ09KJB58pY2piRlJSk/wCCEkMIdIGK7Oxs48R5q1Zw4wbEx8OHH+be5scfYexYqF4dDh4UIv0lwN7enri4uJIeRvHzqk9Giet/ta//JSXpwgWiV68GFKuTn36K5av0+sVclFb+lIR8CnYeJTYcTU5FwTqFbjYBPqsNLgYESEGIj07h4IqrqhTxuu0q4hfsVvAD5gDrkXLPQcqnHoRB4aSPp3v28Pj331Vtz6FDKdu6tfEHKKA4T8iAL09DqEKrlbGE6Q3AT4/mvXchioMrryGTSc9ixVputBpQM3d6QCiSOFeGY/sCAwANoz+5XE70mjVakxKe77+PS6dOuU8sy4ELc+DJRalt4QANp4B1MZkaxt6Aqz+rc/oBzG2gal/we0tvOHtmdDSPfv6ZpDNnVH2mVla49+1LuU6dMDHTMwN1F2nV/KlGnwuSCVwePoGR/8VxZP1NEmJSVX2OLjY071UNr6rS8yWTyfTtLihhhEAXGI+mOAdJhIO2SFeKc5C2a9VKiHTBi8mrPhklrv/Vvv6XlMyoKB7Onq0KLXV7910c6tYt4VEVI6nRcH4WKgezKr3BPR+h08+RiGQptF3JgAD9pmQFJSMti7+XXiZDkYPrF+RK7Xb5cER/FjmwE8m1HSSjroHkciQ3hriDB4lcvFjVdu/XD5e38+EJoDSEK4A4n3gawhTi3NlKWjmvoGe95s6ZSP5dfV01wVG5rjst3q2RW5zfR8rHV4pzf6A/0nOkQC6XE716NdEbN6r69IpzuRyuLILHx6W2qSXUn5RnTn2RkB4LN36HiEPa/eVbQ7UBesPZ5Tk5PN2+nag1a5BlZKj6HerXx2v4cCzd9EwMpSKV6Duv0WcGtABew2DptIzULE5v/4+bJx6p+kxNTajV2pfabf0wL+pwFMFzQQh0gXE8K86VaIp0TXGupIRF+tdff01ycjJz5sxh+/btHDlyhNmzZxf7OEJDQ/n7778ZOnRosZ9bUABe9ckocf2v9vW/pMgyM3nw7bdkK0I6HRs0wNUIB/qXhpxMOPet2mXarT5Ufbdkx6QgPVsq6aWo0EUzT+jqX7TnkMnkHFx5TbWi6OxpR8v3ahTOd+AwkmkXgClSWHsBMsESTpwgYt481cSRa/fuuPXsafwBCphz/jQdvjwFDxWR2s5W8G1D8NYjzm+dfMSRdTdU4jygoRfNelfD1PSZ5/A/YCWgtDSqgvTcaAhLXeLca9gwyr31lu6T/7dRne9tai6lZehxRy8yZNlwfwfcXqs2U4Q83dkBUm/f5tHChaTdU6+2Wzg74zlsGI6NG+t+38mBS8AuQCN6Hh+kVXMDPy9yuZz7F6M5vvk2aUlqBzk3X0ea965euBQOQbEjBLrAOPbuzS3OlYwdK92s3r+v+/EbN/IOEy0GOnXqRCdds7LFQGhoKEuXLhUC/UXgBZ2MKjLE9b/a1/+SIpfLebRokepm2crLi/Iff/xqmcJd/VntNG3rCbU/KRWmcHI5/HgFwhWlt3wd4MOgoh/a2Z13Cb8uxQpb21rQZkgwltaFuA2+CPyl0e4GVM7/YZIvXiR81izkinDjch074t6/v/EHKGBYe0waTDwFkYoIaBdrSZx76vFZu37kIcc23VK1azT1pkmPgNyfoVvAGtTiPAB4l7zF+fDhlOvYUffJHx6CW6vV7ZCPwa2OwesrNNHn4NovaiNFkELqA94Dn7Z6w9lzUlOJWr2a2J07tU3gOnSQTODs9DzBscB24LZGnxXQFmiIQRO45Nh0jv1xiwfXn6iHamlG/U6Vqd7UO/cEiqDUIyyZSzmfffYZQUFBODk5cf36db3brVy5kjp16hASEsKHH35Y9E7sAwbAvHn6H9cnzkHarwDi3MTEhBkzZtCgQQP8/f35559/+OKLL6hduzY1a9bk2rVrADx+/JhWrVpRt25datasyZgxY1RfiposX76c7hp1NCdOnEjlypVp2LAh48aNo57CIfXQoUOEhIQwcuRIgoODqVmzJmfPngWkPP22bdtSr149atasSd++fUlNTVUdv23btvTp04egoCDq1avHPcXN4PDhw7l+/TohISElNkkgMJK8JqP8/XOLMyXKyagXGXH9r/b1v6TE7t1L3P79gJT76TNhgv4b5ZeRB39D+D7pb1NLqDeheB2vDbAtFI5GSn/bmsOEOlAY3ayLu+ejuLQ/DABTExNaDwrEsTDJ7XeBzRrtNkDt/B8m5cYNwqZPR664Z3Nq1QrPYcOMnzgqoDiPSoXPT6rFubsNzGykX5xfOfhAS5wHtqygW5xfB1ajFuc1MG7l3JA4j7kIl39Ut6sNBK/mBq+vUKREwpnpkku8SpybgE87aLUE/HSXTpPL5SQcP86dESN4umOH6j7U2s8P/9mz8Ro+XPd3Tg5wBJiHtjivCXwENEKvOJfJ5Fw99IBNM05qiXPfQBe6T2hEzeblhTh/QRECvZTTuXNn9u7dS4UK+guAhoaG8u2337J3714uXLhAVFQUq1atKvrBfPihYZGui3nzdOduGomjoyOnT59m5syZdO7cmWbNmnHhwgUGDBjAN998A4CTkxM7duzg3LlzXL58mXv37rF582aDx92xYwc7d+7k0qVLnDhxgrt372o9fu3aNQYPHsylS5f44IMPmDhxIgBmZmasXbuWs2fPcvXqVRwdHVm0aJFqv1OnTvHdd99x5coV3njjDWbOnAnAzz//TI0aNbh48SLbt28v8PMhKAZKYDKqVCGu/9W+/peQ1Js3iVy6VNX2HjMGa1/fEhxRMRN3C679rG7X+gAc/UpsOJpcegK/acyHfRwMXkU8b/AkPJHDa9QLHA27VFaZZBWIx0grxDmKdn2kvOB8knbvHmFTpqhykx0bNqT8hx/mQ5w/k3NuXQ4az8hTnEemSOJcWcbO2w6+awRutrq3v7w/jJN/3lG1g1v70uidKrnHeRVYi/p5CQL6IOVOK1AZwj0b1q5PnCfcg7PfSKHmIInkSl0NXl+ByUqF67/DvyMh6pS637kGNJ8LtUaBpYPOXTOjowmbNo0HM2aoHNpNrazwHDyYynPnYhsQoPucD4FFwB7UkxqOSJMafRV/6+FpRBLbfzjLia13yMqUnnRbRyveGBREmyG1sC9rrX9nQalHCPRSTtOmTfH2Nvxlu337dt566y3c3NwwMTFh8ODBbNq06fkMKD8ivZDiHKCXIj+wTp06mJqa0lHxJV63bl3V6rRMJmP8+PEEBwdTu3Ztzp49y8WLFw0e9+DBg/Ts2RM7OztMTU0Z8MxNdUBAgGpFvXHjxioBL5fLmTt3LrVr16ZWrVrs2rVL61zNmjXDV3Hjp7mf4AWjBCajShXi+l/t63+JyIqL48GMGaoVSpfOnXFq0aKER1WMZMRLJamUAsfvbSj/WkmOSEVMGsy8oLKro2claFjE2SFpSZns06jTXbWBJzVb6l/wyJNEJOMzZTpyANCJfNc6z3j4kNBJk8hJkRKN7UNCqDB+vH4372dRinPNnPNG34K9l8HdIpIlcf5EMf7ydlJYu65gArlczvm99zm1XV2Orm67itR/u1JucX4JWIe6DFgI0BPd4lzDrd1r+HD9OefpsXBmmjr326ORVMasqHMf5HJ4eAAODYd7W9SfFStnqP0pNP4OylTSvWtODk+2buXOyJFaDu0O9epRZdEiXN55BxNzHeEgmUh55osBRfQIJkBjYCxS5IEesjNzOL39P/6cfYaY8ERVf/Wm3nSf0JCKIW5GTfIkZcKSa3luJighRA76S0B4eLjWCruPjw8PHz58fidU5mAaWkmqWLFIblatraUZQDMzM6w0atSamZmpwvh/+OEHnj59yqlTp7C2tubjjz8mPT1d5/GUyOVyg19gyvM+e661a9fy77//cvjwYRwcHJg/fz6HDx/Ocz/BC4jy/asvnFmTl1GcieuX/n9Vr/8lQJ6dTfisWaoVLbvAQDwGDizZQRUnshzJsT1dUaPJuSbUGFyyY1KQmQMzzkNSltSu6wp9qxbtOXKyZfzz2xWS46UVajdfR5r1rFZw34EMJHGeoGh7k2uF2Bgyo6O5P2kS2YmSuLKtXh3fiRMxtTBgza11AKU4V5iPWZVVhLUbFufhyTDhFCieDnwdpFJqTla5t5XL5ZzZcVeVFgBQr2Mlar/pl3vjC8Am1DMtdYCuaC0B6hTnhlbOczLh7LeQrgjbdgqA2uP05n0XmNjrcG0ZJKgjBDC1AP8uULmHVEJND3pN4N5/H8emTfW/z24C21C/jwA8gC5IZnAGiLgVy9ENN0l8mqbqK+tuR7Ne1fCo5GR4ZwVyOfz7CJbdAJkM3s3jnIKSQQj0lwTNLwJd+ddFSl7iHKTHf/yxWG5a4+Li8PDwwNramqioKP744w/Vyrs+WrVqxeTJkxk7dizW1tZGpwTExcVRrlw5HBwcSEpKYvny5fj752016+joSEJCQp7bCUoZismo5AdPmBT0k9ZD0658gH1OUpFNRpVKxPW/2tf/gvN4+XJSrl4FwKJcOXzGj9e9mvWycnMFPL0i/W3lDHXGS+7XpYCl1+GO4ifRwxY+DYGiTpU9ueU2j+/FA4rQ3//VwsyigIGjOUjh28rVTidylQwzhuz4eEInTSLriSQ8bfz98Zs8GVNrI8ORdYrzvMPaQxOlOucJCnNvf0eY1gAcdYxfLpdzausdrvwbrupr1KUKQa10KLmzwFbU4rw+ktB85rWMXrs2tzjXt3Iul8OlHyFekfNu7QL1vwSzfD7ZhkiLkcqmPTqi3e/RGGr8D2z1h3LkpKQQtXIlsXv25M8ELgmpJN8VjT5zoDXQDIMTPenJmZzceoc7Zx+r+szMTAh5syLBrX2Nfl9HpcKiq3BeMe/hUDq+DgQ6EC/NS0CFChV48OCBqh0eHk758s+pLqQu92J96CpN9BwYM2YMPXr0ICQkBG9vb95444089+nUqRPHjx8nODgYLy8vGjVqRFxcXJ779e/fn23btlGjRg28vb1p3rw5ERERee5Xq1YtAgICCAwMxN/fX+ShvygoJ6PMdOedAcU6GVXsiOt/ta//BSb+8GGebNsGgIm5OT5ffIG5k1PJDqo4eXQU7m2V/jYxg7qf663VXNzsfQB/KbSfpalkCmdv5OKxsdw4FsH1Y9Jvs5mZCW3+F4RdGR1LxcYgB/4ElIusNsAgwMDXgi5ykpO5P2kSGY+k+tRW3t74TZ1qvFlhZhKcmpRvcX43ASadVkcrVC4DU+uDgx5xfmLzba4dUUdhNu0eQI3mOu4pTyM9L0oaAW+TW5yvX0/0+vWqtkFxDnB7DTxSRCaaWUm1zq2c9G+fH7JS4e4muLcNZOpSZDj4Qc0h4BKsd1e5XE7isWNELl1Klsb9orWfH96jR+vPM5cDZ4C9qFMjQHL87wyU0z9cuVzOf2cfc3LrHdJTslT9Hv5ONO9dDSd34947OTLYHgqrb0OmTN1fX08ZdkHJYxIfH/+cl1sFRUFQUBAbNmygRo3ciSmhoaG0a9eOw4cP4+rqSp8+fXjzzTcZPNhwKJuDgwOmpupZt2dD5XORH3GuSSkN/0xKSsLBwQGZTMaQIUPw8vJi+vTpJT2sUkue74+XDY33e7KZg/4VVCWl9H1eYMT1F9n1y2QykpKSdD4myM2zv035JT00lLuffqoy3/IaMYJyHToU1fBKP0kP4Ogn6tzdmsOgogFBVIzcioPPT4EiJZyPg6GVYX2Zbx7fjWf3wvPk5Ei3ty3frUHVhgUoTq7kAPCP4m8zYDBQMX+HkKWnc//LL0m9Ja0KW7q64j9rFhYuLsYdIDNZEucJinxwVVi74cWYO/Ew6QwotV1VJ0mc2+mYEJHL5RzdcJObJ6QJBBOgeZ/qBDTSETp/Atih0W4KdCC3ON+4kSiNCEXPoUNxeftt/QOOPAHnvlU0TKRa5x4NDV2icchl8GAf3FoFmRrRjJaOENAPfN4EE/3fOZlRUTxavJikc+dUfaZWVrj37Uu5Tp30ewdEI0UYhGn02QIdkfL0DUSNJMSkcmzjLSJux6r6rKzNqd+pMtWaeBmdqnEnHhZchXvqdHVcrGF4TajvKn6bSitiBb2U8+mnn7J7926ioqLo0qULdnZ2XLhwgQ8++ID27dvToUMH/Pz8+OKLL2jbti0ymYwWLVrQr1+/oh3IihWGxXnFivrD3seOBSenUudu3L9/f0JDQ0lLS6NOnTp89tlnJT0kQWmhIJNRxRQxUiyI63+1r/8FJiclhQfffqsS52Vbt8a5ffsSHlUxkpUiuV4rxbl3K/DTk+dbzMRnwIwLanH+tl/Ri/PkuHT++e2KSpwHtaxQOHF+AbU4B+hB/sV5ZiZh06erxLl5mTL4TZtmvDjPSoFTX2mIcydo9E2e4vxWHHx1BlIVVjg1ysLkemCrQ5zLZHKOrLvB7dNSDL8J0LJvDao00PHcHQV2a7RbINXqfkYvxvzxh7Y4HzLEsDhPeaxdTq3G4MKLc7kcos9K4ezJ6pB9TM0lw8QqvQyWG5RnZ/Pkzz+JXrcOWaZ6xd2hfn28hg/H0k3PEnQWcAg4jNrVHqT8/PaAgYXvnGwZVw4+4MLe+ypzQwD/EDcad6uKraNxkSDp2dKK+fZQdQaCCfCWH/SrCjbmUg66oHQiVtBfYfK1gh4VBa1a6a4PrFw50ndTW706HDwI7kVszyooVl6ZFfQVK+AZI6kcTIm29pAa3uUh4iFu6Y8xQ8ev2/LlpW4yKl+I6y/y6xcr6PmjoCvocrmcB9Onk3j6NAA2lSrhP2sWppZFmLtampHLJXGuLBHl6A9NZxdt7m4ByZFJYdZXFIuBgc5SDrR5EdYSys7KYeeP51XO1t5VnWk3PBhTswKe5C7wO2pn8rZAy/wdQp6Tw4PvviPx5EkAzOzsqDhjBjYVjVT5SnEeryiQbVlGCmt3MPxbfCMOvtYQ54HOkjjXVV9eliPj3zXX+e9cFCDViX+tXw0q1fXIvfERpJJgSl5DqgH/rDjfvJnHy5er2h6DBuHa1UB5tJxMOP4ZJCgq33g1l0zhCuPYnnAPrv8KTy9r93s2g+oDDeaZg1Sj/tGCBaRrpJBalCuH57BhODZqpH8F+x5S6P8TjT5npNz8yoaHHBOWyJH1N3j6KFnVZ+9kRdOe1fCpaeSEDnAuRso1j1Z7yeHnAKMDIUAj00X8NpVexAq6wDjc3SWRrSHS0yysOTFnNZHtupFzC8p1+ZBGWFNu7HD1fkKcC1402rWT3rcak1FmyPBMf2TcZFS7dsU21OeCuP5X+/pfYGI2blSJc3MHB3wmTHh1xDlIubVKcW5hD/UmlApxDrDillqcO1vBZ7WLVpzL5XKOrr+pEucOzja8PjCw4OI8CqnWuVKcN0RaKc7nmCJ++kklzk2trPCdPDmf4nxyvsX51acw5SykK1Zua5WDSXV1i/OcbBkHV17j/qVoQMrXb9U/kIohOlaGDwL7NNpvAK/n3uzJtm3a4nzgQMPiXC6HywvU4tzOC4JGF1ycp0TCrdXqPHYlTgFQfRCUq2lw9+ykJKKWLyf2779VfSYmJpTr1Am3vn0xs9Hj7J6KNHlxTqPPFGiO9DwZ8FnITM/m3O57XPs3XGu1O7BlBep2rISFlXHu9fEZ8Mt1OByp7rMwhT5V4J2KRfuZEzxfhEAXGI9CpN/r3I9F3m/yW+vRxJlYw371JpbWw+j5UwtGfT+IRjaJQpwLXjx0TEYB2jnGukpwvSyTUeL6X+3rf0FJOneO6DVrAOlmuvynn+oPP30ZibkAN5XhxCZS/eY8VgiLi8OPYKsiA87MBD6vA2UL6Nemj6uHwlUO1+YWZrz5fi2sdSVaG0MisBztWudvka9a53K5nMe//krcfukGycTcHN8vv8SuenXjDpCVqhDnCidzS0cprD0PcX75KUw5ozYCC3GBL+uCLn2XkyVj/+9XCLsmLfWamZnQenAQvoGuuTfej9a9Hm2AVrk3e7pzJ5HLlqna7v364dqtm8Ex8+AviDgo/W1mDXW/AAtbw/voIiMB7myAsN0g14grt/WQVsw9mhgU/XK5nPiDB3n866+qEngANlWq4D1yJDaV9Sx/y5HqwO8CUjT6KwDvIJVQM8CDa0849sctkuPUDnLlvOxp3rs6rr6OhndWjR32R8CvNyBZ7SVHcDkYGQheRvoQCkoPQqAL8sX8KHc+GfgX2XLdX3KZMlhtVp3Vn53kf75pLHKxyW8VEoGg5HlWpOkyANMUaS+bOBPX/2pf/wtG5uPHhM+erSp55PbeezjUqVPCoypGUqPg/GxUmaZV3wW3uiU6JCWhiTBfo6zUkOpQvYjN5CNuxXLqT3Ud65Z9q+PsZV+wg2UCKyl0rfOY9evVVQRMTKgwbhz2ISHG7ZydBqe/zi3OHX0N7nbxCUw7qxbn9VzhizpgqWPs2Vk5/PPrFcJvPAXA3NyUN4bUokL1ZyzF5Ug5+Ac1+tqhM5rg6Z49PFqyRNV2f/dd3Hr2NDhm0mOl/HAlwR+Co5/hfZ4lM1mqWHB/u9p7AaTnrXJP8O0AZoYnazIePiRi0SJSrqjfrGY2Nrj160e5jh0x0ZdyE4tU01yjjDpWSOkQDTE4qZOamMGJzbe5dzFa1Wdubkqd9v4EvlYBMyOXux+lwMKr0uSMEgcL+F91eN27cFkCgpJDCHSB0Uw7KxmOGDuN/GuYDdF/wZZ2IqxG8AKiFGl79+rPqf7wQ8kAsV27l0+ciet/ta/fSFatWsXixYu5desWM2bMYOjQoarHfv75Z1asWIGJiQmmpqZ8/PHHdDUU6loAZBkZPPj2W3JSpKUrx4YNce3Ro0jPUarJyYSz30KWIo/UvYFkfFUKSMmCb89DhmIxs7U3dDSsMfNN4pM09v9+RRUWXPtNP/xrF/CzmAOsAx4p2mWAfuS71vmT7duJWrtW1fYeM4YyTZoYt3N2OpyeAnGK6B0LB4U49zO427kY+OYcZCnEeUN3GB8CFrrEeWYO+5Zd5uEtKefA3MKMtkNr4VXVWXtDOfAXktGZkg5INbufIXbfPh4tWqRqu/XsiWvv3gbHDMD13yA7Vfq7whvgpePg+shOh9CdUmpHlsbStZkV+L8j/ctjJV6WmUnMH38Qs2kT8uxsVX+Zpk3xfP99LMrpqYGWAxxDmrzI1uiviRRtUUb/OeVyObdOPuL0n/+Rka7e2buqM017BlDG1bjogWwZbLkH6/9Tv+4ALb3g/epQ0KqCgtKBEOgCo9h0VynOc1O1jBQ+dSNe7c6qZEcYfH4S5hj52yQQlCrc3fM2PHuRDdHyQlz/q339RhASEsLvv//ODz/8kOux6tWrs3fvXsqUKcPDhw9p2bIl9erVw8fHp0jOLZfLiVi4kDRFBRErb2/Kf/SR0eWHXnjkcriySF0X29YTQj4uFUtmcjnMvQyRCu1VyVEKtS3KoWVlSEIzI00SOb41Xajbwb9gB5MDOwHFojXWSLXOjYswVhF34ACRv/yiansOGULZN94wbmelOI+9JrUt7KHR9DzF+ZloaSJEef/V2F1/jn9WRg5/L73Eo/+kOt4Wlma0HRaMZ+VnwhrkSPnURzX63gJ03MvFHTzIo5/UZShdu3fH7b338v4cPr0Kj/6V/rZwkPLDjSE7XQpjv7sZMjVqh5mag09badXc2ln//gqSLlwgcvFiMiLVCduW7u54jRiBQ10DESjhSKXTHmv0lUGqAZ+7ErIW8VEpHN1wk8i78ao+a1sLGnWtQuV6HkZ/d92Kg5+uQpiGv5ubjfQZq6sjQ0Hw4iEEuiBPZHKYcCp3//vVYVwIVHGS2jFpsPQ6TD+nNicB+PEKfBoCHvlMKTIxMSEpKQl7+wKGqukgJCSEEydOYKPP5EMgEAgERhMUFASg03W9ZUu15XX58uVxc3MjIiKiyAR67J49xB+UYm9Nra3xmTABM7tXKNnywV/wUJEYbGYl1Yw2UDKqONl4F05JpuA4WMAEPaHWBUUul/Pv6uvERkpu12VcbWnZr0bBJ2eOAsr7HDOgL5BPC4PEU6eI+FFdJsytd29cOnc2buecDDgzFWKvSm0LO2g4DcoYnnA4FQXfaZSua+YJnwTrFueZadnsXXKRqPtS/L6FlRnth4fg7u+kvaEcqYzaMY2+TkCj3MeMP3yYiLlzVeklLp07496/f96vg1wGV9Xh8FTrL4WkGyIzCcL2SKHsmrXMMYHyr0upHbZ5v2hZsbFELltGwpEj6iOYm+Pyzju49eqFqZWeped0JJO8k2jXLWsEvIkU2q6HnGwZl/4J4+Lf91UlAAGq1POgYZcq2DgYF6aRlg2rbsPOUO0hdK4IfavoNgIUvJiIl1KQJ/sfwp0E7b4fmsBHwdp9rjYwsa4UXtNymyTsQfrh+OU6TKpXPOM1xMWLF0t6CIIXkOTYdKZ13KzVN2lXN+ydrUtoRMWLuP5X+/qLgkOHDhEfH0+IsXm4eZBy4waRS5eq2t5jxmBdRML/hSDuJlzTEDi1xuSZo1xcnI2GNQrjcRPgkxBwK4DnlyEu/h3K/ctS7q6ltTlvvl8LK5sCmsJdRbt0WFegUv4OkXz5MuEzZyJXFJYu99ZbuL37rnE752TCmWnwVJH/rBTnToZrch1/DLMugFLvtfCEj4NBl3F9RloWexdfJDpMWnG2sjan3cgQ3HyficWWI5mdHdfo6wI0yH3MhBMnePj99ypxXq5jRzz+9z/jJkkij0FSqPR3mUrg86b+bVMi4f42eLAPZJkaD5hI5diq9smzJjyAXCbj6a5dRK9aRU6auv6YXWAgXiNHYm2ojOx1YDuSgaASDyQTuDyqz0bdi+fI+pvERanD8B3L2dCsVzW8A/Je6VdyKgp+vgZPNNLs/R1hTBBUMhBSL3gxEQJdkCfLnil9XqMsjK2lf/tmnjAwAH67qX2ML+vmP7xtzpw57Nu3j5iYGKZMmUKfPn0AOHPmDOPHjycxMRGZTMbEiRPp1q0boaGh1KtXj5EjR7Jr1y4SEhKYP38+HTp0ALRX5Y8cOcLIkSMxMTGhZcuWbN++nV27dhEYGIifnx+DBg3ir7/+IjIykv/97398+eWX+Ru84KUiMz07741eYsT1v5rX3759e27duqXzscOHD1O+fN43xteuXWPUqFH89ttvRRK9lBUbS/h33yHPkUK1XLp0wal580If94UhPQ7OzgCZ4j1ZsTN457MG2HMiMgXmXFKv7r1XtehDbsOuxnB2txTWbwK06l8TJ/cCRg48ADZqtFsDtfN3iNQ7dwibNg1ZlmSf7dSyJZ5DhxonVHMy4ex0eHJJapvbQoOp4FTF4G5HHknPs3IhpJW3dF9mquOU6SmSOFeWoLO2taD9qNq4lHfQ3lCOJEKVkQQmSAJUx+JK0rlzhM+apZqQcG7bFs9hw4yPYLi7Vf139UFg8sysgiwbIo9LK+bKqAIVJlKuepU+ebraK0m9c4dHixaR9t9/qj5zBwc8/vc/nF5/Xf+4E4AdSAJdtSPS+6QZBs0DM9KyOLPjLjeORaj6TE1MCHrdhzrtKmJuZEhJbLoUnXpMI6Te0lT6bHXy0z0hYzRaEx6C0oQQ6II8ufhEu/2/6nkL7f9V1xboD5IhPjP/pVVMTEw4duwY9+7do0GDBjRr1gwHBweGDRvGrl278PT05MmTJ9StW5emTZsC8PTpU+rWrcvUqVPZu3cvH374oUqgK8nIyKBPnz6sW7eO5s2bs3XrVhYsWKC1TXx8PMePHycmJobKlSszaNAgvL2983cBAoFA8AKzZ8+evDcywM2bN+nVqxcLFiygcePGhR6PPDub8JkzyYqVDK7sgoLwGDiw0Md9YZBlw/lZkKEsKh4olZAqBWTkSLnQKYoyT43doUc+V6LzIj4qhYMrr6na9d6qhE9Nl4Id7CmSY7ty7q0OOut6GyI9PJywyZORpUvLmg7161N+7FgjxXmWZPAXc0Fqm1lDwylQtqrB3f59BN9fVE+CtPaGMfrEeXImuxdd5GmElKxsbWdBx9F1crvcy4E/AaXXkAlSJIGOVOzkixd58M03KlO1sq+/jteoUcaL87SnkKBhe26nuK+SyyD2Bjw+IeWmZ8Rr72dmDRXaQMVOYJdH7TIFOSkpRK1cSeyePaqVfgDnN9/EfeBAzB0cdO8oQ5qo+AvJ2V9JVaRwfwML33K5nNBLMRzffJvUxAxVv2sFR5r1rpZ7YkTvceCvcFh+S/2ZAqjjIuWauxc2KiXmAtxYDsFTC3kgwfNACHRBniRmabcDjYjICdKxTUJG/gX6kCFDAPD396dZs2YcOXIEJycn7t27R/v27VXbyeVybt26ha+vL3Z2dnRW5H01btyYu3fv5jrurVu3sLGxobli1eWdd97ByclJa5u+ffsC4Orqir+/P/fv3xcCXSAQCIzk1q1b9OjRgx9//JFWrXQUTS4Aj5cvJ+W6tJxl4eKCz/jxmJgVYXJzaefmSvWKonU5qDteMscqYeRyWHQVQhWmVd520opuUZrCZaRlsW/ZZbIUtvD+IW4Ev1HAsP5UYIXifwB/pNXifIw3Mzqa0EmTyE6SLtouMBCfzz/HxNyI10OWDedmQMw5qW1mDQ2nQtlqBnd7Vpy/WQFG6zHfS0vKZPfCC6o8fRsHSzqOrkNZj2eiDXSJ8+7ojCRIuXpVK1qgTLNmeI8Zk7/cf7Nn8q33DwL7ClIlgmdFOUgCvkIbyQDO0jhPIrlcTsLhw0QuW0Z2vPqY1r6+eI0aZbge/WMkE7hwzTEgmeTVwuB7JDkuneObbhF2Vb2yZWFpRt0O/tRsWQFTXbMoOghPhgVX4Hqcus/RUvJ+aulVyM9VRgJc/1WqPW9h3GSBoPgp+W91QanH5pl7n8gU3dtpbZOau8+2CN5tJiYmyOVyatWqxeHDh3M9HhoairW1Oi/UzMyMnJycXNvJ5fI8f1CePU529qsZ4iqQbmw+Wt0xV9+rgrj+V/v6DbFhwwamTJlCfHw8u3fvZt68eaxbt47g4GBVGtLkyZOZPHkyAFOmTKF169YFOlf8v/+qa0ubm+PzxReYl3mFki8fHZHqPYMkyut+DlZOJTokJTvD4IAiktfaTPKjsS1gSrgu5HI5h1ZeJz5aurlw9rSjxbsFNIXLBtYASg3limQKl495nuyEBEInTSLrqVR82qZSJXwnTcLU0ojvBVk2nJ8J0QpFbGYFDSaDswHRCBx4CPMuq8V5Ox8YWVO3WEtNzGD3gguqvGdbRys6flAbJzcd4nwLoJgnwAToAYToOObt24ROmYIsU1pSdmzYkAqffJL/CTJLB/BoBI9PqvuSw7W3MTUH90bg1xGc9VykHjIePuTR4sUkX76sPpy1Ne7vvku5t9/WP4GSCRwAjqB+kgHqI9V+N5CdI5PJuXH0IWd23lVNIAH41HChSY+qODgbl9qTlQN/3IU/7mlXRWrtLUWmFupnRy6Hhwckca4syygotQiBLsgTf0e4r/FZXv8fDDA8ycu6O9ptBwsoVwA/pd9++41JkyYRGhrK0aNH+emnn7C3t+fOnTscOHCA11+X4tEuXrxIjRp51LfQoFq1aqSkpHDs2DGaNm3Ktm3biNeYZRUINDGzMC14GOVLgLj+V/v6DdGrVy969dJdd/vPP/8ssvOkh4YSMX++qu01bBi2VQ2HAr9UJIbCJbVDODWG5LnaWlxci4VfNbxqPqwFFYqu+AoAZ3fd48F1SVFb21rQZkgwFlYFiJxQCtL7irYdMACD4utZclJSCP3qKzIeSQXTrby98ZsyBTNbI2KOZdlwfrZanJpaQv3JUC7Q4G77wuGnK2rd2N4HRujRrSnx6exacIGEGGkyw97Jig6j6lDmWac+GdJzcV7RNgF6As8YAAOkh4UR9vXX6lD+evWoMH68cdECuqg7AeJvQ8x5iD4n/W1qAa61wbMpuDfId0UCWUYGMRs3ErNli1ZNc8dGjfAcOhRLVwNmCHeQogg0VqxxRTLIq2j4vLGPkjmy/obKgA+kCdwm3apSMcTN6Emka7HSqvlDjUUwT1sYFQjBhf35SYmEKwvVXgcgPb813i/kgQXPCyHQBXnybhXYr/a4YG84HI2UzOB0EZUq1WfUpHflghlZWFlZ0bRpU2JiYvjpp5+ooHDZ3LFjB+PGjeOjjz4iKysLHx+ffN0MWllZsXbtWoYPH46NjQ2tWrXC3d2dMq/SaoxAIBC8AOQkJ/Pg229VK3dl27ShbNu2JTyqYiQrRcpVzlHks5ZvDb4dDO9TTMSmS2W+lE7i3fz13xsUlPsXo7m4LxSQNOTrAwNxdCmg2eA/wEXF3+ZAfwzmEz+LLDOTsGnTSLsnmdRZuLjgN22acZEcshy4OBceKyzSTS2h/iRwCTK4298Kca7kbT8p1FmX7kuOS2fXgvMkPpFcyu3LWtNxdJ3cz5cucd4b0DGUjEePtEP5g4Lw+eILTC0KESJhYgJlA6R/VftItc1NzMCsYMdMPHOGyJ9/JjM6WtVn6eaG5/DhONavr3/HZGAncFmjzwxoBbTAoErKzsrhwt5QLh8IQyZTL7kHNPKiYefKWBkZQpKSJeWZ732gMQQTeKci9KlSyPKEsmy4uwXurAeZRr6qVwuo+T5YOEKSWE0vjZjEx8fL895M8DLi4OCgVbs2PDxcJYA1Sc0C75WSyZuSslbwS0t4x1/bmORMNAw8oJ03A3ChB4SUsgWopKQkHBQGIQcPHmTAgAGEhobqrOcr0P/+EAgEhpHJZCSJmyCj0fxtksvlhE2bRtIZKRzYpnJl/GfONC6U+GVALoez30CUwlq7TCVoMit3Hm8JkC2DiafUv/chLvB1vUK6Sj9D3OMUtn1/hqxMKWy4UZcqBLUqYDm988Amxd8mwLtATeN3l2dn82DGDBJPnwbA3NER/5kzsTKikgFyGVycJ+X9grRaXO9LcKtjcLe9D2ChxoJHZz/9Rr3JsQpx/lQS547lbOgwunbu8GoZsBlQeNMZEueZ0dHcGz+erCdS9IJNlSpU/OYbzIqgGkNRkBkdTeTSpSSeOqXqMzE3x7VrV1x79tRf01yOFNa/B0jT6K+ItGqeR+WBiFuxHN14UzURAuDkZkuz3tXwrFTWqLHL5ZIz+9LrEKf2kqOqE3wQCH55lIXPk7hbcPknSApT99m4QdBIcJPc/8RvU+lFrKAL8sTWQqqzOPWcui8uA7r/DZUcpVArazM4HAmno3Pv36Z86RPnAJs3b2bu3LnIZDKsrKxYt26dEOcCgUBQiojZsEElzs0dHKSVu1dFnAP894danFs4SKHBpUCcg1SpRSnOXaxhXEjRivP0lCz+/uWSSpxXrutO4GsFnCS+j2T8paQ9+RPncjkP589XiXNTa2v8pkwxUpzL4fICDXFuLr2OeYjz3WGwWG1YzzsVYVA13eI86Wkau346T1KcFILu6GJDx9F1sC/7TG6hDGmS4qKibYokznVE2GfFxRH65ZcqcW7t5yeF8pcCcS7PzubJ1q1Er1+viqwBsK9VC68RIwy/LtFI4eyhGn02SO+Juhg0gUtPzuTUtv+4fTpS1WdmZkJwGz9C3vDDzMK4D8CTNOm11bxntjaD/gHQ0Ve3I7/RZKXCrZUQuht1UoQJ+HeBqu+CeQHyTQXFjhDoAqP4si6cjJZCrTS5mwgLni1RqYGfA6wqmB/Qc2fgwIEMfJXK8wgEAsELROKZM0SvXQtIBqHlx43D0s2thEdVjESfg1urFQ0TqP0p2JaO6z/wEHaESn+bm8IXdSSX6aJCliPj4IqrqhXKct4ONO9dvWCmcDHAakDp3dUQaGr87nK5nMe//kr8QUlgm1pY4DtpEjaVKxuzM1xZBOH7pLaJGdQZD+46iotrsCsMftYQ5139YWCAbnGeEJPK7gXnSY6XlmHLuNrScXRt7JyMEOd90DlRkZOcLOXZR0pC1Mrbm4rTp+svS1aMJF+6xKPFi8mIUOdeWpQti8eQIZRp3lz/eyQLOAQcRv1eAMkQrwNgwDdBLpfz39nHnNx6h3SNmmce/k4061UttzO+HmRy6bVdeQvSNcbQwE3yFCho5oaKyBNw9Wd1GUaAMpWh1mgp+kbwwiAEusAoLMxgS1vosw92hOW9PUD1srCnYxHUahQIBALBK0VGZCQPv/9eVbvYvV8/HGrrqPv0spLyGC7MRrUCFtA3zxXX4uJeonbY9YiaUlhuUXJm510e3pJEhrWdBW2GBGFekGTcZKRyaspI5KpI5bLyofNj/vhDXT3AxIQKn32Gfa1aee8ol8O1JfBgr9Q2MZUmWTwaGdxtZygsua5u96gE/arqFufx0Sns+umCqt52WXc72o+qjV2ZZ0K7dYnzdwEd3rqy9HRCp0whPTQUAEtXV0mcl7BHT9bTpzz+7TfiNSr4mJiY4PzWW7j37YuZnQGRfBfYhtq5HyTvgc5AFcPnTYhJ5djGW0TcVoteK2tz6neqTLUmXkZPGt1PlLwE7iSo+8pawbAa0MSjkKXT0p5I7zVNZ3wzKwh4D/zeBtNXqBTlS4IQ6AKjsbOAre0kM4sfL8OVWN3bedlJXzgf1SpkSQiBoJSQHJfOd13/1Or7fEuX3OGDLyni+l/t6y9uZJmZPPj2W3JSJDvjMk2a4NK9ewmPqhjJyYBz30rmcCAJuso9S3ZMCpKz4NtzkKkoAdXOR6rFXZTcuxDF5QOSY5apqQlvDA4yukyVFllIK+fKexVPpBXjfGiVp3v2ELVqlartPWYMjo0MC2xAEuc3foPQXYoOEwj5BLyaGdxt231YpuGI37MSvKdPnEelsGuBWpw7e9rTYVTt3CUgnxXnZkjPgy5xnpVF2DffkHrzJgDmZcrgN20aFi4ll6coz87m6Y4dRK1dq3KRB7CtVg2vESOw8ffXv3MKUp75eY0+UyQDuFaAAR+3nGwZVw4+4MLe+2Rr1DzzD3GjUdequSdB9JCRA+vvwJb70gq6kvY+MCBAurcuMHIZhO2BmysgWyOZ3rWulGteSiJuBPlHCHRBvjAzlQxKBleTnNx3hsHjVMiWSzloLb3gbV9pxV0geGmQQ0p8Rq6+VwZx/a/29Rczj3/7TbV6Z1W+PN4ffliw0OYXEbkcriyGREUdMDtvCP6okMtrRYNcDt9fhCiFDqjqBEMNl+7ON7GPkvl3jVqhNuxSBc/KxpluaSFHMkJTOmM7IDm2G6epAEg4epTIxYtVbc/Bgyn7xhtGnFsON1fCvT8VHSYQ8hF4tzC425/3tcvVGRLncY9T2LXgPGlJUv51OS9JnFvb6xDnfwDK6loGxLlcJuPhDz+QfPGitKmdHX5Tp2Ll7W1w3M+TlKtXebR4MekP1Bbn5o6OuA8cSNk33tD/vSBHEuW70TaB80EygfMwfN7osASOrLtJbGSyqs/eyYqmPavlq+TmxSdStMnjVHVfBXsYHQg18lE9QCeJoZK3QfwtdZ+VE9QcCp7NSsV3hqDgCIEuKBAmJtDcS/onEAgEAkFRkXD0KCAZcflMmGBcfemXhbA98HC/9LeZFdSbABal4/rX3oGzMdLfDhbwRe2inYxPT87k76WXyM6SknOr1POgZgsjTNh08Q/q0lkWSOI8HxHayRcvEq6RYuHarRsu77xj3M6318HdTep2rdFQvpXBXZ4V530qSyW2dGms2EfJ7F54gbRkhTj3dpDE+bNLsfkR53I5jxYvVn/2LC3xnTzZ8Or0cyQrNlbK+38mnL1su3a49+tnOBc+Bimc/Z5GnzXQDqiPwfSGzPRszu66x/XD4Zr2agS2rEDdjpWwsDLuDZ+QAb/ehIMaJYrNTaVJl+7+hfzc5GRKZdPubgG5RiK7T1uoNhAsDSTTC14YhEAXCBRs376dI0eOMHv27GI535AhQxgwYADNmzfP9djAgQOpV68eo0ePVo1t9+7d/Pzzz3qPd+nSJb744gt279793MYsEAgExUX5Dz/E+lUq7Rh7A64tVbdrjQGHApYUK2JORcH6/6S/TYDxtYvA0EoDWY6M/cuvqlzIXSs40qx3tYJFTpwDFIbpmAC9gHwsAqfeuUPYN98gz84GoGybNrgPGGDczv/9AXfWqduBI8DnTYO7bL0nOeIr6VsFeuvJi459lMyuBedVRmWuFRxpNyJEtzjfhFHiHCB69Wpi90q58iZmZvhMmIBd9SIOjzACfeHsNlWq4DViBLZVDCSMZyMZwB1E2wQuCOgI5FG2LOxKDMc33VKZ7YHCnLBXNVx9jat5JpfDgQhpsiVJo+x4oDOMCoTyhdXOTy5Lq+apahd57LylSaByOqz4BS8sQqAL9NJ4y/M57omuz+e4haVTp0506tSp2M63bNkyo7edOHEiO3bsMLhNcHAw5ubmHDp0iNdee62QoxNoYu1gwahf2ubqe1UQ1/9qX39J4Nq1K2WaGc7XfalIj4Nz36lXxPzfyTMkurh4lAI/XFK3B1WD4CJOST6z4y6P7kg122wcLHnjf0GYF2SZ8T5SCS0l7dErSnWR8fAhYZMnq8ShY6NGeI8aZdxEwb0/pdB2JTXfB78OBnfZcg9+N1KcP3mYxJ6FF0hPVYvz9qNCsLLRI84vKtp5iPMnO3YQvXEjoKiW8PHHONSta3Dcz4PkS5eIXLKE9HB1uSBzBwfcBwyg7JtvGn4NlGX0NE3gnJBM4AIMnzclPp0Tm+9w/7K65pm5uSl1O/gT+FoFTI2sHRiZIoWzX3qq7rOzkFJC25QvZMR5ZiJc/00dXQNSub5KPaByDzATv0cvG0KgC/RyMqrkzj179mz+++8/lixZAkB8fDyVK1fm9u3bODsXLnEnJiaGvn37EhkZiYmJCXXr1uX3339n+fLl7Ny5k02bpNC0iRMnsmHDBsqVK0eLFi04ePAgZ8+e5dChQ4wdO5ZGjRpx7NgxLCwsWLlyJdOmTePKlSt4e3uzdetW7O3tSU5OZsyYMZxW1E7t0aMHkydPBuC1117j008/5a233iIiIoL+/fsTExNDxYoVyclRT/8eOXIEJycn/Pz8AJg6dSpbtkizJzk5OVy9epXQ0FB8fX159913+eWXX4RAL2LMLcyoXC+PpLWXGHH9r/b1Fzd2NWrg3r9/SQ+j+JBlw/lZ6tJI5YKgmpErts+Z9Gz49jykSovJNPeELhWL9hx3z0dx+aCGKdygoIIZMD6hUOXUsp484f6kSWQnJQFgFxhIhXHjMDEzYqLg/k64/qu6XX0QVDQ84b/pLqzQSB9+ryr00lO57Ul4IrsXXiAjTXoh3HyllXOd4nwzRovzhGPHePzLL6q257BhOLUo3omhrCdPiPz1V1V4PSjC2du2xb1/f8Ph7KlIeeaaJnAmQDOgNWDAqFgul3PjaARndt4lMz1b1V++Wjma9QzAoZxxISLZMth6XzKCU5ongvRZeb+G5NReYORyiDgE15dJIl2Jcw0IGg0Or1CE0SuGEOiCUsn7779PQEAAs2bNokyZMvz666907txZpzgfM2YMhzXylDRZsmQJDRs21OpbvXo1fn5+/P333wDExua2o9+xYwc7d+7k0qVL2NjY0P0ZB+Fr166xfPlyfv75Z0aNGkW7du04efIk5cuXp0OHDqxdu5ahQ4cybdo0MjMzuXz5MmlpaTRr1owaNWrQo0ePXNfQokULJk+ezL179wgODqZdu3YAHDp0iCZNmqi2/eqrr/jqq68AGDp0KA0aNMDX1xeAJk2aMHbsWENPrUAgEJRqvD/4wDhR9LJwYznEKuqWWbtIdbJLQVkkuRzmX4EwSa9SwR7GBBWt99STh0kcXqOuKdaoa1U8Kjnl/0CpwHIKXE4tOymJ0K++IuuJtARr4++P75dfYmppRCmasL+kEldKqvaFSoZDBTc/I877VYWeesR5zINE9iy8QIZCRLpXLEO7YSFY2jxzC68U5xcU7TzEefLly4TPmaPKs3fr2ZNyHTsaHHdRIsvM5MmffxKzcSOyDHVYuW1AAJ7DhhkOZ5cjXedupNdeSQXgHfI0gYt9lMyR9TeIDlOLXht7Sxp3rYJ/HXejUytuxcGCqxCapO5ztYGRNaFeYQ3UUyLhyiJ4clHdZ2EH1QZJaRPCBO6lRgj0Us7du3cZMWIET58+pUyZMixatIhq1appbSOXy/nqq6/Yt28fpqamODs7M3/+fPxLyNyjKHBycqJbt24sX76cMWPGsHjxYv744w+d286fPz9fx27UqBFz587lk08+oWXLlrRt2zbXNgcPHqRnz57YKepqDhgwgGnTpqkeDwgIICQkBIA6deoQFhZG+fKSmU3dunW5d09yJ/nnn3/48ccfMTU1xc7Ojv79+/PPP//kEugHDx5UXYe/vz+tW7dWPfbw4cNcrznA9OnTefDgATt37lT1eXh4EBUVRVZWFhYWIuRJIBC8eJgZWjF72Yj4F+5LNbYxNYe6n4NVydabVvLnfTiiSHW1MYcJdcC6CO8a05Iy2bfssqqEVdUGntRoVgDH8Gy0y6m5A70xupyaLD2dsClTVKHVVp6e+E2ZYriutpKHB+HKQnW7Si+o2tvgLs+unA8IgO6VdG8bE5bInkVqce7h70TbYcFYPvtCyIAtqMW5KQbFedq9ezyYPl2dZ9+6NW7vvWdw3EVJ4pkzPP7lFzIi1bnURrmzg24TOCugLVLUhIFds7NyuLA3lMsHwpBp1DwLaOhFg86Vc+fy6yE1C1beht1haJnJdfKTIiEK9TmRZUvpErfXgSxT3e/ZTEqbsC6s/bvgRUAI9FLO2LFjGTBgAH379mXbtm188MEH7Nu3T2ub3bt3c/z4cY4cOYKFhQWzZ89m6tSpLF++vGQGXUSMGTOGLl26UKlSJdzd3aldu7be7fKzgt64cWMuXrzIP//8w+bNm/nyyy+5cOGC1jZyudzgD4S1tTr8zszMLFc7LS1N73Hya3pja2urOp6SVatWsXXrVv7991/MzdUf4/T0dCwsLIQ4FwgEgtJOYihc0phgrjkMyuaRMFtMXH6qnRv9SXARGFxpIMuRcWDFVZIVpnBuPo407RmQf1M4OZIwDVW07ZEc242MkJdnZ/Pgu+9IvSUpZouyZfGbNg1zJ6e8d350BC7ORSXR/N+RVs8NkB9xHh2awN7FF1Xi3LOSE22HheR2Elc+B8ow7zzEedaTJ4RNmUKO4r7CoX59KWqlGFZkMyIiiFy2jKSzZ1V9JqamOHfsiHvfvoYnRQppAhdxK5ajG2+S+ER9P1XG1Zbmvavlq5Tficew5Do8VXvY4e8IHwRB5cLOrcXdkkzgkkLVfdYuUk1z9/qFPLjgRUII9FJMTEwMly5dYuvWrYBkYjZu3DjCwsJUIc1KMjIySE9Px9zcnKSkJLy8Cl//rJF7oQ9RKKpVq4afnx8jRoxg1qxZerfL7wr6/fv38fb2pmfPnrRr1w43NzeSk5O1tmnVqhWTJ09m7NixWFtbs2rVqgJdQ5s2bfjll19o0qQJqamprF69mi+++CLXdq+//jq//fYbkyZNIjQ0lP379/OGot5qrVq1OHjwoGrb/fv3M3XqVA4fPoy9vfYd040bN6hVq1aBxioQCASCYiIrBc5+q14hq9BGKpNUCniSBrMuqFcGe1eGhkV8P3BmZxGZwh1CnW9tjiTOjdRacrmch/PmkXTuHABmtrb4TpmCpbsRF/v4JFyYg+pZ8uso5Z0bELn5Fed7Fl9U5UYbFOd/ohbnJkjRAzV1H1eWnk7Y1KlkKVL7bKtXx2f8+OeeUpKTlkbMhg082bZNtWoPUp6/17BhWCs8dvRyD+k6C2ACl5aUyaltd7hz5rGqz8zMhOA3/Ahu42v0++5JmiTMNf2ZrMwkY79OfmCkl5xuslLh1moI3YnWmnzFThDwHpgXwJNB8EIjBHopJiIiAk9PT9UKqYmJCeXLl+fhw4daAr19+/YcPXqUgIAA7O3t8fT0ZNeuXYU+f2lwW3///fcZPXp0rhzwwnDo0CF++OEHzMzMyMnJYfbs2ZQpoz3t2alTJ44fP05wcDBeXl40atSIuLi4fJ9r0qRJfPDBBwQFBQGSSZyua/nxxx/p378/f/zxB1WrVlWJc4C33nqLqVOnkpOTg5mZGdOnTyc1NZX27durttm9ezdeXl7s3buXbt265XucAoFAICgm5HK48L26VFKZyhA4vFTklGblwHcXIEExb1DXVarHXZTcPfeYywe0TeHsnAogQK4AmgGFPQAjy6bL5XIeL1tG/L//SuOwtMT3q6+wqWiEA170OTg/E+QKR7AKb0rRDwZev2dzzvMS57sXXSArQ1om9qpSljffD9YtzrcBZxRtpTjXU21LGS2Qdv8+IIXy+375JaZWhXExM4xcLif+wAGiVqwgS+MeysLFBY/BgynTrJnhlfsUYA8FNoG7c/oxp/68o3K+BylNoFmvapT1MCKFAZDJpVD2lbchTT23QF1XKdfczdaow+jn8Um4+jOka9i/O/pLpdOcivjDJ3hhMImPj5fnvZmgJLh48SLDhw/n5MmTqr5WrVoxffp0mjZVW5NeuHCBb775hmXLluHo6MjXX39NTEwMixcvNnh8BwcHTE3VU37h4eFUKGU1Z0eOHImnpyeTJk0q9nMnJSXh4OCATCZjyJAheHl5MX369GIfB0jPQ6tWrXLlrmuSmZlJ/fr12b9/Py4uRVwDh9L5/iguUuLT+f7dnVp9n6x9q2A3lS8g4voLd/0ymYykpKS8NxQAuX+bXjpur4Pba6W/LRyg+TywLayjVNGw4Ar8pahy5WYD85qCgxE+acbyJDyRHfPOqfLOm3SrSs0WBfhdeQAsQwp7BmgDtDJ+95hNm3i8YgUghVj7TJiA4zPpcDp5chlOT1FHPni3gpCxYKL//ZqflfOoe/Hs+fmiSpx7V5XEubmlDnG+A1DeHirrvesJoJPL5UTMn0/cP/8AYGZnR6U5c7Aqb+SMRgFIvXWLR0uWkHbnjqrP1MICl65dce3eHVNrA9+fciRRvhu18R+AD9CFPE3gEqJTObrxpipKA8DK2pz6nSpTrYmX0eH8oYmSCdyteHVfGUsYWkNyaS/UnFraU8lc8PEJdZ+ppbRiXrFTsRhFZicnk5KTk/eGgmJHrKCXYry9vXn06BHZ2dmYm5tLX7ARESozMiVr166lefPmOClypvr06WNQyL0IPHr0iNdffx1nZ2dmzpxZImPo378/oaGhpKWlUadOHT777LMSGQdIpdX27t1rcJv79+8zY8aM5yLOX3XkMoiLTMnVp4+YsETWfnWUlPgMbBws6DOlWS5n4jM77nJo9TVVOyE6Ff867gz+Ph93mcWEuP7CXX+vr5tg56p9s3Xxrwec2Pifqp0Yk45vcDl6TzNCJAheXKLOqMU5JlBnXKkR53+Hq8W5pSl8UadoxXl6cib7fr2iEucBDb2o0bwAAjEOyRROKc7rAK8Zv3vsvn0qcQ5S1QCjxHnsDTgzTS3OPZtB8If5Euf9DYjzx3fj2fvzRbIyleLcmTffr6VbnO9CW5z3QK84B3iyebNKnJuYm+P75ZfPTZxnPXnC4xUriD90SKu/TJMmeAwahKVHHuo6Giky4L5GnzXQDqiPQRO4nGwZl/eHceHvUHKy1V/S/rXdady1CraOxkULZORIZdO23occjWXMthVgYDWwL4zNj1wGYXvg5grI1ph9cK0LQSPA9vnnl8rlchKPHSNq7VrcZ8x47ucT5B8h0Esxrq6uBAUFsWHDBvr27cv27dvx8fHJlX/u6+vLgQMHGDlyJBYWFuzdu5caNfS4g7wgeHl5cfPmzbw3fI4oc/9LAy4uLryXh8NqQEAAAQGlw2DoVWfjNydo3K0qDTpV5uK+UNZPOc7YlR20tqn/diXqv62+U5vVYxt127+4lRc0Edevff0bp55g0E/NtLYJaetDSFsfVXvRoAPUeuP5rWYJSgEpkXDxe3W7Wn9w1W1+WtzciYfF6vkyRheF4ZUGuUzhfB1p2qMApnAZwCpAaRtTEamslpGHSTx1ikc//aRqe/TvT1mNlDK9xN2G05MhR+EM5lYfan9icJVzyz3jV851ivOhtXLnR8uRQr6PK9omQHcgRP/Qk86fJ2rlSlW7wiefYBeoJw6+EMgyMniyZQsxmzYhy1S7j1v7+uL5/vvYBwcbPkAWkgHcEbRN4GohmcDlUdwh6l48R9bfJC5KPZnqUNaaJj0C8Klp/MLFxSew8Co81ijfVt4ORgVCYDmjD6ObxFDJBC5e441hWQZqDgWv5sWS5pIZHc2jn38m6cwZTF+lihkvGC9xDNnLwbx581i+fDl169Zl7ty5/KT4Yfnggw/YvXs3IOVply9fniZNmtCkSROOHDnC7NmzS3LYAsErS1JsGg9vPKVuB0lsBr/hS+yjJGIfJevdJ+xqDElP0wls+eKnEIjr13X9yblW4DV5eCOO5Lh0App6Ftcwi4xVq1bRpEkTypUrx9KlS7Uek8lkjBs3jpCQEGrXrs2yZctKaJSlgJwMODdDMocD8GgClUqHX0hSJsy4AMoFx7f9oFUBqp0Z4szOu0Tc1jSFq4WZRT5vQWXAekDp9VUO6IvR5dRSrl8nfNYsVd1vl06dcDHG3ybxviTOlaudrrWlcnim+te4tt7TdsHPjzgvX62cfnH+F3BUo68rYGCOJyMigvDZs1XX7N63L2WaNdO/QwGQy+XEHTzI7WHDiFq7ViXOzR0c8Bo+nMo//pi3OL8N/Ihk+qcU587AIKS8egM6MiM1i6MbbrL9x3MqcW5qYkKtVj50+6KR0eI8IQN+uASTTqvFubkpvFsF5jcrpDjPyYAbK+DIWG1xXqENvLYYvFs8d3Euz84mZssW7owcSdKZM3nvIChRxAp6KadKlSq5yqoBKqEOYGVllW8nc4FAYDzW9hYMmd86V58u4h+nUsbVFjNz6ebTxMSEsh52xEUm4+ylu07RqT//o15H//zfsBYT4voLd/1OHnYkRKdR1lO3KdGF3WEEv1lBtc+LREhICL///js//PBDrsc2bNjArVu3OHfuHImJibRo0YIWLVpQtWrVEhhpCSKXw+WFktADsPNWhEaXvClcjgxmXYQYhfasXhYGVyvac/x3VtsUrvWgQOzKFMCYbDeg1DbWSI7tRhp0pYeFETZ1qko8OrVsiceQIXmv4Cc/hJOTIEsxwVguCOpNBDP9sf9b78FvGuK8X1X94jzybhx//XxJS5y3GaLH0f4AUpkxJV2AuvqHnpOcTNi0aeQoqtQ4NmyIa69e+ncoACnXrhH5669aeeYmZmaU69gRtz59MLPX/Z2vIhEpXP+KRp8Z0BzJU8BAKLlcLufehWhObLlNWpJ6xd61giPNelfDpbxxq8NyORyIgF9vQJLaS46azjA6sAjKC8ZchCsLIVXtIo+dt2QCV67oIxl0kXrrFhELFpAeGqrqs3B2xnPkyGI5vyD/CIEuEAgEeWBuaUbN/ORKPnPPJzdgxZmZls2Fv+7z4fIO+jcqYcT1F+76MXT96dlcPfCQ/y1sUaCxlTTKChW6TN22bt3K4MGDMTMzo2zZsrzzzjts3rxZZ6nJl5rQnRChKJVpZi0JPIvCWj8XDStvSyG9IJlfja8trRoWFU8eJnFk3Q1Vu9E7VfCsZHzNaRUnUYd1myKtnLsat2tmTAyhkyeTkyKtrtrXrk35sWPzFucpkXBiImQmSO2y1aD+JDDTP7nwrDh/ryr0rKx723yJ83+B/RrtTkAD/UOXy+WEz55NRkQEIIWZl//44yKrdZ7x8CGPly8n8dQprX6H+vXxHDw47/x2GXAK+BspbUFJRaTSaXnYMiTFpnHsj1uEX1c7n1tYmlGvoz81WlTA1NS464xIhkXX4LKGgbq9hTRJ9Ub5Qs6hZSTA9WUQcUjdZ2oOlXpA5R5gVphEduPISUkhauVKYvfsUUVRmJiY4PzWW7i/9x4m1tbCwLSUIgS6QCAQFCFOHrYkRKeSky3DzNxUKjMTlUJZT93T8Jf+CcO9olMuE7UXFXH9uq+/jJuNzu2v//sIV18H3Pwci3mkz5+HDx9qVX7w8fHhwoULJTiiEiD2Olz/Vd0O/hAcSkcqx7FIKU8awMwEPq8N5YqwMEN6Shb/LLtceFO4/5Acy5V0AfSsSD9LdmIioZMmkfVUUmA2VargO2ECJuZ53P6mxcDJLyFDqhdOmUrQ4Gsw1/05Bth+P7c476VHnD++G68lzitUL6e/FvwRpNB2JR2ARoaH/2TzZpLOS7XJzB0d8f3qK8xsCz8plB0fT/S6dcTu3YtcpjZhs/bzw/N//8M+JCTvg0Qg1TSP0OizQbquOhj0E5DlyLj6bzjndt8nO0udqO4b6EKT7gHYlzXuDZwtkwz8Nt6FLA3Dzxae8H4NcCpM5Tm5HB7ulz73WRqpXc6BUGsU2D9/rxG5XE7C0aM8/uUXrfJ2Nv7+eI0ejW0VqXybTGbA7VRQogiBLhAIBEWIg7MN3gHOnNt9jwadKnPpnzCcvez1h3dvu0OjLnru4l5AxPXnvv6ynvYGw9trd/DV+VhpoH379ty6dUvnY4cPH85VVeRZNFfs5IZCKV5G0mPh3HcgVwgJ/67gVbT5vwUlPBnmXVa3/1e9CAywNJDJ5BxYfpWkwprCxQBrUUehNAPqGTmG9HTCpk5VrSJbeXnhN3my4fJeIL1uJyZCWrTUdvCBhlPBQn/d7B2h8Is6UCBPca6Zc25QnJ9AMoVT0hbpOTBA6u3bRK1eDUifvwrjx2PpVrhKATlpaTzZsoUnf/6JLD1d1W/h7Izbe+9RtnVrTPIqjZiOVLf+JNpRRXWB9uSZrhATlsiRDTd5GqFe8bV1tKJJt6pUDDH++q7HSqXTwjW0s5sNjAyUapsXiuQIKZz9qUbMvoU9VB8k5ZsXhwnc48eSCdy5c6o+U2tr3Pv2pdzbb2Ni9vzLtwkKz4uX8CYQFJLo6GjatWtHlSpVCAwM5OjRo3q33blzJ9WqVaNy5cp069aN5GT1N/qpU6cICQmhatWqtG7dmsjIyCIdZ9euXTlxQqqPKZPJ+OCDD6hUqRKVK1dm0aJFeve7c+cOTZo0oWrVqjRo0IDr16+rHjN07Z988gnr1q0r0mt4Ven5ZWOOb7rNt523sv/3q/Sa3ASA9VOOc/XQA9V2T8ITeXjjKSFtK5bUUJ8L4vqfuf6vGgOwbdYFbh5Tf0/ERqQQeTuBwNeL2JGrCNmzZw/37t3T+S8vcV6+fHkePFC/3uHh4Xnu89Igy4bzMyFDsXrlEiy5tpcC0rJhxnlIV8wbvOYFbxXxHNGZHf8RcVtafbaxt6T1oKD8e0ykACuQhB1ANaRSW0Ygz87mwaxZpComlyycnfGbNg3zMnlY02cmwqlJkKr4nNp5QcPpYKk/wmVnKCxV/8zSp7Lx4rx8NQPi/AzakQNtgJaGh5+TmiqZwilqW7v26IF9LQP11/JAlpXF0507uf3++0SvX68S56bW1ri/9x5VlyzBuU0bw+JcjpRjPg9pwkEpzl2B94FuGBTnmWnZHN98m20/nFGJcxOgZvPy9JjQyGhxnpwlubOPP6kW56Ym0NUfFjYvpDjPyYLb6+DwaG1x7tVSMoHzebN4TOA2beLOqFFa4tyxYUOqLFqES5cuQpy/QJjEx8e/YlPaAiUODg5aeYPh4eFa4YgvK4MHD8bHx4evv/6aM2fO0L17d+7evYv5MyFvycnJVKpUiX///Zdq1aoxevRoHBwcmDFjBnK5nCpVqrBs2TJee+015syZw7lz5/IlcENDQxk4cCCHnqkVCnD69Gk+//xzDhw4AMDKlStZsWIFf//9NwkJCdSpU4e9e/dSrVpuN5/XX3+d/v37M3DgQDZt2sT333+vEvqGrj06OprmzZtz48YNnfmkr8r7QyAoamQy2Uuf5zdixAhq167N0KFDVX1r1qxh48aNbNmyhcTERJo3b87mzZvzLAf57G/TC8nVpRCqUFfWLtB8HlgVYd2yAiKXS+L8RJTU9nOA2Y3BugjjKe+ee8yBlVLNNlNTEzqMqo1n5XzmnecAv6Guhe0BDAOMCD2Wy+VEzJ+vqvttZmuL/8yZWPv5Gd4xKwVOToSEu1Lbxg2azAQb/S7gu8O0y9P1rgx99XggRt2LZ8/ii8blnF8E/kAtZlshCfQ8CP/hB+IPSn4HtgEB+H/3Xd7h/DqQ5+QQd+AA0WvXkvXkiarfxMwM53btcO3VC4uyRrymT5EmGW5r9JkDrZEiAfLQi6GXYzi+6RYpCepEdWdPe5r3roabn3GfJ7kcjj2WJlHiNPLdq5SRygn6Fza76Ok1uLJAMhRUYusOgSPAzYCLXxGScuMGjxYsIF1jQtSiXDk8hw2jTOPGevd7FX6bXlRe8F9AwcvK7t27CQkJUf2ztrZmxYoVRXLsjRs3MmrUKADq16+Pu7u7zlX0PXv2UK9ePZUIHjlypEqAnz17FisrK1577TUAhg0bxp9//klWVhY3b96kfPny3LsnJffNnj2bDh065Cu8c8mSJfTt21fV3rBhA8OHD8fMzAxnZ2d69uzJ+vXrc+0XHR3N+fPnVTXTu3Xrxv379wlVOHcaunY3NzcqVqzI/v37cx1XIBAIdLFhwwZq1KjBtm3b+Oabb6hRowaXLl0CoHfv3lSpUoW6devSqlUrxowZk6c4fyl4eFAtzk3NpZJcpUCcA2y6pxbndhYwoU7RivOnEUkcXqthCte1av7FuRwpR1kpzu2BARglzgGiV69WiXMTc3N8vvwyb3GenQ6np6jFuZUzNJpuUJzvfaAtzntVlkpy6SJf4vwK2uK8KWBEqfaE48dV4tzM1pYK48blW5zLZTISjh7lzujRRMyfryXOyzRrRpXFi/EaPjxvcZ6NVNP8R7TFeQAwFikSwIA4T45L5+9fLrHv18sqcW5ubkrDTpXp8ml9o8V5dCpMPQszL6jFubUZDK0Bc5oUUpxnJsGl+XDic7U4NzGFSt2h5cJiEefZSUlE/PQT9z77TCXOTUxMcOncmSqLFxsU54LSjchBF+gnJVL6V9TYeUr/DNChQwc6dJBcnVevXs33339P165dc223cuVKneV9QKoPrxSjSp4+fYpMJsPVVR3L5OfnpxWGqeTBgwf4+vpqbRcREYFMJsv1mIODAw4ODkRGRlKtWjVmz55Nz549mTNnDosWLeLMmTP5yr07dOgQn376qcGxnD17Ntd+4eHheHl5qaIBTExM8PHx4cGDBzg4OOR57U2aNGH//v20aWPEVP0rREpCBj8N3qPV98Fv7QtWKugFRFz/q339hujVqxe99JRuMjMzY86cOcU8ohIm8T5cXqBu1xwOZUvHpMTFJ7BKYSdgAnwaDHqsEQpERmoW//x6RdsUrlkB0jeOAcoIXXOgH2Dk/MbTnTuJ3rgRUORff/op9opKA3rJyYSz30CcYmLB0lES5wbuU/4Ol8KllXT3h75VdEcxR91P0BbnAc76xflNYANqcd4AyTwtj9uHnJQUIn/+WdX2HD4cS3d3wztpIJfLSThyhOh168h4+FDrMYd69XB7912VsVie3AW2AU80+soAHYGaGDaBk8m5fuQhZ3feVT1fIOXpN+0RgEM5/SZ9muTIYEcYrL4NGerD0NAdhtcAF+MOoxu5HB4dhmu/qB3+AZyqQq0PwNGvEAc3dghy4g8d4vGvv5KdoB6DTeXKeI8ejU0lI10UBaUWIdAF+rm6BM5MKfrj1p8MDb82atODBw/y9ddfc/jwYRwccte07N+/P/375y+v71mhbGhl25CoNnScPn36cPDgQdq2bcv+/ftxcZFm4cPDw3n77bcByMzM5MGDB4QoXE9r167N77//Dkjuxx4eHnrPl58xa26b17V7eHhw+PBhBNrIc+RE3UvI1feqIK7/1b5+gZFkJsPZb0GmqMns0xZ825bsmBTEpMGsC2rd924VqFc43zAtZDI5B1deI/GpVFDdtYIjTXpUzb8p3A20TdG6A0ZmViUcO0bk0qWqtufQoZRp2tTwTkqvgCcXpbaFHTScZtBpf/9DWKCRZtzVH/oH6Bbn0aEJ7Fl8QSU2vas60+b9WrrF+X9IhnhKY+06SCXHjHgKn+7YoXLrdmzQACdFdF9eyHNySDh2jJg//tCqkQ1gW706HgMGYFezplHHIhmpVv1FjT4ToAlSBEAe85lPHiZxdP1NYsITVX02DpYqEzhj30v/JUivz131YXC2gmE1obF7IVPBUyLhyiL1+wUkZ/9qA8C3vbSC/pzJiIjg0aJFJF9Wuzya2djg1q8f5Tp2zNusT/BCIAS6QD+Bw6Bip6I/bh6r50quXr3KoEGD2LVrF15eXjq3ye8Kerlykk1tTEyMaiU5LCwMHx+fXPv7+PiocsBByhn39vbG1NQUHx8fVdg4QFJSEklJSXh6SteWnZ3N1atXcXZ2JiJCXUukQoUKXLx4UXU8fTnotra2pKWlUVYRRqY8X/369Q2OuUKFCjx8+JDs7GzMzc2lWqjh4fj4+Bh17enp6djYFGZqWSAQCF5B5HK4+AOkPpbaZapAzaGG9ykmMnOkvPOkLKld302/iVlBObvzLuE3pFJm1nYWvDFYzwqxISLRXj1uBRjpb5Zy9Srhc+aoJp1de/Sg3FtvGd5JLpNes6jTUtvMWiqlVsZf7y4HI+DHy+ohdqkIA/WJ8zDFynmGWpy/OVSPOA8DViGFhoN03V0xSpzL0tN5un07ACampni+/36eYlaenU3cP/8Qs2kTmVFRWo/Z1aiBW58+2AUHGyeKZcBppJrm6Rr9FZBK4uVxy5eVkcP5Pfe4cvCBlrl79Sbe1O9UCSsb4+qFp2dLK+bbQ9WvjwnQ3keaQLErTNlxWTbc+1MyglNOwAF4NJHula2dC3FwI4eQmUnMpk3E/PEH8uxsVX+Zpk3xfP99LMoVYRkGQYkjBLpAP0aEoj8vIiIi6NKlC7///js1DczeFmQFvUePHixcuFBllPb48WOaNctdt6Rdu3aMGjWKmzdvUq1aNRYtWkTv3r0BqFu3Lunp6Rw6dIjXXnuNJUuW0KVLFywspF+Azz//nICAAFasWEGrVq2oW7culSsbf0dUq1Ytbt68qZqY6NGjB0uWLKFr164kJCSwYcMG9u7dm2s/Nzc3ateuzerVqxk4cCCbN2/Gz88PP0X+XV7XfuPGDYKDg40ep0AgEAiQbtyjz0h/WzhAvS/AzLJkx4Q0b7D4GtxRBIB42MLHwUVrKH3vQhSX9ocBYGpiQutBQdg757OgejKwElBqnyCMyrsGSA8NJWz6dJVoKdu6Ne79+hneSS6XUhEeHZHappZQfxKUzW28quTwI5h7SS3+OvnB4Gq6n8uYsET2LrpIZro0Jq8qZXlT38p5BLAcUEygUB3ogdEuUQnHjpGtMPoq07Ills9E32mSnZRE7J49xO7aRVZsrNZjtgEBuL/3nvHCXDn2P8ld07wtUJ88JxgeXHvCsT9ukRynVvZlPexo3qsa7v5Oxo0BOB0lvc+faEwQ+DrAB4EQkE8LhFzE3pBKpyWFqfusXSBoBLg3KOTBjSP54kUeLV5MxqNHqj5LNze8RozAoZ6RdQcFLxRCoAtKJcuWLSMmJoaPPvpI1Td16lQ6dSr8iv7MmTPp168fVapUwdLSklWrVqlytr/66iu8vLwYPnw4Dg4OLFu2jC5dupCdnU1QUJDKqM7U1JTVq1czfPhw0tLS8Pb2ZrWi7ujOnTvZu3cvp0+fxtbWljlz5tCjRw9OnDiBdV71VxV0796dPXv28PrrrwPQr18/zpw5Q9Wqkj3suHHjqF69OgDbt29n+/btLFu2DJAM5gYOHMi3336Lo6OjlrmeoWuXy+Xs37+fCRMmFPYpfumwtrdgwKyWufpeFcT1v9rXL8iDqNNwR1nBwwTqfAY2hS2oXDTsfQD/KFKKrcxgYh0oyrdu7KNkLVO4hu9UwatKPhVRFrAaUGaRlEcKbTdCI2bGxBA6eTI5KSmAlC/tPXq0YYEpl8P1XyF8n9Q2NYe6X4CL/uX6o5Ew56JanHfwgSHV9YjzB4nsWXSBDKU4r1yWtkODMbfUIc6jgd8Bpbt4ZaAPebqba5Kk4Ufj3L59rsflcjlpt28Tt38/8fv3I8vM1HrcoU4dXLp3xy4w0Hhhnoa0Yn4a7ZrmdZBK4dkb3j0lPp0TW+5w/1K0qs/M3JTabStS63UfzMyNm52ITYcl1+H4Y3WfpSn0qSJFNxh5GN1kpcDNFRCmmXNhIkWWBvSVQtufM9nx8UQuW0b8v/+qR2Bmhss77+DWuzemVsIH5WVFlFl7hXlVy6y9CCQlJdG4cWNOnTqFnV0RuvgYYO/evaxZs4ZVq1bpfFy8PwSCgiFK2eSPF6rMWkokHBkL2alSu9pAqNytJEek4lYcfH4KFJ5tjAuBFrqzxQpERloWf845Q+ITKe+8Sn0PWvatkb+8czmSY/lFRbsMMAIwwl07OymJ++PHkx4eDoBt1apU/OYbTPOaCL+1Bu4oq6AoJlS8ckfRKTnxGL67ADLF3XI7HxhZU7c4f/Iwid0LzpORJolzz0pOtB0WgoWVDsUdCywBlF8NvsAgIJ+BFzcHDFCthvvPmoVd9erIs7NJ++8/Ek+dIuHwYTKjo7X2MTExwaFRI1x79DDe/A2k1+siUq55ika/G1K+fEXDu8tkcm4cfciZnXdVof8ghf837RlAGVcDBdE1hyGHPQ9gxS1IVUd7E+IivTaFMj+UyyHyKFxbChnx6v4ylSBoNDgVcX6IziHIid27l6gVK1STTyD5AniPGoW1hmlwYRC/TaUXsYIuEJRCHBwcmDdvHvfv3ycwMLBYzpmQkMDMmTOL5VwCgUDwwpOTIZnCKcW5Z1OolLvaSEmQkCGJSqU47+xXtOJcZQqnEOflvB1o1qta/k3hDqEW5+bAexglzmWZmYRNm6YS51aenvh+9VXe4vzuFg1xjuS6bUCcn46CWRfV4vzNCvrFeeyjZPYsvKAS5x7+BsR5AvAranHuBfQn3+IcwLZaNRKOHwfg3mefYe3nR+ajR7lWygFMra0p26YN5d5+GyvPfKYwRiO5s9/X6LMAXseomuZPHiZxbMNNoh9omMDZW9LonSpUqutu9HsnNBEWXIVb8eq+MpZSRENLr0Kmb6RGwZXFEHNO3WdmDQHvgd9bYJpPX4UCkHb/Po8WLiT11i31EOzt8Rg0iLJt2uT/MyZ4IRECXSAopbzxhpEJeEWEvlJJAoFAIHgGZQ5zUqjUti8PwR8WbXJ3AcmRSXWflfm4NcrCQP2p1QXi3O57hF9XmMLZWtDmfwUwhbsG7NNo9wSMqMoml8kInzOH1BtSaL25kxN+U6diXiaPWmxhe+HG7+p2zaHgo7+k6LkYmKExydHaG0YH6hfnuxacJz1VSiR3r1iGtsOCdYvzVOA3IE7RdkVaOS9gxHT5jz4iJzlZ5er9rBu7iakpdsHBOLVogWPjxpjlNyovEzgAHEXtMA9SybS3yLMEntIE7uqhcGQalWMCGnnRoFNlrI10b8vIgfV3YOt90Cyi0aY8DKoGDoWxfJBlw71tcHuttgmce0MIHA42LoU4uHHkpKURvWYNT3fsQC5TP9FlW7fGY9CgvN/fgpcKIdAFAoFAIBAI8sP97RBxSPrbzBrqTiiWnFRj+P0mXFH4fzlbwfjahczFfYbQS9Fc3BcKKE3hAo2uT63iEbBRo90GMCJYTC6XE7l0KYknTkjnt7bG7+uvDRqjARDxr1QeS0lAP6j4tt7NLz6Bb86pxXlLLxhTS7c4j3ucwu6FF0hPkcS5m68j7YaFYGmt4xY7E8kQLkbRdgb+BxQiJNvU2hrfyZN58N13JJ2RjAotXV2xrVkTu8BAHBs1Kpi4kyNNouxC7Q+gHPPbQEDeh9BpAuduR9NeAXhWMt6r4EKMVHc+Kk3d520nTZgEFta8PO6W9mQbgHU5qDkMPBsX8uB5I5fLSTxxgsilS8l6+lTVb1W+PF4jR2IfFPTcxyAofQiBLhAIBAKBQGAsT6/Cjd/U7ZCPDNbNLk4OP4JtodLf5qbwRR3Ir6G6IeKjUji0+rqq3aBzZbyq5rPEVDJSSTGla3kw8Jpxuz7ZvJmnu3YBklmWz4QJ2FSqZHinx6ekcmpKN7NK3aFKT72bX3kK085ClkKcN/OE/7N33uFRVekf/0wy6T2k9wRCqKF3BKUJqIgFULHrrqKry+5vdXd17XXd1dV1V9aOICg2BBGkSld6J4QS0nvvZWbu748zmckkM8lMMiGBnM/z8HjPuXfuPScZM/Oe877f7x8SwcFMcF6aX8X6/xymplLsugZGejNz0VCc3cx8vW4Uw9OL9uEF3I9VKf1t4eDsTPQzz1Bz7hxqPz+cAzsoUlgI/ACca9KnBiYBkxGp7a1QVVbHL9+ebSkCNyOGxKnRVovAldbBR0mwwyhejtoB5sXBvN5ga9KGCQ1VcGaZXgSuiTFb7A0ipf0SLLjV5+WR/f77hoUVEL/LwAULCLz5ZlRqGab1VORvXmJArVZTWVmJp2cb8puSHkdlZaVB7V0ikUh6LLXFcPjvwkMbRLAXOr5rx6QnoxLePWFs/7Y/9OuoxVQT6ms0bP7oOA31Qtyr9/BgBl1t48KEOcV2K/2+S37+mdwmriThjz2G17Bhrb+o8Jjp7ytqJvSzbM16uhhePAj1+svHBcP/DQFHM/FkeWEN6/9zhOoKEZwHRHgx65Gh5n27tQiP9/P6tisird2O9tkqlQp3vdNLu6kHdgA7EWNuJB6xa95GprdOp3B6VyYHf2wuAufHhPn9bBKB25wJn5yBqgZj/yB/eHQQRHTka6pBBO5DqCsx9nvHQeLvwNcG0bz2DkGjofD778n/4gsTrQCvESMIe/jhtjNCJFc88hu3xEBwcDB5eXmUlJS0fbGkR6FWqwkODu7qYXQZ1eV1/G/RZpO+h5dMx927Z1icyPn37PlL9Og0IthrVHYOGAr92vDbvkRUNYiU7Fp9TDQ1XKiN2wtFUdj++SlK84Ugnn+oJ1fd1t92xfbVQLq+7YMQhbOiBLny6FGy3nnH0A6+6y78pk5t/UUlyXDgZdDpI7ywyTD4EYs6Ackl8PxB489wVBA8aaE8oKK4hh//c5iqMuGP5h/qyaxHhuHibmYyjfNuTDxwBu4FulMMpgBJwDqgtEm/D3Adot68jV91YUY5u1clU5BhKgI3Zm4f+owMsfq9klEJ/zkBp5t8FfVyEnXm0yI6KPNQlQsnl0DBYWPfJRaBqzp5kuz33jMIHAI4+fsT+pvf4D1hghSBkwAyQJc0wcHBgVBbVT0lkh6ATqMYBJGa9vUU5Px79vwlek5/DMX6KMs1AIb9CVRdbwenKPDWMcjSuzHFeMEiC2Jm7eXIxlTSThYC4OKmZtoDg80LoLXGToyK7U7AXViV3l1z8SLpr76KohWRc6/ZswmcN6/1F5Wnwv7nQauvfQ4eA0MXW/yhXCiD5w6CXoCdEYHwVwvBeWVJLT++e8RQV+0X4sHsR4dZFjvbDDTGg46IRQk7Lp50mCJEYJ7cpM8RmIBQaG9DfK2+VsPh9Smc3JFhYoneb1wYo26wXgSuXgurzsN3F421/wDXhMMD/cCnI+uhOg2kfA9nv+gyEThNWRm5n35Kydathj6VSkWvG24gaOFCHN2tyy6Q9AxkgC6RSCQSiUTSGpnbIHWdOHZwgpFPgUv3UFVedR7260t9PZ3g6RFga+zcGumnCjm8IQUQm6hT7h1kdaqygTPApibteQhrsTaoLygg7fnn0dYIdTDvMWMIfeih1ncZq3Jg37PQUCnavRKF17mD+a+8qeXwzH5jKnViL3hquPn65qqyOtb/9wgVxWI8vkHuXPe74bhZkhD/BWElB+KHNx/ofBtt67CUzt4Hkc5uRRl72okC9n6TTGVpnaGvPSJwRwvhvZOQU23sC3WHRwYJb/MOUZwEJ/4DFenGvkssAleyaRN5n32GponnuFt8POGPPtq2hoKkRyIDdIlEIpFIJBJLlKXA8f8a24MWXZI6VWs4VAAr9UJeKuDJoRBix424soJqti87ZdgZHXl9byL62SibnQt8iVGHaxpWKbZrKytJe+45GoqFJL17QgKRTzyByqGVrIWaIvj1b8baYt8EGPU3cDQfQGdWwt/2Q4U+OB/gB8+MAGczwXlNRT0b/nuEsgIRRXoHuDG7teD8GGJnupHrge4gyK0g0u1/xDSd3QsxxkG0mc5eWVzL3m+TDVkVAGq1A8NmxjL4miirReDK9CJw25uJwN0SB/N7m/89WE19BZz5DNI3Num8tCJwNRcvkv3ee1SfOWPoc/TwIPjuu/GfObP197KkRyMDdIlEImkDFw8nbn9hQou+noKcf8+ef4+mvhIOvWpMi42a2ap39qUkvxrePGqMe+/sC8M6KN7dlIY6LVs+Pk5drcj7jk0MYsi0aNtuUgksQ+zWgghQr2n7Zbr6etJeftlQp+sSGkr0M8/g4NJKnnN9Oex7Bmr06QRe0TD6OYuBWE4VPL0PyvRj6+sLz40Ec+5otVUNrP/vEUryRB2Bl58r1z06HA9Ledfnga8x/nKuBjp/s7ZtChDq7Oeb9NmQzq7T6ji5I4PDGy4axAIBIvr1YsK8BLwDrAt6FQW2ZApLwIomInAD/OB3gyGyoyJwWTvg9EdQ38QfzqcPDH4UfDs/hUFbU0P+ypUUrV1r4mnuO3kyIQ88gJOfHdUbJVckMkCXSCSSNnBycWT0nO6Sl3jpkfPv2fPvsSg6OPJPqM4TbZ94GPjbrh2TnjotvHLYGNyMDhK2U/ZCURR2rDhNcY4ISH2D3Jl0h42icFpgJcZd2nDgFtrcnVUUhcy336bq1CkA1D4+RL/wQute3g3VsO95qNQLb7mHwJgXwdnL7OX51fDUPijWZ2b39oYXR4E5jbe6mgY2LDlCcY5Imff0deG6x4bjacm/LhehVN8Yl41C+Lx3JXXAz8Ae2p3Onp9axq4vzxh+DgDuXs6MvbkvccOCbBKB++9JOFVs7PNwgvv7wfSOisBVZgkRuMJjxj5HV6HcH3Ndp2tGKIpC+d695Hz4oamneXi48DRPTOzU50uuHGSALpFIJBKJRNKcsyuh4JA4dvaGkX8Fx67PnFAUEeCk6MWyQ93hj0PsKwp3bEuawcPa2VXN9N8kmvf2tjhIYC2Qqm97IcTR2tihBchbupSyXbsAcHBxIfq553BpTcBWWw8HX4Yyfa6/iz+MeQlczXuYFdWKtPZCvX5cjBe8NFoEic2pr9Xw05KjFGaI2mF3L2dmPToMr14WdorLgKUYMwb6AzdilY1cp6AghPl+Aiqa9Nugzl5X3cCBdRc4syerqVs4/SeEM/KG3uZt5cxgSQRuchg82B98OyICp22AC9/C+a+Mqv0AIeNh4G8uiQhcXU4OOe+/T8WhQ4Y+B2dnAufPJ+Dmm3Fw6vq/HZLLBxmgd3MuXLjAokWLKCoqwsfHh/fee49+/fq1uO7UqVM8+eSTFBQUoNPpePbZZ5kzZ04XjFgikUgkksuc3H1wbpU4VjnA8D+Dmx3zxzvA+nT4OUscuzoKUTh7VlxkJRdzcN0FQ/uauwfiG+Rh201+AQ7oj9WI4NwKTb2ideso+O47QChcR/75z7jHt1Lvr9PC4TegSG8A7+QFY18CD/MeZqV1Iq29UYws3EME5+bKyDX1WjZ/eJz8NLES4urhxOzfDbf8s6gBPgUaXcYigAVAV5UZZyPS2dOa9KmBq4DJtLlYoigKFw7l8evqc9RUGpXPe4V7MXFBAkHR1oskdqoIXOFxOPEeVGUZ+9wChVZE8KgO3rxtdPX1FH73HQVffYWuwbg44DVyJGEPPSQ9zSXtQgbo3ZzFixdzzz33sHDhQtasWcNjjz3G5s2mfrzV1dUsXLiQJUuWMG7cODQaDaWlpV0zYIlEIpFILmcqs+HoW8Z2v3shoHukpiaXCFGtRhYnQrT5LO52UVFcw7bPThp2SofPjCVqoI0R1DmEAFkjNwGRbb+s/NdfyfngA0M7dNEivEe1EmApChx/F/L2ibajK4x+HrzMe5hV1Au19kY7uhB3eHm0+Z1bbYOOLZ+cIPu8EJtzcVMz+9Fh+IVYCM61iLR2ffk7/sDdWJUxYHeqEIr5B8HU9wyxa26Fxl9ZfjV7vk4m66wxD93J2ZERs+MYOCkCB0frVh1K9SJwO5qJwN0cCwv6dFAErq4Mkj4RDguNqBwgdi70vR3UFkoQ7EjFkSPk/O9/1GUbJ+gUECA8zceN6/6e5tIttNsiA/RuTEFBAceOHWP16tUAzJkzhyeeeIK0tDSio41CLd988w2jR49m3DihQKJWqwkI6Px0HolEIpFILHHx4kV+//vfU1xcTF1dHTNmzOCll17CoTsrF2tq4dBroNFv9YVOhLi5XTqkRkrq4LUjxvTgm2JhQiuZ37aiqdey+aMT1Or9xiL792L4zFjbblIAfIHxi/8kYFjbL6tOTibjH/9AUcQLA+fNo9esWZZfoCjClz5T7yntoBZq7X59zV5e1QDPHoBUfZp3gCu8MhrMaZppNTq2fXaSjCRRQ+zk4sisRcPoFW5hJUQBVgMX9W0P4D6gI0Jn7UEL7Ef4rtc26e+FUGdPaPsWmgYtx7ekcXRzKlqtMXqLGRzIuFv64ulnXdCrKLAxA5YmG+3rAAb6w6OD7CACl7EFkj6FhiZ5+74JkPg78I7pwM2to6GoiJyPPqJs925Dn8rRkYAbbyTo9ttxcO38xYEOk4d4r9zQ1QORmEMG6N2YrKwsQkNDUavFr0mlUhEREUFmZqZJgH7mzBlcXFxYsGABWVlZDBw4kFdeeUUG6RKJRCLpMv72t78xe/ZsHn74YWpra5kyZQpbtmxhxowZXT008zTuyFakirZnBAx53L7F3e1Eo4PXD4v6aYBB/nC3FQGXtSiKwq4vz1CUJQIe7wA3rrl7oG07gDXAcozBYX/g2rZfVp+bS9pLL6GrF2nUvldfTfBdd7X+onOr4OIafUMFw56AgCFmL63VwAsH4bxe0NvPBV4dA0Fm7Oh0OiGOl3qiAAC1kyPXPjSEwGhvy2PZCRzWH6uBu7Bql9qunEeksxc06XNGKOZPwKpv+1nJxez5OtlgIwfg6efK+Fv7Ej3I+vKOtAqhkZBUYuzz1IvATeuoCFxFOpz4LxSfNvY5eYgsl6hrO/3/VUWjoWjdOvJWrEBXa1wF8RgwgLBHHsG1yXfzbks1sBX4FbgM1hF6KjJA7+Y0/3BsXF1uSkNDA9u2bWPz5s2Ehoby8ssv86c//YmlS5deolFKJFc2NRX1fPyHbSZ9D/xrimX/2ysMOf+ePf+OUF4uCnJra2tpaGggODi4i0fUChfXQvZOcax2g5FPXxKvZGv45Ayc1gc8Aa7w52EiVdhenNqZyflDuYBIZZ7xYCIu5iTNLaEDVgGNtthBwHzaFCDTlJeT+txzaMpE9OyZmEjE73/f+sJA6no4u8LYTnwMQsebvbReCy8dMgaLXk4irT3UTKa6oijsXnWGC4eFar+j2oEZv0kktHcrllgngKY22/MA8xn2nUMRsB5IatY/HJgBtLKu0Eh1eR2/rj5nmDeAg4OKwddEMezaWJxcrMtDr9PCl+dg9UVosvnONeHwQD+w5EhnFdo6OPslpKwGpYkMffjVMOABcPHtwM2to+r0abLfe4/aNGNRv9rHh5D77sN3ypTun86uBfYhgvOaLh6LpE1kgN6NCQ8PJzs7G41Gg1qtRlEUsrKyiIiIMLkuKiqKiRMnEhYWBsC8efOYN29eVwxZIrki0TbouHAor0VfT0HOv2fPv7289tpr3HbbbXzyySeUlpbyxBNPMGSI+V3OLqfopKhnbWToH8UOejdgZzb8kCqO1Q7w1+EdVLxuRl5KKfu+P2doT144AL9QG3OQNwBn9cduiPrrNsbY6HXeWL/rGhlJ1FNPoVK38tU0ezec/J+x3f9+i770Gh28fgSO692uPJzg5TEQZSZTXVEU9q0+R/KvYiwODiqm3T+Y8ATzSvAAZCK8zhuZhvB5vxTUIGzTfsHUNi0Ckc5uxSKBTqeQtDuTgz+mUK/3ugcIjvVh4vx++IdZ/x44mA9LTkF+k8Av3EOIwCV2NJsg/5CwTqtu8jfYPRQGPwKBQzt487bRlJWR++mnlGzdauhTqVT4zZxJ8F13ofayowhEZ3EOWIdphoUTQixQ0i2RAXo3JjAwkMGDB7Nq1SoWLlzI2rVriYqKMklvB5g7dy7Lly+nvLwcb29vtmzZwqBBg7po1BKJRCLpCcyaNYvk5GSz53bu3MnSpUu57bbbePzxxykoKGDOnDmMHDmSyZO72bfC2mI49LrwPQfoMx9CxnbtmPSkVcC/TxjbDw+Avr72u39VaS2bPzmBTie2PBOnRBE7NMi2mxxC+GuDUCy/AyGS1gqKopD51ltUJ4mtXyc/P6Kffx5Hj1bU4vMPw9E3MRS4974Vet9k9lKdAm8egwN60TZXR3hhJMRZ2FE+vOEiJ3YID3UVQrm+VXG8CkQ6f2NcOxyRTt7Z6BDib5sRYnCNeAIzEfX+VmzkFqSXs/urMwb7OABXdydG39iHvmNCrd4NLqqFD0/Dnlxjn9oB5veGW+I6KAJXUwSnP4ScPcY+B7X4vfeZB46dm8Gk6HQU//QTecuWoa0y/rDdevcm7JFHcO9rXu+gW1GAyLBo/md6KKL8xAtT+z1Jt0EG6N2ct99+m0ceeYS33noLLy8vlixZAsBjjz3GrFmzmD17NpGRkfzhD39gxowZODo6EhYWxttvv921A5dIJBLJFc2GDRtaPf/+++9z9OhRQCw4T5s2jT179nSvAF2nEaJw9foC5YChkLCwS4fUSGUDvHJIpA6DqN+dYYUaurVoGrRs+fgENRWi9jss3o9R1/e27SbpwPdN2nMAK26Rt3QpZXtE4OXg6kr088/jHNTKwkBJMhx6Vfy+ACJnQL+7zV6qKPDv47A7R7SdHeDZkZBgIVP9+LY0Dm+8aGhfdXt/4oa1UoqhBVZiDGxiEEr1nZ3hfB4RbDUJhlEjasyvps2MBYC6mgYOrkshaXemiYB3wpgwRs/pjaundUGvToEf02D5Wagxbr6T2AseGQjhHRGB02kh7Uc4sxy0TdTueiWKXXPP8A7c3Dqqz50j+733qDl/3tDn6OFB8N134z9zJqruLHQJIsOisc68abJXJELJvzHDQiaCdVtkgN7NiY+Pb2GrBvDuu++atG+//XZuv/32SzUsiaRH4eyu5pa/jGnR11OQ8+/Z828v0dHRbN68mTvuuIOqqip27tzJH/7wh64elimnPoSSM+LYLRCGPyGsmroYRYE3jxp9o+O8YdFA+2pg7f3mLPnpQiPAy8+VqfcOsto+C4AyYAXGFOsxwOi2X1a0fr3R69zBgai//AW3uDjLL6hIh/3PizpkgJDxkPio2R+GosAHp2Gr3hK7sSRgsIU066Q9WexbYwzCxt3cl4SxYa1PYB1Gb3FvRMZAR3aK28LSLuhgxK55KyXyjSiKQsrhPH5Zfc6wIAPgF+LBxPn9COnta/VwzpXCe6eMonsAPs7wQH+4OqyD79GSs0IErjzF2OfsAwPuh/BrOl0ETlNRQd6yZZRs3Gii+eQ3dSoh996L2te3U5/fYSzVmfsgdsyH0PkLSRK7oCotLZUueD0ULy+v7m13I5FIJFcAOp2Oioqel0d47NgxnnzySSoqKtBoNFx33XU8++yzbabPXrLPpoytcOxtcezgBOPfAN8+nf9cK/jiHKzUl4V7OcHbE8yrjreX5F+z2fmFSC9Xqx244Q8jCYiwoZa2HvgQ0AfCxAL302agWn7gAOkvvWQIfsIeeaR1O7XqfNj7JNTqC8kDhsCo58DRvIDd8mT46oI4VgF/GQ7jQ8zf+sKhXH5edsqwkzzyut4MmxHT+gT2Y8wYcAR+Q+eJwlUiAq39mPpVhwOzET9zKyjNq2LP18lknzPKqqudHBkxK5aBkyNxtFJtsLoBPj8H61JNh3NtJNyTAB3SzGyogjPLIG0DJnePmikyJZw7t85bURRKtmwh79NP0TT5W+0aHU3YokV4DBzYqc+3C8mIhZymdeZqRJ35VQhV/2b01M+mywG5BSCRSCQSicTuDBkyhI0bN7Z9YVdQdkHs1DUy+NFuE5wfKhABOogg88lh9g3OC9LK2fO1cTt24m39bAvOFeA7jMG5L3A7bQbnNefPk/H3vxu9zm+5pfXgvK4M9j1rDM594oWyvoXg/NsLxuAc4PeJloPz9FOFbP/8tCEUTJwSxdDpbVhkZSGszBqZS+cE5w2Imv7tiIWQRnwQyuxDsWoXVFOv5cimVE5sS2vpaX5zXzz9rfc035Mras2L64z90V7C07y/FTv4rd48eyec/gjqSo39XjEind2/fwdubh01Fy6QvWQJ1U30NBxcXQleuJBe11/fumhhdyAPEZifa9Y/FLFr7nOpBySxB938XSeRSCQSiURiR+orRN25rkG0o2dB5NSuHZOe3Gr451HjHuJdCTC0Fa0yW6mpqGfLJ8fRakTx6YAJ4cSPCrXtJjuB4/pjZ4Riexs1x/UFBaS9+CK6OhHh+Vx1FcH33GP5BZoa2P8CVOlXATzCYfRzFm3v1qfB0iYp4A8PhKkWRPhzzpewtYkwXv/x4Yye06f1zI56hI1cYzr/OGCE5cvbhRY4gtg1b5I+jjMwCZiI2V1Qc6SfKmTvN2epKDbmOXv5uTLORk/z3Gr43ymxaGQYjgPcHg9zYzto9VeZJdTZC48Z+xxdoe8dEDsHHDqzbgC0lZXkLV9O8YYNJunsvpMmEfLAAzj5t6F02NVUAluAA5imNEQh6sztqFchufTIAF0ikUgkEknPQNHBkX8aLZt8+8KA33TtmPTUaYUoXKV+3WBMMNzaSmm2rWg1OrZ8coLKUhEkB8f6MPZmG5Wok4FNTdrzAQu71IbnVlWR9uKLNJSIFGv3/v2JWLzYckCsbYCDr0KZfkvQxR/GvAgu5rcCf84SFl+N3NUXrrOwGV6YUc6mD4+j0S9QxA0LZvy8hLZVy9dh9HhvTDG3Fwri57oB0/RkFTAKYd9mpehaRXENv3x7lrSThYY+R0cVg6+JZti1MaitlFVv0Ao/81Xnob6JkNioIOEk0KGMDm09nP8aLnxjFP0D4Zww8LdCC6ITURSF0q1byf30UzTl5YZ+l4gIwh5+GM/uagXZiAbYi7DZa5LRgA9CkyARWWd+BSADdIlEIpFIJD2D5BVQcFgcO/vAiL9aTJm+lCgKvHsCUvXloOEe8IdE+2piHVh7ntyUUgDcvV2Ydv9gq+uPARE8folxt24aMKD1lygaDemvv05taioALqGhRP/tbzg4W9gKVhQ49i8oPCraTp4w9iVwN6/wvi8P3j5ubN8aB/MtVCqU5lfx0/+OGTy/I/v34uo7B+Dg0MYP+SjC2gyEd/R87CcKdxFhmZbarL8fIj25FTH5pmg1Ok78nM6Rny4aFh9AKPOPvzUBv5BW7OuacaII3jsJmU1s3AJc4aEBYtGoQ+/J/ENw8n2ozjH2uQXBoIcg2AqFwQ5iKZ096PbbCZgzp3unsyvASeAnoKRJvzOiznwi4v0puSLoxu9EiUQikUgkEjuRuw/OfyWOVQ4w/Elws2P+eAdYlwY7ssWxqyM8PQI87PhlO+VInsHn29FRxfQHBuPubYUvVyM1wGcYd+wG0qbvt6IoZL33HpV6qz21lxfRzz+P2tuCGbmiCFX97F2i7eAMo54FL/OF3seL4O9HhOUXwOwouDvB/K0rS2rZ8N5RaipFUXdwrI91CxRFtLSRs8cGby6wkZbK7JGIXVArBeAAspKL2fN1MmUF1YY+dy9nxsyNp/eIYKs9zcvq4JMzsC3L2OegghuiYWFfcOtIxFBTJOrMc3Yb+1SOEHcTxC8AtXX18O3FUjq7z8SJhD7wAE4B3ePvgEUygB8RtoaNqIDhCF2CztXQk3QBMkCXSCSSNqipqGf5UztN+u56dRJuHZKtvXyQ8+/Z878iqMyGo28Z2/3vg4DErhtPE5JK4OMkY/sPQyCyIz7SzSjOrmTHCuMDxtzUl6AYG5SjtMAXQLG+HQLMo8002sJvv6VEbxPr4ORE1DPP4BLWioXZ+a8hVa/CpnKAEX+xKBKWXAIvHYQG/Wbx1WGi7txcLFpb1cBPS45SWSI8tXuFeXLtQ0OsS/deh1GobTgdrzvPR9SYn2jWHwBMBwZhdXpyVVkd+74/x4XDeYY+FTBgUiQjZsfi4mbdCo+iwMYM+CzZWF4BkOALjwwSFn/tRqeF1HWQ/Lmpp7n/IBi8yOLii70wqLMvXXp5prOXIEpKjjXr740os7BRPkJy+SADdIlEImkDbYOOpN1ZLfp6CnL+PXv+lz2aWjj0Kmj0O4yhEyH2xq4dk57iWnjtMDSKbN8cZ1l5vD3UVjWImusGoW4WPzKEARPDbbvJRqDRKtwduIs2xcrKdu8m97PPDO3wxYvx6N+KInf6ZkhebmwnPg7Bo8xemlYBzx+EWr1g25hgodhuLjhvqNOy8f2jlOSJfG3vADdmLhpqXfB6HuMOtzdi97y9FAHbEOnyTQW9fBClAkOxOm1eq9Fxelcmh9an0FCvNfQHx/gwfl6CTYr8KeUinT251Njn4QT3Jgj7tI55mp+BE+9B+UVjn7M39L8fIqZ0uqd59blz5Lz/fst09ttuI+DGG7t3OnstQsV/L6LmvJEARGCegKwzv8Lpxu9OiUQikUgkkg6gKHDsHahIE23PSBjy+04PDqxBo4PXj0CJPm18sD/cbaNmW2soisL25acMSt6Bkd5MvK2f1SnPgAgoG7OSHYCFQBu2WtVnzpD5ljFbIfjOO/GdNMnyC3L3wfF3je1+91pU1c+thmf3G3d6E3vBk0PNq4lrNTq2fnqC/DSxc+ru5czsR4ZZl9qvIETbGpmJ1QrqJhQgAq2jmAbmHoi64THYVDecc6GEvV8nU5xjLBB3dXdi9I196Dsm1OrfbY0GVpyFtammw5oSDvf3Ax8bqh9aUF8BSUshY5Np/yXyNNdUVJC3bBklGzeaprNfdRWh99/fvdPZtQhV9q1AEw0A3BALOaOxn/6BpFsjA/QezOwfoayh7eskkp6OugpGNuub9SNorNfduayR8+/Y/H2cYFUr8YmkE0n53lj3qnYXPtqdXO9qLR8nifR2ECJcfx4Gjh2xrWrG4Z8ukpEkPMTdPJ2Z9sBg1E42fLvPRPidN3IdbdZG1+fmkvbSS+gaxJcLv6lTCZw/3/ILipPg8BsYwsTYG6H3zWYvLamDZ/YbvbjjfeBvI8BcprqiKOxYcdowfxdXNTMXDcWrl3mbthacBBp1zMIAWzOhs4FdCDu6phGwG8IybRw2BfzV5XXsX3OecwdzDX0qoN/4cEZe3xtXKwULLHmaR3iIdPbBvawfk9mbZ2yBpE+hocLY7x0nPM39LAgE2AlFp6N440byly9HU2F8vmtkJKEPP4xnYvcoaTGLJSV/R8R75RrEe0fSY5ABeg/mYAEU1bV9nUTS01Fr1NSMMS0+PFaqRlPZRQO6xMj5d2z+vTqyGyVpP4Un4MxSY3voH8HTxvTuTmJ7lhCGA7H7+9TwDu5aNiPtZAFHfhKpxSpgyj0D8fSzYWGiHFiOMb12FDC29Zdoq6pIfeEFQ62vZ2Ii4b/7neVd3YoMOPAi6PRF3mGTYcADZrMbKhvEznmuvkoh0hOeH2VZuGzf9+cNtdlqtQPTf5tIr3Abdm6bSk5Mx7p0YgU4i8g4uNDsnBswARFs2RBo6bT6dPYNFw3q8wABkV5MmJdAULT1WgI5VcLT/LDRgQ1nB1jQR5RWdMjTvDxVpLOXNBFTULtBwl0QPbvTPc2rkpLI+d//qElJMfQ5urkRdMcd9Lr++u6dzp4NrAdSmvUPRij5d3M7dknn0I3fsRKJRNI90KjVHBoyqKuH0WXI+ffs+V+W1BbD4b8L33OAPvMhZEzXjklPajn856Sx/fAAiPe13/1L86vYvvy0YeN25PW9Cetrw7d8DbASaNyEjEHUX7cSpCoaDemvvUZdZiYALuHhRP31r5YDo5oi2PcsNOhXuQKGwtDFZoPzOi28eNBoQRfkBi+OAm8LO9Anfk7nxHYhd60Cptw7iNDebeTlNyUTaJScCAPaKjuoBg4B+zAK6TXihrC/GgfYmLiRc76Evd+YprO7uKkZdUMfEsaFtW0Pp6deC9+mwNcXjKJ6ACMD4aGBENIRT/OGaji7Uoj7KU1uHjZJLLa4dm502VBSQt7SpZRs22bS73v11YTcdx9O/t04ui1DCMAdxTTLIgpRZ965+nmSbo4M0CVWodZoGHLqjEnfsYH90HTnVUk7Iuffs+cvkUguI7QNcPBVqC8T7cBhkLCwa8ekp7IBXjksgk6AaREwI9J+96+v1bDloxOG3da4oUEMmRZt/Q0U4AeMdk4+wB20WvdqsFM7JqSm1d7eRD/3HI6eFqToG6pg/3NQq9/K9ekNI58Ch5afJxqdENFrLAXwcYaXRkOAhV3oC4dy+fX7c4b2xAX9iB5soy/a6SbHozG/MKEgPMwPI9LYNc3O+yMC8+HYXLteVSbS2c8fyjXpTxgbxugbeuPqaf0NDxeIXfMcowMbAa7w2wEwtiOe5ooi7PBOfwx1TVYlPMJh0MMQOLSdN7by8RoNRT/8QP4XX6CtqTH0u8XGEvrQQ3gMHNipz+8QtcAOYA+m7xt/xI65DUr+kisX+e1aYhVODRqu2nfIpO903z49JkCT8+/Z85dIJJcRpz+EUr1ys1sgDPuTsO3qYhQF/nnUmKbd2xsWWbAGa9/9FXZ9kWRQLPcP9WDSHQNsE4X7FSFSBeIb4p1AG5ZvhatXG+zUVGo1UX/7Gy6hFvyftPVw4GWjaJ97CIx+XqRDt5gPvHMcDulrct3U8MIoCLOg/ZCVXMyOFcboevi1sfQb346ShqYfa1pAhxDIq0HsrF9A7HqWmXltH0RQP0D/GhvQanSc2pnB4Q0XTdTZAyL06ew2WOMV1sDHZ2B3jrHPQQVzY+H2PuDakY/uyiw4uQQKm3h/OTgLP/O4m8DRBtW79jz+6FFyPviA2owMQ5+jhwfBd9+N/7XXonLspipqWmA/Qs2/uQDcNYgsi246dMmlR3677sGMDLReJE5d1bJvRGDPEolqjpx/z5m/RNIRfDr3+6qkKRlbIE0vv+3gLEThnDti5Gw/vjhnDDa9nUXduTVW3NZy4ud0Uo7mA0IUbdoDiTi52PCAFITvdyM3AW3Et2W//ELe0qWGdsQf/mDZTk1RhBd9sT6/39kHRr8ALr5mL/0wCbZni7aTAzw7AnpbiFGLsyvZ/PFxtHq/un7jwhg+qw1FO0s0zYb/AdiCSE8vsXC9C8IffSzCBqsdZCUX88u3Zw2LKyDU2Ude39umdHaNDn5IhZXnjDZ0AAP9xWJQdEcE1DW1cP4rSFkNuiZbv0GjYNBD4B7cgZu3TX1+Prkff0zZ3r2GPpVKhd+MGQTfdRdqH+sXMC4pCnAKkc7epP5fCsBJWkMG6J3Anj17mDBhQlcPo03WXwcOVq7wVhbDM2+Z9m24Djy7cXmPPZHz79nzl0g6gk4HTUSFLzmNVkM27aRejpSlwIklxvbgR0T6dDfgUAF8qfcSVyGswYI6UvvbjKzkYvavOW9oT75zAD6BNjygBFF33lgLexUwrPWX1Jw/T+abbxreX8ELF1q2U1MUOPUh5OwRbUcXGP0ceIaZvfzrCyLQBPHz+vMwGGRBYbyqrI6N7x+lQV83ED0ogAnzEtr/fu+NCMhr9e0a/b+mqBC16UOB/rTPgg2oKK5h3/fnuXgs3+TW/caHM/K6OJvS2U8Xw3unhE98Iz7OcF8/YZ/WoXT2vH1w6gOoaSIx7hYEA3/b6doOuro6Cr79lsJvv0VXX2/od09IIPShh3CPj+/U53eIdIQAXHqz/kRgBlIATmIRGaB3AvPnzycgIID58+dz++23ExcX19VD6jCOTg70nxjeoq+nIOffs+cvkVyOLFu2jCVLlnDhgpCU7t27N4sWLeLuu+/u4pF1AvUVcOhVoyJ49CyLXtqXmtxqkdreGPvenQBD7GjFXFFcw7alJw33Hzo9xra66wbgc4TYGUA8oha2tZcUFgo7tTphBeN79dUELlhg+QUp3wshMRDlBiP+Cr7mA6uN6bD8rLH9eCKMsbA5W1+rYeP7x6gsFeMIjPTmmrsH4dARvzpv4P+AJIT1VWNJewgioyAMSKDN1P/W0DRoOb41nWObU9FojOJqQdHejL81gcAo67M+yurg0zOwNcvYpwJmRcFdCeDZkQyeqhyxsJJ/wNjnoIa4myF+vlho6SQURaF8715yP/6Y+gLjwoDa15eQe+/Fd8qU7rvoWAhsROycNyUWmAnYUXdCcmWiKi0tVdq+TGILlZWVrFmzhlWrVrFnzx5GjhzJbbfdxk033YSvr29XD8+Al5cXDtZuoUskEomkXeh0Oiou8Rb6yy+/zJIlS/jtb3/LqFGjADhw4AAffvghDz/8MH/7298u6XhswebPJkUH+5+HgiOi7dsXxr3e6bWw1lCrgSd+MSqQjwmGp4fbr+5c06Dlh3cOUZghHhA5oBczfjPE6pRoFOAroLGc2B94lFZTbnW1taT8+c8GSyv3/v2JffllHJwt7PZm7YAj/zS2Ex+HqOlmL/01F149bFzMuDcBbrGQBKHV6Nj0wTEyk4VImZefKzf+3yjcvNq5nW2JxsHY4XemKAppJwr5dfU5KoqbiJt5OjPqht70HRNqddCpU+CndFh2FqqalCv28YFHBnbQGUBbD+e/gQvfgK7JzQOGChG4TrYrrE1LI+eDD6g8ftzQp3J0pNecOQQtWICjRzetr6tE1JjvR2gXNBKIWPTqT7cSgOuKzyaJdcgAvZPJzMzk66+/ZtWqVaSmpjJz5kxuv/12ZsyY0eUrfzJAl0gkks6nK74ExcXF8cYbb3Drrbea9H/zzTc8+eSTpDTxC+5u2PzZdGa5qI0FUdd81dvgZsct6naiKPDWMWMddbgHvDkePOy4brBzZRLJ+8QDvAPcmPunUbi42fCAHYidPhBp2g8jdootoOh0pL/6KuX79omXhITQ+5//tFz/W3hcKLY31iz3XQh9bzN76alieGa/0Qpsbiw8YLGcXWHPV8kk7RXbxi5uaub8YSS+wd00cANKcqv45duzZJ01qp47qFQMnBTBsFmxNv3ekktgySm4UG7s83CCu/vCzCghCNdu8g7AqfehOs/Y59oLBjwIoRPst7pkBk1FBfkrV1K8fj2Kzhjheg4dSuhvf4trZDfdeq4HdgM79ceNeAJTgZF0SwE4GaB3X2SKeycTERHB6NGjSUlJISUlhdOnT/Poo4/i4eHBv//9byZPntzVQ5RIJBLJFYZWq2XYsJZFxEOHDkWjae4JdRmTu88YnKscYPifu0VwDrA+3RicuzrC0yPsG5yf2ZtlCM7VagemP5BoW3CejBCuamQerQbnAHmffWYIzh09PIh+9lnLwXl5Khx8xRicR80USt9mSKuAlw4Zg/NrwuH+fpbHcWpHhiE4d3RUMf03id02OK+raeDIhouc2pWJTmfcEwvv68e4m/viF2p9rnxFPXyWDJsyTK2zp4aLWnOfjmScV+eJOvO8/cY+lSPE3gh9bwe1jUbuNqDodJRs2kTesmVomgSMzsHBhP7mN3iNHt3lm1pm0QKHgK1A0zjXGaHjcBXt1ieQ9GxkgN5JXLhwgS+++IKvvvqKiooKbrnlFjZs2MCIESNoaGjgH//4B4sWLeL06dNt30wikUgkEhuYP38+H3/8Ma+++qpJ/9KlS5k/f34XjcrOVGYLVfBG+t8PAYO7bjxNOFMCHyUZ24sTIbIDNcvNyU8tY+83yYb2xNv64R9mwwOKgFUYo7wpQBvW0cWbN1Pw3XcAqBwciPrLXyzvaNYUwv4XQKMvbA8aJVKjzQRZBTXw3AFjmvbwAHh8sOWN2vRTIkW8kUl3DCC0t5/5i7sQnU4h+ZdsDv54gdomOehefq6MmRtPzJBAq4NORRFB+WfJUNEk4zzGCx4eKFTa2422Hi58K1LadU22f3sNhkGLwKtzd62rTp4k58MPDSUTAA4uLgTOm0fATTdZLp3oShSERsFGoIluHirEbvlUhJaBRNJOZIDeCUydOpXjx48zefJknn/+ea677jpcXIzLmk5OTjz44IP84x//6MJRSiQSieRK4qmnnjIcq1Qqli9fzs8//8zIkSMBOHjwIFlZWSxoTczLznz00Ud88MEHODo6olKp2LZtG66udtiJ09TCoVeMAWDoRIid0/H72oHiWnjtsLC8ArgxBiZYsAVvDzUV9Wz++ITBUmzgpEjiR9nwgHqEKFyjSnl/REDRCpUnTpD93/8a2qEPPYTn0KHmL26oEsF5rd5Tyicehj8JDi1zfCvqRXBepB9LvA/8dTioLVQ4FGVVmAjiDZsRQ5+RbWz7dwE550v45btzFGUZt1XVagcSp0UzZGo0ahv89c6Vwv9Ow9lSY5+bGhbGw/XR0BE9PLPp7C7+MOABCLuqU9PZ6wsKyP3kE8p27zbp9500iZD77sMpoHtkwrQgDdhAS2X2AYg6cxv0GSUSS8gAvRO44YYb+PzzzwkNtfyBGRQUREmJJVNNiUQikUhs43gTQSWAIUOGAHDx4kUAevXqRa9evThz5swlGc+PP/7I119/zebNm/Hx8aGgoAAnJzvkeCsKHHsHKvTfkD0jYcjjnRpMWItGB68fgWIhKs4gf7i3lVRtW9FpdWz99ATV5eIBob19GXNjH+tvoABfA43xWCAitb2VH11dTg4Zr72GohU2Zr1uuIFes2dbGKAGDr0GFami7R4Mo581mx5dr4WXD0FGpWiHecBzI8HVwjfTmop6Nn1wjIZ6MY64oUGMmN29XHIqimvYv+a8wY++kbhhwYyZ0wdPf+sXpyrqhZr9T+mm6eyTQkVtvg23aklVrkhnb6rOrnIUi1zxt4GTHT0Am2HJNs0tLo7Q3/4Wj4FtpHJ0FfmIkpDmia9RwCwg+pKPSHIFIwP0TmDx4sVdPQS7U1+rMfFYBRh9Yx+cLX2SXmHI+ffs+UsklwPr1q3r6iGY8O677/LXv/4VH32NcmCgnbaWLq6FHP2um9odRj4N6lZkxy8hn56BJP3ae4Ar/GWY5d3g9nDghwvkXCgFwMPHhan3DcbRlgf8jNH6yRW4U/9fC2irqkh78UVDXbDXiBGEPvCA+YsVBY7/Bwr1kvBOXjD6BXDxbXGpThHWc6f1PytfF3hxlOUaaq1Gx5ZPThjs1IKivZl854BuU5fcUKfl2JZUTmxLN7FN6xXmybhb+hLax/oUfEWBzZmw9IxpOnukJywaCIMt+MFbhbZOr87+rak6e6/BogTBK6oDN28dRVEo27WL3E8/paGw0NCv9vEh+O678Zs2DVV3FC4uR9SYH8R0paSbKrNLrgzkt+tuzoULF1i0aBFFRUX4+Pjw3nvv0a+f+eX42tpaJk+ejJubG9u3b7frOOqrNXz7+j6TvqHTY3pMgCbn37PnL5FIbOfMmTMcOXKE1157jbq6Om677TYefvjhjt206CQkfWJsD/1jp1s+WcuubFibKo7VDvDU8A6KdjXjwuE8jv8ssgYcHFRMvW+wbZZiZxCBBoiAYgGtpuMqWi3pf/87dZmZALhGRhL5xBOoHC2kZ5/7EjL1D3BwglHPmP3dKAp8cBp+0e/iuzrC8yMhuJVN21+/O0tuSikgFiamP5iI2qnrZbEVReHc/lwOrLtgyGoAYZs28ro4+o4Ns97yDvPp7K6OcEc83BDTgcUeRYG8fcLTvKbJ7v4lSmevOX+enA8/pKqJ7pLK0ZFe119P0O23d0/btBqEKvseoKmupheiJGQE3VKZXXJlIL9dd3MWL17MPffcw8KFC1mzZg2PPfYYmzdvNnvtSy+9xKhRozh58uQlHqVEIpFIehqzZs0iOTnZ7LmdO3ei1Wq5ePEi69evp7y8nOuuu464uDhmzJjRvgfWFMGh14XvOUCf+RAypp2jty+p5fDOCWP7oQEd9KFuRnF2JTtXGlXnxt4UT3CsBfV0cxRiKgo3A0ho/SU5H31E5RHhLa/28iL62WctB1IZW+HsSn1DBcP+BP7mPdJWX4Qf08Sxo0osZPRuZSpJuzM5vceo2D7tgcG4e9tx5aOd5FwoYd/q8xRkGL3OHB1VDJwUydBrY2xS1C+vh2Vm1Nntks5emSXqzAuOGPsM6uy3dWr2SUNJCXnLllG6dSuKYpyZ14gRhD74IC4REZ327HbTAPwKbEcE6Y24AJOB8UhldkmnIwP0bkxBQQHHjh1j9erVAMyZM4cnnniCtLQ0oqNNi1327t3LhQsXePTRR2WALpFIJJJOZ8OGDa2ej4iI4JZbbsHR0RE/Pz+mTZvGoUOH2hegaxtEbXN9mWgHDIWEhbbfpxOobIBXD0OdKI1mSjhca0fh67qaBjZ/fBxNg3hA39GhDLjKhsCmFlgONG7wDgImtf6SovXrKdKXTKjUaqKefhrnEAtibIXH4Pi7xvaA+yF0vNlLd2aLMoBGHh8Mw1rZxc+5UMLeb88a2hMX9Cco2oaFiU6grKCaA2svcPG4aZ159KAAxsyNxyfQ+vptnQIb02HZWfE+aiTSU6izJ3YknV1TC+dWwcXvjVZ3AAFDYOBDnarOrmtooGjNGgq++gptjTHKdQkPJ/TBB/HSC1d2K3TAEWALUNak3xEYC1wNdMONfsmViQzQuzFZWVmEhoaiVotfk0qlIiIigszMTJMAvaqqir/+9a988cUXXLhwoVPG4ujkQO8RwS36egpy/j17/hKJxHZuvfVWtm7dyuTJk6mtrWXPnj3t12g5/SGU6nfr3YJg+BPC97yLURR46xjk6MXke3vDo4Psly2sKAo7Pj9NeaEIcgIivJgwP8H62utGUbhGK6hA4BZarZmtPH6cnPffN7TDf/c7y8JdFelw8FVQ9KsTMdeLnVkznCyCfzXRMbyrL0xpZZ2hpqKebUtPGbzDB18dRd8xdpTDt5HaqgaObkrl9K4Mg4I+gH+oJ2Nviic8wTavs6QS+N8pSDFuwBvU2a+L7mA6e/YuUQpSW2Tsdw2AgQ9CyPhOS2dXFIXyX34h99NPqc/NNfQ7engQdPvt9LruOlTqbhZ6tGaZNhSYBnQ/Fz/JFU43+79E0pzmH8JNU4QaefbZZ3nwwQcJCwvrtADdzcuZ3300s1PufTkg59+z5y+RSGznkUceYfHixYwZMwaVSsWNN97IDTfcYPuNMrZCmn633sEJRj4Fzt3DZPirC3BAv5Hq5STStW1w0GqTY1vSSDspBLVc3Z2Y9sBg22qvtyGCDxBicHcjUnUtUJeTQ/prr6HoRBlBwE034TfVggdbbYmp13nwaBj4G7PBX0YlvNzEem5GJMzrbXkciqKwffkpQ113WLwfo+e08oJORKvRcXpXJkc2XqSuxrgT7eblzKjrexM/OtSmOvOSOvjsDGzNMu2/JhzuTehgOnt5Kpx8H4qbZFI6qCHuZugzz6yavr2oSUkh9+OPqWziJqFSqfC79lqC77wTtU/XZj6Y5SIiMG9umdYXmAl0Pwc/SQ9BBujdmPDwcLKzs9FoNKjVahRFISsri4hmNTu//PILmzZt4o033qCuro7S0lLGjh3Lr7/+2kUjl0gkEklPx83Njfeb7MS2i7IUOPGesT34UfDpmkCtOUcKYIU++1oFPDEUguzoTpVxuoiD6y4Y7n/1XQPx8rehXri5KNxtQCsp09qqKtJeeAFtpfA98xo5kpB77zV/saYWDrxkFBzz6QPDzGc1lNbB8wegSp/CPSIQHhnY+ibukY2pZCYXAyIQnnLPIBw6ZPhtO4qicPFoPgfWXTBkMIDwMx90TRRDpkXbJJSq0cG6NFh5DprE+cR4iXT2gbZtwJtSXyE0AFJ/xKSKPWiUWDTx6LzMg4aSEvKWL6d0yxaTTSTPxERCf/MbXGNiOu3Z7SYXEZg3l9CIRATmsZd8RJec7HMlnPg5jbELesBkL0NkgN6NCQwMZPDgwaxatYqFCxeydu1aoqKizNafN7Jr1y6eeeYZu6u4SyQSiURySamvgIOvgE7vlRw1EyIt7OZeYvKq4Y2jxlDojvjWa6ltpbywhp+XnTTcf/isOCIH2FCQXAh81aQ9A7EraAFFqyXjjTeoyxLbuq5RUUKx3ZztlaKDo29C2TnRdg0Qiu2teJ3n6+PbOG9hPddarJ2RVMThDSmAWFeYcvdA29Tq7UDO+RL2rzlPfrox/1wF9BkVwsjreuPpZ9tO9NFCoVzf6PkO4OEEd8bDrKjWfx6touggfROcWQYNFcZ+91ARmAePaueN20ZXX0/R2rXkr1qFrrbW0O8SGkrI/ffjpc+c6VYUI2rMj9EjLdMURSHnXAmHf7pIzoVSXNxlGNhdkb+Zbs7bb7/NI488wltvvYWXlxdLliwB4LHHHmPWrFnMnj27i0cokUgkEomdUXRw5B9NdmjjYeBvu3ZMeuq1QhSuUdRrVBAs6GO/+2satGz5+LghnTp6UADDro2x/gZ1CFG4xphpIG2KwuV++ikVhw8DesX2Z57B0d1COkDSUsjVZ+ip3WD0c+DacvtXUeDNY5BcKtoBrvDcSGht07mmop4dn582xE4jr+9NWN+ObC3bRklOJQfWXTCUFTQS1sePMXP7EBBpW2lFXjV8nGS0lAMR/82IFDX4HbLhK04S6uxlTUobHV0hfj7EzgVH61XkbUFRFMr37BF15vlGoTxHd3eCbrsN/+uvx8Gpc57dbiqAn4EDgLZJvw+ixnwoV7RlmqIoZCUXc/ini+RdLGv7BZIuR1VaWtqyqFnSI/Dy8sLB3Oq4RCKRSOyGTqejoqKi7QslgP6z6ewKOK/fAnb2gaveBreALh0XiKDznePG+uEwD3hrvNgNtc/9FXZ9cYbkfdkA+AS6M/f/RuHsZuV+igKsABrtpgOBR2i17rxk61Yy334bEN7UsS+/jMegQeYvTvsJTvxXHKscYPTzEDjM7KWfJAlLNRBe3m+Mg9hW4ltFUdj6yUmDOnrUgABm/DbxkuzCVpbUcmh9Cuf255hsrPqHejB6TjwR/f1tGkedFr65AN+lQL3O2J/gawcLvtpisUiS9bNpf9gk6H9fp/5/Un3uHLkffWTqZ96d68xrgF0IL/MmKvm4AdcAY4ButpZgTxRFITOpmMM/pZCfVm5yzifQneGzYwjsbce6HIndkDvoEolEIpFIug8FR4zBOSoY/mS3CM4BNmYYg3MXR3h6uP2Cc4Aze7MNwbnayZHpDwy2PjgH4d3cGDtZIQpXlZRE1n/+Y2iHPfyw5eC84CicXGJsD1pkMTjfmG4Mzh1U8JfhrQfnACmH8wzBuau7E5Pu6N/pwXltZT3HtqRxalcmWo0xknb3dmHkdXE2C8ApCuzJFbvmhcasb3xdhADclPAOCKhr6+HiGjj3FWib3NwrBgY9BL0s/N7sQENhIbnLllH6s+migOfQoYQ+8ED3qzOvB34BdmLqZe4MTAAmIoL0KxRFUUg/VciRn1IpyDANzP2CPRh2bQyxw4IBRS4ed1NkgC6xioY6LUc2XjTpG3ZtLE4uV3BOUBPk/Hv2/CUSySWkaRDY/z4ISOy6sTQhuQTeN24c8vhgiPKy3/3zLpbxy7dG1aqrbuuHX6in9Tc4h6ivBZFHvYBWReHqCwpIf+UVFI1Ipe81ezb+My24dVRkCB96RR/Ext0E0eavPV4ES04Z24sGCmG41qgur2PvN0a/8wnzEzq17ry+VsPJn9M5/nM6DXXGnGcXVzWJ06IZNDkStY1y/Knl4v1xstjY56iCOTFwWx9wb+9CjqJA3n44/RFUG63LcPKChDsh6lpw6JzPYl1tLQXffkvhd9+hq6839LuEhxPywAN4jRzZverMtcBBhHtB07jTERiN2DW34X+pyw1FUUg7XsDhjakUZZkG3v6hHgy7NpbYoUGG31mjhaGk+yEDdIlV1FU18MVze0z6BkyM6DEBmpx/z56/RCK5hDTadoWMh7i5XTqURkrr4LUjRpuwG2JgUpj97l9TUc+WT04Y/LUHT46kz0gbPJ6KgS8xCl9NBRIsX66rqyP95ZfRlIl61EbFbbPUlcGBpnZqY6D/vWYvza6C1w5Do034jTEwM6rt4e/9+iy11SIHOW5oEHHDgtt+UTtoqNNyelcmx7emGZ4HQpl9wFURDJkeg6uNKRHl9fD5Wfgp3VR3bEQgPNgfIjoSEFZkwOkPRVaJARVEzxLBubMdV4iaoOh0lG7bRt7y5TQUG1ccHD09CbrjDnrNmtW9/Mx1wHHEAlWTBRKDl/lU4NJJGVxydDrhOHB0UyrFOZUm53qFeTLs2lhihgR2r8UUSat0o/+7JBKJRCKRSADPCBi6uAP5wPZDq4M3jkCRPqt4oD/c389+99fpFLZ9dtLg+R3a25dRc2xQnasHPseYypuA2Cm0gKIoZP3739SkCKV055AQIv/yF/MBl7ZBKOlX61XOvONg2J/M2qlV1MMLB43ieSMD4f7+bQ8/7USBIbXdzdOZ8be2srLQTjQNWpJ2Z3FsSxo1lcadYAcHFQnjwhg2IwYPX9uU2TU62JAOK84ZLeQAQt3hNwPE/Nv99m2o0tumrTNmLQD0GizEEr1j2nnjtqk8fpzcjz82vD9AaBP0uv56AhcsQO3VOYsC7UIBkoDNQF6zcwOA6UDnrPV0C3RaHSlH8jmy8SKl+dUm5wIjvRl2bQxRgwJkYH4ZIgN0iUQikUgk3QdHVxj5N6EQ3g1YdhZO6Hfl/F3gz8NAbUd91YPrLpB9rgQQtc9T7xuMo7UPUIBvEb7OAAHAfFq1iir4+mtKd+4EwMHVlehnnjEfdCkKnPgPlCSJtos/jHrWrJ2aVgevHxE76ADRXsIXvq3yba1Gxy/fnTO0x90cb9fU9oY6LUl7Mjm+LZ2aCmNgrgJ6jwhh+KxYfAJtF8k6XAAfJZnaprk6CjX/G2PAqb3JZYoO0jfCmeWmtmluQaLcI3RCpy1a1WVmkvvpp5Tv32/S7z12LCH33otLeHinPLfdnEcE5hnN+uMQtoJWZG5crmg1Oi4cyuPIpouUF9aYnAuK9mbYtbFEDuglA/PLGBmgS6zCQa1q4cHqoO45/+PL+ffs+UskkkvIwIfBs3sEA3tzhRI3iHrivwwHv45YYzUj5Wg+x7amAeCgUjHlXhs9v/cAJ/THLsBdtCp+Vb5/P/mffw4I9e3IP/0J1ygLkcyFbyFzmzh2cBZe527mi9o/OSNqzwF8nOHZEdbVXJ8/mEtFsQgwwuL9iBtun+3OupoGknZncXJ7hsmOOYgU+uGz4vAL8bD5vlmV8PEZOJBv2j81HO5OAH/bNuFNKTwOpz6EilRjn4Mz9JkHvW8Gx86pydeUlZG/ciXFP/2EojPu1rvFxRHy4IN4Dh7cKc9tN+nAJiClWX8EIjC3o+Vhd0Or0XF2Xw7HNqdSUVJrci4kzpfhM2MJ6+snA/MrABmgS6zC3duFP664vquH0WXI+ffs+UskkktI8MiuHgEAmZXwr2PG9gP9ob+f/e5fklvFzhVG1bnRN/YhtLcND7gI/NSkPQ9hq2aB2owMMv/5TxRFVEoH3Xkn3mPGmL8491c485mxPez/wNd85LMlE9amimO1Azw9AoKs2JTW6RSObk41tEdd37vDgUVNRT0nt2dwencm9bUaQ78KiB0axLBrY/EPs70ovLIBvjwH69KM9fUgbNN+OwD6+nZg0FW5kPQp5O417Q+bJGr93dpQ2Gsnuvp6itaupeDrr9FWG9OjnXr1Iviuu/CdMqV7BXq5iB3zpGb9wYhU9v60mjlyOaNp0JL8aw7Ht6RSWVpnci4s3o9h18YSFm/HP06SLkcG6BKJRCKRSCRNqNHAK4egVi/wPSkUro+23/3razVs+fg4DfXiAfEjQxh0daT1NygDViLEsQAmIWpuLaCtqiL95ZfR1ojdap+JEwmcN8/8xeUX4cibxnbCXRA63uylSSXw35PG9qKB1i9iZJ0pNqTnhvf1Iyim/R7apflVnNyewbl9OWia2KWpgLjhwQybEWObIr4erU5Y631+Fiqa1Jn3chW2aZPDOlJnXg3nv4aL34POuJiATx9RZ+5vRQF/O1AUhbIdO8hbtoz6ggJDv4OrK4G33ELATTfh4GLHNJGOUgBsRWSKmJjUA9OARMCOJSfdCU29lqQ9WRzflm7QqGgkol8vhl8bQ3Ccb9cMTtKpyABdIpFIJBKJRI+iwDvHIVNfTx3jBY8Ntl/pr6Io7F51xiDq5B/qycQF/azfrWxAiMLpx0cfxA6ipefpdGT885/UZQt/dbfYWCJ+/3vzz6srgwMvGX22wyaLFGszFNaIRYzGePj6aJhhwxrDmV+yDMcDJ9nwQj2KopBzvpQT29JJP11ocs7RUUWfUaEkTo3CN8j2VHaAQwXCz7xpnbmzA9wcB7fEgWt7v0ErOsjYCsnLoK7U2O/iCwl3Q+S0TqszrzxxgtxPPqHm/HlDn0qlwm/6dIIWLsTJvxtJnZcg7NIOYxqYewNTgBEI+7QrkPpaDUm7szjxc3qLEo2oAQEMuzamQwtaku6PDNAlEolEIpFI9KxJhT160TUPJ3hqeAeCMTOc3J7BhcNCctrZVc20Bwbb5rm9DmiMbf2A22g1UMlbvpyKgwcBUHt5EfX00zi4mimW1mng0KtQo99V9e0LQx43Gyw2aIXtXJk+dhgaICzFrEVRFDKThPKem5dzC42T1qiv1XD+QC5JezIpzqkyOefk7Ei/cWEMnhJlsyp7IxmVIjA/VGDaPzkM7kmAwI5oFxadFHXm5U0KqB3UEDsX4ud3mjBibUYGuZ9+SsWBAyb9XiNGEHLffbhG2zE9pKNUAD8DBxC+5o14AJOBMUB7PeW7OXU1DZzemcnJ7RkmNoAAMYmBDJsRQ0CkdxeNTnIpkQG6RCKRSCQSCXCyCD49Y2z/IRFC27cBa5bss8XsX2PcvZx8R3/bVMQPIQIXEN/g7gRaeXnpzp0UfPMNACoHByL/8hecg80IsSkKnFgCxfqaeBd/GPm0RWGyD5LgbKk4DnaDJ4eCow1pxuWFNWgaRPQVHOuDQxsv1ukUcs6VcOFQHilH82io05qc9/R1YeDkSBLGheHi1r7orawOVp6DnzJA16zO/MH+0K8jJb5VOZC0tGWdech4oc7uYYPnvQ00lJSQv3IlJRs3GrQHAFxjYgh94AE8hw7tlOe2i2pgJ7AXaJLxjyswUf+vc3TyupzaqgZO7cjg1I4M6pprJwwTJRrt0U6QXL7IAF0ikUgkEkmPp6hWWIU1Bme3xsEYO3ooV5XWsu2zU+j0gdLQ6THEDAmy/gZZwJom7blAqOXLay5eJOuddwztkAcfxDMx0fzFqesgY5M4dnCGUX8DV/Ppzpsz4Kd0cezsAE+NAFud0eqa7A7WlNejKEqLlHtNg5acc6VknC7k4tF8qivqm9+G4FgfBkyMIHZokPXWdM2o18IPqfDVBahuEhgGuMJ9/eCq0A76mZ9bBak/mNaZe8fBwN9Ar0HtvHHr6GprKVy9moLvvkNXa1T7dvL3J/juu/G95hpUDt2kcLsG2I1wJGj6K3YCxgNX0eoi1OVMTUU9J35O5/SuTIMeBQhHh94jghk6IwbfYDuuEEouG2SALrEKTb2WkztMzSYHTY60mJZXkFbOymd3U1Vah5uXE7e/MJGQ3r4m1xz44QLbPz9laJflVxM3PJj737zG7uPvKHL+PXv+Eonkykajg9cOm6Zs35Vgv/trNTq2Lj1pqCeN6NeLEbPjrL9BJaLuvDHGGw0Mt3y5pqKC9FdfRVcvnuc3dSq9rrfgxFF4DE5/ZGwPeRx8481eerYU3jP+2eaRQRDXjozbXmFeePm7UVFcQ15qGduWniSify9qKxuoqainNK+KnHMlJoJvjTg5OxI3PJgBV0UQEGHGv91KFAV25sBnyVDQxEra1RHm9Ya5sWBL5YEJOq3wMz+7AurLjf2GOvOpoLJ/gKxotZRs3kz+ypU0lJQY+h3d3Ai49VYCbryx+wjA1SN2y3chgvRG1Ij399XAFbppXFVay/Ft6ZzZk2XyHndwUBE/KpQh06Nty6yRXHGoSktLlbYvk1yJeHl54WDlCmplcS3PTF1l0vfS1gV4WjD9/O9vNzLq+t6MntOHo5tT2b78NIuXzW71GW/MW8O1Dw1lyLRuVAulR86/Z89fIukIOp2OioqKrh7GZYMtn0324v1TwkILRI3x2xPA247ptL9+d5YT+kVOTz9XbnpiNK4eVqZi64BPMPo+RwG/wWLduaLVkvr881QePQqAW3w8ca+/joOzmQlV58GuP0CD/v3Z+1bof4/Z+1bUw+/3GIPZ66Lh4YHWTcEc2edK+PE/h6261tFRReSAAHqPCCZqYIBtNftmOFkkvNvPlRn7VAiRu4V9O+B1ryiQfwiSPoHKJovaDk4QdxP0ubVT6swVRaFi/35yly6lLjPT0K9ydMR/5kyCbr8dtU83ERVrAPYBOzAKHYJQYh8JXAN0k6Ham4riGo5tSePsr9lom/j1OTqqSBgbRuK0aLz8O0eHwBzys6n7InfQJXanoriGzKQiHn5PyMoOmRbNd3/fR3F2pcUamrSTBVQU1TJosu1Krt0NOf+ePX+JRHJ5sTPbGJyrHYQonD2D8wuHcg3BuaOjiqn3DbI+OAfYgjE49wTuoHVRuGXLDMG52seHqL/+1XxwrqmFAy8bg/OgkdDvLrP3VBR454QxOO/vZ5sonDnC4v0YdX1vDq67gLmdIg8fFyL69yKyfy/CEvzaXVvelMxKWJoM+/JM+4cHiHT2mI7ob5WnwumPofCoaX/YJOh3D7jbUM5gA9VnzpD76adUnT5t0u8zfjzBd9+NS3h4pzzXZjQI/YTtCCG4RlTAUGAqwjrtCqQ0v4pjm9M4fyDXUOICoFY70H9COIOnRuPh000yGyTdAhmgS+xOaW41PoHuhnowlUqFX4gHJTmWA7R9359n5HVxODp1k5qoDiDn37PnL5FILh9Sy+HfJ4zthwZAHzvu3hVnV7JzZZKhPfamvgRF2/CAJERAAyKQuR1hM2WBst27KfjuO3G5oyNRf/kLzoGBLS9UFDj2DlSkirZHOAz7k8W067WpxqDWy0mIwrWz5NuEodNj6DMyhIzTReh0Cm6eTrh5OePu44J3gJv11nNtUKIXgNvUTAAuxgvu7wfDzPyIrKa2GJI/h4wtmPiB+fWDAQ+Cnx1rJZpQl5VF7mefUf7LLyb97v37E3LffXj07xwfdZvRIsQNfwaaZCygAgYjAvOO/Py7McXZlRzdnErK4TyTRSgnZ0cGXBXB4GuicLNVwEHSI5ABusQqVI4qguN8WvRZfoFpU2mlkKK+RsORjRf5/dLWU6C7Ejn/nj1/iUTSfnbt2sWNN97I66+/zm9/+9uuHo6BqgZ49TA0CoJPDYdr7ZjEU1+jYcsnJww1pgljwug/0YbdzALgqybtmUCs5ctr09PJbCIKF/rgg3gMsiBCduE7yNktjtVuQrHdybwY1blSsevcyB+GQIAds3A9/VzpP6FzdnlrNPBdCqy+aPw9A/i7wJ19YWoEOLR3DUBTCynfw4VvQFtn7HcLEsrsoRM6xc+8obiY/C++oGTTJhSdsX7ZJTyckPvuw2v0aLstbHQILXAU4WVe0uzcAGAa0Dni9V1OYUY5Rzalknrc1KvPxVXNwKsjGTgp0rYsGkmPQwboEqvw8HHhL9/Otepa3xB3yvKr0Wp0OKodUBSF0rwq/ELN754e25JGcKxvCxGx7oScf8+ev0QiaR8VFRU8//zzTJ8+vauHYoKiwL+OQ061aPf2FoJn9oprFEVh15dJlBWIB/QK92L8vL7WB071wAqgMe4bhLCZsoC2qor0V14xKHb7XnMN/tddZ/7igqNw5jNje+j/gZf5lYmqBvj7ESGiB3BTLIzqnExtu6LRCaX5L88bhf8A3NRCnX9OTAe87RUdZGwVu+Z1xcZ+Jw/oMx9irrdoT9cRtFVVFH73HYXff28Q/wOhzB50xx34TZuGyrFjtfl2QQccB7YCRc3OJSAC826SdW9v8i6WcWTTRTJOm07c1cOJwddEMWBiBM5uMvSStI18l0jsjpe/G+EJ/hxan8LoOX04tiUN/zBPy+nNa84xdm6fSzzKzkPOv2fPXyKRGHn66ad5/PHH+emnn7p6KCasvmiasv3U8A4odpvh5PYMUo7mA2LXbNr9g1E7WfkABVgN5OvbQcCttMhMMlyuKGS8+SZ12dkAuMXGEv7oo+YXA6rz4fAbGFKx42+HkDEWh/L+acjT150n+MLdnZOtbTcaldk/Pwu51cZ+RxXMjoYFvaFDpb4FR0WdeWNpAIiygOjZ0Pd2cO5IEbt5dPX1FG/YQMGqVWiaCHoZlNnnzMHB1bxg6yVFAU4gAvOCZuf6IALzqEs9qM5HURRyzpVwZFMq2edMUwXcvV1InBJFv/HhOLl0g8UTyWWDDNAlncL8v41j5bN72PLxCVw8nLjjpQkAfPnCXgZNjmDQ1eKvdGFGOZlJRTz4ztSuHK7dkfPv2fOXSCSwefNmysrKuPHGG7tVgH68SFhrgYh5/28oBNnR0SjnfAn715w3tCffOQBvW3LC9wPH9MfOwEL9fy1QsGoVFQcOAKD28iLq6afNW2lp6+HQa01E4UaJoNICe3Lg5yxx7K62X915Z6AocLgQliVDSrnpuatC4a6+ENoRO+nyVKHMXnDEtD94DPS/FzwjOnBz8yg6HaU//0z+ihXUFxgjXpVaTa/ZswmcP797KLMrwCmEmGF+s3NxiBrzVkozLlcURSHjdBFHN6eSd7HM5JynnytDpkbTd2yo9QtzEkkTpM1aD6YrrGwkEomkp3GlWtnMmjWL5ORks+d27tzJggUL+P777wkMDGTRokUMGzbMqhr0zvxsKqyBxXuMac8L+ohaZHtRVVbH6n/sp6ZCPGDo9BhGXd/b+hukAx8i6ndBiMINtnx5xZEjpD33HIqioFKpiH7hBbyGDTN/8bF3IWOTOHYPhav+ZbHuvKQOHt0JFQ2i/cchcE03TUtOKhELLqeKTfuHBsDdfSHetwM3rykUqeyZ2zARgPOJhwH3Qy8LNf4doNEyLW/ZMmrT0w39KpUKn6uvJnjhQpyDg+3+XJtRECKGW4DcZueigemIAP0KQ1EUUo8VcGRTKkVZpn/XvQPcDMKHjt11NasJV+pn05WA3EGXSCQSiURiMxs2bLB47pdffiEvL48pU6YAUFxczE8//URhYSFPPfXUpRqiCRodvH7EGJwPD4A74u13f51Wx7alJw3BeXhff0bMtiFCqQRWYgzOJ9BqcF5fUEDmP/6BolfhDLrzTsvBefpmY3Du4Awjn7IYnCsK/OeEMTgfHwJXh1k/jUtFSrlIZT/QbNe2tzfc208E6O2moQrOfwMX14KuaRF7EPS7W1indYIQW9XJk+R+9hnVZ86Y9HuNGEHwPffgFtsNtqIbA/OtQE6zc5GIVPY+WCzJuFzRanSkHM7j2JY0SvKqTM75hXgwdHoMccODcWi36qBEYkQG6BKJRCKRSOzKuHHjOH/emOZtyw56Z/HJGUguFcdBbvCnoR1Q8DbDwR9TyE0RD/D0dWHKPQOt/7KuA1YBjenZ0QjVdkuX19eT/uqrhppkr1GjCJw3z/zFZSlwcomxnfgYeMdYvPe2LNivD3p9nOGRgZ0Si7abjEpYcRb2NNu1DfcQ2RATQjowXm0DpK2Hc6uMpQCgF4BbADHXdYoAXE1KCnnLllFx6JBJv3tCAiH33mtZjf9SogDJiMA8q9m5cERg3pcrLzBv0HF2fw7HtqRRUVxjci4g0oth02OITgzsHsr5kisGGaBLrEJTryV5n+lSacKYUNT2VNXpxsj59+z5SySSy5td2fBDqjh2chCicPa0H049ls+xrWkAODiomHLvYFw9bXjAVuCC/tgTkdreyp/XnPffp0a/AOIcEkLkH/9oPkBoqIJDr4NOvx0ePRsirrZ43+Ja+OC0sf3Y4A6KqtmRnCr44jxszzJJNifAVWRCTAkHx/ZmFSsKZO+E5OVQnWfsd1ALVfY+88HZqyPDN0tddjb5K1ZQunOnSb9rZCRBd92F99ixXR/4KcBZRCp788A8DBGYJ3DFBeYNdVrO7M3i+LZ0qsvrTM6FxPkydEYMEf38u/73I7kikQF6N+fChQssWrSIoqIifHx8eO+99+jXr5/JNTt27ODFF1+ksrISBwcHZs+ezd/+9je7/tGorWzgo8e3mvS9tHUBnv49I0CT8+/Z85dIJB1jyZIlbV/USaRXwL9PGNsPDYDedtTWKs2vYvsKY1Q7ek4fgmNteMA5YLv+WIUIzlsRAy/ZupXiTSJd3cHZmainnsLR04xLhqLAsX9DtX5x1SceBjzY6lA+S4ZqjTieEg5jukGpc241rDovdvZ1TSJzXxeY31t413dorbjgKCR9CuUppv1hk0U6u7v9feUaiorI//LLFl7mzoGBBC1ciO8116Dqao2gxsB8K5DZ7FwoQvytP1dcYF5X08DpnZmc3J5BbXWDybmIfr0YOiOa0N5+XTQ6SU9BBujdnMWLF3PPPfewcOFC1qxZw2OPPcbmzZtNrvH19eXjjz8mJiaG2tpa5s6dyzfffMM8S+luEolEIpH0AKoa4JXDUKuv654SDjPMW363C029lq2fnKShTjyg9/BgBl1twwNKgC8xbgnPoFXF69q0NLLfe8/QDnv0Uct1yanrIHevOHbygBF/AUcni/dOLhFBMICnEzzQ39pJdA651fCVPjDXNgnMvZzg5ji4ProDXuYApeeEH3zhMdP+gKFCmd3HBnE/K9GUl1PwzTcU//ijiZe52tubwPnz8Z89Gwcny7+jS0JrgXkIIjAfwBUXmNdU1HNyewand2dSX6sxORczOJCh02MIjLa/jZ5EYg4ZoHdjCgoKOHbsGKtXrwZgzpw5PPHEE6SlpREdHW24bsiQIYZjV1dXBg8eTGpq6qUerkQikUgk3QZFETvn2Xo9pzhveHSQfeup935zluKcSgD8gj246rb+1mevaYAvgMay1gRgkuXLtdXVpL/2miGw87/2Wvz0InwtKD0nbMEaGfKHVneCFQU+TDK27+wL3vYvtbaKnCqxY/5ztumOuYcT3BQLN0SDe0di2Mosocyes9u03zsO+t8HgUM7cHPzaGtqKPr+ewpXr0ZbY6xjdnR3J+Cmm+h14404utlgxdcZtBWYTwEGcsUF5pUltZz4OZ0ze7LQaIzZDCqg94gQhk6Pxi/UTIaKRNKJyAC9G5OVlUVoaChqtfg1qVQqIiIiyMzMNAnQm5KXl8eaNWv46quv7DoWlQP4NTMRVXV/Bwm7Ieffs+cvkUguP9amwl69kJiHE/x1eAdToZtxdl8OyfuyAXBydmTq/YNxcrHhAesxBkL+wHwsBj+KopD17rvUZYktbre4OEItCe41VMPhf4BOvwsYdxOEjGl1KLtyjAJ6kZ4w045ZBtaSXgFfXYCd2aY15h5OMCcabowVx+2mpgjOfSnU7BVjIIZ7CCTcBWFX2V0NT1dfT/GPP1LwzTdoyo0G7Q5OTvhfdx2B8+ah9u7iXdlG8bdt9KjAvCy/mmNb0jh/MAdtkxQNR0cVfUaFMmRaND6B7l04QklPRgbo3ZzmK/GNdirmKC8v57bbbuPxxx9n6NChdh2Hh68rz66/1a73vJyQ8+/Z85dIJJcXSSXwaROnqj8mQogdv2sXZlaw5yvjAybMT8AvxLxtmVlOAL/qj9XAHUArG6jFP/5I2W6x4+vo4UHkX/6Cg7OZLW5FgRPvGevOfRNEHXUraHSwrImd/YP9OyC21g7OlcI3KcbFlEY8nGBuDNwQ08HAvL4Czn8NqT+aWqY5+0Df2yHqWiEGZ0cUjYbiTZsoWLWKhmKjQbvKwQG/GTMIWrAAp4CO+MDZgUa7tJ9pKf52BQfmxdmVHN2cSsrhPJOFILXagf4Twhl0TRSefq5dNj6JBGSA3q0JDw8nOzsbjUaDWq0WK+hZWURERLS4tqKigltvvZVZs2bxu9/9rgtGK5FIJBJJ11NSB68fNtYt3xoHo+0odlZX08DWT04Y0mH7TwgnflSo9TcoAlY3aV+PUMO2QPW5c+R8/LGhHbF4MS6hFp6X9TNk7xDHancY/kSbweemDMjTZ10PDYDhgW3OoMMoChwtFIH58SLTc97OcGMMXBfdwcBcUwMpayBlNWiqjf1qd5FVEDcX1PYNxBStltLt28n/4gvq84xq8CqVCp9JkwhauNDy7+5S0ZqP+RVcY56XUsrRzWmkny406Xd2VTPgqggGTY7EzZ7WDhJJB5ABejcmMDCQwYMHs2rVKhYuXMjatWuJiopqkd5eWVnJrbfeypQpU3jyySe7aLQSiUQikXQtWh28cQSK9a5Iib1EPbW9UBSFnSuTKC8SEW1gpDfjbrLhAQ3ACqBW304ERlm+XFtVRcYbb6BoRLp6wI034j12rPmLq3LgRFO/89+Be+srE4oCa1KN7bvt+LMyh0YHu3Ng9UVIKTc95+8ixN+ujeyg+Ju2HtI2iF3z+jJjv4OT3jLtVnC2b1q5oiiU7d5N/ooVhjKERrzHjiV44UJcY2Ls+kybUYCTiB3zZtkKhCJ2zK+wwFxRFDLPFHNscyo5F0pNzrl5OjPo6kj6TwzHxa2LhfkkkmbIAL2b8/bbb/PII4/w1ltv4eXlZbCqeeyxx5g1axazZ8/mf//7H4cOHaKqqop169YBMHfuXP70pz915dAlEolEIrmkfH4WTuozinu5wpND7ZuufWpnJqnHCwBwcVMz9f5BODrZ8IC1GIOjAOAm2qw7r88VL3Dv25eQe+81f7FOA0f+CVp95B85TdRUt8HZUqOI3mB/iPe1ahY2U9UgdurXpkJhrem5MA+4ORauCe+gRoBOAxlbRJ15bZNteZUDRE6H+NvAzb5p5YqiULFvH3krVlDbTJzXc+hQgu+6C/e+nbzq0RY6RGC+Dchvdi4MEZhfYXZpOp1C6vECjm1OpTCzwuScp68Lg6dE029cGGp7ilJIJHZEVVpaarmoWXJF4+XlhUNX+2xKJBLJFY5Op6OioqLtCyVA+z+bDuTDiwfFsaMKXhsL/e1oV5yfWsa6fx8yCErN+E0i0YNsyAc/CjTqtzoBjwCtbHAXrV9Ptn5R3tHDgz7//jfOQRaU2M9+AWdXimP3UJj0b6vStz9Ogu8viuPfJ8K0lhV0HSKjEtalCqu0Rqu7RuJ94JY4GBcCDh0JDnVayNoO576A6jzTc2GToO9C8GylhqAdKIpC5aFD5K1YQc358ybnPAYOJPjOO/EYNMiuz7QZLXAc2A4UNDsXjkhlT+CKCsy1Gh3nD+ZybEsaZQXVJud8At0ZMi2aPiNDcFTL774gP5u6M3IHXSKRSCQSyWVNXjW81cTO+t5+9g3Oayrq2frpCUNwPvjqKNuC8yLg+ybtG2k1OK9JSSH3o48M7YjFiy0H56Xn4NwqcaxyEHXnVtZWnzLqlzHGsgubTWh08Esu/JTRsr4cYHSQsEsb6N9B0XRFgexdYmGiqpnKWfAYSLgTvGM68ABzj1SoPHqU/BUrqE5ONjnnFh9PyN134zFkiPVWe52BFrEYtB3xvmtKJGLHvC9XVGDeUKcl+ZcsTvycTmVpncm5gEgvhk6LIToxEIcOrQRJJJcOGaBLrELToCX1mOkSbMyQQNROPSM9SM6/Z89fIpF0X+q18NphqGwQ7bHBQmTMXiiKwo4Vpw1f/EPifBl1Q2/rb9AArAQaBcSH6/9ZQFdbS8Y//oGuQUyo1w03WK4719bD0X+Bot+e7jMffOOtG5YWLujrwCM9oaP6WJmVsCVT/CurNz3n6ghTwoUie0RHLaUVBXL3isC8It30XMBQEZj7JXTwIc0fqVB1/Dh5K1ZQnZRkcs4tLo6gO+/Ea+TIrg3MNcBhRGBe2uxcDCIw780VFZjXVjVwelcmp3ZkUFvdYHIutLcvQ6bHENHPv2t/LxJJO5ABusQqaisa+O9vNpr0vbR1AZ7+PSNAk/Pv2fOXSCTdl4+SjIFmqDssTrSvnfWxLWlkJImtSDdPZ6bcO8i2FNl1GNWyA4A5rV+e8+GH1GUKQ2q3uDhC7rvP8sXJn0Nlhjj26Q3xC6weVr0OdPoix17tFDOvqIedOSKF/Wxpy/Oh7kKNfVpEBxXZQQTmefvh7Aoov2h6zn8QJCyEXvZPK688fpz8lSupOnXKpN81OpqghQvxHju2awPABuAAsAsoa3YuDpHKHnupB9W5VJXWcnJ7Bkl7smioN62diB4YwJDpMQTH+nTR6CSSjiMDdIlEIpFIJJclO7Jhg34T1dkBnhpuh0CwCXkXyzj0YwogNh6vuXsgHj4u1t/gGCJ4AqPfeSs71WW7d1O8aRMADq6uRD75JA5OFiZUkgwp34tjBzUM/aNNft5Nte3K60X8a02cWVQLv+aJNPYTxcYgvxG1A4wLhplRQniuw7GrITBfCeUppuf8+oka84Ah9l2VASpPnBCB+cmTJv2ukZEE3n47PhMndm1gXg/sQwTmlc3OxQPXIHbOryBK86s4viWd8wdzDOUmAA4qFXHDgxkyLRr/sI6maEgkXY8M0CUSiUQikVx2ZFXCf04Y24sGQYwd3bNqKurZtvQEOkUEAkNnxBCe4G/9DYppWXceYvny+vx8sv7zH0M77KGHcAkPN3+xTgPH30V4ZwF97wSvKOvHhgjQQ90hp1pYnv2YBrOiTFXvFUUE76dLRDB+oghSLWhKxXmLNParw8CWNQyLtBaY+8SLHfPA4XYPzKtOniRvxYoWgblLeDhBd9whAvOuFNitAX4B9uiPm9IfuBpRa34FUZBWzrEtaaQez6fpepCj2oGEMaEMnhKNd4Bbl41PIrE3MkCXWIcKPHxdWvT1GOT8e/b8JRJJt6JWA68eNiqDTw23rwK5oihsX37KUHce2tuX4TNtyBPWAquARr2qobRad67odGS++SbaKuF55jtpEr5Tp1p+QcpqqEgTxz69IW6u9WPTo1LB/f3glcOi/f5p+PiMCNr9XYSXfH4N1Gkt3yPYDcaHiMDcbosjigK5vwi7tOap7D59oO8dEDTyku2Yu4SHE7hgAb6TJ3dtYF4J7AZ+xahnAOKzeBAwGWGbdoWgKArZZ0s4tiWVrLMlJuecXdUMmBjBoKsjceuoeIJE0g2RNms9GGmzJpFIJJ2PtLKxjbY+mxQF/nUcftYLd0d5wpvjwdWOWw5HN6dyYN0FANy8nLnpidG2pbb/BOzUH/sDvwNaqfMu+PprcpctA8A5KIg+//43jh4e5i+uzoPtj4CuHlDBVf8SQXo7ee0w7M1t+zrE0+jjAyMDhT1ajJcd42RFBzl74NxXUJFqes6ntz4wH2XXwFxRFKpOnCD/iy9aBuZhYQTedhu+kyahcuxCvZUyxHvpAEIIrhEVMASxY24nBf7ugE6ncPFoPse2pFGUZfp3093LmUFXR9FvQhgubnasZemhyM+m7ovcQZdIJBKJRHLZsDnTGJy7Ooq6c3sG53kppSZ151NsrTs/izE4dwDm02pwXpOSQt6KFeJ5KhURf/yj5eAc4NQH+uAciL2hQ8E5CFG93t4izT2rSvxr0Ima/kA3CHITiyCJvYQ1mj1r/AGRrp+9UwTmze3SfOKh722dE5gfOyYC89OnTc4Zdsy7OjAvBHYgLNOaZjE4IrIxJgG9Lv2wOgtNg5azv+Zw4ud0yotMc/e9A9xInBJN/OgQ6R4j6RHIAF0ikUgkEsllQVoFvN9ETPvxwRBuR02ouuoGfl52ylB3PuzaWML62lB3Xgl83aQ9E2ilNFxXX0/mP/+JohURWMCtt+IxcKDlF+QdEHXZAC7+QiCtg7ipYX6fJmNSoFoDHmq7Z5Gboq2HjC1w4VuoyTc955sAfW+3e425oihUHjpE/pdftvAxdwkPJ+i22/CZNKlrU9lzEFZpJ8Gk4NoJGA1MBK4ggfK66gZO787i1I4MaipN/fkCI71JnBZNjPQw7xS0uq4egcQSMkCXSCQSiUTS7anVwN+PCHswgNlRcJUda24b/c4rSmoBCI71Ydi1MTbcAPgWqNK3+wITWn9J3rJl1GYImzS3uDiC77jD8sXaBjj1obE94AFwcrd+fFbioALPzswebqiG9J+EAn2daW0x/oOEVZydVdkVRaFi3z7yV62i5vx5k3OukZEELliAz1VXdW1gnoYIzJOb9bsCYxHvpVYSKy43KotrObk9nTO/ZLewSgvv68+QadGE9fWTHuadQK0GNmbAlkx4ObGrRyMxhwzQJRKJRCKRdGsUBf57EjL0dlIxXvBAf/s+49TOTNJOFgLg6u7ElHsG4eBoQ8C2D2Nw5QncSqtimlUnT1K0di0ADk5ORPzf/6FSt/K1LGMTVOsN1f0HQdhV1o+tO1BXBhfXQtqP0FBlei5wBPSZB71ayR5oB4pOR9nu3RR89RW1aWkm51yjowm67Ta8x4/vusBcwVgS0UwPDw/Ebvlo4AoSKC/OruT41jQuHM5D18SjTwXEDgtmyNQoAiLtaMcgMVBcCz+kwoYMqGoALxkFdlvkr0YikUgkEondefHFF1m3bh1OTk64uLjw3HPPMXny5Hbda1MGbM8Wx25q+MswcLZjKWphZgX715wztCffOQBPv1YKx5uTB6xv0r4FEaRbQFdbS+Y776DoU+mD774b16hWcuG1dXBulbE94P5Ozj+3I1U5Yrc8Y4uxdh4AFYSMg/j5Ha6jb46i0VC6fTsFX39NXXa2yTm3uDgCb7sN77Fju253VgucQtSY5zQ75wNcBYwErhCBckVRyDlXwvFt6WQkFZmcU6sd6DsmlEHXROETaP+MEIkoDfr+ovgbqpFp7ZcFMkCXWIW2QUfW2WKTvvC+/jg69QwVeDn/nj1/iURiO+PGjeOJJ57Azc2NEydOcP3115OcnIyrqw2BL+LL5QdNdLzsXXeuqdeybelJtFoRLA++OoqogQE23ABhqdaosD0WSGj9JXnLl1OfK6TTPQYMoNecOa2/IH2TMR08ZDz4xls/vq6i9Bxc+E4oszctpnZQQ/g10Ptm8LSjNx6gq6ujeNMmCr/7jobCQpNz7gkJBC5YgNfIkV0XmDcAh4FdQHGzcwEIq7ShCCG4KwCdVsfFo/kc35ZOYaapWriruxMDropgwFUR0iqtE1AUOFEM36XAoQLTc2oHuDoMbo7BVOdA0m2QAbrEKmoq6vnXnT+a9L20dQGe/rZ90bpckfPv2fOXSCS2M336dMPxwIED0el0FBUVER4ebvU96rQt684nhtp3nL98d5aygmoAAiK8GHWDjbu5G4FGm7JgYFbrl1edPk3RDz8A4ODsTPjvf996irW2QQipNdL3dtvGdylRFCFil7Iaik+ZnnN0heiZEHsjuNmwAGIF2qoqin78kaK1a9GUlZmc80xMJHDBAjwGD+66wLwGUQKxB6NGQSPhCKu0AbRaEnE50VCnJfnXbE7+nG7QdGjEy8+VQddEkTA2DCeXK2Qlohuh0cGuHLFjnlJues7DCWZFwg0x4O8KOh1Il7XuiQzQJRKJRCKRdCqff/45MTExNgXnIHbOO7Pu/MLhPM78IlKg1U6OXHPPQBzVNmQGpSCCLhDfqBYg1LYtoKuvJ+vf/zamtt95Jy5hbSjdZe+EWn1acPAY8I6xfnyXCk0tZG6Di2ugyjSlHBdfiJ0DUbPA2Y6pD4CmtJTCNWsoXr8ebXW1yTnv0aMJnDcP93797PpMmygDdiM8zOubneuD2DGP44oJzKvK6ji9M5Ok3ZnU1WpMzgVEeJE4JYrYoUG2aTtIrKKqQQi//ZAKhaZrIgS5wY0xMD1SlAhJuj/y1ySRSCQSicRmZs2aRXJyc8lpwc6dO4mIEOnLO3bs4I033mD16tU23f/nLFF7DuDiCH+2c915RVENu1edMbTH39oX3yAbZLLrgG+atGcAIa2/pOCrr6jLEl7f7gkJ9LrxxtZfoCgi6G2k9y3Wj+9SUFMAF9dBxsaWwm+eERA7FyKuAUf7pjDX5+ZSuHo1JZs3o2toMPSrVCp8rrqKwHnzcI2JseszbSIPkcZ+FGha86sCBiE8zG1bq+rWFGdXcmJ7OhcO5hpKRRqJ7N+LxClRhMZLRfbOIL8a1qaK4LzWVAyfeB+4KRbGh4BcE7m8kAG6xGqcXXv220XOv2fPXyKRmLJhw4Y2r9m9ezePPvooX3zxBfHx1tdNZ1XCeyeN7UcGQoQdN191OoWfl5+iXr/L12dEMH3H2Jg7vw4o1R/H0qalWm1GBgXfilR1laMj4Y891rZ6eOlZKNfLe/v2Bb8u3A1uRFGg6ASkroPcX2lRxNorEeLmQtBIuwvZ1Vy8SOE331C2a5chCwFApVbjN3UqAbfcgkuonWsgrEUBUhGK7M3XrdTAcIT4W69LO6zOQlEUss+WcOLnlsJvjo4qeo8MYfDVUfiH2TdrQiJILoHvU2FPTssy8jHBMDcGBvpfPlqSElPkN26JVXj6u/L3XxZ29TC6DDn/nj1/iURiO3v27OHhhx9mxYoVDB482OrXaXXwj6PG3aCp4TDFvlpiHNuSRt5FUavs5e/GhHn9bNvdOw0c0h87I1TbW3m5oihkv/ceikYsCATecguu0dFtPydnt/E4ambXfttuqIasnyFtPVSkm55zUEP41RBzA/jE2fWxiqJQdeIEhd9+S8Xhw6aPdXXFf+ZMAubOxalXF0W+OsT7YReQ0exco4f5eFpV9b+c0Gp0pBzO48TP6RRlV5qcc3FV029COAMnR+Lh49JFI7xy0erg1zxRX36m1PScs4P4Ozk3xr4impKuQQboEolEIpFI7M5jjz1GXV0djz76qKHv/fffZ+DA1r2uv0qBC3pxo3APeNi+1tgUpJVzeEMKIGLqa+4agLMthZnVwPdN2tcD/q2/pGzHDqpOipQA55AQAhcssO5ZufvEf1WOwpKsKyhLEUF51g7QNitudfGH6Fnin4uPXR+raLWU7d1L4XffUXP+vMk5tbc3vebMwX/2bNReXnZ9rtXUIxZp9tBSkd0HkVExmivGKq22qoGkPVmc3pVJdXmdyTkp/Na5VDXA5kxRX55fY3rO1wWui4LZ0eB9hbzXJDJAl1hBYQ18ckbUAubViOy2YHeYHgH394OgK9y2sqQOlp6BDemQWw06BQLdYEo4PNgfQm0oWbwcKauD5WdhXRrkVAuF0ABXmBwGvxkAkXKlViKRmOFws91Oa/kxVfxX7QBPDgN7Vtc01Gn5efkpdDqRFDpkWgzBcb623WQd0Lhx2A8Y0frl2upqcj/5xNAOe/hhHJyt+CbdUAXVepNs33i7C6y1+eysHZCxCcoutDzv1x9ibxCLBg72/Sqpq62lePNmir7/nvr8fJNzzsHBBNx0E37TpuHg0kU7tBXAr/p/zYIlQhBp7IlcMVZppflVnNyewbl9OWiamWgHRXsz+JooYhIDpfBbJ5BbLYLyTWbqy2O8YG4sTAoFpyvkvSYxIgN0iUVyq+Gvv8IX54XVTVNOlcC2LHjuACzoA6+PhbArLFAtrIG/7YdlZ6FG0+xkCWzPhhcPwS1x8PexEN1Fi/idRWkdXTXKoAABAABJREFUPHsAPkmCqubzB3bmwCuHhTLoG+Ogj303TyQSSQ/nngSI87bvPX9dbbRUC4z0ZvisWNtucAYh/AXgBsylTQXugq++oqFEeJh7jxmD14g2IvpGGmvPAXxstH5rD4oCRceFGnv2btA1kx13dIWIKWK3vBOU5BuKiihat47iDRvQVpkKzrn17k3ALbfgM348KscuikZyEbvlR4Fm34nogwjM+3BFKLIb6su3p5Nx2rS+XAXEJAYx+JpIgmJ9pPCbnVEUOFUMa1JhX17L+vKRgXBjLAzpJevLr2RUpaWl0qK+h+Ll5YWDBYGapBKYuQ7SK82ebkG4B/x0HQy6QsRPUsrh2nVwvqzta0FYWPw4G0YGde64LhUZleL3f7rEuuv9XGDtLPv7E0skVwI6nY4KaTZrNYv2edHXz4Hn7Kwxln6qkI0fHAPAydmRm54YjY8tKWA1wL8R1llA9ePVKF6tf4VSNBrq8/JEQ6XCOSgIldrKvRFNLdTpgyNnb3DqpFVgXQNoakBTDUrzyBNwcAK1B6jdOyUi0NXWUrZsGaU7dxpq9BvxGjGCgJtv7joPcwU4h7BKO9/snANip3wi0IZT3uWCpkHLhUN5nNyeTnGO6SKJk7MjCePCGDgpEu8Aty4a4ZVLvVb4l69Nbelf3lhffmOMvcUy5WdTd0XuoEtakFkJ03+ArKq2r20kqwqmr4Nfb778d5Lzq2HaWrhow9+s/BqY+SPsvQn6+nba0C4JJXVicSLJyuC88TWzf4TdN0HiFbJII5FIugYfF/hDon1jwdqqBnZ9kWRoj70p3rbgHGADhuCcPqB4KXh4WJE65tPe9CIPLp3kt+8lek5LirOyKNm2zdBWqdX4Tp5MwE03WSek1xk0IHbK9wD5zc65AqMQwm9XSOZYVVkdZ/ZkkbQni5pK08wJTz9XBk6KIGFcGC5uTl00wiuX4lpRQrkhHcqaJa34u8D1MTAzErxkfXmPQgbokhY8vLNlcO6CjgVeZYwLFl+afs2DLyt8qFWMO/C51fCb7bDphks7Xnvz+z0tg3MndMzzKGNCKKhVcKAAVpb7UK0zzr+oFu7dBntuurzTjp78pWVw7qjouMWjjElhYiX3UAGsKPOhssn8Kxrgzi1wbP7lPX+JRNK1PDRABOn25Jdvz1JdIb79Rg7oRcI4G7c8LwAH9cfOwM32HJ0EwNHTk16zZ+N/3XU4+behutdZVAK/APuB5psUfgjht5FcMcJvBWnlnNyRwcWjeS38y4NjfBh0daSsL+8kzpWK3fLduULbpynxPiKNfUKI0OKQ9DxkgN7NuXDhAosWLaKoqAgfHx/ee+89+vVr6YO6bNky3n77bXQ6HZMnT+bNN99EbW0aXRPOlsKPaaZ9o4Ng2eh6/jdnrcHa0xc4vnYB9xxw5Zc847WbM0XtzMAu+mztKJmV8FUzPZzEXrBybD0f3biWc/o+T+DkugU8eMiVbVnGa3/JgwP5MDr4Uo3YvhTWCEG4piT4whdj61l281pDhp8bcHr9AhYdcTV5v5woFtoEU+1siSSRSHoO9s7CSTmSx/lDuYCwgbpqgY2Wag2YqrZfi/gQtCHLTGIZlVpN2MMP4zd1Kg6url0ziGxgL3CMlvXlUYg09gGItPbLHK1GR+qxfE7tzCQv1bSOz8FBRezQIAZNjiQo5gpJD+hGaHSwJ1cIvyWXmp5zUMH4EJgTA/185UZHT0cG6N2cxYsXc88997Bw4ULWrFnDY489xubNm02uSU1N5dVXX2Xnzp0EBgZy++23s3z5cu677z6bn7fklGnbx1nUVrs2VypFqJf/eB30WQHFTRw3lpyC/1xl86O7BR+cFirtjbipYf1s8KlveW2gG6yZBX1XCnXzRv578vIN0D85YyoI6OQgfv/BZsoS/VzgmxkwcJVpvdR/T8oAXSKRdA9qKurZ81WyoT1+XgIevjYGgbuARp2sKISvtcRuOAcH4xFnX+90q2j0L/8FuNjsnAoYjNgxj7zE4+okairqObM3i9O7s1rYpLm6O9FvfDj9J4bj6ddFiyRXMCV1sDEd1qeL46Z4OcHMKJgdBbK0X9KIDNC7MQUFBRw7dozVq1cDMGfOHJ544gnS0tKIblKXtXbtWq6//nqCgoRC2f33388777zTrgD9+2YfUr/pL/5gVJoJ0EEEaQ8PhFebuOl8l3L5Buirm83/nr4Q7gmVzT1O9Xg6wWOD4al9pvdYqlyeq5/Nf//ze0NvH8vzd1WLWtHHdhv71qUJsRNnafshkUi6EEVR2P3VGWqrGwCIHRJE7xE2rp4WA9v1xyqsUm2XdHNqEOUKvwClzc65IrzLx3HF1JcXpJVzamcGKUdaprH7h3owcFIkfUaFoJZeXXZFUURW6g9pYte8eRp7jBfcEANXh8nvS5KWyAC9G5OVlUVoaKghVV2lUhEREUFmZqZJgJ6RkUFkpHGJNyoqiszMzLYfUHgcsYQsUIDASvBr8vf7Dl+EQEqpGZ+twuOgUXObD2xo8hpVFejyL89MsF4VMKzJXO70pc35L/CGr5t+5tVDTQ64X4b/d/mVm87/Hn/anP88b/ik6fy1UJYpMgwkEgmAA7jYaOcl6TCpxwpIPV4AgJunMxPmJdiuBL4OaPzzNwHhc20Fubm5vPLKK/z4449kZWURFBTE0KFDWbx4MVOnTrVtDGZYunQpixcvprS01ObXKorCCy+8wAcffEBJSQljxozhv//9LwMHDmz1daWlpTz99NN89913lJSUEBsby5tvvsns2bMBqKio4JlnnmH16tXk5+czbNgw3nnnHUaNGtWeKdqfXERQfgTj77SRQERQPpwror5cq9Fx8Wg+p3ZmkJ9mKgmuAqITAxl4VQSh8X7SJs3ONKqxr0tr6QSkAsaFwA3RohRU/ugllrgMQ4ieRfM/nIpi3tKl6XWWrmnBmmlGCxfEH479za/RC6t6Av96oNm5TeI/g4HDzU7xlXVD6G5sb96xS/yntfnHYWb+39l3XJeKH5t37BX/aW3+wZiZ/w92HphEcjnj0gvmX2j7OondqKtuYO83xtT2cbf0xc1WGeTTCN9zAC9ginUvS01NZcKECfj6+vLGG2+QmJhIQ0MDGzdu5NFHH+XMmTNt36QTeeONN3jrrbdYunQpffv25eWXX2b69OkkJyfj5WXehqW+vp7p06cTFBTEN998Q0REBBkZGSbXP/jgg5w8eZLly5cTFhbG559/zrRp0zh9+jTh4eGXanqmaIEkzKexA/RFqLHHc0VkRlSV1nJmbzZJe7OoqTCtzXNxU5MwNowBkyLw8pcr6PYmr1oosW/KEKK5TfFygmsjYVYU2GoeIemZyAC9GxMeHk52djYajQa1Wo2iKGRlZRERYVrgGxkZSXp6uqGdkZHR4hqz3LiFpjvoANesNbV5+OswmNe79dt8fxFePGRsu6th99y2H98duW69aT357wfDPQmtv2ZTBvylSYq7gwr23QyOl+GH/bxNcKHJYvtv+sOi1jdV2J0Dj+8x7ds1FzzkXxeJRM/lmE90efPr9+cMqu3RAwOIGxZk2w00wPom7esQ6c9W8Mgjj6BSqdi/f7+JDdvAgQO5//77De309HQee+wxtm7dioODAzNnzuTdd98lOFik4R87dozFixdz8OBBVCoV8fHxvP/++1RWVhpK2BoX55977jmef/75NsemKApvv/02Tz/9NDffLKToP/vsM4KDg1m5ciUPPfSQ2dd98sknFBcXs3fvXpychNVW00y+mpoavv32W9asWcOkSZMAeP755/n+++9ZsmQJL7/8snU/PHtRCRwCfsVojdeIMzACoSUQeGmH1RkoikLOuRJO784i7XgBOqV5GrsnAydH0GdECGqZS21XFAUOFwpx5YP5IhO1Kb29RRr7VaEyjV1iG/IrdDcmMDCQwYMHs2rVKhYuXMjatWuJiooy+VAEUZs+c+ZMnnzySQIDA/nkk0+45ZZb2n5AQCI4mH5x9A6H7anG9ovpcOtYy2k4igIv7YAjTc7PDANs/C7UXfCLgPXnjO0X0+HOCdCaw8gre03nPyEEHC9TkbhekfDNaWP7pXS4fxK4tPLB8vpB0/kn9gIPGx2MJJIrGp0OKiravk5iFzKSiji7LwcAZ1c149uT2r4HUX8OEItIFbOC4uJifvrpJ1555RWzHum+vr6ACKrmzp2Lh4cHO3bsQKPR8Mgjj7BgwQK2b98OwMKFCxk2bBhLlizB0dGRo0eP4uTkxPjx43n77bd59tlnSU4WWQKenp6ACIqXLl1Kamqq2fFdvHiR3NxcZsyYYehzcXFh8uTJ7N2712KAvnbtWsaNG8ejjz7KmjVrCAwM5I477uDPf/4zjo6OaDQatFotrs1U2N3c3Ni9e7fZe3YKGYig/Dgt1dh7YUxjvwJ00OprNJw7kEPS7ixK8kwtBRxUKpHGPimCkN6+Mo3dzpTXw5ZMsWOeW216Tu0AE0Pg+mjo6yvT2CXtQwbo3Zy3336bRx55hLfeegsvLy+WLFkCwGOPPcasWbOYPXs2MTEx/PWvf+Xaa69Fp9MxadIk7rrrrnY976EBwpexkZPF8NwBeGGU+T8yrxwWq4dNebiNHdfuzEMDYEWTAD2lHP70C7w13vz83zkOO3NM+y7n+f92ALzfJEDPqYbHdsH/JovMgOZ8ktTSlu/hAZ07RolEIrGEpl5roto+5sY+tqtSV2EqDHc9Vqc/nz9/HkVRzNqhNmXLli0cP36cixcvGjRkli9fzsCBAzlw4ACjRo0iPT2dJ554wnCv+Ph4w+t9fHxQqVSEhJgWxQcEBNC7t+W0t9xcYTfXuEvfSHBwMGlpaeZeAkBKSgrbtm1j4cKFrF+/nnPnzvHoo4+i0Wh49tln8fLyYty4cbz00kv079+f4OBgvvjiC/bt22cy7k6hHhGQ7wOymp1TIdLYx3HFpLEXZVWQtDuL8wdzaag3XYVw83Km//hw+o0Ps92tQNIqiiKs0dani8zBhmaib4FuIoV9RgT4uHTJECVXEDJA7+bEx8e3sFUDePfdd03a99xzD/fcc0+Hn3dtJMR5m9pmvXRI+HsvThR/eFQq2JwBb58Q6d1NifSE60w3+C8rJobCYH/h593I28fhUAH83xBhhaFWwfZsEZz/0Oz7TIAr3NoFbjH2YnggjAvGxNv+wyQ4XgR/GipWhJ0dhQDKf07ANymmr/dygjv7XtIhSyQSiYEjG1OpKBa2I6G9fUkY1450nu1AoxXScCDU+pc2asC0tWOZlJREZGSkicDrgAED8PX1JSkpiVGjRvHHP/6RBx98kOXLlzNt2jTmzZvXavAN8Lvf/Y7f/e53bY7TnL5Na2PW6XQEBQXxwQcf4OjoyIgRI8jOzuYf//gHzz77LCAWGO6//37Cw8NxdHRk+PDh3HHHHRw+3EKlxD4UIIRzDgG1zc65IdLYxyB2zi9zNA1aLh7NJ2lPFnkXm+fsQ0icLwMmhhMzJAhHtSypsSc1GtieBRsy4GJ5y/NDA8R3o1FB5jcyJJL2IAN0iQmODvDviXDDetNami2Z4l9b/HuiSO+5XFGp4J2JMP0HaOpGsitH/GuLf00Q1mOXM2+Nh8lroL7J6vC+fFGf3hZvjANbdZgkEonEHpTmVXHiZ7Fq6uioYuKCfran9pYiUqRBfEOaZtvL4+PjUalUJCUlMXfuXIvXWQqIm/Y///zz3HHHHfz4449s2LCB5557ji+//JKbbrrJtkE1oXHHPTc3l9BQ48pDfn5+i131poSGhuLk5ISjo7HeqX///uTm5lJfX4+zszO9e/dmx44dVFVVUV5eTmhoKAsWLCA21o4OBlqEeN8+IMXM+TBEbXkiV4Qae1l+NWf2ZnF2X47BLrARJ2dH4keF0H9iBP5hnl00wiuXlHKRwr49C2qblUt4OsH0CLFpE9aykkUi6TCXeSgh6Qyui4b/XgWP7LLtdW9PgLnt/BzOSi4mP62cYTNi2ncDC+xcmUR9rYZp9w+mOLuSlc/uJiu5mIBIb/5v5fUtrv/mtV/pPTyYj6+O5ZmVuUz49SBOGg0qncLmqyeQExxEQFExE/YfZs0s/Tc3RZieP5VzlNmefVAUD8MXrLL8aj754zZ+v+w6HBxUFKSVs/LZ3VSV1uHm5cTtL0wkpLdvi3Ec+OEC2z8/ZWiX5VcTNzyY+9+8pt0/i19Xn2PrpydQFIX40aHc+texZlfak/65nSculJOqL5kNKCrhhxnXkBIT1er9/zLMfHp/fY2GL1/YQ8bpIlQqFdc/PpzEqS3TLHIvlLL8qZ2Gdk1FPXVVDbyy43bbJtqEtBMFfPXyLzTUavENcWfhy1fhE9hSQnXLJyc4stEo8VuUVcnYufHM/VP77YE2fXiM/WvPAzB8ZhyzHx1m9rp37llPfa3w/NFpFXIvlPLEqhsI6+vf7mdLJD0NRVHY+81Zg89z4tRofIPb8c15C8ba5fHY7IXt7+/Ptddey3//+18ef/zxFnXopaWl+Pr6MmDAANLT001sUk+fPk1ZWRn9+/c3XN+3b1/69u3LH/7wB26//XY+/fRTbrrpJpydndFqmxdZt01sbCwhISFs3ryZYcPE36T6+np27NjB3//+d4uvmzBhAitXrkSn0+Gg1645e/YsoaGhODubRsIeHh54eHhQUlLCxo0beeONN2weZwtKgAMI//LKZufUiIB8DBDBJU9jv+uuuygra7mr3V4UBTR1GuprNTTU61qcd1SrcHFzwsnVEVWyCj6326N7PIoC1Rqo0rRMYQdwdgAPJ8ARvlfB93Z6ro+PD8uXL7fT3SRXAjJAl5hl0SBRT/PbHVBS1/q1Ps6wZBLc3oEys6zkYk7tzLRrgF5fq2HHitM8+fUcAFw8nJj96DBqKhv4acnRFtcrisKZvVlc//gI4qqqOfPrbr6+dhoX3H1x1GhR678MFfbyR6dSEZGdS2ZYCIPS07jNuYgBXlrSThay/r9HmPunUXj6ubL5o+NMXNAfB33e01ev/MK4W/oyek4fjm5O5csX9rJ42ewWYxl1Q29G3WBMZXxj3hpGzGo7d/6n/x3FP8yT0XP6mPQXZVWw4b0j/OnLG/D0d+XjxdvY9/05xt/aUqL+vn9eDYiMid+vLOSq77aQFmnZIsdNDW+MheI/fwPrb21x/udlp1A7O/L02pspyqrgnXvW02dUCO7epkVaIb19eWLVHEP729d/xZpvWucP5rJ/7XnueHGiSb+iKHz+9C4WPDuePiND+HnZSda8eYC7X5/c4h7T7h/MtPuFCpSmQcvzM75mxOy2f94rn93N6Dl96DPStA70wqFcDv90kSe+moOjowPv3LuBuGFB9Bvf8uf4+8+Mv/+jm1PZ9MExq4LzF2d/w7Nmft4SSU/k4tF8ss6K2iRPP1eGTo+x/SYFCI9sECJiLf9UWMV7773H+PHjGT16NC+++CKJiYloNBo2b97MkiVLSEpKYtq0aSQmJrJw4ULefvttg0jc5MmTGTlyJDU1NTzxxBPceuutxMbGkpmZyYEDBwwCsDExMVRWVrJ161aGDBmCu7s77u7u/Oc//2H16tVs3brV7NhUKhWLFy/m1VdfJT4+nvj4eF599VXc3d254447DNfdfffdhIeH89prrwGwaNEi3n33XX7/+9/z2GOPce7cOV599VUef/xxw2s2btyIoigkJCRw/vx5nnjiCRISEgyK8zajBZIRgflZWkpk+yN2y4cDXWhdVVZWxtq1a7tuAJLLnjlz5rR9kaRHIQN0iUVu7S3Sd1aegyWn4GgzMbjB/nCfSz58f4jMjQ28AcxaNJTB10SRfqqQ7/6+n/paDU7ODtz4p9HEDQ2isriW5U/vpKKwBlQqIvv7c/3vR7BhyVFqK+v5x4K1RA8OZP7fxnV4/Me2pBE7LAgXd2EJ4+HjQtywYM4fzDV7fcbpIoKifXD1cGLb0hNcdWMczz7ky6rz8N4pRw7kG1P7kvvEMf7CWSbOD+Hu+2MoO+/Jew9tIvtsCb/9zzScXBxpqNNyZFMqc/4wEoCK4hoyk4p4+L3pAAyZFs13f99HcXZlq+lpaScLqCiqZdDkSIvXWPOzGDwlCq9ewvt0/K0JbPvspNkAvZFpEfBsw3lSpsQxOszRpC4doI+PEIS7tx/0coUXLdznyKaLhuC5V7gXccODObk9o8UiQlM09Vr+n737Do+juho4/JvtXb3ZapbcjY3BNFNsSug1ISGAQyghoYcSagiEEvggoYYWSIfQktBLCJ0AxgaMG1gu6rZkdWl7n/n+WHYtWZItq1iSfd7n0WPt7uzM3ZGs3TP33HO++k8NFz9x9A69zu42rmlHb9Slguf5p07jP48+Tzyqojf2vw5j9QcbScu1UTRz8AsXl79dy34nTcZsTfzu7X/KZL56q6bPAL27z1+pZP9TRrigkhC7mGg4zpKXtlT3nP+9qYNrJ/URW4LAQ0isY95KSwDuXwVv1UM4bsGsT7xPXjlnS3/jSZMm8dVXX3HHHXfwi1/8gs2bN5OTk8O8efNShV4VReHll1/msssuY8GCBT3arAHo9Xra29v58Y9/THNzM9nZ2Xzve9/j1ltvBeDAAw/kwgsv5Ic//CHt7e2pNmttbW1UVVVt82Vee+21BINBLr74Yjo7O9l///15++23e/Q0r6+vT82UQ6Kd69tvv82VV17JnDlzmDhxIpdffjnXXXddahu3280NN9zApk2byMzM5NRTT+WOO+5ItWUbsE4SM+VfAls3P1CAmSRmy8vZJYq+CSHE1iRAF9vkMCYqe/9sJjQHoCVRe4ccKzijYe4+9QPOvecwJs3NRVU1gt4IsWicv179IT+8aT7TD5xI9fJm/nbNh9z46nf58s0qMic4uOixRIsXvzuMPc3MsRfN5Zv/bUrN3m5t3ZJGXr3/yz4fm3lIIcdfunev+6u+bGLSnIE3OV39fj17HJZI426qdpM1wcFfL3kbf1eI6/bKY/4F8+hQDaiAuSuHpy/4nMtmw6r36qj7uo39TprMpLm5/PP2xZxy9b40VbvJLnJisib+m3U1BUjLsaXSyhVFISPfTufmbQfoS1+uZJ/jy7YZVG5P52Y/GQVbjpE5wUFnk38bz0h86F353xou/fMx3DAF2kOJdiIxNVEMb4J9YO1Dupr8ZBRsSfPMnOCgc/O2j73qvToyJzqYOG3wad6dm/1kdnvNFrsRs82Ipy3Q41xsbenLG4YcJHc2+Smft2VWPbPAwcp3+6+QDNDV4qdyWRNn3n7wNrcTQvS04p1a/O5EqlfRjCxKZmfv+E462TJ7biWR3t5NMAZnvZsooNkUBFUDSFwEWNUBT66D+fnwjyMSdUgKCgp4+OGHefjhh/s9ZHFxMa+88kqfj5lMJp599tltDvmxxx5LBfxJt9xyy3b7oSuKst3tkq3eups/fz5LlizpvfG3TjvtNE477bRtHrtfcaCCxGx5Jb1ny9OAfYF9ANfgDiGEEOOFBOhiwPJsia+kbz5uJa8snUlzE03PdToFe5qZxg2dGIy61Gxh2V55ODMsbF7fSensHD56uoJX7v2C8nl5251RTJp2wIQe6c8D0dUSYOaCwgFv//VHG7noD4kLB/GYSuWyJi76w1GYbUaeu+VTvvz7Ck66IjEbHnNZ8baHiEdVZh9ezJwjSnjrDyso2SObuUeWoCgK7uZGXFlbTcFsFdBqW38I2UokGGP5f2u4/G+90+CTPnl+LZ+9uB4Ab3sQvVHPR08neqUdfcGezDk8sd67ezCt9fr009uq9+rIKXYyYUoGkJglz9qqa8sfLn4Hb3viqo2nNcjvfrglze8Xz5yA7tsG8go9Dr5dS7czk5xcWw6JcxRwh1PH7nHBptf53vbBO5v81Cxv4az/W9DvNm88/BVrPk5UTOxq8lOzoiV1Eea0X82nZHbiopCyYy+ZL16tYuYhhf22hFLjKvee+Xrqdvfz7cyypjIzhNideDuCrP6gHkgUhpv/vamD6/n8KVv+ox4IdFuBE4zBgpdhZXvf61JVDRoDiRalh7wMH58y/ouF7lR+4EF6ry1XgBkkAvMpwDguQCuEEDtC3kLE8OsvCFIUSufkcPVzJ7J+6WZWvlfPm4+u4Opnexdr29pgZtBNlkSa+UC01LmxOk24shMBdWaBncJpmal10nsdPYn3//51avtYOI7eoOsxq33MhXN77NNoMRAJx1K30/NtuFsCxGMqeoMOTdPoavZvczZ35bt15E1K77OQXNLBP5zOwT9M9Mntbw16RoGdjsYtn346G/1k5G+7gNKSAcwkdw8Kbzvu331eREnPt9Ox2YcjMxF4dmz2MfPg/i+cdDT6qF3Zyjm/O7TfbUpm56SO1d8a9K1fc8gfJRyI4sruf7Hi569UMmthIfZtNDE9/tK9U79v/a1Bz8jf6nxv9m3zfGuaxuevVnLq9fv3u41Or+txfvs730KMJVVVVVx00UW0t7eTlpbGo48+ut0e4Tviy9ericcSUfOsBUWk5Q5iMXKQRDo1JD4VHdDz4bPe6z847y6qJrb70Xvw78Gvztn9BOkZnKeTCMrnIbPlQojdkgToYtAm7ZnD87e5qVnR0iPFPXdSGrGoyobPNzNlvwJqVrTg7QxRMCWd9gYvabk29jqqlBkHTuCmI54nHIhhsRsJ+SL9HmswM+gFUzJp6aNfaF9Wv1/P7MO2rPHe+5gyXvv9Mo6MxDGY9Kxd3NCjcFdzjZuCb2eW+zNxWiYttVuaZjozrUyclsmyN6vZ76TJrHy3jswJjm2nt7+ygQNO6X+t9kDNOaKEh879D0f/bE8cmRYW/3sdex3df8n99gYv9V+38ZP7Bl81PmnukaV88vxazrztYNobvFQta+YHN/ZfY+DzVyuZfXgx1iH2ayuckUUsEqfyyyYm75PPZy+sY/ahxf0uFdA0jc9fqxyW+gdzjyzlhbuWctBp09DrdSx9ubLfKu4AVcuaiUVVph4wiJ7NQoxhV1xxBWeffTaLFi3ilVde4bLLLuOdd97Z5nMev/Rdwr7Exc29jill4Zkz+9yubZOXymVNBDxhWms8+NqDfPbilrXop1y9L6XbWeb00TNrWP6v2kR7NQAHcBGp4p0tAfisafvBeVJUTWzfGkwsBRMDpGfLbPlkdqu15Yqi8NJLL3HKKadQW1vLpEmTWL58OXPnzt3uc8855xy6urp4+eWXR3ycO2JHX8euYqz+PMT4IwG6GDSby8x59x7GK/d9QTgQQwGOvXguexxazLn3HPptkbgvMJp0nPPbhZitRlZ8WctH/1iDolPQVI0Tr9gHq9PElP0K+ODJb/jdaa9SMmd4isTt+Z0SnrvlU469OBEYxSJxfnPii8QicUK+KLcc/S/2Ob6ME34+j9UfbGTRb7bMwE6am8usBUX87vTX0OsV8svTewSVFYsb2LOPVmHdZU5w4Mgws7mqk4LyRDB/2q/m88zNn/Lun1djths58/aDUts/d+ti9lhYyB6HJtbBt230sKminfMfPGLI5yK70MkxF83l9+f+B1XVmLJvAQd8OzvubgnwxGXv9rgAsvTlSuYcUYLFMfRGsoedPYvnblnMHSe9iKIonHr9/qkZ6k//tQ5PayD1M0rOJJ9x60Hb2uWA6HQKP/rNIfzrjiVEw3HScm0sumPLz/iJS9/lmIvmUjwrsV51w+dNoMHU/Qv62+WATd4nn7lHlvLbHyRS0Pc+ehIzDkos56j/po23HlvBzx7e0mB5ycsb2O+kyalq/0LsClpbW1m5ciUvvfQSkKhUfM0111BXV0dJSf9/PzetaSfgTlyw3VaA/fkriTaGakzD7w6n1qEnbeuib1Jno5+62tYtd3QBm7bcvH9VovbGjmgKwn0r4f8O2P62ArAD15G4OLKLaWpq4o477uCNN96goaGB3Nxc5s6dyxVXXMERR/R+by8qKmLz5s1kZw+ijsIA7K6BsxDjjdLV1TWQ5ZFiF+R0OntUad0VbR2E9cXdGuDxS97l2n8ObIY+Fo1z/6I3uOjxo/pdL5y0/O1aqpY18f0b5JOaELsrVVXxercuR73rW7FiBRdccAFLly5N3Xf44Ydz++23c9BB/V+E++3Jb6YC9IWLZnLK1fv22qZhXQdvPpqo6haPqqz7rLHXNhc88p3t1jl5+eYv+Oi1Nb3uv3/52QDs9U9Y0b7NXfRpbhYsH2S9tN2N3+/v1S9+PDnh+BN4/Y3Xe91fW1vLQQcdRHp6Orfeeitz5swhGo3y3//+lyeeeIK1a9cCPWfQd9SOztjurAB9d70QMNgZ9JNOOmlUWvXtru9N48GuHZ2J3d53r90vVcSsP2k5tgEH5wAdDT6Ov2zv7QbnAHsdVUpuaRqqKtfBhBC7n60Ltm2vUONAaJrGF69taSU2db8hZL00b/vh2ABT23s9T/7k79Ki4Tjrl27m9d8vw9Me6nObiy++GEVR+Pzzz/n+97/P1KlTmTVrFldddVW/1fBra2tRFIUVK1ak7vvmm284/vjjcblcOJ1ODjnkkH5b6S1btozc3FzuuOOOPh+fNCmxtG2vvfZCURQOPfRQIBGo3XbbbRQWFmI2m5k7dy5vvfXWNs+BqqrcfffdTJ48GbPZTHFxca/jVldXc9hhh2Gz2dhzzz357LPPejz+wgsvMGvWLMxmM6Wlpdx77709Hu/s7OTHP/4xGRkZ2Gw2jj32WDZs2LKMpa6ujhNPPJGMjAzsdjuzZs3izTffTD2+Zs0ajjvuOBwOB3l5eZx11lm0tW3VM/hbmqaRk5PDCy+8kLpv7ty55Obmpm5/9tlnGI1GfL6tKxr2dOutt5Kbm4vL5eKCCy4gEtl+No8Q3UmALnZpOcUuZi0YfP/wvuSWpjHzkIFXh19wxgxJXRZC7HYmTpxIY2MjsVhiPbmmaTQ0NFBYOPC/n32p/6aN1o2J+h5ZExwUTEkf3I6iwHZmxw2D/JRkkD/5uxxN09hc2clHz6zh6V99zEfPrGFzVVef23Z0dPDWW29xySWX9JkdkJ6ePqBjNjQ0sGDBAiwWC++//z7Lli3jvPPOS/2f6u7DDz/kiCOO4NZbb+XGG2/sc3+ff/45AO+++y6bN2/mxRdfBODBBx/k3nvv5Z577mHVqlUcffTRnHTSST2C4a3dcMMN3H333dx0002sWbOGZ555hry8vB7b3HjjjVx99dWsWLGCqVOncsYZZ6TGvmzZMk477TROP/10Vq9ezS233MJNN93E3/72t9TzzznnHL788kteffVVPvvsMzRN47jjjiMajQJwySWXEA6H+d///sfq1au5++67cTgSayU2b97MwoULmTt3Ll9++SVvvfUWzc3N/bYCVBSFBQsWpFoMdnZ2smbNGqLRKGvWrEmd43nz5qWO0Zf33nuPiooKPvjgA5599lleeuklbr311n63F6IvsgZdDElrnYdnbv4Ef1cYq9PIGbce3GfF8S9eq+LDf3yTuu1uCVC2dx7n3du7CNkr935B0cws9j62bNjGmWzLFQ3FSc+3seg3h5CW03e138ovm3j1/i+JhGJoqsYZtxxE6Z6JInivPfAlaxc3osZVJu2Zy/dvPACDUT9s4xRCiF1FTk4Os2fP5vnnn2fRokW8+uqrFBcXb3P9OUDhzKxUkbiMCT2DG03T+OqtmtTteceVoWmk2ht2t90aGhsgw2SnJD0nsQ46s/cmxxQn+pzvSBKUTkk8T+wavO1BNny+mQ1fNOHpIyNPr+99NaayshJN04bcseCRRx4hLS2N5557DqPRCMDUqVN7bffKK69w1lln8fjjj3PGGWf0u7+cnMT/k6ysLPLzt3Qfueeee7juuus4/fTTAbj77rv54IMPeOCBB3jkkUd67cfr9fLggw/y8MMPc/bZieUg5eXlHHxwz24qV199NccffzyQmFWeNWsWlZWVTJ8+nfvuu48jjjiCm266KfW61qxZw+9+9zvOOeccNmzYwKuvvsqnn37KgQceCMDTTz9NUVERL7/8Mj/4wQ+or6/n1FNPZfbs2QCUlW353PjYY4+x9957c+edd6bu+8tf/kJRURHr16/v8zweeuihPPHEEwD873//Y88996S4uJgPP/yQmTNn8uGHH6ayDvpjMpn4y1/+gs1mY9asWdx2221cc8013H777bv8slIxfCRAF0Pyzzs+Y/6pU9nvpMmseKeW525dnKp+292+J5az74nlqdu//cErzOsnAD/83D146Ly3mHv0pGGZedY0jX/c+DE/vPlAJu+TzwdPfs0r937Bj+9a2Gtbd0uAZ27+hAse/g55ZelEw3FikUSrtqUvb6BxQye/ePYE9AYdz9+6mP89U8HhZ+8x5DEKIcSu6IEHHuDiiy/mvvvuw+l08thjj233ORc8/J1+P8huWttB28bEmsmsiU6K98hGUZQ+33e2qwIWls5kYelM+DHQRyx15Rx4cl2iz/lA5VlULpgSxNMaQAuFUMNhNHX7ufKKXo9iNKIYjeiMRhSTCUU/hi4AR4Hwt199XbAwABYSPeR34K17UH3rR1g4GKV2ZSsbPt/c5yy5yWKgfO88puxXwGuX9y7Xn1zKMdTXtmLFCg455JBUcN6XpUuX8vrrr/Ovf/2L7373uzt8DI/HQ2NjY6+6EAcddBArV67s8zkVFRWEw+E+C911N2fOnNT3BQWJpSgtLS1Mnz6diooKTj755F7HfOCBB4jH41RUVGAwGNh//y3tR7Oyspg2bRoVFRUA/PznP+eiiy7i7bff5jvf+Q6nnnpq6pjLli3jgw8+6HO2u6qqqt8A/fLLL6etrY2PPvqIQw89lOLiYj766CN+9rOfsXjxYq644optvuY999wTm23LBND8+fPx+Xxs3LhxuxcnhUiSAF0MmrcjyKaK9lQv7D2/U8KLdy+lo9G3zdZhdV+34m0PscfCvlPPnZlWsiY62PD5ZqYNQ9upjWva0Rt1qV7V80+dxn8efZ54VO3VcuvTf61ln+PLyCtLB8Bo1mM0Jz4gNa7vYOr+E1Iz5jMOLuS/T6yUAF0IIfoxZcqU7bZV2xGr3qtLfT/3qNLBB0AakMzeNQDlfW+Wa4P5+fBq7cBarRl1cGCBjtIsO4lp+USwFmlqIrhhA9q3a1E1TUMxGDBmZye+srLQmYbeNWPYtQArvv3q6uPxNGAusDew7Y52Y54aV2lY18mGLzZTt6qV2FYFCBRgwtRMpu5fQOmcHAym/i+eTJkyBUVRqKioGFTxtySrdfu9+srLy8nKyuIvf/kLxx9/PKZB/h71VS+iv/9fAxkX0OPCQnJf6rcXq/raf/caFf3Vq+j+vPPPP5+jjz6aN954g7fffpv/+7//49577+Wyyy5DVVVOPPFE7r777l77SF4s2Noee+xBVlYWH330ER999BG33XYbRUVF3HHHHXzxxRcEg8FeWQIDNRYvRImxSwJ0MWhdTQHScmzov12kpygKGfl2OjdvO0Bf+nIl+xxf1m8/aoDSObmsX9p3gP7i3Uup+qrvyj6n/Wp+r1THzs1+Mgu2jMdiN2K2GfG0Bcgo6DnOpmo3WRMcPHrB2/i7QpTtlceJl8/DZDVQNDObz15cz0E/mIbBpGP5f2voaNx2oRAhhBDDo6XOTeOGTiBR3HN7Pc63qQ3wfPt9KdD/BCX/OAIOeRlWtm87SDfqYM+sxPbdKYqCuaAAcz9BwZjjAVaRCMp7F8cHE7AHsBdQxrjuWa5pGu0NPiq/aKJqWRMBb+9iXum5NqbsV8DkffIHVBwWIDMzk6OPPppHHnmEn//8573WoXd1dQ1oHfqcOXP4+9//TjQa7XcWPTs7mxdffJFDDz2UH/7wh/zzn//sd9tk8B6Px1P3uVwuJkyYwCeffMKCBQtS9y9evJj99tuvz/1MmTIFq9XKe++9x/nnn7/d19GXmTNn8sknn/S4b/HixUydOhW9Xs/MmTOJxWIsXbo0leLe3t7O+vXrmTFjRuo5RUVFXHjhhVx44YXccMMN/PGPf+Syyy5j77335oUXXqC0tBSDYWDhTnId+iuvvMLXX3/NIYccgtPpJBqN8oc//IG9994bp9O5zX2sXLmSYDCYuoixZMkSHA7HkGtviN2LBOhiaLZ6Y95egd5IMMby/9Zw+d+2nY7ozLZS3U8Q/r3r9u/z/m3qNc6+BxqPqVQua+KiPxyF2WbkuVs+5a3HV3DSFfuw74nldG728/BP3sJkNTB1/wI2fNG042MRQgixw1a9V5/6fs7hxUNbAlXf7ftJ297UYoD/nQJnvQefNSX6nHdfk65TIN+amGn/xxGJ7cedILCGRFBeTe8UdgWYQmK2fCaJIH0c83WGqFrWTOWXTXRs7n2h3WIzUj4vjyn7FpBd7BzU7Oejjz7KgQceyH777cdtt93GnDlziMVivPPOOzz22GOpNO1tufTSS3nooYc4/fTTueGGG0hLS2PJkiXst99+TJs2LbVdbm4u77//PocddhhnnHEGzz33XJ9BaW5uLlarlbfeeovCwkIsFgtpaWlcc801/PrXv6a8vJy5c+fy17/+lRUrVvD000/3OS6LxcJ1113Htddei8lk4qCDDqK1tZVvvvmGn/zkJwM6P7/4xS/Yd999uf322/nhD3/IZ599xsMPP8yjjz4KJC4CnHzyyfz0pz/l8ccfx+l0cv311zNx4sRUavwVV1zBsccey9SpU+ns7OT9999PBe+XXHIJf/zjHznjjDO45ppryM7OprKykueee44//vGP6PtZPnLooYdy5ZVXstdee+FyuQBYsGABTz/9NFddddV2X1ckEuEnP/kJv/rVr6irq+PXv/41l156qaw/FztkPL6NiDEiPd+GuyVAPKaiN+jQNI2uZn+vWenuVr5bR96k9D4LyXUXC8dTqeVb29EZ9IwCe4+Z7pA/SjgQxZXdu0hcZoGdwmmZ2FxmAPY6ehLv//1rIHFl9egL9uToC/YE4Ku3asgrS9vm6xBCCDF03o4gtStbALA6TUzeN387z9iOTd2+H0BBN6sB/n00tAbhvpXwVn2ilZrh24JwV+0JOQPL+h07osBaErPl64DehcFhIomgfA6w7YnDMS8SjFGzsoXKL5vYvKGz1zUIvV6heFYOk/fNp2hmVio7cLAmTZrEV199xR133MEvfvELNm/eTE5ODvPmzRtQLQZIrLl+//33ueaaa1i4cCF6vZ65c+f2Wi8OkJ+fz/vvv8+hhx7KokWLeOaZZ3oFoQaDgd///vfcdttt3HzzzRxyyCF8+OGH/PznP8fj8fCLX/yClpYWZs6cyauvvsqUKVP6HdtNN92EwWDg5ptvprGxkYKCAi688MIBn5+9996bf/7zn9x8883cfvvtFBQUcNttt3HOOeektvnrX//K5ZdfzgknnEAkEmHBggW8+eabqQyBeDzOJZdcwqZNm3C5XBxzzDHcf//9AEyYMIFPP/2U6667jqOPPppwOExJSQnHHHPMNoPlww47jHg83qMY3MKFC3n55ZdZuLBn7aJzzjmH2traVOV3gCOOOIIpU6awYMECwuEwp59+OrfccsuAz4sQAEpXV5d069xNOZ3OIV/Re/j8t9jvpMmpInEfPvUNVzx5/Da33+e4Mg74Xu/iHN39647PyC9P55DTZ2xzu4FQVY07T36R0399UKpI3MZv2vnx3b2LxNWsaOG13y/j4j8chcGk58W7l4Ki8L1r90sVjLM6Tfg6Qzx24dsce/Fe/a6lF0IISKy59Hq9oz2McaOv96alr2xg1fuJae95x5ax9zHbmfbenj+RmCkG+BXQd1OPXj56Zg2djf4e92VMsLPwzJlDG8/OEgcqgZUkZsz7as+cCexJIjAf5+vK4zGVTWvbqfyiifqv23qtKwfIK01j8j75lO2dh8W+jbUO/TjppJN49dVXh2O4Ypw59NBDOfTQQ4ccgI/W75C8N41dMoMuhuS0X83nmZs/5d0/r8ZsN3Lm7Vuu6j5362L2WFjIHocmpifaNnrYVNHO+Q9uu+qnpmls+LyJ7/xkzja3GyidTuFHvzmEf92xhGg4TlqujUV3bCny8cSl73LMRXMpnpXNpLm5zFpQxO9Ofw29XiG/PJ0f3DgfgJAvwsPnv4WiU9BUjQVnzpTgXAghRlgsGmf9ks1AYpZzxkETh77TZP9zKwMOzgGWv1VL3erWHveVzM4Z2wF6HKglMVP+NYl09q3ZScyS7wkUMe7XlbfUuKn8sonq5S2EAtFe27iyrUzeJ5/J++T323JViG3xer1UVVXx+uuvj/ZQxC5IAnQxJLmlaf22tzn91wf2uJ1d5OKuTxdtd59rFzdSPDubjHz7drcdqNI9c7nmnyf1+djPHv5Oj9tHnLMHR5zTuzK7M8vKDS/teAsTIYQQg1e3qjUVZE2am4vVOcQF0BqQXPW0q65SUoE6tgTl/j62sQCzSATlk4Ax1NFtMDoafYmg/KtmvJ2hXo9b7EbK9spjyr755JS4pKq2GBKn08nGjRtHexhiFyUB+m5MHUBf1tEQ8IY5/rK9xuz4hBBiR8jfsh2z9fmqWt6M2Zb4uDL9oIlDP59RthQ5SycRzA6Q2WHAlmbqdd+Y+BmrJNbWV3z71T0oT66PNwJTSRR6K6dnUD4GXsKO8neFqVnZQu3KVrqat7zg5O+L3qCjaGYWk+bmUjA5HZ0+sXRC07R+i8UKMRpG42/ImPi7Jfoka9CFEEIIIYQYhEWLFkmasxiSE044od+K+WL3JDX/hRBCCCGEEEKIMUACdCGEEEIIIYQQYgyQAF0IIYQQQgghhBgDpEicEEIIIYQQg+B0OjnhhBNGexhiHHM6naM9BDHGSJE4IYQQQgghhBBiDJAUdyGEEEIIIYQQYgyQAF0MWFVVFUcddRTz5s3j8MMPZ+3ataM9pJ3q2muvZfbs2aSnp7NmzZrRHs5OFQqFOPPMM5k3bx4HH3wwp556KnV1daM9rJ3qu9/9LgceeCAHH3wwxx57LKtWrRrtIY2Ku+66a7f8PwAwe/Zs9t13Xw4++GAOPvhgXnzxxdEe0i7nqaee4sADDyQrK4snnnhitIfTp/H0Xjje3rfG43vNeH1vGC9/y8fb391wOMw111zD3nvvzQEHHMDPfvaz0R5Sv7q6ulLn9eCDD2bevHlkZWXR2dk52kPb7ckadDFgV1xxBWeffTaLFi3ilVde4bLLLuOdd94Z7WHtNCeffDKXX345xxxzzGgPZVScc845HHnkkSiKwhNPPMEVV1zBSy+9NNrD2mn++te/kp6eDsDrr7/OpZdeyv/+97/RHdROtmLFCr788ksKCwtHeyij5u9//zszZ84c7WHssubOnctf//pX7rvvvtEeSr/G03vheHzfGm/vNePxvWG8/S0fT393b7nlFhRFYdmyZSiKQlNT02gPqV/p6el88sknqdsPPfQQn3zyCRkZGaM4KgEygy4GqLW1lZUrV/LDH/4QgJNOOom6uroxf2V7OB100EFMnDhxtIcxKiwWC0cddRSKogCw7777UltbO7qD2smSH8AAPB4POt3u9eczOStwzz33pH4PhBhus2fPZtq0aWP2/9d4ey8cb+9b4/G9Zry9N8jf8pHj9/t5+umnufnmm1PnNj8/f5RHNXBPP/00Z5111mgPQyAz6GKAGhoaKCgowGBI/MooikJhYSGbNm2ipKRklEcndrY//OEP42pGZrhccMEFqavN//73v0d5NDvXnXfeyWmnnUZpaeloD2VU/fSnP0XTNObNm8evf/1rsrOzR3tIYieS98Kda7y814yn94bx+Ld8vPzdrampITMzk3vuuYcPP/wQq9XK9ddfz8KFC0d7aNv1+eef09HRMS7+v+0OxvZlPjGmbH2lVdOkAcDu6N5776W6upqbbrpptIey0z3++ON888033Hjjjdx8882jPZyd5vPPP+err77i/PPPH+2hjKo333yTTz/9lI8++ojMzEwuuuii0R7SuHPsscdSVlbW59emTZtGe3gDIu+FO8d4eq8ZL+8N4/Fv+Xj6uxuLxaitrWXatGl8+OGH/Pa3v+W8886jra1ttIe2Xf/4xz84/fTTUxcfxeiSAF0MyMSJE2lsbCQWiwGJDyQNDQ3jZv2SGB4PPfQQr732Gv/617+w2WyjPZxRc+aZZ/Lxxx/T0dEx2kPZKT799FM2bNjAnDlzmD17No2NjZx66qljdt3tSCkqKgLAaDRy0UUX8dlnn43yiMaf//znP1RXV/f5NR7eT+S9cOcYr+81Y/29YTz+LR9Pf3eLiorQ6XScdtppQGLJTklJCRUVFaM8sm3z+/289NJL/OhHPxrtoYhvSYAuBiQnJ4fZs2fz/PPPA/Dqq69SXFwsKX27kYcffph///vfvPzyyz3W3O0OPB4PmzdvTt1+7bXXyMzM3G0KqVx55ZWsXbuW1atXs3r1aiZMmMALL7zAkUceOdpD22n8fj9dXV2p2//+97+ZPXv26A1IjAp5Lxx54+m9Zry9N4y3v+Xj7e9uVlYWCxcu5L333gOgvr6euro6pkyZMsoj27aXX36ZWbNmMXXq1NEeiviW0tXVJblZYkA2bNjAxRdfTEdHB06nk8cee4wZM2aM9rB2mquvvpo333yT5uZmsrKysNvtLF++fLSHtVM0NDQwa9YsSktLcTgcAJjN5tSb0K5u06ZN/PjHPyYUCqEoCtnZ2dx+++3MmTNntIc2KpIBynipqjscamtrOeuss4jH4wCUlJRw1113SWA2zJ5//nluvfVWurq6MBqN2O12nn32Wfbcc8/RHlrKeHovHG/vW+PtvWa8vzeM9b/l4/Hvbm1tLZdccgmdnZ3odDquu+46TjzxxNEe1jYde+yxLFq0SGbQxxAJ0IUQQgghhBBCiDFAUtyFEEIIIYQQQogxQAJ0IYQQQgghhBBiDJAAXQghhBBCCCGEGAMkQBdCCCGEEEIIIcYACdCFEEIIIYQQQogxQAJ0IYQQQgghhBBiDJAAXQixW3r66afHbO9XIYQQQgixe5I+6EKIfqWnp2/z8ddee41DDjlk5wxmB1VWVnLrrbeyZMkSvF4v+fn57Lfffjz88MOYTCaCwSB+v5/s7OzRHqoQQgghhBAAGEZ7AEKIsWvdunWp7x944AGWLVvGU089lbovIyNjNIbVQyQSwWQy9brve9/7HnvttRf/+te/SE9Pp6amhldeeYV4PA6A1WrFarWOxpCFEEIIIYTok6S4CyH6lZeXl/qy2+0YjcbU7VtuuYVLL720x/bHH388v/nNb1K309PTeeqppzjxxBPJz8/n8MMPp7a2lo8//pj58+dTVFTEz372M0KhUOo5LS0t/PjHP2bixImUlJRw6aWX4vf7exzjV7/6FZdeeilFRUXcdNNNvca9du1a6uvruf/++5k7dy6lpaUcdthhPPDAA6mgvHuKe11dHenp6b2+LrrootQ+X3jhBfbff3/y8/OZP38+r7zyyvCcZCGEEOPKf/7zH4qLi1FVFYBVq1aRnp7e4/3oiiuu4Cc/+cloDVEIMY5JgC6EGFH33HMPF198MR999BEGg4Hzzz+fe+65h0cffZR//etfvPvuu/z9739PbX/BBRfQ0NDAG2+8wXPPPcfixYv55S9/2WOff/vb3ygvL+ejjz7qEUQnZWVlodPpeO2119C07a/iKSwsZN26damvt99+G7vdzvz58wH46KOPuPbaa7nhhhtYsmQJV111FRdeeCFffPHFEM+OEEKI8ebAAw/E5/OxatUqAD799FOysrL49NNPU9t88sknHHTQQaM1RCHEOCYBuhBiRJ133nkce+yxTJs2jQsuuIAvv/ySW265hb322osDDjiAk08+mU8++QSA9evX88EHH/DII48wd+5c5s+fz29/+1uefvpp3G53ap9z587lyiuvpKysjNLS0l7HnDhxIrfddhvXXHMNkyZN4gc/+AGPPPIIXV1dfY5Rr9enMgOcTidXXXUV3/ve9/jxj38MJC4yXHfddZxyyimUlpbygx/8gNNOO40nn3xy2M+XEEKIsS0tLY3Zs2fz8ccfA4lg/OKLL+brr7/G6/XS3NxMZWUlBx988CiPVAgxHkmALoQYUd0rpefk5AAwffr0Hve1tbUBsGHDBpxOZ4/H9913X2KxGDU1Nan75syZs93jXnrppaxbt47777+f8vJyHnnkEebPn8/mzZu3+byf//znWK1W7r333tR9a9as4eabb2bixImpr2eeeYba2trtjkMIIcSu5+CDD+aTTz5B0zQ+++wzjjvuOGbMmMGSJUv4+OOPyc3NZerUqaM9TCHEOCRF4oQQg6LT6YjFYj3u2/o2gNFoTH2vKEqf9yXT0PtKR08+pzubzTagMWZkZPDd736X7373u9xwww3MmzePv/zlL9x44419bv/QQw/x8ccf8+GHH/YoPOf3+7n99ts54ogjemxvsVgGNA4hhBC7loMPPpinnnqK1atXo9PpmD59OgcddBCffvopXV1dkt4uhBg0mUEXQgxKdnY2zc3NqduRSIQNGzYMaZ9Tp07F6/Wydu3a1H2ff/45BoOBSZMmDWnfaWlp5ObmEggE+nz8f//7H//3f//Hk08+SUFBQY/H9thjD2praykrK+vxNWHChCGNSQghxPiUXIf+2GOPcdBBB6EoCgcddBCffPKJrD8XQgyJBOhCiEE58MADWbx4Ma+++irr16/nyiuvJBqNDmmfU6dO5fDDD+fSSy9lxYoVLFmyhOuuu45FixaRlpY24P0sX76cs846izfeeIPKykrWrVvH7bffTkVFBUcddVSv7Zuamjj33HO54oorKC0tpbm5mebm5tS696uuuoo//elPPPLII1RWVrJ69WqeeOIJXnzxxSG9XiGEEONTch36P//5z9Ra84MOOoiVK1fK+nMhxJBIgC6EGJSjjjqKSy65hCuvvJLjjz+eWbNmDWht+Pb84Q9/oKCggOOPP57TTjuN+fPnc+edd+7QPoqKisjPz+e2225jwYIFHHXUUXz88cf8/e9/Z+HChb2237BhA+3t7dx5551MmzYt9XX99dcDcNxxx/GnP/2J559/ngMPPJBTTjmF//73vxQVFQ359QohhBifDjnkEOLxeCoYT09PZ9q0aWRnZzNt2rRRHp0QYrxSurq6tt+DSAghhBBCCCGEECNKZtCFEEIIIYQQQogxQAJ0IYQQQgghhBBiDJAAXQghhBBCCCGEGAMkQBdCCCGEEEIIIcYACdCFEEIIIYQQQogxQAJ0IYQQQgghhBBiDJAAXQghhBBCCCGEGAMkQBdCCCGEEEIIIcYACdCFEEIIIYQQQogxQAJ0IYQQQgghhBBiDDCM9gCEGEvS0tJGewhCjHtut3u0hyCEEEIIMS7JDLoQQgghhBBCCDEGyAy6EEIIIcYMu92OTifzB2Lo4vE4gUCgx332V/ZFF+4YpREJMXao5kz8J38x2sMQfZAAXQghhBBjhk6nkwBdjBhduANduH20hyGEEP2SAF0IIYQQQohRUPprsBjAYoRgFM7dH64/aniPEY3Dnf+FZ5eBXgcmA5RkwC3HwdzCoe9fuQy894DDDHPvgs+uAqtp8Pt74AM4cx/Idfb9ePKcmQ0QiMLMfLjuO3Bg2eCPOdz+tiQxnqm5oz0SMR5JgC6EEEIIIcQo+fdPYI8J0OiGmXfA4VNhv9Lh2/+5/wBfGD77BWTYEve9thq+2dx3gB5XE4H8YKy4fvDjTHrgQ/jOtP4DdNhyzgBeWQXH/QH+ezHsXzr04w+Hvy2FbIcE6GJwJEAXQgghhBBilE1Ig2m5UNeZCNAPfRCuPgJO2CPx+Pf/DCfMgnMOgHOeApsJ1rdAfSfsUQDPnZuYHe9uQwu8tAo23rYlOAc4cfaW7/+2BJ77CnIdsKYJHvoBfFaTmHGPqWDUJe5LBr8vroBfvpbY33Gzeh6v+2z6hha44gVo8UEkBhccBBcv2LLdXSfBiyuhxQs3HwvnHgC3/SdxoeL7f05kFfztR9uf5T95Dlx8MNzzHvzrJ4mMgZteh/fXQyQO0/PgDz+EdBv8aTHc9wGY9IkLEX86M/G6KpoSY93sSezz4kPgwoOhyQM//zfUtkMoBqfMgduOT2xT+utExsN/18JmN/xkPvzqmMQxvqxPPO9Xr8OdJ/Y+T0JsiwToQgghhBBCjLK1TdDmh0MnD2z7FQ3w3qWJoHzBA/DCCjhjn57bLN8Ek7Mh077tfX1SBcuvgynfzvhOzoarDk98v6QGfvIMfP3LRDD902dh8VUwLQ9++27f+4urcObf4amzYHo+BCJwwL1wwCTYuyixjcUIS69OBMf73QNn7ZsI1P+ypOcM+UDsWwIvr058/7t3ExcIPr8mcfv2t+DXb8KD34dfvAQVv0pcDInGIRyDWBxOfgJ+cwKctnfiOW2+xL9nPwU3Hg0LJie2O+FxeGklfHfPxONdwcS5aPXC5NsSFxnOPxD+8UXPiytC7AgJ0IUQQgghhBgl3/8zKAqsa4H7vwc520jt7u57e25Z671fCVS19b2domz5vqoVTv1zYr37gnL445mJ+w8u3xKcQyKwv+NtaPeDQZeYWY/EYEltIsCelpfY7mcHwnWv9D7muuZECv3pf9tynzeU2E8yQF/07cWEGfmJYzR5oDBjYK99a5q25fuXV4EnBP9ekbgdiUN5duL7w6fCj59MZBAcOzORgv7N5kSmQDI4h0R6uj+cmIVv9m653xeGtc1bbi/aN/FvjhPKsqGmHSamD+41CJEkAboQQgghhBCjJDlb/O5aOPGJRBA5e0IiaI2rW7YLRXs+z9LtU7xelwgyt7ZXIWxohc5AIiW9PCexTvxvS+D1b7Zs5zBv+T4SSwTxH/4c5hWDJwhp1yYC3e6B8LZoJILcba1Jtxi3P/6B+qI+keafPPajp8Hh03pv9+L5sGwjfLgBjnssMWs+u5+ZelVLXNz44how6vt5Dd1/BsrQXoMQSdLHRAghhBBCiFH2nelw0cGJdcuQCKaX1ia+r2mDT6p3fJ9TcuHk2fCTp6GrW0t4f6T/54SiifTvom9nsx/635bH5k9KzK6vb0nc/tNnfe9jWi7YjPDk0i33VbZCh3/7Y3ZZwB3a/nZJr6yCxz6Bqw5L3D5pdmKdeeDb1xiIfDtLHk9kGexTnEg///5c+LwuMVaTHv61fMs+23zgtMAh5XDXO1vub3TDps4BvobgwF+DEN3JDLoQQgghhBBjwE3HwORbYVl9onXYD/8K//1tIojcv2Rw+/zbj+CO/8L+9yZmqjOsiQrp1x/Z9/Yua6IQ2n73QHFGIuBNynXCE6fDiY9Dlj0R5PbFoIfXLoArX4R73k9kAuQ44Omztz/eny9MVJ63mfovEvf9PyfarPkjiTZrb16YWN8Oidd1639g/3u2pPdf953Euvpz/wGdwUR2Qo4D/rooMdZXfgaX/itRpE5R4JJD4IKDE+O96kWYfWdiPw5zouDc9lLxf3ZQYr37796TInFixyldXV0DTFYRYteXlpY22kMQYtxzu92jPQQxjjmdTnQ6SfATQ6eqKl6vt8d9zn+Wowu3j9KIhBg7VHMW3tOqRnsYog/yDiiEEEIIIYQQQowBEqALIYQQQgghhBBjgAToQgghhBBCCCHEGCABuhBCCCGEEDvRK6tgxm9g7l2wunG0RzM6Xl4Jn9cO3/5q2yF7G23dtvbqarjwuR3b59y7ILiNCvgAhz4Ir3/d+/4mT6JwXSw+8DGK3ZME6EIIIYQQQuxEf/gkUSl9xfW9+3CP5QAuvlWf76GM9eVViTZno+XG1/qvZN+fFdeD1TS44+W7YP9S+McXg3u+2H1ImzUhhBBCCCF2kp//Gz6ugnUtcP8HsPgqUC6De06B176GfUvg6sPhwucTvcM1Eq3HfnZQ4vmlv4Yf7wfvroNNXYk2Xu1+ePoLaA/An8+EQ6f0Pq47mGj9tbQWdArMK4a/LIJb3gRfGO75bmK7hz+CL+vhb2fB35bAc19BrgPWNMFDP4AD7+s51puPSbQiW9kAoRgcOCmxnVGfmE3evxQWV0OjB46cBn84Hd78Bl79Gt5dn+ilfukCOP/ALWNd1wwnPQHrbgJNg5wb4IKD4I4T4b11cOfb8N5lfZ/fq19KnF9fGB76Phw+rfc2H1dCuhVKs7bc99cl8OCHieMZ9fDvn/R+nnIZeO9JtFuraIIrXoDNnsRjFx8CFx7cc/t/L4ff/Bde+Emir/2Z8xI/g3MO6HvsQoAE6EIIIYQQQuw0v/8+rGqAq4+AE/bYcn84Bh9envj+h3+B6Xnw0k+hxQvzfgtzJ8J+pYnHg9FEYP9FHSx8MBEwf34N/PMr+OVrice2dsULicBy5fWg00Grt/c2ffmkCpZfB1Ny+x7rz56FBZPhj2cmgtufPpsI8q88PPF4VVti20gMZt4Bn9Uk+oKftAfsUwyXLux9zGl5iddY35G4+DA1F95fn3js3XXwnT6CbkhsO3tC4mLDkho45Y9Q9Wuwm3tu92ElHFjW7faGRK/4j6+AgjQIfJvG3tLPOYrF4eQn4DcnwGl7J+5r8/Xc5t73Ehch3r8MMu2J++YVw/JN4A/3HpMQSRKgCyGEEEIIMcrOm7/l+3fXwcpvZ7RznfC9PeG99VsC9B9+GxTuXZQIZJNB4rwiqG7re/+vfwPLrkkE5wA5zoGN6+DynsH51mN9eRUsqYV730/cDkbBpN/y+Ol7g16XSA2fW5gI2OdP2v5xj5iaOA/tfvjRPvDE4kQWwLvr4NHT+n6OyQBn7Zv4/oBJibTylQ09g3FIZB5M7/aa3vgmkZVQkJa4bdtOGvu6FoipW847QLZjy/e3vAkT0uDti8Fs3HK/UZ+Yud/sgck52z6G2H1JgC6E2CV0dHTQ0NDAHnvsgaIooz0cIYQQYoc4tppR3fqtrPtNy7ef4PXfBtsW45bbsa3WiW+PQddzbXkotu1xbX2fBrz8UyjL7nv/lm4Bql438HXr35mWCJw7Aomsgw2t8OJKqGlPXIgYqL4+EtiMiQsJI2X+JPhvRWKs0/N7PhaKgdXY9/OEACkSJ4TYRaiqSjgc3mnHW7duHR0dHTvteEIIIXYf35kGT3ya+L7VCy+tgsOnDm2fJ+0Bv3sPVHXLfgHKsxNrzlU1kdr9wood3+9d72wJvDsDibXz2+OygjvU/+PfmZbIGqjrSKS4f2ca3PofWDh5SxbASyvhx09ueU4kBk9/mfj+89pE5fQ5E3rtmjkTYW3zltsn7gFPfp7YHhLnIbCNau3TchNZAv9avuW+7inuR8+AP50JJzwOKzZtub/Zk7ggMiGt/30LIQG6EEIMgs/nIxodwcvvQgghdlu//z6saoQ5/weHPQQ3HrUlvX2w7v9eIujc4/8S7cJ++Vri/lPnJtLoZ94B3/tTIg19RzxwaiLonHt3YrzfeTjRnmx7ztoXnvkyMZY/Le79eJ4L8pxb0uEXToFGd8/155Wt4LJsuZ1lT9y3/z1w7tPwzNl9r/U+YVaikFwyc2DBZPjV0XDUI7Dn/yXW9bf6ej8vyaCHV36WuIgy+87E6976wsaCyfDsOXDqnxPr7gHeqoDv7tn3rL4QSUpXV5c22oMQYqxIS5NLmuNVa2srdXV1zJs3b6ekuC9btoyioiJyc3O3v/Fuxu12j/YQxDjmdDrR6WT+QAydqqp4vT2rfDn/WY4uPIDoUYwLp/4J7v1uz2rsA3Xx83DYVPjBXsM/rv4ccj/88Yzeae+jQTVn4T2tarSHIfog74BCCLGDNE1D0zRZ6y52aVVVVRx11FHMmzePww8/nLVr1/a53ZNPPsnee+/N3Llzufzyy4nFei5g1TSNk046ibKysj6fL4QQg/XC+YMLziHRhz4c2/52w6XZAxcdMjaCczG2SYAuhBCDJAG62JVdccUVnH322SxbtozLL7+cyy7r3XS4traWO++8k7feeovly5fT3NzMU0891WObJ554guLi4p01bCGEGJBsB/xo3513vDwXnLnPzjueGL+kirsQYsRFo1EaGxvJz8/HbB7Zxp87I2jWNFkZJHZtra2trFy5kpdeegmAk046iWuuuYa6ujpKSkpS27366quccMIJqaUe5513Hg8++CDnnnsukJiFf/HFF3n00Ud58803d/4LEbu1vv5Wq+bMURiJEGOP/F8YuyRAF0KMuFgsRmtrK5mZmSMeoO8MyQ99MoMudlUNDQ0UFBRgMCQ+JiiKQmFhIZs2beoRoG/cuJGioi39joqLi9m0KVGyWFVVfv7zn/O73/0utR8hdqa+AnT/yV+MwkiEEGLgJMVdCDHixlMgq6oqHo+HtrY2VHXbzWTH0+sSYkdt/fvdX+ZI9+26b/PQQw9x4IEHMmfOnJEZoBBCCLELkkvaQoidZiymhquqSiAQwOPx4PV68fl8qXE2NzdTXl6OxWLp8RyZQRe7uokTJ9LY2EgsFsNgMKBpGg0NDRQW9uy/VFRURH19fer2xo0bU9ssXryYb775hueee454PE5XVxezZ8/m448/Jj09fWe+HCGEEGLckABdCDHixlIgq2kawWAwFZB7vV5UVUWv1+N0OiksLMTlcqFpGlVVVaxZs4bS0lIyMzN77APG1usSYjjl5OQwe/Zsnn/+eRYtWsSrr75KcXFxj/R2SKxNP+aYY7j22mvJycnhL3/5C6eeeioAzz//fGq7uro6DjvsMFavXr1TX4cQQggx3kiALoTYaUZjBl3TNMLhcI+APBaLodPpcDgcFBQU4HK5sNlsvQLumTNnUldXR3V1NV6vl6KiInQ63ZjMBBBiuD3wwANcfPHF3HfffTidTh577DEALrvsMo499liOO+44SktLueGGGzj66KNRVZUFCxZw1llnjfLIhUjQ6XTY7fYe9ymKIhdXhSCRQej3+0d7GKIPSldXl3zSFOJbaWlpoz2EnS4ajRKPx3ulcQ+nSCTCqlWrmDJlyoid49bWVurq6thnn32IRCKpgNzj8RCNRlEUBbvdjtPpxOVyYbfb0em2X4ZD0zTa2tqor6/HYrFQXl6OoiisXr16RF/PeOZ2u0d7CGIcczqdA/q/KYQQYvBUVcXr9Y72MEQfZAZdiN1ca2srra2t7LnnniN+rJGaeY5Go6mrwKtXryYcDgNgs9nIzMzE5XLhcDjQ6/U7vG9FUcjJycFut6dS3idOnJh6TAghhBBCiOEiAboQuzlFUUY8ZXu4A9l4PJ5KV/d4PASDwdRjLpcLl8uF0+kc1tZONpstlfK+ceNGYGwWvRNCCCGEEOOXBOhC7OZ2RoCeNNjjqKqKz+dLBeTJ2XKTyYTT6SQ/P59wOMzmzZt7FbEaTnq9nkmTJmGxWGhsbKS+vp7JkydjtVqHvG9N02RGXgghhBBiNycBuhC7OUVRttvveziOsSM0TcPv96cC8mTrM4PBgNPpJDs7G6fTidlsTu27paVlJIbei6IopKen09jYiKZpVFRUUFJSQlZW1pD2W1lZiaIoTJ48eZhGKoQQQgghxhsJ0IXYze3MFPf+jpNsfZYMyJOtz3Q6HU6nk4kTJ+JyubBarWNiljn5OiZNmkRbWxs1NTWpKu+DWecOiSyB4UzJF0IIIYQQ4498GhRiN5eslrwzU6yTrc+6B+SxWAxFUXA4HOTn56cqrY+FgHxryQBdr9dTWlqK0+mkvr4ev99PWVnZoFLeJcVdCCGEEEJIgC7EGOX1euno6BjRNdXQc3Z7pALE5H69Xi9utxuv10skEgHAbreTnZ2dqrQ+ntorJfvpZmdnp6q8V1RUUFxcTHZ29g7tS9O0cfXahRBCCCHE8JMAXYgxKhwO09raSnFx8YjOrG4v/XywYrFYaobc4/EAiZZuVquV9PT0VKX1waaEj6bkuer+c7FarcyYMYP6+npqa2vxer0UFxcP+PXJDLoQQgghhJAAXYgxKjmbqqrqiAaxyaBwqMeJx+P4fL5UynogEADAbDbjdDoJh8MUFRWRl5c3LOMeTX0F6LClynv3lPfy8vIBpbyrqioBuhBCCCHEbk4CdCHGqOEKnAd6nB2dQVdVFb/fnwrI/X4/mqZhNBpxuVzk5uamKq1rmkZbW9uIvo6d2ZN8e8caTMq7pLgLIYQQQggJ0IUYo7rPoI+kgQbomqYRCARSAbnP50tdPHC5XBQVFeFyuXq0PtvRY4wX/c2gd5dMed+4ceOAUt4lxV0IIYQQQkiALsQQxeNxNE1Lfen1+mGZKe5eXX0k9Rc8a5pGKBRKBeRer5d4PI5Op8PhcDBhwoQx1foMdrzf+kgfr3uV97q6um2mvEuKuxBCCCGEkABdiCEKhULEYrHUbbPZPKwB+kjPoHe/EBAOh1MBucfjSbU+s9vt5OXl4XQ6sdvtg0rF3hn91ocieUEi+do1TaO4uBiz2dzntjDwCwJZWVnYbDaqq6upqKigqKiI7OzsHs+XGXQhhBBCCCEBuhBDNFJB1c4I0KPRaKrC+vr164lGowDYbDays7NxOp04HI5xWWl9IJKvP/kVjUZTvdjD4TBr1qxh0qRJpKen93jejgbokEh5nz59Ohs3bqSurg6v10tJSUnq3MoadCGEEEIIIQG6EMNsuGaJRyJAT7Y+S84Sh0Kh1GMOh4PMzEycTicGw/D/aRgLM+jxeLzH6w8Gg0AieM7MzEz1Ytfr9cRiMWpqaqisrKSgoIAJEyYMeS391invgUCAsrIybDabpLgLIYQQQggJ0IUYqq2DquEKQrtXcR+sZOuzZECabH1mMplwuVwUFBRgNBpZv359qur6SNrZAbqmaalK8x6Pp1el+fz8fFwuF0ajsddzDQYDkydPpqmpiYaGBnw+H2VlZRiNxkHNoHeXlZXVo8p7UVERgMygCyGEEELs5iRAF2KIxlKKe7L1WTIgTwakBoMBl8tFTk5OqtJ6UnIWfWcVoxtJ3QvbJdfSx+Nx9Ho9Tqdzm5Xm+xtzQUEBdrud6upq1qxZQ3l5eY/HB8tisaSqvNfX16fGL4QQQgghdl8SoAsxzHZminuy9VkyIO/e+szpdFJYWIjL5cJisfQbTO6savEjdYzkOvL29nY0TePrr7/uUdjO5XJht9uHFEy7XC5mzpxJdXU169atS61JH+pFB51OR0lJCTabjbq6OhobG7Hb7dhstiHtVwghhBBCjE8SoAsxRCOd4t59f1tXGt+69VlBQQEulwubzTbg4HFn9Sgfrhn0ZNp+cpY8uY48maY+ZcqUESlsZzKZmDp1Kg0NDTQ3N6fGMhzHSU9Pp66uDkVRqKiooLi4uFeV98EIBoN4PB5yc3NlfbsQQgghxDggAboQQzRSgY+iKOh0OiKRCG1tbamAPFlp3G63k5ubm5ohHuz65Z0VoA/2GANdR97e3k5jYyNpaWkjMPIEnU5HUVERsViM9vZ21qxZw+TJk/vsa74jkuelqKgIj8dDXV0dHo+H0tLSIV0A8Pl8bNy4kdzc3CGNTwghhBBC7BwSoAsxRMM9gx6NRlMz5KqqsnnzZiDR+mzrSuPDYTiK0e3Icbanez/2rbMEXC4XRUVFOJ3ObabtjzSr1Zq6gFJRUUFJSQlZWVmD3l/y3Ov1ekpKSnA6ndTW1qbWvA825V1VVXQ6ncyeCyGEEEKMExKgCzHMdjRAj8ViqZRtr9ebStm2WCypoLS0tHREWp/B2JhB735RwuPxEIlEBrWOfGcFosme5dOnT6e+vp6amhp8Ph9FRUWDymRInpfkczMzM7HZbFRXV6eqvOfk5Ozw60sG6EIIIYQQYnyQAF2IIdrRGXRVVXsE5H6/H0iscXY6neTn5+N0OjGZTKxevRqz2TxiwXn38e/MNej9rSO3WCykp6fjcrlwOp3Dvo58OCmKkupr7nA4qK+vx+/3U15e3qNK/kD01bbNYrEwffr0VJV3r9dLSUnJDv0uSIAuhBBCCDG+SIAudmnvvfcet912G6qqEovFuOyyyzjzzDOH9Rh9zWpqmtYjdTwQCKQCcp/Pl2p95nQ6yc7Oxul09tn6S6fT7ZTUc0VRRjRA1zQNVVXxer2sW7cudQ66ryNPXpQYD7r/fBVFIScnB5vNRlVVFWvWrGHSpEmpSu8DkfwZ9/XzT6a819XVUVFRQVlZGXa7fcD7lQBdCCGEEGL8kABd7LI0TeP888/ntddeY4899qCuro799tuPE088EafTOaLHTbY+S34lA6Vk6zOn05lax7wtOp1up6SeD3eA3t86cr/fj8vlGlD7t7Gse4CeZLfbmTlzJjU1NVRWVlJQUMCECRMG9Pq2TnHfWveU97Vr1w445V1V1XF5foUQQgghdlcSoItdntvtBsDr9ZKZmbnD6cfboygKwWCQJUuW8L///Y8f/ehHqcAo2frM6XQOqhf3zphBh+EJ0PtaRw7gcDjIy8ujtbWVjIwMiouLh2PIo6q/c2UwGJg8eTJNTU00NDTg8/koKytLtYDb3v629fuRTHnftGnTgFPeZQZdCCGEEGJ8kQBd7LIUReFvf/sbZ511FjabDbfbzVNPPTXsadSqqjJr1iwsFgv77LMPZrM5NUs+1OBoZwboO3qcZMp6coY8EAgA/a8j7+joGNHZ3J2RadBdf69FURQKCgqw2+1UV1enKrE7HI5+99VfivvWdDodxcXFqSrv20t5lwBdCCGEEGJ8kQBd7LJisRj33XcfzzzzDAcccABfffUVixYtYvHixWRkZAzbcfR6PZ9++in5+fmpAMvhcAxLYKTT6YjH40Pez/YMZAY9mbqfnCHfeh15sif7ti6A7OwgeqT0leK+NZfLxcyZM6murmbdunUUFhaSm5vbb80C6D/FfWsZGRmpNe9r167td9/JavNCCCGEEGJ8kABd7LJWr15NU1MTBxxwAAB77703+fn5rF69mgULFgzbcZIzpt0NVyA6mJntwR6nrzF3X0fu8XhS/ciTa+l3ZB35eFkLHYlE8Hg8GI1G0tLS+txmIAE6JCrzT506lYaGBjZu3IjP56O0tLRXdfqBpLhvzWw2p1LeN27ciNfr7dWOLx6Pj2gHACGEEEIIMbzkk5vYZU2cOJHGxkY2bNjAlClTqK6upqamhsmTJw/7sUaqCvrOSnFPFqOLxWI9AvLkOnK73Z6aIbfb7YOelR2LM+jxeLzH2vlQKJR6LD8/n4kTJ/Y5Mz3QYFqn01FUVITD4aCmpoY1a9YwefJkrFZrapuBprj3te/uKe9r1qyhrKwslU4vM+hiKKqqqrjoootob28nLS2NRx99lOnTp/fa7sknn+SBBx5AVVUWLlzIvffei8Fg4JtvvuHqq6+mra0No9HIvvvuy29/+9thrwMihBBC7EokQBcjprq6mldeeYUlS5awceNGQqEQWVlZ7LHHHhx66KEcd9xx2y2eNRS5ubncf//9nH322akA+t5772XChAkjdsyk4QpER7qKe7IneywWo729nebmZmBk+pHvjBn0gVZMDwaDuN3uHqn6JpMJl8vFhAkTcLlctLW1sWnTJvx+f69Cb4P5mWRkZGC1WqmqqqKiooKSkhKysrJ67G+w5yiZ8r51Or2sQRdDccUVV3D22WezaNEiXnnlFS677DLeeeedHtvU1tZy55138r///Y+cnBzOOOMMnnrqKc4991wsFgu/+93v2GOPPYjH45x//vk8/PDD/OIXvxilVySEEEKMfRKgi2H31Vdfcdttt/H555+z//77s9dee3H44YdjtVrp7Oxk7dq13HrrrVx11VVccsklXHTRRT1mE4fT97//fb7//e+PyL6723oGfTgD9OGcQe9vHTmQKm63vXXkQz3+aEimrSe/YrFYj1R9m82Gw+HoESDn5+engt6KigrKy8t7FGMbTDCdrMReX19PTU0NPp+PoqKi1Iz8UC5imM1mpk2blkqnT7a2kwBdDEZraysrV67kpZdeAuCkk07immuuoa6ujpKSktR2r776KieccAK5ubkAnHfeeTz44IOce+65lJeXp7bT6/XsvfferF+/fue+ECGEEGKckQBdDLuLLrqIyy67jKeeemqb/cZXrlzJn/70Jx5//HGuuOKKnTfAETBSs8PDEaBv3Y+8e3A6ceJE0tLSqKmpwWq1kp2dPUwj721nrkHvXmHe4/EQDAYBsNlsZGdn43K5UmngyfOi1+ux2WxAomVcsvjdjBkzUsXYSkpKyM7O3qEU963p9XpKS0txOBzU19enesMPx/lJptM7nU5qamqIx+PEYrEh71fsfhoaGigoKEjVMFAUhcLCQjZt2tQjQN+4cSNFRUWp28XFxWzatKnX/vx+P08++SS33HLLiI9dCCGEGM8kQBfDbsmSJQMKNvbcc08eeuihMbkueahGs0hcch15MkANh8NAYh15Tk5On+vIRzqVHkZunT4kznc0GkVVVdavX4/X6+1RYT4/Px+Xy9UjTT2ZSZA8v52dnaiqSmtrKyaTidzcXIxGY6oYW319PbW1tfj9/lSf+8FSFIWcnJxUJfbm5uZhvYCRnp7OzJkzWb16Ne3t7VitVvLy8sZNoT4xNvRVe2F72/W1TTQa5bzzzuOwww7j+OOPH95BCiGEELsYCdDFsOsvCOjq6gISwcNAth9PBvpBdkcNZAY9uY48OVvcvR+5y+VKrSPfVjXvkQyeR0o0Gu2Rth6NRlOPJTMD+qowH4vFcLvdqQsXmqbR3tXBkvUrMVvMzM4rx+l04na7UxkFOp2OkpIS7HY79fX16HS6YVmWYbfbmTlzJhUVFYTDYRoaGpgwYcKw/J8wm80oioLdbmfTpk14vV4mTZokVd3FgCSLbMZiMQwGA5qm0dDQQGFhYY/tioqKqK+vT93euHFjj22i0SjnnHMOeXl53H333Ttt/EIIIcR4JZ/UxIiKxWLce++9/PGPf6SjowOAzMxMfvrTn3LVVVeNaJG4nWkkU9w1TeuRUp0sctZ9HbmqqhgMhgH3I+/vOCNpqBcB+rsQYbVayczMJBaL0dXVxdSpU/t8vqZp+Hy+HuvuI5EIyzZ8w/LqCiKxCGaLiQmObBRFwWq1EgqFsFgsqfHn5ORgtVpZt24dfr8fr9e7zWUcA2EwGEhPT6etrY3Nmzfj8/l6FaUbLE3TyMzMJD8/P1VBvnuVdyH6k5OTw+zZs3n++edZtGgRr776KsXFxT3S2yGxNv2YY47h2muvJScnh7/85S+ceuqpQOLv/3nnnUdGRgYPPvjgLnExVgghhBhpEqCLEfWLX/yCt99+m5tvvpl58+YBsGzZMu6++24aGxt58MEHR3mEw2MkZ9ABQqFQKjjtvo7c4XCkqo5brdZBfwAeizPomqYRCoV6rJ/vfiEiLy8Pp9OZuhDR2NjY7+sPh8N0dXURj8eBRLC/sbWJZfXrqG/chKLFCUfCBD0+vnZUs3DKXvh8vlSKe/f9OhwOnE4nfr+f9evXU1RURE5OzpCDD6PRSHl5OdXV1axZs4by8vIhBdLJzAudTkd6ejqzZs2iqqqKdevWMXHiREl5F9v1wAMPcPHFF3PffffhdDp57LHHALjssss49thjOe644ygtLeWGG27g6KOPRlVVFixYwFlnnQXAiy++yGuvvcasWbM45JBDADjggAO45557Ru01CSGEEGOd0tXVNbY+lYtdSmFhIU8//TQLFy7scf9HH33EokWL+iwmNJrS0tIG9bxgMJjqGQ6JYCtZcGwwYrEYXq+X1tZWPB5P6n673Z5KWx9KP/KtVVVVEYvFmDZt2rDsry/r169Hr9f3qOy8tb76sCuKgsPhwOVykZaW1u+FiMbGRlpaWpg7d27qvng83qNIHIDH5+WruvWsrl6P3qRH1TS8be2o6DBazFisZuaXzaIoI4+srCyysrJ6BcobNmwAEmnkLS0tZGVlUVJSMuifR319PV6vl1mzZhGJRKiursbv96fapQ0mkI7FYqxYsYKysjIyMzOBRNDe2NhIU1MTaWlpI5by7na7h32fYvfhdDql+4AQQoywZEFdMfbIDLoYUenp6eTk5PS6PysrC5fLNQojGhlDnUHvnr7t9Xrx+/0AqTTnkpISMjIyRmz98GjNoKuqit/vTwXkyded7MOelpaGw+EYVB/25H6TrysajbJh80Y+rlhFTImjMxnxdnRgsNowORJp6tFICE97gBWmGiam5+D1ejGZTFit1h5j0DQNvV5PcXExdruduro6gsEg5eXlmM3mQZ2H5O+QyWRi6tSpqXZpPp+P0tLSHT4Hyde9dTHAwsJCHA4HtbW1fPPNN5SVlQ05TV8IIYQQQgwPCdDFiLrpppu47rrreOCBB1Izp1VVVdx4443cdNNNozy64bOjM5zbW0eek5OD0+kkHA6zfv367RZ5G6qdEaAnK9J3T1tPVlHX6/Wp1z2UPuyKohCNRunq6koVjdM0jdauDj74+itaQ35AJeD2ojcZQGdCbzZiMBhxt7SixTX0Fhv+YIANbQ1MyylK/ZwyMjJSx+leEyArKwur1UplZWVqjfeOZmJomtYrkC4qKsLhcKTWjk+ePHmHCtN1T3HfWrLKe3V1dSrlPT8/X1LehRBCCCFGmQToYtjNmjWrxwf99vZ29t13XxwOB4qi4PV6MZvNVFVVccYZZ4z4eMLhML/61a947733MJlMzJkzhyeeeGJEj9lXsBsOh3v05h7IOvJkkDnUXujbM5JF4pLp+sFgkFgsxtdff52qLl5QUIDL5cJmsw05OEymarW2tqbuCwaDLNmwhnVtmwlHY0QCfoxGHZFoHLvdis3mwNvegclkwmBzoFcSheM8XR6+qqmmNDM/9ftqt9tTFw62Plc2my0V8G7YsGGHA97++qpnZGRgtVqpqqqioqKCkpISsrKyBnw+oO8AHRIz9dOmTaOhoYGGhoZUlfddpXCjEEIIIcR4JAG6GHY33njjaA+hh1tuuQVFUVi2bBmKotDU1DTsx+grxT0ZmCYD8u79yLOzs3G5XDgcjm2utUw+NtIB+nDOoGualkovd7vdqbR1nU6HwWCguLgYp9M5qLT1/gSDQdra2lJrzePxOOsbNvLFxmrafG7UcBiDyUjQH0BxOXHl5BB0dxE3qqDoUYwG7DYb7uYWYjEVndlCLK7y1cZq5pdOw+/3YzKZUss1+gqoDQYDU6ZMobGxkYaGBvx+P5MmTRrQ69xWX3WLxZLqw15TU4PP56OoqGi7a3S3F6BD4udeWFiI0+nsUeVdUt6FEEIIIUaHBOhi2J155pmjPYQUv9/P008/zZo1a1IBUH5+/rAfJ7nv+vp61q5dy4wZMwiFQkCikNhA+5FvbbwE6OFwuEfaejweT6WtJy9GbNy4EVVVSU9PH7Zxd+9prqoqmqbR4e5iSe0GKpoa0CIRjFYLfrcPk8OOPSeHuD9ANBImEo1jMpiw52Tjb2sjGomht9kxALFoDL/Xy9pYPVPzJqbarvn9fux2O9D3sgZFUZg4cSI2m42amhoqKiooLy/fbmr61inuW9Pr9ZSWluJwOKivr8fv9293vftAAvSktLQ0SXkXQgghhBgDJEAXu7SamhoyMzO55557+PDDD7FarVx//fW9qsoPVW1tLSeeeCKVlZUcfvjhPPTQQ+Tl5eFyuQZVNCwpGVyNtR7l8Xg8lR2QDJAhkR2QfN12u71HgDecwV5fPc2j0SgVTY18uLmOUCSEwWLG29FBLBLHkpmFFo2AohAKhDAoCs6cXILuLoIeL6piQK/XYXG58DS3okZjKCYLisHAktoqjp+xJx6PB6PRiNVq7TclPSmZml5ZWUlFRQWTJk3qsYa9r9ezvUA62YfdZrNRVVXFmjVrmDRpUr8XPJIB+kDPezLlPZkBICnvQgghhBA7nwToYpcWi8Wora1l2rRp3HLLLaxevZpTTjmFpUuXkp2dPWzHKSws5Fe/+hWHHHIIaWlpKIoy6JZt3e3MGfRtHUPTNAKBQI+0dU3TMJlMpKWlDTg7YDguNITDYdxuN7FYDEicm4a2Nl5esYyN7a1MLCkm1BFAH4ygtzoBDaPFjLvLgz4Sw56TQzTgIxoKEYnE0RsVXDnZeFpaibV1oLfaiGsBAAIeHw2hCGvzJjA9Myd1DrYXoEMiNX3GjBnU1tZSVVVFfn4+EydO7PN5yQKBA2G325k5cyY1NTVUVlZSUFDAhAkTeu13R2bQk5IZAN2L00nKuxBCCCHEziMButilJdfqnnbaaQDMnj2bkpISKioqOOSQQ4btOCaTiRNPPLHHfQMJ4rYn+fzRSHGPRCKpgNzr9aaK2rlcLoqKilLZAQN9jcNRBM7tdvfoae73+/nf2rWsaWnEH48RDYUJuj2AEc2gx+Zy4m1pIxbuwuxKQ4tF0DQIBaMYwhqO7FxCXZ10NrUQCoaJRCIYHVH8nR7i4TBpWVmYjCa+qq2k2O5E/23BuO4977dFr9dTVlZGU1MTDQ0NBAIBysrKegXjA5lB785gMDB58uTUfn0+H2VlZT1muwcToCdtnfI+YcIECgoKJOV9jOjs7CQYDJKZmYnFYhnt4QghhBBiGEmALnZpWVlZLFy4kPfee4+jjjqK+vp66urqmDJlymgPbUB29gy62+3G7Xbj8XhSa+htNluq/Zndbh9UwJc02Bl0v9+P1+tNnYdYLMbXdfUsrqkiGFcJBUJEo2HiEZVoVMWelUWwox1fexc6ow1Ni2Ewmwn4/USCESzODPxdXfg2bybgTuzXmZONLq6iC8XIKZiIt7MTny9AyB8i4nCwrKmJQ0pK8fv9uN3uAVdTVxSFgoICbDYb1dXVqZZpNputx3nZ0eA3uV+73Z7ab3l5OQ6HI7VPGFyADj1T3hsbG/H5fJLyPkr8fj8vvPACr7zyCl988QU+ny/1WHl5OYceeiiLFi1i7ty5ozdIIYQQQgwLCdDFLu/+++/nkksu4ZZbbkGn0/HAAw8Me6G4voKr4ZpB3176+WAle7G73W7a2tpQVZUNGzZgNBpJS0tLtX4brv7rgzkXffU0b2lv58P1VdS5u4j6fRisZmLBCKFgGEtmNoqmJ+DxosZ1oIA93YW3vY2Ojc1gMhPyeyAYIhIIotcga+JEtHAYnU5B58xEVWNYbQ6Ia8SDTQRjcfTRGBW1tUxypVGkKEQiEQKBwA69luSsdFVVFWvXru3RMm1bVdy3x+Vy9ZjtLiwsJDc3N7XPofwOJlPenU5n6iLApEmTcLlcg96n2DEPPvggDz74IFOnTuXII4/kkksuIS8vD6vVSmdnJ2vXrmXp0qWceuqpzJkzh7vuuotp06aN9rCFEEIIMUgSoItRM2fOHBYsWMAvf/lLJkyYMGLHKS0t5Y033hix/cPwFkDbmk6nG7YAPRqNptLWu/diT/b3njVrFhaLZcRez0Bn0DVNw+PxpFq0AYRCIT6vrGF5w2Z8bg8mp51oJEo0GMGamU0o2kQ4FEFV9KjRONb0dLpaW3FXVhNTIR6LYXUZUBQjumiciUXlxKNhNE1DU/TEQ3GsBWkE3V68LS2Ynelk5hbhDXrpam4m4PayJD2HCWlpeL1eAoEAsVhshy5gmM1mpk+fTl1dHTU1Nfj9fgoLC3c4xX1rJpOJqVOn0tDQwMaNG/H5fFit1iHts7vkRYCamhrWr18vKe87UXV1Ne+99x6TJk3q8/F58+axaNEi7rvvPl544QVWrVolAboQQggxjkmALkbNGWecwaZNmzjxxBNZtmzZaA9n2A1X5XWdTjfofamqis/nSwXkyfXbNputRy/29vZ26urqRjQ4H+h+g8FgqlUbJCrG12xu4aN11TS1tWJJTyMWjxNracfodEEkjKZqRKNxwj4fUXs6vq522js7UeIqep2OjIIJqMEgajSCLScPNRpNtGWLq4T9fpx5+cRMIXytbRgNFvRGGzq9iVg8iCESJzO3AL/fT8XatXxiMpCvVwiFQjuU6p6k0+koLS3FbrezceNGAoHAkGbQu++3qKgoVeDN6/UO688yeREgmfIeCoUoKysbtv2Lvj344IMD2s5gMPDDH/5whEcjhBBCiJEmAboYNTfccMNoD2HY9JfiPhx2ZAZd07RU4OjxePB6vWiahtFoxOVykZ+fj8vl6rWOODn+4UjL3974+tO9p3lSl8fLRxWVVNRtwuhwosZV/K3tGAwGVEPi3Lj9foINjegdTgxGMz6vGwMKZoOZjOJCouEAsUAAs8mCqjOg1xuIhSOE29tx5OSjM5gJuj0YdArEwOiyQySCr7kFa3o6towswqEQVkMYbyjEkvU1HF5WiM/nIxgMEggEeqwnHwhFUcjNzcVqtVJdXU00Gk2l8Q9VssXb2rVricVitLe37/BFhP50T3kXO9+JJ57IU0891au1nsfjYdGiRbz22mujMzAhhBBCDBsJ0MWIeumllzjggAMoKCgY7aGMuB3tJT5Q2wvQo9EoXq83FZRHo1EURcHpdDJx4kRcLhdWq3WbgXf3AH2k9Hf8/nqaL6+qY/HaKuIGM/F4nKi7Cy0eJxQKY3A48HR2ENnUgMnhxGC1YTDoiYfBjg5XwQSi4TAhrxcdoMTBZHcSDvjxNDdjT89C58wgFg5DPE7cH8Cam4fOFMPb0ozF7sTsTENRdGiqRtwfwGxzYra78HV28EVdA5l2G11dXRgMBiwWy6DSyZ1OJzNmzGDVqlW0tbVht9vJzs4e8kUSi8VCRkYGHR0d1NTU4PP5Uh0NhoOsQR8dn3zySZ8XckKhEEuXLh2FEQkhhBBiuEmALkbUb37zG2pqaigpKWH+/PnMnz+fAw88kPLy8tEe2rDbOkAfrmB36yJxqqqmKol7PJ5UsTKr1UpmZmaqJ/mOBGPJbUcyQO9r/332NG9t571V62gPxggFgsSUMEG3m5A/iMGZRjQYQvN5sFmdODJsOHKyiQSDuJubMFjt2JzpRIJhtGgENRTEkptDzBjF3dyM1WbDYnUCOlDjhNwd2LJysWVa8Xd2YTKaMOjNKAYTeiDQ3orFlYYjJ5+Q1000HMRqstIVjLK+pQOdbj1msxmz2UxGRsagzonJZEJRFOx2O3V1dfj9foqLi4ccTCf71Ofm5lJfX4/f76e8vByz2Tyk/Yqd79lnn019/9JLL/XIYIjH4yxevLjfNepCCCGEGF8kQBcjatmyZbS0tPDZZ5/x2Wef8ac//YmrrrqKjIwM5s+fz9///vfRHuKIGc4APRqN0tzcnEpbV1UVg8GAy+UiNzcXl8uVKvQ22GMM55i3dQzou6e5z+fnk28q+WZzO11tLcR0OgLuTqL+ILasXIxWBQPgcLpQNBVbVg7hQJBAaxsWZxomq4OQpqGqcUJeD87sPPRmK8EuNwa9AYNOj95khXgMf0cL1rQMbBm5xMIhDDo9ROPorGYsFhve9hYsdjvWtAzUeJx4NAqROHqzFR06wu31bGgyUOC0snz5cvbaay9sNtugg19N08jMzCQ7O5u6ujoCgcCQg2lVVdHr9eTk5GCz2aiqqkpVYd86RVqMbb/5zW9S399///09Lt4YDAaKioq49957R2NoQgghhBhmEqCLEZebm8vJJ5/MPvvsw957783bb7/Nyy+/zHvvvTfaQxtWw7l2OxaL4fF4UjPkyarmDoeDgoICXC4XNptt2I65MwL05P637mkejUZZvq6Sj7+po7GpEc1oJhrwQTSMK3cCxnQNg9mGFgsTDQSxpecQDYXxd3ZgNBgxGM3oDUY0xUC4tRElPQubK4twMIRO01BDUYwZLvQWC77WFiwOJ1Z7GmgKmhon6vWhT0/HnpWNr6MNk9GMyexA0enQ6QyE3F0YLXas2dmEujqIRIKoqoKmM9Csmpms07Fq1SpisRjTp0/f4Z9J937l2dnZWK1WqqqqqKiooKysbNDp5KqqpgI5u92eqsJeWVlJQUEBEyZMkCrs48Q333wDwAknnMA//vEPucAihBBC7MIkQBcj6sknn2Tx4sUsXrwYv9/PAQccwAEHHMBFF13EnnvuOdrDG1ZbBzs7Euwm12Eng/JkezGLxYLRaESv1zN9+nT0ev2wjjlpZwTosViMzs5OMjMzU+3e6jY18PHXNdRsbsLocKE3mCAeITO3AFQVo9VCNBwi2NmOKysPvcH87bpyBSUax+hIR4lG8bW2oKDH7EgHVUPVVCIeN/bMbGwZVvyeDkxGC0aTDUVvQNHpCbS1YHam4cjMI+R3QzSOHh2K3oDFYsXX1ozBYsGenks06CPq96OoCiaTFb3ZRMjnpapuE1P23xNTJMCGDRtQVZWZM2fuUOCbPOfJ59jtdmbMmEF1dTXr16+nsLCQvLy8HQ6muwfokJhpnTx5Mk1NTTQ0NODz+SgrK+tVMFCMXa+//nqP27FYjNWrV1NUVER2dvYojUoIIYQQw0kCdDGiLr/8crKysrjwwgs599xzh62a9HiwvWA3uf46mbYej8fR6/W4XK5UCzSz2Zyq8j1SwTlsCQ6Hq996d5qm4Xa7aWpqIhgMsm7dOjweDxUb26jY1ILZlUF6Zg6KpmKwWFHjccxmG5FICG97K3ZXNjZHBtFYFGJxon4/jqwc9AYzvs4OzCYzBrMNNa6iRKP429uwpWdiy8glEvBjMBogDliNmC0m/O3NmGx2rOnZqNEw8VgENRJD1ZuwZebgaWkiHo1gtqehxWLE41HUSBRUFVtGNt6OVtRgCMWZhl5nZMm6er6370yCQT91dXVomsb06dMH3B89ec67B+BGozHV13zTpk34/X5KS0t36HcguQyiO0VRKCgowG63U11dzZo1aygvL8fhcAx4v2L0XHnllcydO5ezzz6baDTKUUcdxYoVK7BYLPzjH//giCOOGO0hCiGEEGKIJEAXI+r111/ns88+Y/Hixfz+979nwoQJHHjggalicRMnTtyp47nrrru46667WLx4MTNnzhzWfW9vBj0ej6dmyD0eD+FwOFUcLC8vD5fLhd1u77WfHWmzNtSxD+cMejgcpqWlhYaGBtxuN6qqoqoqnb4wyyqbiBpsONOziakxdCjEIzFMmRlEo2G8HS1YrA4sVieKToG4QqijHXtmHrYMC2G/H71Ohy6uoTNZMGng3lSNZjBhyylAi8dQiRELBtAb0nCkZeNzt6GaTBgsTlBAp+gIBwLozFbsGbkEPe2EuuKYzDYUBfRGE8GADyIR7OlZhHwegl3tGPQGjGYrBpOFoN9POBRkWV0zB04tRFVVmpubUVWVKVOmYLVat3ueuqe4b/0zKSwsxGazUVtby9q1aykvL8disQzo/Gua1m+hOZfLxcyZM6murmbdunUUFhaSm5srKe9j3JtvvsnZZ58NwBtvvEFHRwcbNmzg2Wef5Te/+Y0E6EIIIcQuQAJ0MaIOOuggDjroICAxo7d8+XIef/xxLrroIlRVpaOjY6eNZcWKFXz55ZcUFhaOyP77CtC7p637fD4AzGYzLpcr9bW9WdGtq7iPhOGo4q6qKl6vF4/HQ0dHB21tbUQiEex2Ozk5ObS1d/LJsrVELJkEo6CoYdRIlHg4gCUzh5jRir+rE5PRiF4xYTBbiMVi+NpbsDkzsbqyE8XaFI2o348xPQery4rf3YHJZMFgchDT4ugUHUFvF2ZHGo6sXEI+N5F4HD16dIoBo82Gr60Z1RjFlp5DJOgjHPCiqKDpwOZw4e1oJRYOYXFkEA8HiQT8oGpoqoo1Ix1NbSEW9GOxOzGZTHxduZHiDAdFuRkUFxfT3t6eWkO+vfXCW6e4by0zMxOr1UplZSUVFRUDLvK2dYr71kwmU2qWfuPGjfh8vh2epRc7l9vtTqWyv/3223zve98jOzubU045hbvuumuURyeEEEKI4SABuhhRsViMr776isWLF/PZZ5+xZMkSwuEw++67L/Pnz99p4wiHw1xzzTX88Y9/5MQTTxyx40QiET7++GNUVaWsrCxVSdvpdFJcXExaWtoOV+YeqzPomqal0vTdbjc+ny/RrzwaxWAwUFBQgMPhQFVVVq7fyAfLamlq6iR3ohUVCHW148zIRW8wE/EHUDQNojH0dheKwYK3vRWL1Y7Z6gJNh6JA0NOB1ZWNIyOXUNCDQWdEUXUoOiMmqx1/0yZi9jSsrjziMT/RUJh4JA4mE7a0LDwdzcTD4UT6ejxKPBolHomgKToc6dkEPO34u9owWawQj6PFVdRYlGg0hjM7l5DPi7e1BZ3OgMVmx2g0Evb5URSFJZWbyc90EQ6HmThxIj6fj8rKSiZMmEBBQUG/AXhfKe5bs1qtzJgxg9ra2gHtM7nf7bVq0+l0FBUV4XA4qKmpoaKigvLy8gHN/Iudr7i4mGXLlpGZmck777zDX//6VwA6OjrkZyaEEELsIiRAFyOqqKgIk8mUCsgvv/xy5s2bt9N7Md95552cdtpplJaWjtgxkumnNpuNn/3sZ+yzzz5kZWXhcDiGlDo8lgL0eDyO1+tNBeWRSARFUXA4HKn6AgaDIdUTvqGpjU9W1VNTv4mgqoFOj6+9DbsrC6sjg1g0DppKyOfBmZmL3mQh6OvCqDdi0JnR663o9OBvS1Rft7lyicdDxFSVWDCC3m7Gnp6Fv6OVaCSMyeJE0etQ0IgFQuiM4MjIJdDVTtDTidFgQ1E0DAYTwYAfLRzBnpZNOOAm5O1A0XREoiFimh7iUQgEcGTkoI+ECHR1YtQbiSt6DCYTKDqiAT86kwWdzkDr5ma+WGtj/qwSQqEQWVlZuFwuGhsbCQaD/c5O95fivjWDwUB5eTmbN2+msbERv9/PpEmT+l3rPpAAPSkjI6NH9fiSkpLdql7EeHHttddywQUXYDabmTlzZio76cMPP2TOnDmjPDohhBBCDAcJ0MWIeuutt5g9e/aAA4WR8Pnnn/PVV19xyy23jOhx9t9/f/773/8ya9YsFEXBYDBgt9uHvF+dTjfi7c/6KxKnaRrBYDBVzM7n86FpGmazmfT09NS6eZ/P16Onud/vZ+nXG/ni6w3oTDbQmwh0NICmw2JOQ6fo0FQNv7sVe0Yu9rRcIqEgekWHFo6jd7kwmHT4utowm62YrS4UnQ4FiPq8GK0unBl5BH0dhOMxdHoTOn0co6Ij6vehGqJYXdmJ9HW/FwUNLa5gS3Ph72ojHu7A7EgnHgni93qJBP2EAkEMdidBnxct2oLZnoHDbiUSDKJocdRwFENGGioKwc0NOCYUYXZmEg0HiQR86BQdazd1UDYhG0VRMJvNZGZmYrPZqKmpYe3atUyePLnXxantpbhv/XOaMGFCqshbRUUFkydP7nP2VFXVHbowZLFYmD59OvX19dTU1ODz+SgqKhrV/7uip+9///sccsghbN68mdmzZ6d+vgcffDDHHnvsKI9OCCGEEMNBAnQx7DRNS31wHEgrte7bj4RPP/2UDRs2pGaYGhsbOfXUU/n973/PkUceOWzHycnJwel0pm4PV1C9s2fQkz3Yk0F5NBpFp9PhdDopKirC5XKlCpX5/X7a29tT44vFYqyrbeKDJWsIqQZUTATdnZiMBgwYiBmM6M0WvB0t2Bzp2BxZaLE46DQiXi9WVxb2tGwC3g4MRhMGgxmdwYTOYMTf0YzR4sCWlkc05CMSDkBMRdPpsLtceOs7iUajuCYUflt9PZZYsx6NYc/4Nn29vRWdyUw0HsTr9RJwdxIO+bFn5KIzmFGiQXKzsgENfyhCR0cnFr1C9sRSdHoLvs5WjGYrepMDBQNaXCMWDGGy2AAdvs52Pl6xge8t3BOPx4PRaCQnJ4cZM2ak1pBv3dt8ICnuW0tLS2PGjBmpGe/S0lIyMzN7bLMjM+hJer2e0tJSHA4H9fX1+P1+ysvLd3rGi+hfXl4eNpst1Rt90qRJzJs3b5RHJYQQQojhIgG6GHb77bcfV1xxBSeffPI22zetXr2aJ554grKyMq688soRG8+VV17ZY/+zZ8/m+eef3+lV3AcrGaCP1IWM5Cw5wMaNGwmHw0BiRjUzM5O0tDQcDkePYC8ajaZS3JP7aGt38+6SNTS0hYlG48TjIRRNQ4nG0NtcGG0q3pYGNIcdi8WJojOiUxS8nU3YHJmJWfRIAE2LQ1wDkxGb3YqvqxmDyYzZlgHE0OIq0VAIvRFsabn4PW0E3B3o9EZ0cTWRvh7yQySMLS2LcMCD391ONBwhFAqiGK2EfG60eJS03Hxc6RmggNlgJx5OpMlHoyGMagCXK4NAwE/r5k1kZmSgVwwoigG93kjQ3YoxMx9HRh5BbyexaAAUA52+EMs3NDFvWgF+vx+j0Uh2dnYqoF6/fj1FRUWpqukDTXHfWnLGu66ujurqavx+P4WFhal9bquK+7YoikJOTg42m42qqirWrFkz4MJ0YmQFg0F++ctf8vTTTxONRoFES74f/ehH3HHHHbIOXQghhNgFSIAuht3jjz/O7bffzrXXXsv+++/PnnvuSV5eHmazGbfbzfr161m6dCkej4dLLrmECy64YLSHPCxGKgug++z2cB0jGo32mCWPxWJAYp1zfn4+aWlpmEymXs/TNA2Px4Pf70/dFw6HWfzlBr6u7cDr9YJOAU0j5HPjSM9Fb7QQ8ntQI2EU9Oj0VvQGHX53MyarA5sjG4ijqvHEmm6bE3taDn5vO8Qi6HQWFJ0eo9GM3+1Gr49id+YQDXsJ+TwoqoKmKJhtLgL+zYQ8HZgsaYSDbtpbW4gEvETCYSyuLDRNhxLxk5s/AR2ATgdqnEgoiNGeBjrDt7P7LqyOTOLE0aHR2bwZt8FCeno63q4W1FgUW3YeajxGPBpG0cBodaLF4wQ9HpZ/U8WkgoxUqrvP58PhcDB16lQ2bdrExo0bCQaDFBcX71CK+9b0ej2TJk3CbrezceNGAoEAZWVlqcB8KOnpdrudmTNnUlNTQ2VlJQUFBUyYMEFasY2ia6+9lsWLF/Pss8+y7777oigKS5cu5frrr+f666/nwQcfHO0hCiGEEGKIlK6urpFd3Cp2W5WVlbz66qssXbqUTZs2EYlEyMzMZI899uDQQw/lmGOOwWg0jvYwe0hLSxv0c6PRKIFAIHVbUZQeqcyD1dHRQXV1NXPnzu23INj2qKqK3+9PBeTJcdpsNtLS0nA6naxfv55Jkyb1WxwsGAzi8XiIx+OpfVbWbmbxyk00bm5BrzehKiq+rhbsrlwUdMTVMDoUIgEvcb2ZQDCEw6rHZDSjagp6owm93oC/qxmj1YnJYiMc9KLTGYlHgujNFiwWJ57OZvQGPQaTHU2NoOhNRPxeFL2CPS2HgKcDt7sDVdNh0OuJK3oiIS9aPEpGbiHEY+iIY7bYUdU4RouVaDhEOOTFmZ5HPB4n4u/CaLERj6sYTBY0NU7Q24HVlYmqKTQ1VGI02XDYXXh9btKzc1FDfowWO1a7C19XC/FYDA09FquZ3OxMTjpkBhaLmaysLHJyclI/v7a2Nurq6rDZbOTk5FBbW8ucOXP6vCgyUF6vl6qqKnQ6HSUlJWzYsIHy8nIyMjIGvU9IXJRpamqioaEBp9NJWVnZdv/fut3uIR1T9K20tJTnn3+e/fffv8f9S5Ys4fTTT6e2tnZ0BjbMnE6n1D4QQogRlmyPK8YemUEXI2by5MlcddVVoz2MnWYkU9yhdwG37QmHw6lZcq/XSzwex2Aw4HK5yMvLw+VypQKt5Fj7GnMsFsPtdqdS3wE6Oz0sXl7L6rW16AxWdHoTfk8rFnsaNnsmaBoocSJeN1ZnFlZXDp0dTcTDQXSWdNCZMZvM+DqbMJpsWOxZqGqEeCxGLBRGb9Jhc+Xgd7cSiEUxGq0oiobeYCDo8aIpYexpWfjdbbQ21RMLxwhHY5hsTmKREDpdmLy8QnRAPB5DbzQRiYYxmqyEI8HELLkrG6s9m6Dfi9FgQNH06HRmFJ1GoKsNiyMNe1oekbAbg95EZkY+gWCIYCRCJOAnHgrhSssjFOgi5HWj15nQW0zE4iohv59NoQirKrOZOzWXQCBAV1dXqod1dnY2FouFqqoqNm7c2OPnPFhOp5OZM2dSVVVFZWXlsOwTEr/XBQUFqcJ0a9asoby8fJvLV8TIiMVifaaxWyyW1IUzIYQQQoxvEqCLETGQtHWDwUBubi4LFizgsMMO2wmjGll9pf4OR1p6MsjaXsCfvBKaDMpDoRAADoeDvLw80tLSsNlsfY6nrzZrmqbh8/lSldshkSWwas0mPlm6johmBMVMwNuB2WTBaHSgw4SmA39XMyaLC4szh1gshB6NWDgImhGrI4uAtxU1ZsJgsqPT6dDrDYR9neiMKnZXDpGQh7Dfg4IO4josThc+dxuhYBt6vZWgvwu3208sEkTTNDKyJ6J5O4n6OsmfOAm0OGgKmhYlGgpgycwFvQFvZwsWmxOrPR0tHgc01FAYncuGyWkh4G7DbLFjdWaixqOo0QhqREW16HCmZ6OpmwkEgphtGcQi4UShOlVFVcDmyMTnbkeNh1AUI0a9iS+/rqIwx46iKJhMplSqe/LnMmPGDNatW0c8Hqejo4Pc3Nwh/a6YTCamTZtGTU0NnZ2dtLS0DNtspMvlYubMmVRXV1NbW5vqViB2nsMPP5xf/OIXPProo0yZMgWA9evXc+2113L44YcP+/Gqqqq46KKLaG9vJy0tjUcffZTp06f32u7JJ5/kgQceQFVVFi5cyL333pvKFnnrrbe46aabiMVi7LHHHjz22GNycUcIIYTYBskhEyNCr9dv9ysej/P111+zaNEi7rzzztEe8pCNVLDS3wy6pmmEQiGam5vZsGEDK1asYMOGDXR0dOBwOCgrK2Pu3LlMnz491ZprW2NUFCV1jHA4TGtrK16vF03TUFWVhs1t/OPfi/lgSQ3BqEIw6EGNhVBUBRUjis5AZ+smfB3NxON6wiE/xOKEfV2E/H4czlzikSCRoBdF1aNoeqxWF+Ggj5DPg8WRhV5RiIXDxMNR4tEYVnsm0WiY1oYafN4A7i4vXV4v0XAUgxqjYEIZBflFGJQoJqMRg8GEXmdAVVWCvi4MBjtWRxY+dwfE4xgNVhSdCUXRE/J2omg6bK5sgj43aiiI0WAHnQ5F0REJ+IjFY9ic2cRCQUK+LsxmF06HCxQFr9tDR2uiHZuiKQTc7YlidkYHeqOeUMBHyO9jydeNqKqaymRIrveHRECdn58PQH19PfX19UPOvNDpdBQUFADg8XhYt25dqpjfUJlMJqZOncqUKVMkOB8F9957Lw6Hg/3224/i4mKKi4s54IADcDqd3HvvvcN+vCuuuIKzzz6bZcuWcfnll3PZZZf12qa2tpY777yTt956i+XLl9Pc3MxTTz0FgM/n47LLLuPpp59m+fLl5Ofnj8g4hRBCiF2JrEEXo+6dd97h8ssvZ82aNaM9lCGtQe9rLc9wzF4GAgHWrFnDjBkzsFgseL3e1FrycDiMoig4HA7S0tJIS0vDYrEMKnhavnw5ubm52Gy2rXqaB/jg4woq630Ew0Fi8QioUQJeNyZrGmgagUAHBp2RmKqiNxjRNPB2bcZiSQOdDk2LYjTZ8XncFEwowWJz4fe1oOgM6HUmUFQMRitBfyeapmEwO/B0tRKJRlBVDT0atrQctFgInRLHmVZAPBoirsbRKRrxWISoYiLgcWOz6LA6s0GDuBpGr9MRCYWwOtNRVZWApw2zLQ2D0Uwk5MVgNBIJhzGZrRhMFnzuZozGRO/1cNCNYjCgReOg12GxOelqa6Kzq5PsvELcHS3oDXrS0zJQFBWzLZ1wyEs46ENnsKDXGdCIsf+ek5k3qzD1M0qmugO0trZSV1dHUVERGzduxOl0Ul5ePuh6A5AIjNauXcukSZPYtGkTmqZRXl7eow3gSJI16CNrw4YNbNiwAU3TUhdMhltrayvz5s2juroag8GApmlMmzaNd955h5KSktR2v//976mvr+eee+4B4O233+bBBx/kjTfe4OWXX+aZZ57hn//8JwBr167lBz/4AatXr97msWUNuhBCjDxZgz52SYq7GHX777//LpviPlSapqVmP+vq6ggGEyndJpMpFew5nU70ev2QjxMMBmltbU2lWcdiMVZ9U8/nyxtobm1H1WJo8Rh+TzsWeyZ6o42gvwM0CAeCRA0xQKGjqY5YOEIs7KctVo0tqwCDTofBGMJgcYEaIxR0o1OMKOgxmW14OpoIRztQdFbCITcqfgzEMBoMZORMJBoNoKlhzM4MNDWGGougxWPEokEcablEI2HcrQ2gM2GxpYOqoWlxooEARlcWVrsVv7sVs9mO2ZqOommosShqNEpcMeFwZuPpbMIYjWCxpKOqUaKREFpMRY1Hsbuy8XvbCHjaMVucGA1+FDTS09Job2/FZ7RjtRiJdjZjcWRgtRkJhxPZAnq9ga/Xt1I6IaPPVPfkMoi8vDysVivV1dVUVFQwefLkQbfNSmZCOByOVFr6+vXrKSwsTLV3E+PXlClTRiQo766hoYGCgoLUhSJFUSgsLGTTpk09AvSNGzdSVFSUul1cXMymTZv6fWzz5s2oqioBuBBCCNEPCdDFqHO5XDzyyCOjPYwRMZh05VgshsfjSa0lT/Y7BigsTMzCms3mYQuyIpEIXV1d+P1+zGYzoVCIjZta+GRpDVXVm1B0ieDf62nFbHaiN9gI+DuJxWIEvV0omgLRAL7OzcQjEaKhIPFv12VHY1Fi4TDphZOJxUKEuoI4rAasNid6g53O9kYi0Qb0OjMaMSxWK1arFT060rJKCYd8RMNejAYTqmZErzMQiYQJh904XPnoDBb83k5MBhNGvZlwHMLhKFqsC6stC0daNgFfJyajBYPempixVwwEfK0YLU5szhxC/nZCvhhGox1F0dDpDERDfjSi2B2ZBAOdBDxtGPRmIEZcg3hcJRb0kp45AZ3OTHt7I9GgGYfDCXHQNBUtGsFkcaKqKp6uVv73uZ6Tj5yN2+3GYDBgsVgwGBLp+MmfpcvlYsaMGVRWVlJRUcGkSZMGVYU9GaArioLRaOzR3s3v91NSUjLkizpi5/vRj37Efvvtx89//vMe9z/00EN8/vnnqdTy4TLQwpfdt9t6G7kYJIQQQuwYCdCFGCb9FYnbHk3TCAQCqbR1n88HJCozZ2ZmpqpnFxQUDLllVneqqtLe3o7H4+kWpAdY8kUVK1bWEkdPPKbh9TWi1xmIRmKEgi3EA178HY3oNSAcJBzyE0MhGougaqDoDYTCATRFw2CwEg968W2uBYsDFBNum4vOzk6MRgtGgwm7xY4tLYd4xI+mxrA58olFgwR9bnSKgk5VMZpshEMBfO4WrPYsLDojkbAPPQrE4hjMVpxpOoLNdXg6o7hcmcRiIdCMoCmg6LFY7fjcTRjNdmzOXKIRH5GQH01V0HQKFqsDv6eNeCSM2Z5BLOIjFPSgQ09ci4FixNPVRcDvxmxJw2xxEvB1YTQYyUzPJhSO4/Z6CPg6yMouwu7IIRDoQI1FQTHS2hHgq28a2Wf2RPx+PwaDgezsbDRN6zGbaDabmT59OrW1tVRVVQ2q/3gyQE/uV1EUioqKsNvt1NbWEgwGmTx5Mmazedh+n8TI+/TTT7nxxht73X/kkUfywAMPDOuxJk6cSGNjI7FYLJXi3tDQQGFhYY/tioqKqK+vT93euHFjapuioiI+/vjj1GP19fUUFBTI7LkQQgixDRKgi91CKBTivPPOY926dVitVvLy8rjvvvt6pGoOB0VRelVC70s0Gk3NkHs8HmKxGHq9HqfTSUlJCS6XKxU8de87PhTJdPloNEpnZycdHR2pfUejUTZUNrJmXSuqZiEei+D3txOPhYkFPSjxCGrEj9/djk5JzPqqahxUhXA0jF7Ro9ebiMaCKFocg84AqooWi+INBQj4PNhc2SjOTPy+TmwmC3ZXOjl5k/B7O4gG3VisaajxCLFoBDUeIx6NYHHmEIkE8blbMFscmM0uNFUDVSUaCmCyZ2OxWfF6W7FaHORkFRMI+fB4utArUTKyCrDZs/B7WlBNUcyWDDQthhqPoUViaDoNmyMbv6+VgLcdo8kOaoR4NIwaixHwBzBYnHi73ESjDVis6VhtaZjMZvRAJBpFb03DpBiIxzqxWWyEglE6uprJcGWhQ4fR6iIaiRD0drF8VZxJRVk9Ut37qvSv1+spKytL9R8PBoNMmjRpwLPeWwfoSZmZman2bmvWrKGsrGxIdRfEztW91WF3qqoSCASG9Vg5OTnMnj2b559/nkWLFvHqq69SXFzc62/mSSedxDHHHMO1115LTk4Of/nLXzj11FMBOOKII7j66qtZv349U6dO5c9//nPqMSGEEEL0TQJ0sds455xzOPLII1EUhSeeeIIrrriCl156aViPsXWAnpRsWZYMypMfpm02G9nZ2aSlpWG32/ucWRpom7WtxeNxIpFI6isajRKJRFI9zWOxGOFwmKamVj7+pIKK9Q3oNC+KGkSNBYiEvOg00OJxgqEwigJaTENR4ijoCIdD6BUDJqOJWDxKXI2gKKCoKjpVIRQLoarfjl/RoYYCxKNh9GmZWCwOwoEg7a312G1p6HRm9DoDsXCISLQTpyMPvS6K39uByWjCoDOh15uJRyMEvW1Y7VnYbdkEA25MZjNGnRlIdAcwKHFsFivhiJHOtibSM7PQK5b/b+/Oo+S+yjv/v++936W23tTa15YtyftuEkMwJGZsbJxAMjlkwg9IHDN4Jg4OcEJOIAwhkxBMBgIEe7CBCUnIMkBMMBAwDJAFA8MY2xKWLWvfd6nVS23f9d7fH9/qimRLtpZudVt+Xufo2F1V/a1b3W25P3Wf+zzgDJ7xiVp1bJJQqs4iikZpN4fxVAlIcQ6ajSatRh2/1EuapNDeT6Xah+/1EpZrOJcRt8cxpp9SpURjtBjdFoR9hMriewlHDu3FZR5z5s6jXR/GKdA6xFrHv/xgA79082WMj4/jeR5RFJ1w9N2CBQuedS69VCo97/f+6BL3Z6pUKlx00UVs27aNTZs2sWjRIubPny+lyC8A1157LZ/61KeetVt+//33c/XVV0/683384x/nzjvv5KMf/Sg9PT3cd999ANx1113ccsstvOY1r2FoaIj3vOc9vPrVr8Zayyte8Qre/OY3A0Wzt0984hO88Y1vJMsyLr744u41hBBCCHF80sVdvCitXr2a22+/ndWrVx9z+5nuJtbr9W44StOUNE1ptVqMj4+T5zme59Hb20tfXx+9vb34vn9S133sscdYsmTJCedkH707PhHIJ9ZhrSWKIo4cOcLIyAhpmtJsNhkdPczjj69jz+4DNFvDtMdHQUGeWpTS4BxploEFZx1GmyKYpzFGe3hK4VwGGOI4xjnw8UjzBK0NWhmU1qAMWZ6glMbDp2f2PLzB+fT1DuJ5HnPmnUee5WRpnVJ1EJvnWJtjlCNJIiq1WWR5StQ6QqncD8ojzyOMMSRRiyDswfMC6uP7CUo1/KBMmjRJ05wjw4cIS2XmzltMuzWMVoag1E+WNcBpnMuI4hZa9zA2dpAsbVKuzEbroolVpVpG5Y6w2kuaJcStUcqVWaA94vYoYalMlqZov3iDod04jB/2oD2PfXu34wcBfbV+jG9IsgybRHhBiZ+65iJecsV8arVat9z9sssuO+H3v91us3nzZrIsO6ld7/3797N3797nDG3OOfbu3cu+ffvo7+8/pR365yNd3KfGmjVreN3rXsfixYv5mZ/5GQB++MMfsnv3bh588EGuvPLK6V3gJJEu7kIIMfWki/vMJTvo4kXp/vvv5+abb5706+7Zs4dvfetbfO1rX+ONb3wjl112GdVqlXnz5tHb23vCWeR5nqOUOuEvpVrrbuB2znXD/0Qgn5it7Zzr7pxHUUS73WZkZISDhw4yVj9MvXWEZnOEvbv2smvXMPWxOs5CnlmazXF8HeD7AVmWgnNkucUzHsbTZGla7FD7BpdbUutIkhiFwTdF2TsafBOitCbwvE4H+pzABKRpTOosSeMIuc1xlUFSZzmwdzMDAwsJg17ILc7mJFGdWs9cQh1Srx+mHNQI/D5Agc1Joya61Eu5Ootm/TCBXyIs9YHKsXlOlqQY47Nw0Qr27t3Mvj1bmTUwD2McadomajZot9uYoId2qwVujJ6eQbzePvxO+XoctyiHfSRpQmPsEKVyH6XSLLIsxvNycGCdIij10B4/iAuqVGpzSZJxbOYYHJhDvT5Oo5XgmxzP9zBeGYXi8cc3smB2mcWLivD9fGGkXC5z0UUXsXXrVjZt2sTixYuZN2/eCXe9n3mu/XiUUixatIhqtcq2bdt4+umnOf/880+7c7yYeldeeSWPP/44n/rUp1i/fj3OOW699VbuuOMOBgcHp3t5QgghhJgEEtDFi86f/dmfsXXrVj72sY9N+rX/4i/+gh/+8Ie84hWv4JJLLuGiiy6iWq0+7+cNDw9z8OBBarUaAwMDeJ6H1hrnHNZaGo0Gw8PDaK27Ybw4B267IT2KIprNJu12m9HGKIdGDnDw0AEazSPEWZMkiqiPNNi7Z4yxIw1QFLvkUQvf81HaJ3MW7TJSm+H7IeXAJ0kScqvIrSXPcrT2iJMIg8EPQxQKoxXkoJQm9HziOCXJU7RnIC92o7MU4rSNy6vQrnNk51r8gYVUa320202sBUdEpTKbSmU2UWsU3wvxVYDSPhpHu3GYUqmfWs8cWq1RlLV4OkQpg2dC2q0joDOq1UGi9ghZ2mDOwDzGxo4wOlbHZi3SJKJcm41SPhAxZ3AWWin8sEqWRsTtOn5tLuVSQKN+iDCsEfg9WOvQ2pElbYzqpVyZRatxCBdmBKUBrE3IshiXWVAp1dogzuY06nXahFTI0b4ij1OcF/DDR3fzC7PKjI2NUS6XiaLoOcvXPc9j5cqV7Nmzh927d9Nut1m2bNlxg/ipjLHq7++flM7x4uwYHBzk93//96d7GUIIIYSYIlLiLl5U7rnnHr70pS/x4IMP0t/f/6z7z7TEvdFodBuvQdGR+/nODGdZxrp16xgbG2N0dJQ8z1m0aBG1Wg1jDFprnn76afr6+pg9e3b37HiSJLRaLRqtJsPNMcabdcaaY4w2R6mPj5LFEXmSYvOcuNXiyME6R4brOKdwuSVPU7QxKCxaG/I8JU9jSqUyRqvi/LVTJEmEpmgCl2UJnudhTBGYNYo0z1BOE3jFzrtDdeaMW3w/LM6n5zme8YnbbZTWlMISgTLUBhej+hbiedDbO5dqbRDjGYxWxHGzKCcHmo3OiDcTkuUttPbI0gjtlSmVKjTqBzDaxy/1YtMmFkXcbhFHMZgyreYouISwPIDnQ6lcITQeWZ4QlvvIsog4GqdcGcShSOJxwrBMmiR4QQmFJmoN44d9BGGJVusIvhfinAbtMCYkicdxTlPrHaDVOAKA0WUsMe12TqMxgmegt282Ns9xKuWSC8/nwhVltNZcc801zJkz56TKzIeHh9m+fTvlcpkVK1YQBMEx9+/atYuxsTEuvfTSk/7ZzfOc7du3MzIywvz581m0aNFpn0uXEvfJ86Mf/YjrrrvupB7baDTYuXMnF1988RSvampJibsQQkw9KXGfuWQHXbxo3HvvvTzwwAN85StfOW44nwxa62MC+sk0dtu1aw+7DzSJGnWODB9geHiYJ598klmzZtHb20sQBOzatYtyuUytp5fRKGI8btNI2jTTiHbSJo9joqhF2mqRxRHO5mBzsjSlMdLi8ME6uQXlNFme4huN87wioCtIkwhrc7QXECcp1jnyNMX3fUrVKnmW4WmF8UsowBhNmmbk1uJ5flFWn6U4LM5CKQhIVUZqY4wXoI0G6/DDgFbUwqQG5WsaI/sIbYbtW0C73cDh8D2Pnp75lEuDtJuHCcIqYdBXzBa3liyJ8TxNuTxIs3mQKE/wTQ+5jWjUx4iaRRO8sDyLLMsIaDJ79lyMUTSaLaJWE5vG1OYuwylNs36QUqmfUthHlkR4xkNZh3MGP6wVawiqlCpzSNI6aeJQeFgHYVih3RjGqqLpXNIepd0YxdMB1qZ4nk+apBjVZtas+YweOczw8H56qr34QYnNWw6iqHHesl6azSa+759UqfLg4OAx3dhXrFhBrVbr3n8qO+gTJjrHHzhwgN27d9NqtTjvvPPwPPnfxHT6nd/5Hfr6+vjVX/1VbrrpJubPn3/M/Wma8vjjj/PlL3+ZBx98kA9+8IMv+IAuhBBCvJjJDrp4UdizZw+XXHIJQ0ND3SAThiHf/e53j3ncme6gt9vtzrnrQhAEz3mmt16v8/TTT3P48BGe2HSEbbv3kkbjRO1xxseOEFZCqv2DjDcb4HuElQrWZVibYm1CblNcmpInMVmaQJaDsjjraLfaDB8YJ88VyjpsnqE0uDwrStqdIoobxW3WoTo73w5HGAT4niG3GUYrbGYx2uGZgCiKAYfRxWxkjcK5HOcUpSAkTVKstXjaYPO82J23rgjLJiDLM9rtCN83hH6JaljB1OaiKj309y+mXO4hCAyVSi9pmuF5PsaEtFqH8bwyYdhDHI+glEeSZCRJG+c8ms1RlHL09M4Fl2B8QymoYG2CH9TI82Jmu6NCvT6KMSmzBhfgcgdGoVAk0ThhuQ9jAlqtIpiDwdoE7QVkSRutDaVKH836IZQyBKUe8rQFSqOwZHlGWOonSZpF07nqYKc6IUJpzaHDh1DKo1z2CYzBOcMNP7ecyy9bxeDgIAMDA/T09JzUz1uapmzZsoVms8nSpUuZM2cOANu2bSOKIi666KLT+jkeHx9n69ataK1ZsWIFlUrllD5fdtAnj3OOf/iHf+Czn/0s/+///T/mzp3LvHnzCMOQsbExduzYQa1W4/Wvfz133nknS5cune4lnzHZQRdCiKknO+gzlwR0IY4y2QHd9/3nDDe7du1iw4YNjI6OsnfvXnbsGmb7vjpj9WHazTpR3CbNE5zShD0Vaj01lAaU63Q7z1CuOButjMYCWZwwenCcOE6xucXZDKMdNktRnRnmWRoXgRyL5/t4xpCmMVprwsBHOYtWurhNKfzAJ0syFBZlNBpQyuDyFGcVvucV68lBKbC5xfd88syS2RTfD4pnyx0aS5La4s0Ep9CBpr/Sj6nMwe+bR7lcRWuPWbOKsN5oHCQIKnhehSQZJ00t7XaTJMnw/RppOkYY+vT0zEOptNNsT5HnKb7fR5q2SNMG5fJsrM1JsxY2d4yMDBOGNfr6eknTOmHYj+f5tNojBEGJPKc4X+/5JHEdjKFS7qfVHEZrgzElnItROsDZhCSJqfXMIYqbZGmLsNSLsxZLjq8McdIkKPdh84wD+7eTWejrnUWStpk92Mdv/NorKZdLzJo1izlz5jyrbP1ErLXs2rWLQ4cOMWfOHJYsWcK2bdvIsowLLrjgtH+W4zhmy5YttNtthoaGTqkJmQT0qbFv3z4eeeQRdu/eTZIkzJo1i0svvZQrrrjinKp0kIAuhBBTTwL6zCUBXYijnGlAj6KIOI67H3ue95xN4iZGXa1Zs4annnqKkZERDh06wvZdRxhvtonTFnG7RbPdwPMMPbNmUy5X2BsZrDEsn+WhNFiXkWUpY4fH2TeWQ5bQ52XgIM9i0B5OAVmO8RRYix8E+IFHHkdoozsBOyMMS2RJhDEeXhCQ5ykGh3OgFfi+RxrHoCZ2yS1gcLYYj1YKSuS5xeY5njFYB8pRrCXP8Y2PtZY4TYuGajhKYZlSWMKU+in1L6a/fyF53mJgYD5KhcRxkySBOB7DOU1Pz2zyvEUY+pTLfVibAgalMpIkplqdTRy3yLIGpVI/eWd0m+8boqhFGPaSJAmHDm0nDPuYPXsOadrC90PSNEVrjyCo0GoNo7RPpdxHqz2KMQqFj3Mxnt9LmjTI84RyZQ5Z1iJJW5TCGnmW4rRBa0XSLnblldG0W6OEQRVnNSNjB2k26xijKZd6eOUrr+K6n1pMtVplYGCAOXPmnFJIOXjwILt27epWiGitWbly5Wn9HE+w1rJjxw6Gh4eZO3cuixcvPqk1SUAXZ0ICuhBCTD0J6DPXufOWuxAzwKk21Tp61JXneWzduhUAz1PsOzDO7n3gSoosy0mSiPrwIXa7kJG8AkGJuJEzr+xo1VukiWMkSmmlCowiJqWv7KNNgHIWpTU6cPieX+yOx23AotBYq8idJnfgOYcXVnEuw+Y5OEfuHJ7vkcfFSDejPZyFTGlQ4FxCGJaxeUaapxit0K74BdtZS5oVZfXG08R5imc0ARodeLTjlGa7Dgr6/Sbx+F7GlMJaaDbbVCpzca6N75eYO3cpWRbheRqt+3GuqApwLiJNm1Src7FW02weolSqAcWbBVpDkjTxvAHCsIcoGiEIasybN8Tw8EEOHTpItRqgtU+p1EezeQjICMN+sqxJHDfwjCHPc4IgJEly2q1hyuVBIKbVOkK5VMYonzyn6HQfjxOW+ilXBmm1RiiVKvi6hssztPHoqdbQKFqtiKhdZ82azSyY18PQkML3fTzPO6Vd67lz51Iul9myZQt5nh9zJv10aa0ZGhqiWq2ya9eu7rn0k93dF5Nr165dx71dKUUYht0jDkIIIYR44ZIddCGOcqY76EmS0G63ux8bY046KOV5zpo1a/jBD37A/v372b9/P2NjdXbubnHg4DCOhPEsZ7yVgLWgPTAeJU/TGzpGE0XifHAp+CFoRdnEDFYMeZrgGR+lDTaL0KZ4b05pg+drbNwmtxaUIvA0GkOeF5UAvh+SpQlKgdEACs8rur6TW/zAx+UZzlk0qujY7nnYzrn3wPdwtmgSp4wiTzM85YFSpEmMVh5JmpJnGaWghFZgSoMEPYsol0v09PQzd+5yIEMpD7A4l+P7vcRxkzxvU6kMkqYxWRZRKpVJ0wRjSkBOHNcpl2cBhnb7CKVSlTwvZsprHRDHo9TrMVqH+H5KtdqDUiWsjdA6ABKyLKdU6qfdruNc3NmVT8hzSxj6RFGLIKh1xt6NEQR9KOURx2OEYZksy1BK4XllkmQM5zzK5V7a7REOHz5AlkGlEjJv3iJ+8RcvY/bsHgYHB+nv76e3t/eUfgbjOOapp57CWst5553HrFmzTunzT6TRaLBlyxYALrnkkucsqZYd9KkxMDDwnG8CTpxF/+AHP0gYhmdxZZNLdtCFEGLqyQ76zCU76EJMoZPp4j7BGMM111zDokWL+Pa3v43WmiA4jDEKTzd5apem3kzA5pBn4BoARF5AFPvF4W/jAA1xC7ShTc6I0wxUKuQ2RTuHNQati93sPG6RWoVWBmcducvJsxzfd/j+xI64QxsDrvhnlqakSYwxHjmQJEXzuTyzhGFYjEDLYjzfoI3BOoXWltzmaOuhPY8ojjHGYIHUxmhdrD3NI8qlKp5q4rtRwnAIyBgbO0Ct1gsklEqzSJJxougwYdhHlkEc1wkC0+mgb1DKJ45HCcMegqCPKKpTKpUwxpBlOb4fEkWjGJNTqQyi1AjtdptWKwca9PSUAEeSNKhWZ5PnddrtYUqlHtKUzrg5RZq2sTYkCKrE8QhB0EsQ9JKmbYKghDEeWZYTBDWi6AjWWoJggCQZJ0nq+H4J3w8xRhFFbXbv3sq//EuNX/iFCxgdHcUYQxAEzzuq72hhGBKGIdZatm7dSqvVOqORaRNqtRoXX3wxIyMj59R55xeSv/zLv+RP/uRPeNvb3saVV14JwJo1a/jkJz/JO9/5TpRS/PEf/zFhGPLBD35wehcrhBBCiNMiv2UJMYnONAQBzJ8/nze+8Y088sgjfPvb3ybPc0ZHR6kOOurOQbPo1I6zYDNI2pCnYELIAd0J6xgwYbHTm1lcp0O70R5J1EIbg1YGUGAMGHA5YAy5s+jOLjM2xfkBeZqQxxnKGHAGnEJ5HnkW45sSnvJI0oTANyjjkVmHcpB1xrVp3xDHEVobUI4sj1HaQ+dgPE2p3EOr1aTZblBxVbQ+RFt7RGEvlUqC7wd4XkAUDRMEVbIMsizGmCIowwCeZ2i1DhOGvZ0GcglB4KFUTpqmBEGVdnuEony9lyRpkKbFeLNih1sxPn6INI2YPXsJzjU73dwrZJkqyvuNKkrevT7CsI8oGiEMa/h+jSyL8f0QpWzn33uJ42HiOCcM+0jTBnnexPM8smxil19jTEqlMptGo87atWupVBS33HIJo6OjaK2ZM2fOKYVi5xx9fX0EQcDu3btpt9ssX778jIO17/vMnTv3jK4hTt9HP/pRPvKRj/DKV76ye9sVV1zB0NAQ/+2//Tcefvhh+vv7ede73iUBXQghhHiBkhoyISbRMwP6qeygP/M6V1xxBa9//eu59NJLWbhwIVcvrrFkySyY0w/VfvB6wK+BqYB1kLYgqUPWKnbZsxYlWvSSYqMYrMLGKXnq0KYEBOCF5M5hswxlinPPLs+J44QoirBoMgxJmqM9H4yH1iEoQ2pzQKNMSJrEWJthnSLOIHOaJLVYVeyiR0lElGTkzmJtjgmKGexGK4JyiDOQ2pSgFKB8Q2JjmlGLVmsPKm+SJAnj4weJonpnxJzFGE2SjAOGIOih1TqCUjlBUCka2xmHczFZluH7PWRZkyxrEYa14ny9TdFad0J+CaUcvp8xMDCPKMo4eHAXxmiKknqH1gFpOlbMR/drtFojQILnlcjztFOSW3RzD4Je8jwlTcfx/d5uYDfGkCRRJ5j7RNEoSoX4fi/GKHp7qxjj8+Mfr+fhh58iiiIajQZHjhw5pZ+l4utjmD9/PitXrqTRaLB+/XqiKDqtn0cxM2zcuPG458xnz57N5s2bAbjwwgsZHh4+20sTQgghxCSRgC7EFDqVUDWxU75jxw7Wrl3LU089RbPZ5DWveQ1veMMbuOKKK/iln17BhcsWwKz+4o9XBlUG1QncGIgjiJt4LqdHW7T2ijbqnTPmznYav6URadTCuaI8PUsz0tyCNnilGrnqBHPtoZTCOk1uFXHSBgfWaaIkwVpHjsGZAOUF5LklzTMs0I5i4jzHKYVz4IcVHIbcKUwQYj2FNRbjGTAOExiCwCN3jjzPiOM2UWMPSXSQen2UdrtOs9mi1RoGNEHQ2ylVtxgTdIKyT54X4+5KpV6yrIW1bXy/Sp6nOOdQynW6tldxDqJoFM8roZTCGMvs2QMkSZsjR8a759SVAs/rJUnGMSbHGJ80TfE8H2vjTll7H9bGZFmTIKjgXI61aae0vonWIcYEtNtHUEqRJDlJUicIFMaA1j49PT2EoeX739/CY4+tZ2xsjEajwcjIyEn/LFlru28W9fX1deehP/3003I+/AXs2muv5fd+7/fYvn1797bt27fznve8h2uvvRaALVu2sHjx4mlaoRBCCCHOlJS4CzGJjlfiXgTC45e+R1HE2NgYY2Nj1Ot1nHOEYUh/fz99fX3dZknVapXBwUGyLGPpD37A//juDg4eOgyVOjTrUI8gbhSl76SgMyouImpHeGmAMh5aUeyAYzvn0AOczdFonALnFMYrY/MYbI7SiiSJsc5hFDiX4fkhaE2Ow3g+WZZitQKrabfbKKVwWMgtnvFQnR10Yww2S7FOYcISzqVYVcxSt3mO7yvyxJIkCcYYvMCQZRlZmpHZnD4PvFAxPm7p7/dwzqLUKLVaH1lmSNMMzwtJ0zHAEYa9RNEYeU5nhzvB9ys4V5wrL5d7ybJG55x6mTSNyLKYIAiIogZB0Mfg4HyGh/cyNpbR09NDmrYolcrkueqUyxcN35LEEgQ9xPEYWaY6TeEiwOvs8jcolXqBjHr9IM4FtFpNsmyEIAjxvOI4QnG9cZyDUqmXNE343vc2E4Zw8cWr8DwP3y8C/POx1h7TZKtUKnHRRRexdetWNm3axKJFi5g/f/6kHMkQZ8/999/PHXfcwdVXX01PTw9KKcbHx3npS1/Kpz71qe7j3v/+90/jKoUQQghxJiSgixeVLVu28Ju/+ZsMDw/T19fHJz/5SS688MJJu/7zBZ6JjpkToTyOY5RS9PT0sHjxYvr6+gjD8FnX0Vqjtea6667ji41Lqe/6Aej/B2P7wD8ElRY0QhhvAB7YiHaeUyYnj2O0l4Px0S5DeWExPo0cpR1ZGhfnysnJ0xRjNLnNUb6P9hRpmkEpwMMvZqEbTZrE5E5js5yo3cIYD4VCa40fhDgvRymD0XTmqCu052NtBhacgyzPim7vOeSu6PzurMMp3dlN9nBOkaUJY2NjDA4UbwzU65pabQBrY4wJCIISSTKO75c7QbmBMRrfD0jTmCCo4VxCHE8E83HieJwgKJGmbfI8w/c9oqiBMX0YUyaORwjDXmbPXsDIyGHq9YxSKUApD9+vkSSjnbnqfaTpOHkeEQRl0rSNUsWbKmk6hu/3kWVtDh/eCxiSJEbrNpVKFdCUSn34fkiSjGFtUTrvXIM8zwgCjbXwwx/uwbmMPM/RWuN5HuVy+YQ/Y865ZwV0KJoQrlixgr1797Jnzx7a7TbLli3DGHNaP+vi7Fu8eDHf+MY3WL9+PVu2bME5x8qVK7ngggu6j7nhhhumcYVCCCGEOFMS0MWLyjve8Q5+/dd/nTe+8Y185Stf4a677uLb3/72lD5nFEXdUF6v1zudvAP6+vq6u+TPF5K01lhrec+P4C931Ghf8GpYcDVs+TfYvRbG90NpHHrqRUhvxqRZC01G2cRYW4wx01qTeQlKG3JijB+gFDirUMagcYBBaQN5jucF4BxJFJNphXI5zmmcUug0wQ8C/NBD6WL0ms1TcKBNQJ7FgMY5RZykGFMEboXFBCGQYZVDBwEuy7E5FE+bgTE4BdbmYDziNOXw2Ag9PWlx1t1Cb+9sxsf3MTAwD9+vdErMFcYY0jTC8ypYm3W6pdewtkUcNwiCkDSNsNbi+z5xXMeYXjyvQhyPUyr14JxPmrYJwzL9/VXGx2NarZw0HaFW68X3q53S+QjP8ztvBFRQyhDHR3CuTKsVkSRH8LwS1uaUSj69vbNxLkbrCp7nd8rzwZhyp6mcxve9osLAKrTOieM2GzaMY8ym7tnyid3045k4VnG8MVVKKRYtWkS5XGb79u1EUcT555//gh7J9WJ04YUXMn/+fAD6+/undzFCCCGEmFQyB128aBw6dIhrrrmGrVu3Fru1znHBBRfw7W9/m2XLlgFnPgfdOcf4+Dg7d+7koYce4tWvfjVpmqKUolardUN5qVQ6pfLi4eFhfu/7EQ8cWshY+ozPG94Fmx6G/U9B/RC0jhRj1poNaLQpqZSqinDk5HmGIwdVlJ0br+he7nQxE11rjaPY/c5tjnMWYzzSPCu6rxuF0eAFIY4MA2jP4LIUhyvK5V3x2CIoOnzPx9kMh8PTBmsztAajNTYtdvGVgyxPMZ5fnJXHFUE1zztvIDjSJMb3PWb19YMaoFJbRBiGGOMza9ZcnLNY6wiCHpKkjlIQBFWSpIVSRaiN4zZhWMW5vPPvRTO3PI8plyvEcQKozq52A/AJwzLt9lhnxr0lDKG3dxZKWZIkJQxrRFGDKGrhXFEi75ylWu0BHKVShTAsdRrahQRB2BkLVyHPwdoY3690zq7nlEq9JEmTLItxznSqJwwXXTSHhQtLLF26lFWrVjFnzpzjvrGTZRlr1qx53hnorVaLzZs3Y63l/PPPP6nS+ZMl59ynhrWWP//zP+eTn/xktxHc7NmzufPOO/nt3/7tc2Z2uMxBF0KIqSdz0Gcu2UEXLxp79uxhwYIF3VFTSikWL17M7t27uwF9Mrz+9a/nn//5n7nqqqu44YYbWL58OX19fWc04mpgYIBZ5cNFd/Zn/Gfbv2AJF1z0Bp7avJPGuu/DoY0wdgCq4+j+cWa7JuW4Rb0ekZuMLE9wNiNLEtKkjfECtOehVdaJ3A6tc9AeuXU4ZfH9AJwltxabO6yL0J5XdHLPi9FtzmUov9glt9aiTQAuI7UpnjLFmwM2Q2vVaT4HRiucBc/30Uphcfi+T54Xu+TaGPK8eINDG58oTjgyPkZYiskd5PlsyuUqR44cZHBwAdZGZFkdzyuRpq3OznZAu1OG73kT4biGMSFp2qBcrtJqxURR3CmXb5CmthPuG+S5JghKaA1aK5rNMY4cOUhv7yCtVpvR0WGUKpFlCUGQMzg4SJ4XI9R83ydNG6Spxvd7SdPxTuguk2Xtzmg2Q5I0CMOeTnO7cTyvOC+fZSmQY23Gli3j9PaW2LlzZ7fcfc6cOc96o8faYjze8wWcSqXSPZe+ceNGlixZIiPUZrg//MM/5POf/zy///u/z0te8hIAHnnkET70oQ8xOjrKH/7hH07vAoUQQghxxiSgixeVyRqD9lzXf+9738tnPvOZbulptVo94/nTWmvuftUg+XeO8Bc7BhjLi+v1B/BfL4G7r1O850fLuH/2Mkb3bIft/4/g4FNcXjrMcn2YVqvF2FidgwcjGo2ENE3wvOL8eZ7FZGlS7IAbH608nKdQxhH4Hi63WJeiAQU4pcgyi9dpFGddjlIahyZLU5TRKGfJ087Mc+vIjEVpryhfVwbP19jc4rQBlxVj3jwPmyTEeYY2CpumKOOB8nEuJQwDUEXneEeMMQdpthy+XyLPm4yO7qe3dzbWZiiVEoYl4riJMTV8PyBJGp0ydI8kaVOplGm1Ytrtduf8eIM4Bt+vkCQNPM/DmJAsi/D9Mlob8ryJ75eJojqHD+8hCGp4Xokg8OntHSSKWiilO7vjLfLc4Xll0rS4vRjV1sbzajjnkSRNgqBGnttON/caadoiSdqUyyXa7ZQ8V51O82M8/XTA9dcvZd++faRpyhVXXMG8efOO+Vl5rhL3Z/J9n5UrV7J792527txJu91myZIlsns5Q33+85/nvvvu41WvelX3tksvvZQlS5bwW7/1WxLQhRBCiHOABHTxorFo0SL27t1LlmXdEvc9e/ZM+kiiq6666pjgP1lvAhhj+PCNg6jvDPOZ7QMorfmvl2juvq64f+Kf9zOEWrSMOxbs4SXt/8f69evZt28f4+PjDA6OMzIyyoEDTUZGmijloRRoMpyzuDzF6hwyDw/IlcMoD6xC+x50yt7THJxN8F3xdUziFBP4KCDPipnpDovRGqUNthO6tRdg8wSnNc5abGLRRpOlGdqC8QKsTXDOoDyDdRmBMeS5R5Jl+J6HIyeOUooy+P1oY/BNL1pblNL09fWTJG2UqqLU0cHcEMdtKpUK7XaLVquF71c689HB8yqkaaNTDl/qXCMgSRJGRkZRyiPLEjxPUa320WyOk+cxs2fPJcsi0jTB98tkWROlShhTIU2blEoVjAmJ4za+XwEsaVonDItO8sW/V4njJnHcolwOabcbJImH71dQqkGWgbWGsbFhHn3U8KpXreLIkcOsXr2aK6+8snseGU5+B32C1pqlS5dSqVTYsWMH7Xab888//4Rn3MX0qdfrLFq06Fm3L168mEajMQ0rEkIIIcRkk4AuXjTmzJnDZZddxhe+8AXe+MY38tWvfpWlS5dOank7FLvok70zP0Frzf/4D4OY7w5zpJXy7ouqwL+fm7/7OvA19PqKd121GFjMvn37+P73v8+Pf/xjnHOkaYrWijlzSoyNpTSbOc1m3Gm+lpPlCdbmJHEblWiM8UBDlhqM9lEGjHNkscVllrBcRhtHnuZo30drV7RpR5HEKdrTKKfIO2fxi5lu4HshNs/AgSmVsXmKtQ7wsGRoVXR4b0cxnufjrCbKUowXgJeTWEueJrjWPmplRZqG5Hkxr7xcLsaiFaPLDFHUolqt0m43abejzui1Jnmu8bwySVLH8wxahzQaY4BHFDWJ4zZBUEMpRalkqNUWkGURzhlqtXkcPLiXw4cP0t8/izyPO+fFi2BenHX3iaI2QVAFss7tNfK8RZLUCcMKcdwmSdqEYUiStEiSopt7mjbQuoTWFbRud5vjHTx4iO9/v8QNN5zHyMgIq1ev5vLLL+8Gt1MN6BNmz55NqVRiy5YtrFu3jhUrVlCtVifjx1ZMkuuuu44/+qM/4r777uv2yxgdHeWP//iPue6666Z5dUIIIYSYDNIkTryobNq0iTvvvJMjR47Q09PDfffdx0UXXdS9/0ybxAHdTu0TfN+nUqmc8XWPZq1ly5YtjI+Ps2LFimPW7ZzrzlcfHR3t7qxlWcbevXvZtm0b+/fvZ2RkhHq9zvj4OI1GiwMHGjQaCVmWk9sUm1tymwAOhYJOszLteWhjQCnSPAMcQRgWzdycQ2kNLkd5HiiNwnZK1R1KUYxxyxNwCt83RXm7c1hAYdHGw2Y5qKLbPc7irMUzBmuLpnHaWbIs61xP4XsBvT2L8XWFarWHnp5ZeF6AMYpKpYd2u4FzpnO+vdgZV0qTZcV4tFYrIorqWOthO6+5UukDijWUShWiqIFSAeVyQBQ1USoAFEeOHAC8TmMrR6lUJcsSsizrNKFrkueOIKgSxxFa207juSbGhPi+IUmaGFNBKd05j140kMvzmCAok+d5p/O8Ruuicd/VV1/ItdcuZWxsDOccF154IUNDQzQaDTZs2MCll15KqVQ65Z+tJEnYvHkz7XaboaEhBgcHT/ka0iRuamzdupU3vOEN7Nq1i/POOw8oRkcuW7aM//2//zfLly+f5hVODmkSJ4QQU0+axM1cEtCFOMpkBPQkSbDWkqZpN6hXKpVJLxm21rJ161bGxsZYvnw5xphj5qtrrenp6el2jp8YpZVlGZs2beLRRx9l69atHD58mOHhYaIoYny8wchImyNHWrTbCXmek+UxzuWdLukZKIVSGuN5YDQ2B6UVvh+gjUIpA54GmxVz07RC4WBibBrgBx55muIoSue1shhtsLlFUYw/y2yGsjl6ovO7Ahy4PEd3RsJleY4xGpylVCrR37OQwFQplWoMDAx2upx7BEFInkco5QOKOC6axrVabaKoiTEllHJ4nqFa7QeKkXRBUKLdbqK139nhbqBUWIyry9oEQYU0zRkbG8Y5Q6kUEAQ+YdhDlrVwzuF5VZKkidaKMJy4XokgMMRxC88LAdPddc8yi7VJJ5gnnc7uNZKk3ek4T2e33+P66y/hiiuW0Ww2abVanHfeecyZM4ctW7Zw+eWXF29wnObP1o4dOxgeHmbevHksXrz4lKYOSECfOtZa/uVf/oVNmzbhnGPVqlXccMMNp/T9mekkoAshxNSTgD5zSUAX4iiTEdCdc7RaLbIsO+b2yQ7pSZIwOjraPVcPHDNfvbe39zl/yXXOsW/fPtasWcOTTz7J3r17u7vucRwzNlbn0KGIZjMmSRKcy8nzHDsR1l2G0gqtfKxzaM/D+AalVNF1XRtQFqc0zoFnDNr3sHmOU66YwU4R2C0WpRy+8cizDJvneF4x6k0rjdaAdRjfw2hFlibF+XYczuYorbB5RlgqMbtnKQqfUqlGX98sSqUQYwLy3NJujwE+cRyRZSmVSi++rzHGdMJxjNaKIKjQbtdRynR3z7UO0NqQZcV89DR1WBtTKtXIspixsXGyDHxfUatVO53ko841QlqtFloX88uTpIUxJUCTZUUwT1MLFOfYi5FrljDsIUmaFOftK6RpiyyzxdcDRxj28rM/eyEXXriEPM8ZGxtjzpw53QZyZ/Lz5pzj4MGD7Nq1i97eXs4777yTbnYoAX3y/Mmf/MlJP/a9733vFK7k7JGALoQQU08C+swlAV2Io0xWQI+iiCRJnnXfmYR05xzNZrO7S95qtYCiS7y1lna7/byzr0+k1WqxadMmHn/8cXbu3MnBgwcZHx+n2WzSaDQYHY04dKhOFKVF+Xae41xxZt25HOscNs8xXoAfhBjfwzqF72tQBhTk5Bil0drHkhWj2BSgFdoUc9MdFs/zKZq/OwyQ25ygE/zzOC126XHYzjl/m+eAIwh8sjTFD0vM7l1A2a/g+xW0Lka/lUqVYhScgmq1nyDQWOs649ZijNEEQUi7XQTpUqlMFDUAD2M8rJ0Yw5bjXNwZi5aQpmk33EdRm1YrRmtHX98A5XKZVqsI957nkWUT5fVF0C+VqiRJinMZQVDtjInLO7PV22hddIHPsjbWKiqVkFarSZY5tFYYo6jVBnj1qy9j4cJBSqUSBw8epFarccEFF9Db23taP2tHGx8fZ8uWLXiex4oVKyiXy8/7ORLQJ8/P//zPn9TjlFJ87Wtfm+LVnB0S0IUQYupJQJ+5JKALcZTJCOjw3CH9VMauZVnG+Ph4N5RnWYYxprtLPjFf3VrLtm3bGB0d5bzzzmNgYOC0175nzx6eeOIJ1q5d2+3+PjY2RrvdptFocuRIzNhYmyiKsBbyvJjVndmUNI1RShcl2MYD7aE0eNqAMTgcBo0yndJ3rYtz6wa08XAUIR0FSlkUxS55niYYozGeT24tRhflvFoV5d5ZEqOswwQezmZ4folQ1Qi0T7XaT6XSQ7Vao1brwxhFkiR4XkiWJRhjCIIi+GrtUS6XiKJWZ1656Yx4KxHHGc6llEpV8jwlTdPOOLUEpRxah50y9OJ7n6YRAwNzijcOsjZKlQBFnrcplWrEcQJk3WtkWd4pmY9QCjwvfMYOfBNjAowJSNMGWVZ0rVfKMXfuPG699Sr6+3vIsoxGo0Fvby/Lly8/rTdsnimKIjZv3kySJFx44YXP21NBAro4ExLQhRBi6klAn7kkoAtxlMkK6FCE9Ha7TZqmz7rvRCHdOUccx4yOjjI2Nkaj0cA5R7lc7gbyWq123POmzjm2bdvGkSNHTnsn/WgToWzNmjVs2rSJgwcP0mg0qNfrnSZ0TYaH6zQaaffcfW4z0jQGioZwSimU9tDGw/eLpnFOg9EaBxRd3hTKgSMHDNpTgKZ4iQ7lexitsVkKGrTSKG3xlCZPM6yzaK1I06TYxTcGz/MISyX6/AEq5Sp9fXMolyt4nofvl8nzFN/30DrolK97VCoVoqiFtaroXE/aKVOfCOY18jwhSTJ8v4xzxZsvxWzzCKU05XJRGp/nilarTZZF9PfP7uzAJ50d8wRrM8Jw4oy57Yx7a6OU6ox4a6GUoVwu0Wo1MCbEGI8sa2FMiHO6s6te9DgwxmPhwoXcfPOlpGlKu91m2bJlxHHMggULWLhw4RmfUc7znP3797NgwYLnDU8S0MWZkIAuhBBTTwL6zCUBXYijTGZAh5ML6RN/QR7d4E0pRU9PD/39/cc0eDuZ55vMkD5hdHSUdevWsXr1anbt2sWRI0cYHx+n1SrmiY+NtRgZadNqFQE2SSIcGQ5XdGF3tui+bjyUNiht8Hwf60BrhTIGrYtQbp1CGQVKYYwmsymKie7vOUo7yCGnmKFucws4PN/Dw3XOvyuczSlVe+gPZlHyS5RKNSqVCj09vWgddBvFVas12u3xzsg1H6VytA7IsqL0vFyukaYJaRrj+xNl8qozFz1CKY9SqUS73QB8giDojGLzGB8fJUki5syZX0ye6+yYW5uSZdlxgvm/75gXbxwUDenyvI3WEzvwRWd3azOSJMY50Bq09li5cgWXXjqLdrvNy172MtI05ciRIwwMDDA0NNRpmjf1JKCLMyEBXQghpp4E9JlLAroQR5nsgA7HD+lRFFGv13HOdcey+b7fDeQ9PT2nHaacc2zfvp3h4WGWL19+WmOyTsRay65du3jyySd56qmn2Lt3b3e3P45j6vU6o6MR4+Mx9XqTPLdAXpSuO4dzOWCL7u1aF43iOqXuKIVWqmj45kBpUMp07uuMYXOQO1sEenJQCi/00J3z6BqLzS0a0EaTO4vnBcypDlIp1ahWezHGp6enj2q1h3Z7HGuLbu3OZTinsNahVEqp1EuaxqRp0bgNbFGOrwx5nnTOqRc73EoZgqBElrVxzkMphbUJYVjh8OFhkqTFwMA8wtCQZRmeV+o0pNN4XkgUtTBGUypVaLcbaO2jtYe1EcaEWFsE8zCsYG1GmiadHfiiYsFa1Qn5hvPOW8Ty5T1ceeWVzJo1C2MMe/bsoVQqsWLFitPu7H4qJKCLMyEBXQghpp4E9JlLAroQR5mKgA7/3tn9n//5n/nc5z7H//k//4ff+73f47WvfS0DAwMMDBQNxSZrVNJUhvQJcRyzbds2nnjiCTZu3MiBAwe6ZfmNRoMoitm7d4R6Pep0KHdYm2FdhnOWPM+AYqY3yqC1j/G9Yqfd83HQ2XW25DgwClRR4q4VGG06O+55EdgVKKNRFOPcjFJ4vkee53heQJ9fY/bAbEql4ohAuVyjUukBEtLUAQqlckqlKnGckOfFjrkxkKYZxnikaYznBZ0z6w1AUy6XybKYPC/edJg4px5Fcbf52/j4KM1mnd7eWfi+j+eBMWF3F79UKneDvueF5Hkbzws7Z/xjwrCEtY40TTpn1ovdfc+rkCRxMVceRZqmZFnOxRcv5nWv+zkqlQqzZs2iUqmwfft2nHOcf/751Gq1Sf95OJoE9Be2VqvFXXfdxeOPP47Wmve///289rWvPe5jH330Ud7xjnfQbrdZtGgRn/70p5k/fz5RFHH77bezYcMGyuUy8+bN46Mf/SjLli173ueXgC6EEFNPAvrMJQFdiKNMVUCHIjT/zd/8DWvXruVVr3oV1157Lb7vo5SiWq1Oevmxc44dO3Zw+PBhhoaGmD179qRe/2jNZpOnn36a1atXs23bNoaHhxkZGaHdbjM8PMz4eIN22xFFOWlqSdIEsFibdmajO3JrO2fMTTFrXevOGXaNMV7xT89DKQ2aYhfeaLRSOF38NVY0a1Mo5cizHJfbomGdb1BO0+NV6Kv1MWvWPIzRhGEJzws6HdF9siwlzzOCoIzWiiSJiq7z1uL7BmP8zhxz3Sl9jzpjz3yUyvD9EnGc4FxGGFaBnCRJMCag2azTbrcol3sIQ4NSAZXKREM6jecFOBejlI9zHLVjnpMkaXfH3NriDHxx1t8CRSl9FLVxDoLA0N8/yKtf/VOsWrWY3t7ebmXGtm3baLVaDA0NTcmbNhMkoL+w/emf/inbt2/nvvvuY/v27dx000088sgj9Pf3H/M45xxXX301n/jEJ7j++uu55557WLNmDX/xF39BFEV873vf48Ybb0Qpxac//WkeeughvvzlLz/v80tAF0KIqScBfeaSgC7EUaYyoMOJZ6SfCyF9wpEjR1i3bh0/+clP2L59O0eOHOHAgQOMjo6SZRlpaqnXY8bHY5IkI89T8nyiyVxRAq90cQbd98Li3Lpn8IwpStZ9H6cottc14OiE9KLZHJqiu7mnAQu2s0tvwA9K9Fif/t5Z1Go9lMtlwrCKc6BUThAUVQxx3EKpAGNUp6pBkaYxWk+cWa+T5+B5AUrlnfPoebeZnHM5SRLjeSWcyzsN8zwajWJ0XU9PXzGCDo8gCHEuQSmvu2NedIrPu6Xs1hYj7TwvJI7bnfPxmnp9HOcUlUoNYzKcM50dekOt1sNNN13NwoWzGRgYoK+vj1mzZrFz506Gh4cnrXnc8UhAf2G77rrr+OQnP8nVV18NwG233caNN97IG9/4xmMe9/jjj3PnnXfyox/9CIB6vc7KlSvZtWvXs8ZJrl69mttvv53Vq1c/7/NLQBdCiKknAX3mOrlZT0KISaGUolKp0Gw2yfO8e/vEjPPJDulKKZYtW4ZSqlviPGfOnEm7/vHMmjWLl7/85bz85S/n4MGDPPnkk/zkJz9h48aN7Nq1izzP6euLabfbjI01GB83tNte57y6xbqc3GYoHFkWQeYgNWTaw/g+SZahtUGbiZ12hVPFQDZMcV7dKQW5w2lVHGbPMsgVysWMqBQ3DmmakGW95LmlVusFAqKoidYBvl/qnPUG5zJ83++MRmsyNjZCqVRBqQxrk05pe0QYVnDO0G6P43klPM/H2hQwndJ4y8DALIxRjI+PEQQlarUyeV68AWBtRKlUReuQdrtBEBTXSNOoc90WaTqKUoY4buP7AQMDg0CO1j6+73dK7TPyHBqNMf7P/1nDa15zNcaYTvM8zbJlyyiVSuzZs4d2u83y5cvPWvM48cKwe/dulixZ0v146dKl7N69+1mP27Vr1zGP6+npoVarsX///mNuB7j//vu5+eabp27RQgghxDlCAroQZ9nEbvmJQnqtVpvU3SOlFEuXLgVgx44dOOeYO3fupF3/ucydO5cbbriBl770pTz55JM88sgjPP300xw4cIBWq0WtVmPevIw0TanXW4yPx7RaKUmSY22KVgHWZUVoT1PyLEFrA9rDeEVIVyjonEdH2WL+OkVrc0UR2HOli5yeWpQHI3mEbVnCsIRzdazNqNX68P2QLEtptxM8z8fzNEqFxHFElqWUyzWyLCaOW2jtobUjDAOyLKfdHsOYMmFY6TRu87A2w/MslUqVVqtJvT5GuVzD83xGR0cZGUmp1crUajWsDWi16nheiSAouszHsSVJ2t2ydmsdQaBZsGAR1qbkucP3y52u8XTeFLDdM/ONxhH+5V+e5FWvurQb0LXWLFiwgHK5zNatW1m/fj0rV648K83jxMxwyy23sGHDhuPe973vfQ/gmMoK505caPfMCozjPfbP/uzP2Lp1Kx/72MdOZ7lCCCHEi4oEdCGmwXOF9EajMWUhXSnFzp07AaY0pE+82TA2Nsbo6CjtdhutNTfeeCM33ngj69atY+/evYyMjLBv375jRsw1m00ajXanE3xElvs4m5HbDJvnWJvj8og8cSjj4Rmv2E3XBu35xVx046EtRQm88ooO7VYVY9qcRTnFiG1jD+2jr6cfpRTOWSqVvuLMulZkWUqWge8bSqUyaZrQbI5jjE8QeOS5I88hy5p4XkAY1kiSNu121unOnmNMEbTTNKFc7iXPY+K4jTEe/f19NJstms0mWZZ2ntun0ThCHBdN9IxRVCo9nYAeUK3OIs9TkqRoIqd1inNpdxa7MT7lchlokiQpWnscPryf73/f54Yb/E4X+qJsv7+/nwsvvJDNmzezbt06VqxYMeXN48TM8NBDDz3n/YsXL2bnzp3dIzG7du3ixhtvfNbjlixZ0v37BIoS90ajwfz587u33XPPPXzta1/jwQcfpFKpTNIrEEIIIc5dEtCFmCbPt5NerVYnPaQvWbKkG9Kdc8ybN2/Srm+tZXx8vDt2LU1TjDH09/ezYMEC+vr6uqXUc+fOZcuWLZ0wCU899RTr1q1j//79jI6OUq/XabVaxHHM2FiTRiPtzFhPsc7iyLDWYm1OliVFGTwajFecfVUJShdj29Aaoz2c6nRqzyHLwQQ+43mOGxvB9zXO1VBqjCAojhlobVDKkiRpEexVgO/75HlGFIExCq09lCrCcTGOLcQYS5Y5oijF83IqlRpx3KbZHMP3w87Zc0uWKYLA77yGjMOH9+KcwfM0QRBQKvXgnMXzFGE4iyyLaLcb+H4ZYxSQ45whTRO09imVyp1dd4cxAVpPzKRX7N69i4cf1txwwxXdgK61pre3l4suuogtW7awYcOGKW8eJ14YXve61/GZz3ym2yTuBz/4wXF3v6+88kqiKOLhhx/m+uuv56/+6q+49dZbu+fP7733Xh544AG+8pWvPKvBnBBCCCGOT5rECXGUqW4SdzzWWprNJtbaY27XWk96SIfiDYDdu3dz4MABFi9efMxu16lK07QbyMfHx7HWEoYh/f399Pf3U6vVTtiEbHx8nM2bN1Mul1m5ciXWWjZu3MjatWt5+umn2bdvH6Ojo52RbRHtdpt6PWJsrE0cF93RrctxLsdZi8V2OsIrtKfR3TJ4H4XG+B6201hOK4fSGi8og9JUraO/v49arY9qtUYYFjt9nmcA1R33BqpT9q5J06L7vOd5GFOcN0/T4ny853lorYjjFKUsvl9Ga0eWZeS5I0lSoChFLzrFF9fwPEO1WsPaiWBeIUnaZFlOEJRQymJt8WZEnqd4XoDvB0RRA6V8PM/HuaKBnHNgbQwU3fCN8TjvvKX87M9eTm9vL7NmzWJgYICenh6stezYsYPh4WHmz5/PokWLzqh5nDSJe2FrNpu87W1vY82aNWit+YM/+ANe97rXAfDZz36Wffv28d73vheARx55hHe+851EUcSCBQv49Kc/zcKFC9mzZw+XXHIJQ0ND3cqMMAz57ne/+7zPL03ihBBi6kmTuJlLAroQR5mOgA7TE9L37NnD/v37TymkO+eIoojR0VFGR0dpNpsA1Gq1bigvlUonvY5Go8GmTZsIgoBVq1Z1d97iOGbz5s2sXr2adevWcfjwYUZGRo4J641Gwvh4RKsVk2ZZEdQ7DeZsbkFZcEBnhJo2RQk8AErhaQ3G4AUh2vhUsMwaGKBSqVEuV6lWezsz1E2nC7sBXGc33XTCu+tUPyg8z++UxmedgF3MdgdHHMed23PSNMe5Yi58qVSmVit2ylutFq1Wi1IppL9/kDRNsLYI5lpDlmUoZcjzomndxMg3pXRnBFuCtUUHe8gJw6IsP0kirFWUSgFae1xwwXJ+6qdWMTAwwODgILNmzaJWq+Gc48CBA+zevZv+/v4zah4nAV2cCQnoQggx9SSgz1wS0IU4ynQFdJiekL5371727dvHokWLWLBgwQnX1Wg0ujvlcRx3y6Mn5ms/c6TSqWi1WmzcuBHP81i1atWzmpVFUcT69etZs2YNGzZs4ODBg92wPlEGPz4e0WwmtNsJWZYXTeXyDLTD5kUgLkq7QXsBynidXWWN0govLGGUT0lBX28vtVoPlUqN3t5e8twdVRZe7EQnSYbWxe75xKy3LMvRWuP7HtY60jTrlL7bTqi3gKFUCimXS/h+SJrG5LnthHBFHCeMjo5gjGZwcDZaFx3gndOda2tAE0VttDZUKtXODrvFGB9jHFr7pGmGcymeV8IYTRQ1SdMcYzyM0axcuZxXvOJyBgcHmTNnDvPnz+/+fI2OjrJ161bCMGTFihWEYXjK31MJ6OJMSEAXQoipJwF95pKALsRRpjOgw3OH9OcqFz9dJwrpWZYdc548z3N83+/ukk/2L9BRFLFx40YAVq1adcJd+Farxfr16/nJT37C+vXrOXjwIGNjYzSbTdrtdmdnPaJej2m3c9IsBmexrmi6hioCOzhQGu15RYm656G9EkEQEihHX08vvb29eF7IwMBA52tVfB+stQRBWMxtzy2+b3BOoZQmyxLa7Rhrc6y13dLyIPAolSp4nkeeZ2RZjudpfD/EuZw0zQCF7xusVYyNjZJlCX19A5RKZSAnTRXOZWjtUS6XiKIWeZ51d9iLGeoWyPD9Sifwt3HOUJx5j0jTFN8PqVSqXHPNZdx888tYvnz5s95gabfbbNq0CWvtaTWPk4AuzoQEdCGEmHoS0GcuCehCHGW6Azr8+471M8cVGWOoVquTHtIB9u7dy969e+nt7e12knfOUS6Xu6G8UqlMyXNPSJKEDRs2YK1l1apV3QZyJ9JsNtmwYQNr1qzh6aef5tChQ4yNjdFoNGi320RREdQbjZh2OyXPLbnNwGVYigDtnAVnUaY4q+4FAX5QpqShWqnS3z+LMCzR1zeA1gZrHZ5nyLKMIAhQShFFRSl6ksSAw3R258OwCPyeV5wHL3bYVeecePGxUhatvc5jHGlq0dqhlKbZbJEkMaVSiVKp3A3RaRoDxfWLnfsM50Ap8P0A51RndnpRMZDnxSz2ogrAEQQlwrDE+ecv5/bbf4m5c2cf9+ubpilbtmyh2WyybNmybkfvkyEBXZwJCehCCDH1JKDPXBLQhTjKTAjocHZCunOOVqvVPU/ebreBopHTvHnz6OvrO63y5jORpikbN24kSRJWrVpFtVo9qc9rNpusW7euWwZ/4MABRkdHabUmxphljI+3aDYzWq2YLM9xLiO3OQrXCbFFMFbGww9KlHyPaqXKwMAAnhfS09NPqRSS5zlae7RaLZzLUaoI4KVSGc8z+L6H74fkedaZTW5wDozRndFs/x7unXM4V+zEG/PvQT3PM6xVJElMHEeUSqVOB3iD5wXkedYN3r5fvCGQ57ZTDl8857+fjQ8IgoBqtYrnaebN62doaB4rV57PnDlzWL58+Qm/rtZadu7cyeHDh0+peZwEdHEmJKALIcTUk4A+c0lAF+IoMyWgA+R5TrPZnNSQfqJRaH19ffT399Nut9m3bx8LFixg4cKFU7pjfiJZlrFp0yba7TYrV66kp6fnlD7/eGG9Xq9Tr9e7DeaazZhGI6Ed5WRZXIRkO9ForriO1grPGMqlEgMDA4RhlTAM8LyANHWEYUgQ+GjtUa2WyfMcY4pd7jzPCcOg00nd4vseWZZ1dsE1SZISBEVpPBTf0zRNOs3nFEp5WFuUwkdRTJomlEolgqDUOWdu0NrrNJ3LOm8UKIwxnU72hjAsEYYhpVKFvr4yy5fPY8WKpQRB0Lm9RLVaZdGiRc8693+0o5vHLV26lLlz5z7v90ACujgTEtCFEGLqSUCfuSSgC3GUmRTQ4cQh3fO8ky45T9OUsbExRkdHT2oU2r59+9izZ8+kjNs6XXmes3nzZhqNBitWrDjt70uj0eiG9aeffprDhw93R8LFcUwURTSbMePjMXFSzFS3NsfhQIGzYHSOVqoTkCsMDMxiYGAWnhd0xqkVodj3Q4pdeNMJ3HGnDJ3OufWAOE7xfYMxfufNEd3ZPS+ay6VpRpZZwtDv7IRrsizvVAGkhGFIpVIjz9NOiXzR6M654ox8EASUyzV839DX18O8ef0sWTKXJUsWdsruQ8rlMpVKhVqtRrVaPekqiXq9ftLNCiWgizMhAV0IIaaeBPSZSwK6EEeZaQEdTj2kP9cotImd8lKp9JzBe//+/ezevXtaQ7q1lq1btzI2Nsby5cuZNWvWGV2v2Wzy1FNPsXr1atavX8/hw4e7X59Wq0WSJNTrbcbGItrtiDwvZqYrpTAaPFN0bff9AGOCzrn8XoLAx/PC7k6552msBd8PybIMz9N4nt8J8UFn3FqxA1+Uyxucc52Raj7OKbIsxfd9rHVorTFGMT5ep9Fo4nmanp5efN8jz//956BcruD7hvnzBxkamkN/fy9z5swBoFwuUy6XqVarVKtVKpXKaY9QOxkS0MWZkIAuhBBTTwL6zCUBXYijzMSADs8f0qHY4ZzYKZ+MUWgHDhxg165dzJs3j8WLF09bSN++fTtHjhxhaGjolBqVPZeJBnOPPvooa9euZffu3Rw+fJhWq0UUReR5ThxnRJEjyRQudziXo01OuVTsOBuj0NojCKqUyxWq1RqgCIKi/BzodHt3neZsQafMPeiE8IRSKex0e9cY43W6rPudM+UZAEmSdprLeeR5TqPRwvN8BgdnU6tVqdV66O8vsWTJXBYtmovv+/T29mKMOWa3vFKpnLWeAhLQxZmQgC6EEFNPAvrMJQFdiKPM1IAOdMJZ45iPt23bRk9PD81m85hRaH19ffT29p7xL7kTIX3u3LksWbJkWkK6c46dO3dy6NAhlixZwrx58874evV6ndHRUUZGRhgdHWXnzp3s2LGDvXv3MjIywvj4+DHd4LMM2m1Hq5XiHJTLRfj+9/nqGqWgUimjlI/vh4RhgFLF7rkxutMB3gMUWtM5y5523jhRWFucUY/jBKVcJ8gXZevW/vuoPa194rhFX1+FSy4ZYtGiBcfslBdd30v09vZ2d85P9/tW7Ozbbq+Ck32TRwK6OBMS0IUQYupJQJ+5vOlegBAvJJs2beLXf/3XOXz4MP39/fzVX/0VF1988Vl57onmcDt37uTuu+/mG9/4BtVqlQceeICBgQFmz5496WPY5s2bh1KKnTt3AkxLSFdKsXTpUowx7Nq1izzPWbBgwSmtI8/zbnO80dHR7psZAwMDLF++nFe84hUopWi1Wt0z60899RT79+/vdoPPsgylFGNjLVoti9Y+nqc7u+V5dwyb1pY0TUiSiFIpoNVy+L7f3VX3fdPp/D5xf9INv2maUiqVu53hK5UqxmgqlQp9fT309PgsXjyXwcF+Dh06hOd51Gq17rGFnp4eKpUKpVLptEvYjw7laZpirQXolPefWhWGEEIIIYQ4NRLQhTgF/+W//BfuuOMObrvtNh544AHe8pa38H//7/89a8/veR5z585Fa80999zDT//0T1Or1QCmbMdp7ty5KKXYsWMHzjmWLl06LSF90aJFGGPYs2cPeZ4/b9l9lmXdQD7RHK9UKjF37twTznWvVCpce+21XHvttURRxLp161i9ejVr165l3759tNtt+vv7iaKoc4wgxJiAKMqxVgPFXHOtXff74fsa3zdkmaVcLmNtgu+Xcc7ieQFhqDEmQClHqVTMfq9WK9RqVapVn/nzBxkYKM6Nz507F2stlUqFoaEhhoeHSZIE3/dZunRpZ4f+1BWj3XLSNO2ckbfH/Xo656alikIIIYQQ4sVCStyFOMpzlbgfPHiQVatWcfjwYTzPwznHggUL+NGPfsTQ0NDZWyRFWJpo/nY03/fPqKT5uRw6dIgdO3YwZ86caQnpEybK7mfPns2yZcuOWUeSJN3S9YmyrWq1Sn9/PwMDA5RKpdN6ziiKWL9+PY888gjr1q2j1WpRr9dpNpuEYUi1WsU5RatlcU4RxxlgyLLirDo4PC8AbPcceHF+3cP3ParVEj09ZYLAZ86cAcplj97eHvr7+1FK0dvbi1Kquzteq9Uol8uEYcjBgwfZu3cvg4ODLFu27KTfqDk6lKdp+qz+BsdTLpefcyTbBClxF2dCStyFEGLqSYn7zCU76EKcpF27drFw4cLuLuVE6fXOnTvPekCfaA7XarWOuT1NU1RnJNhkB+g5c+aglGL79u04554Vjs+WefPmYYxh+/bt5HnO/PnzGR8fZ2RkhFarhVKKnp4eli5dSn9//0kFyudTKpW48sorufLKK4njmHXr1rF27Vq2bt3Knj17ume+lVJorTsd3X2yLMeYoNPB3Ucp8LwQ31ed+e6OarUKFOF3Yub7rFmzyPO829itUqlQrVYplYrZ5kd/3RcuXEgYhmzfvp0kSTj//PNPuJN+OqEcip913/entPO7EEIIIYSQgC7EKTneSLPp4vv+cUN6kiQAUxLSJ7qob9++HWBaQrpzjlKpRF9fHyMjI4yMjKC1pq+vj3nz5tHX13fapd4nIwxDrrrqKq666iriOOapp57iiSeeYGxsjAULFhBFUafZW9xp8GYxxqCU6r5ZUCqVsNbS399Pmqb09fXhnKNSqXSrICYavgVB8LyvZ3BwkCAI2Lx5M+vXr2flypXdnXrnHFmWkWXZKYVyrXX33PnE+oUQQgghxNSSEnchjvJ8Je4rV65keHh42kvcj5am6bNCOhRB8pm7rZNleHiYbdu2MTg4yNDQ0JSHt4kyrIkz5WmadqsI6vU61WqVlStXTtsOb6PRYNOmTVhrmT9/Plpr6vU6xhharRZBEBDHMaVSiTzPCcMQrTVBEHT/eJ7XDcSn+/Vst9vddSxfvryzi5+dUij3fb/T1E6f9jqkxF2cCSlxF0KIqScl7jOX7KALcZLmzp3LVVddxd/+7d9y22238aUvfYmhoaFpDedw4p30OI4BTvvc9XMZHBwEYNu2bQBTEtLzPO/OdR8bGyPPc4IgYGBggIGBAWq1Gkop6vU6mzZtYuPGjaxcuXJKd89PpFarcdFFF7Fx40YOHz7MqlWrWLBgwVldg3PFGLehoSF27tzZDeoXXHDBc37eZIVyIYQQQghx5mQHXYijPN8c9A0bNnDbbbcxPDxMb28vf/3Xf80ll1xyllb33JIkod1uP+v2MAynJKQDHDlyhK1btzJr1iyWL19+xuEuTVPGxsa6s8idc5TL5W6TtxM1wGs2m2zatAnf91m1atW0jQNLkoRNmzaRJAkrVqzonimfKhPl6xNnyidYa3nwwQd5+9vfzt/+7d/yyle+8pjPOzqUT0XVgeygizMhO+hCCDH1ZAd95pKALsRRni+gz3QnCukTzcWmwpmG9DiOu53XG40GQHe298R875PRbrfZuHEjWmtWrVo1Za/3+WRZxubNm2k2m5x//vn09/dP6vWdc91AnmXZcz7uf//v/80//dM/8Xd/93fd8vmJnfKpJAFdnAkJ6EIIMfUkoM9cEtCFOMoLPaDD9IT0kZERtm7dSn9/P8uXL3/OX66dc7Tb7e558onO6729vd1Qfro74HEcs2HDBgBWrVo1ZZUDz8day7Zt2xgZGWHZsmXMmTPnjK53sqH8mYwxeJ5HEARnNfBIQBdnQgK6EEJMPQnoM5cEdCGOci4EdCiCahRFz7p9ukK6c45Go9EN5XEco7XuBvK+vr5JK7VOkoSNGzeSZRmrVq2iUqlMynVPlXOOnTt3cujQIRYuXMiCBQtOqbrAWtstXz/VUH62dspPRAK6OBMS0IUQYupJQJ+5JKALcZRzJaDDiUN6uVyelNngxzM6OsqWLVvo6+tjaGiIZrPJyMgIo6OjZFmG53nd8+RT+Ut4mqZs2rSJOI5ZuXIltVptSp7n+Tjn2LdvH3v37mXOnDksXbr0OUO6tba7U57n+Uk/z0T5uud5MyLYSEAXZ0ICuhBCTD0J6DOXBHQhjnIuBXSAKIq63dyPNlUhPc9z9u7dy4EDB7q3hWHYDeXVavWsdQmfOAvearVYsWIFvb29Z+V5j+fQoUPs2LGDgYGBZ1UXnCuh/GgS0MWZkIAuhBBTTwL6zCUBXYijnGsBHaY+pKdp2m3yVq/Xcc4RhiFxHFOr1aZ1Pnme52zZsoV6vT4lDdtOxcQRgGq1yvnnn98N5qcTys9kVvrZIAFdnAkJ6EIIMfUkoM9cEtCFOMq5GNCdc8RxfNyQXqlUTqshWxRF3fPkR3deHxgYoL+/nzAMGRsbY/PmzfT09LBixYpp+4XbWsvWrVsZGxtj+fLlzJo1a1rWkec5o6Oj7NixA2MMc+fOPanz8RO75DM9lB9NAro4ExLQhRBi6klAn7kkoAtxlHMxoEMR0qMoIkmSZ913MiF9ovP6xHnydrvd7bw+MDBAX1/fca8xPj7Opk2bpj2kO+fYvn07w8PDk9JV/WSf8+jydWstUPQGuP/++/n617/OF7/4xePu6k/sknue94IJ5UeTgC7OhAR0IYSYehLQZy4J6EIc5VwN6HDqIX2i8/pEKE+SBGMMfX19DAwM0Nvbe1Kl6+Pj42zevJlqtcqKFSumrdz96K7qixcvZv78+VPyHMcL5c80NjbGW9/6VubNm8c999yDUuqYM+UvxFB+NAno4kxIQBdCiKknAX3mkoAuxFHO5YAOzx3Sq9UqWmvGx8e75etZluH7fncc2un+4lyv19m0adOMCOl79uxh//79LFiwgIULF55xGHbOked5dxzaiUL5M2VZRpIkzJ49+5wI5UeTgC7OhAR0IYSYehLQZy5vuhcgxIvZbbfdxne+8x1mz54NwI033siHP/zhKXs+pRSlUgnnHGmaAkXA/P73v8+iRYu6O8ClUonZs2fT398/KZ3Xe3p6WLlyJZs2bWLz5s3TFtKVUixevBhjDHv27CHPc5YsWXLKr+/oUJ6mKc6d3PucSqlu+box5pwK5UIIIYQQ4sxJQBdimr373e/mbW9721l7PqUU5XIZgD/+4z/mc5/7HOPj43zmM5/hp3/6p5kzZ86UzA0/OqRv2rRpWru7L1iwAGMMO3fuxFrLsmXLnjcsSygXQgghhBBTTWrIhHgRmgjpq1at4t5772Xz5s3ccsstzJo1izzPybJsSp63p6eHVatW0Wq12LRp0ymNGJtsc+fOZfny5Rw+fJitW7cetzR9otKg3W5Tr9dpNpskSfK84VwpRRAEVKtVenp6KJfL51wZuzh3tVot3vKWt3DVVVdxzTXX8NWvfvWEj3300Ud5+ctfzjXXXMNrX/ta9u/f/6zHfOhDH6K/v59169ZN5bKFEEKIc4IEdCGm2Uc/+lEuv/xyfv7nf541a9actedVSnHbbbdx0003USqVjrmv2WxOWXiu1WqsWrWKdrvNxo0bpzWkDw4Ocv755zM6OsrmzZvJ87wbylutFvV6nVardVKhXGtNGIbUajUJ5eIF7Z577iEIAlavXs2XvvQl3vWudzE6OvqsxznneOtb38rdd9/NY489xo033sh73/veYx6zZs0aHn30URYvXnyWVi+EEEK8sElAF2IKXX/99cyePfu4f3bt2sWf/MmfsHnzZp544gne8pa3cMstt3Tnip8NSikqlQqe9+zTLlMd0leuXEkURWzcuHHKduxPxsDAACtWrKDRaLBhwwZGRkZotVonVcZ+dCiv1WqUSiUpYxcveF/+8pd561vfCsDQ0BAve9nL+PrXv/6sx61evZowDLn++uuBoqfG17/+9W5/iziO+d3f/V0+8pGPyH8TQgghxEmSgC7EFHr44Yc5fPjwcf8sWbKERYsWdbsV/9Iv/RK9vb1s2LDhrK7xRCHdOXdWdtKnK6QfvVMOsHDhQoaHh3nf+9533C73E565Uy6hXJxrdu/ezZIlS7ofL126lN27dz/rcbt27TrmcT09PdRqtW6Z+wc/+EF+5Vd+haGhoSlfsxBCCHGukIAuxDQ6+pfeH/3oRwwPD7NixYqzvo6JkP7Mpm1THdKr1SoXXHABcRyflZDunCNJEprNJuPj492dcoByuczAwAD/+q//yq//+q8fsxZjDKVS6VmhXIgXoltuuYXzzjvvuH8m/k46+g2n56okeeYbUxOPfeSRR3j88cf5z//5P0/BKxBCCCHOXdLFXYhpdNttt3HgwAGMMZTLZf7hH/5h2maxK6WoVqvPCuQTIb1Wq03JbOJKpcIFF1zAxo0b2bhxI6tWrTpuyf3pmtgpn5hT/lyWLFnCN77xDf7xH/+RIAgIggDf92UmszinPPTQQ895/+LFi9m5c2d3/OOuXbu48cYbn/W4JUuWsHPnzu7H9XqdRqPB/PnzeeCBB9i0aROXX345AHv37uWXf/mX+cQnPnHcawkhhBCioEZHR09uVpAQLwLTFY5nkhPtmiulpiykQ9E5euPGjfi+z6pVq/B9/7SvZa0ly7KTCuVHM8Z0R6JJKD99Y2Nj070EcQbuvvtudu7cyX333cf27du58cYbeeSRRxgYGDjmcdZarr76au655x6uv/567rnnHlavXs1nP/vZZ13zsssu4wtf+AIXX3zx8z5/T0+P/PcnhBBTzFpLvV6f7mWI45AddCHEMZ5vJ71arU7pTvqGDRu6O+mnEtKttd2d8lMpyfc8D9/38TxPQoEQwG//9m/ztre9jauuugqtNR/5yEe64fyzn/0s+/bt473vfS9aaz796U/zzne+kyiKWLBgAZ/+9KenefVCCCHEC5vsoAtxFNlB/3fWWprN5rPmg2utpyykA7TbbTZs2HBSO+kSymcm2UEXZ0J20IUQYurJDvrMJf8HFEIc14mC+ImC+2Qpl8tccMEFZFnGhg0buk3cjn7+OI5pNBrU63WiKDqpcO55HuVymd7eXqrVKkEQSAgQQgghhBAzivx2KoQ4oekO6Xmes2HDBqIoIoqiUw7lvu8/K5TLOLRjxXHM2972NlauXMkll1zCm970pulekhBCCCHEi5acQRdCPKeJkP7MQD4R0mu12pSEXt/3GRoaYtu2baxfv5558+ZRqVRO6vMmytcljD+/d7/73Wit2bhxI0op9u3bN91LEkIIIYR40ZIz6EIcRc6gn5i1lkaj8ayZyMYYqtXqGYdh59wxZ8on3gxIkoSPfexjPPzww3zxi188bkiXUH56ms0mixYtYvfu3dRqtUm7rpxBF2dCzqALIcTUkzPoM5f8H1AIcVK01sfdLc/znGaz+azgfjKcc2RZRrvdptFo0Gg0iOP4mJ36IAi44447yLKM//E//gdQdJr3fZ9KpUJvby+VSgXf9yWcn6ItW7YwODjIBz7wAa699lquv/56vvvd7073soQQQgghXrSkxF0IcdKOLnc/OpBPhPST2Ul3zpHneXen/GSC/cDAAF/60pfwfZ9qtYoxRsL4JEjTlK1bt3LxxRfzoQ99iJ/85Cf8h//wH1i3bh1z5syZ7uUJIYQQQrzoyA66EOKUnKikPc9zWq3WcQP30Tvl9XqdZrNJkiTPG86VUgRBQLVaZcGCBcyZM0fK2CfRsmXL0Frzxje+EYArrriC5cuX89RTT03zyoQQQgghXpwkoAshTtmJQnqWZd2QPhmhvKenh3K5LKF8isyePZtXvepVfOtb3wJgx44dbNu2jQsuuGCaVyaEEEII8eIkTeKEOIo0iTs1Jzp/rpQ6pTPpWutuozettYTxs2jr1q3cfvvtDA8PY4zh/e9/P7/0S790RteUJnHiTEiTOCGEmHrSJG7mkv8DCvEC9NnPfpbLLrsMz/O49957j7nPWstdd93F+eefz4oVK/jkJz85ZeuY2El/ppMJ51prwjCkVqtRq9UolUpytvwE/vt//+8opXjyyScn/drnnXce//qv/8ratWtZs2bNGYdzIYQQQghx+qRJnBCn4c1vfjODg4N8/OMfn5bnv+aaa/jiF7/I3Xff/az7/vZv/5Z169axceNGxsbGuPrqq7nhhhu48MILp2QtEyG92Ww+72OP3ik3xkzJes41jz/+OD/60Y9YunTpdC9FCCGEEEJMMdlBF+I0PPHEE1x22WXT9vxXXHEFF1100XHLQL/whS/wX//rf8UYw6xZs/iVX/kVPv/5z0/pejzP655Jn+iyPsEYQ6lUolar0dPT090pF88vjmN+67d+i09+8pNSWSCEEEII8SIgO+hCnKI0TVm/fj2XX375dC/luHbu3MmyZcu6Hw8NDfHoo49O+fN6nkdPTw9KKcIwxFrbPVMuTs8f/MEf8KY3vYnly5dP91KEEEIIIcRZIL85C3GK1q9fT5Zl7N69m8svv5yenh5+5Vd+ZVIbbVx//fXMnj37uH927dr1vJ9/9G7rqTRrO1MTz+v7PmEYSjg/A//3//5ffvzjH3PnnXdO91KEEEIIIcRZIr89C3GKnnjiCTzP47777uNv/uZv+MY3vsHDDz983PPgp+vhhx/m8OHDx/2zZMmS5/zcpUuXsn379u7HO3bskPPLL0D/9m//xvr161m+fDlDQ0Ps3r2bV7/61Tz00EPTvTQhhBBCCDFFpMRdiFO0du1a+vv7efDBB6lUKgC89a1v5Z//+Z+neWWF17/+9XzqU5/iP/7H/8jY2Bhf+MIX+OY3vzndyxKn6N3vfjfvfve7ux8PDQ3xT//0T1x66aXTuCohhBBCCDGVZAddiFP0xBNP8Bu/8RvdcA5QrVax1p61Nfzt3/4tixcv5h/+4R943/vex+LFi1m9ejVQdJi/4IILWLVqFS95yUv43d/9XS666KKztjYhhBBCCCHE6VGjo6Nn74CqEDNcX1/f8z5m8eLFfPjDH+YNb3hD97Zf/dVfZWBggPvuu28qlyfEC8LY2Nh0L0G8gPX09Ej/CiGEmGLW2kntnyQmj/wfUIhTcOTIEfbs2UOe593b9u7dy4MPPsiv/dqvTePKxNkSRRG/+Iu/yKpVq7jyyiu5+eabjznzL4QQQgghxOmSgC7EKXjiiSeoVqt84hOfYN26dTz22GPcfPPNvOENb+ClL33pdC9PnCV33HEHGzZsYM2aNfz8z/88d9xxx3QvSQghhBBCnAMkoAtxCtauXcu1117Lb/zGb/CKV7yCm266ide+9rV85jOfme6libOkVCrxmte8pjtS7rrrrmPr1q3TvCohhBBCCHEukDPoQhzlZM6gC3G0X/u1X2NwcJCPfexj072UGUPOoIszUa1W5Qy6EEJMMWstzWZzupchjkPGrAkhxGn64Ac/yKZNm7j//vuneylCnDPkF0YhhBAvZhLQhRDiNHzkIx/hH//xH/nOd75zzMg9IYQQQgghTpcEdCGOIqW54mTce++9/MM//ANf+cpXUErJz40QQgghhJgUcgZdCCFOwZ49e7jkkksYGhqiVqsBEIYh3/3ud6d5ZUIIIYQQ4oVOAroQQgghhBBCCDEDSJtUIcQ5Y8uWLdx0001cc8013HDDDaxfv366lySEEEIIIcRJk4AuhDhnvOMd7+DXf/3Xeeyxx3j729/OXXfdNd1LEkIIIYQQ4qRJQBdCnBMOHTrET37yE/7Tf/pPALz2ta9lx44d7NixY5pXJoQ4F7RaLd7ylrdw1VVXcc011/DVr371hI999NFHefnLX84111zDa1/7Wvbv3/+sx3zoQx+iv7+fdevWTeWyJ81kvP4oivj//r//j2uuuYaXv/zl/PIv//KM/jv6ZKuyPve5z3H11Vdz5ZVX8va3v50sy7r3ffOb3+QlL3kJV111FW9+85tpNBpna/ln7Exf/1NPPcUtt9zCS17yEl72spfx9re/nTiOz+ZLOCOT8f0HcM7x2te+lvPOO+9sLFucAySgCyHOCXv27GHBggV4XjGcQinF4sWL2b179zSvTAhxLrjnnnsIgoDVq1fzpS99iXe9612Mjo4+63HOOd761rdy991389hjj3HjjTfy3ve+95jHrFmzhkcffZTFixefpdWfucl6/bfddhuPPvoo3//+93n1q1/NO97xjrP3Ik7RyVRlbd++nQ9+8IN885vfZPXq1Rw4cIC/+Zu/AaDRaHDXXXfxd3/3d6xevZr58+fzZ3/2Z2f7ZZy2M339pVKJD3/4w/z4xz/m4YcfZnx8nHvvvfdsv4zTdqavf8KnP/1pli5deraWLc4BEtCFEOcMpdQxHzsnPTCFEJPjy1/+Mm9961sBGBoa4mUvexlf//rXn/W41atXE4Yh119/PVAE0q9//eukaQpAHMf87u/+Lh/5yEee9XfWTDYZr79UKnHTTTd1X/dLXvIStm/fftZew6k42aqsr371q/z8z/88c+fORSnF7bffzgMPPADAd77zHa666ipWrVoFwFve8pbufTPdZLz+888/n0svvRQAYwxXX331jP1+P9NkvH4oduH/8R//kXe+851ndf3ihU0CuhDinLBo0SL27t3bLS1zzrFnz54X1A6VEGLm2r17N0uWLOl+vHTp0uNW6OzateuYx/X09FCr1bpl3h/84Af5lV/5FYaGhqZ8zZNpsl7/0e6//35uvvnmqVnwGTrZqqxnvt6jvy7Hu2/fvn1Ya8/CKzgzk/H6j9ZsNvnc5z43Y7/fzzQZr99ay2//9m/z4Q9/uHsdIU6G/LQIIc4Jc+bM4bLLLuMLX/gCb3zjG/nqV7/K0qVLWbZs2XQvTQjxAnDLLbewYcOG4973ve99Dzi2Sue5KnROVM3zyCOP8Pjjj/OHf/iHZ7jayXc2Xv/R/uzP/oytW7fysY997HSWe1acbFXWc31dXkhVEs80Ga8fIE1Tbr/9dn7u536OW2+9dXIXOYXO9PXfc889vOxlL+Pyyy+f0b0WxMwjAV0Icc74+Mc/zp133slHP/pRenp6uO+++6Z7SUKIF4iHHnroOe9fvHgxO3fuZPbs2UCxc3bjjTc+63FLlixh586d3Y/r9TqNRoP58+fzwAMPsGnTJi6//HIA9u7dyy//8i/ziU984rjXOpvOxuufcM899/C1r32NBx98kEqlMkmvYHIdXZXled4Jq7Ke+Xp37drVfcySJUt4+OGHu/ft3LmTBQsWoPXML2CdjNcPRTi/7bbbmDdvHn/6p3961tZ/pibj9f/whz/kqaee4vOf/zx5njM6Ospll13Gww8/TH9//9l8OeIFZub/DSGEECdp5cqVfPvb3+axxx7jX//1X7noooume0lCiHPE6173Oj7zmc8ARWOoH/zgB7zmNa951uOuvPJKoijqBrO/+qu/4tZbb8X3fd75zneyfv161q5dy9q1a1m4cCFf+tKXpj2cn4zJeP0A9957Lw888AAPPvjgjA4pR1dlASesynrta1/LP/3TP3Hw4EGcc3z2s5/ll3/5lwF41atexeOPP87GjRsB+Iu/+IvufTPdZLz+LMu4/fbbGRgY4M///M9fUNUEk/H6v/CFL/Dkk0+ydu1aHnroIfr7+1m7du2M/rkXM4MaHR2VLkpCCDFDXHfddbz73e/mF3/xF3HOcdA4JbEAAAjuSURBVPXVVzN//vzu7taHP/xhHnvsMT7/+c9P80qFeHFpNpu87W1vY82aNWit+YM/+ANe97rXAfDZz36Wffv2dbuVP/LII7zzne8kiiIWLFjApz/9aRYuXPisa04EgIsvvvisvpbTMRmvf8+ePVxyySUMDQ1Rq9UACMOQ7373u9P2up7Lpk2buPPOOzly5Ei3Kuuiiy7irrvu4pZbbum+QfHXf/3XfPzjH8dayyte8Qo++tGPdt+Q+MY3vsH73/9+sizj4osv5r777qO3t3c6X9ZJO9PX/8UvfpE77riDSy65pBvOr7vuOj7ykY9M58s6aZPx/Z+wY8cOfu7nfo6tW7dOx0sRLzAS0IUQYgZ59atfzZve9Cbe/OY3861vfYu77rqL2bNn88Mf/hBrLVdccQUf//jHedWrXjXdSxVCCCGEEJNMStyFEGIG6e3tpdlsAvCZz3yGO++8k/HxcaAY2eP7PjfccMN0LlEIIYQQQkwRCehCCDGD9PX10Wg02Lp1K2vXruVNb3oTY2NjAPzlX/4lt99++wvqHJ8QQgghhDh5EtCFEGIGmdhB/8xnPsOb3/xmZs2aRbPZZNeuXTz88MO86U1vmu4lCiGEEEKIKSJj1oQQYgbp6+vjwIEDfOtb3+J73/seWmsqlQr/83/+T37xF39Rur8KIYQQQpzDZAddCCFmkN7eXr7yla/wMz/zMyxatAiAnp4e/uZv/ob//J//8zSvTgghhBBCTCXZQRdCiBmkr6+PZrN5TBjv6elh4cKFXHnlldO3MCGEEEIIMeVkzJoQQgghhDjn3Hrrrbz0pS/lv/23/zbdS+Huu+/m3/7t3/jmN7853UsRQsxwEtCFEEIIIcQ5Z2RkBN/3KZVKzJ49m6997Wtcf/31U/68N998M6985St5z3ve072t0WiQpikDAwNT/vxCiBc2KXEXQgghhBDnnIkwnGXZpFwvSRKCIDitz63VapOyBiHEuU+axAkhhBBCiHPOrbfeygc+8AGuuuoqAH7hF36B/v5+fvM3fxOAPM/5wAc+wMUXX8zixYu59dZbefLJJ7uff/fdd3PzzTdz7733cuGFF3LDDTcA8NGPfpSf+qmfYsGCBVx99dXcf//93c/5zd/8TX70ox/xp3/6p/T393PZZZcdc60JzWaTu+66i2XLlrFo0SLe/OY3c/DgwWOuc8cdd/CBD3yAoaEhLrzwQu69996p+2IJIWYM2UEXQgghhBDnrO985ztccMEFfO5zn+Onf/qnKZVKAHzoQx/i29/+Nv/rf/0v5s+fz9/93d/xS7/0Szz22GP09vYCsHbtWhYuXMiXv/xltC72tYIg4M///M9ZsGABq1ev5m1vexvnnXceN910Ex/60IfYuHEjL33pS7nrrrswxhx3Tb//+7/PD37wA/7+7/+earXKu971Lv7Lf/kvfPnLX+4+5qGHHuL222/nO9/5Dt///vd5xzvewc/+7M9y6aWXTvFXTAgxnSSgCyGEEEKIc9bg4CBQlLzPmzcPgCiKuPfee/nud7/LxRdfDMD73vc+HnzwQR566CH+03/6TwAopfjEJz5xTIn62972tu6/Dw0N8b3vfY+vfOUr3HTTTfT19eH7PtVqtftcz1Sv1/m7v/s7/v7v/56f+ZmfAeCTn/wkP/VTP8XTTz/NRRddBMCiRYv47//9vwOwYsUK7r33Xn74wx9KQBfiHCcBXQghhBBCvKhs27aNdrvNjTfeeMzt7Xab7du3dz8+77zznnV+/Fvf+hYf+9jH2LJlC61WiyRJukH7ZGzfvp0sy3jJS17SvW3VqlX09fWxadOmbkCf+OeEuXPncujQoZN+HiHEC5MEdCGEEEII8aLSbDYB+Kd/+if6+vqOue/oTuuVSuWY+7Zv386b3/xm3v72t3P33XfT29vLxz/+cbZu3XrSz+3cyQ1Q8n3/mI+VUif9uUKIFy4J6EIIIYQQ4pxljEFrjbW2e9uqVasIgoD9+/d3m8idjJ/85CeUSiXe+973dm/bsWPHMY/xPI88z094jeXLl+N5Hj/+8Y+56aabANi4cSNjY2OsWrXqpNcihDg3SUAXQgghhBDnLKUUixYt4nvf+x4XX3wx5XKZ3t5e3vrWt/I7v/M7JEnCFVdcwcGDB/nmN7/J61//+meVl08YGhrqniF/6Utfype+9CVWr17N5Zdf3n3MkiVLePTRR9m7dy+VSoX+/v5jrtHT08Ob3vQm3vOe91Cr1ahUKrzrXe/i537u57jwwgun8kshhHgBkDFrQgghhBDinPZHf/RHfPGLX+SCCy7gd3/3dwH44z/+Y97ylrfwvve9j5e85CX8xm/8Brt372bWrFknvM4VV1zB+973Pt7//vfzyle+kp07d3Lbbbcd85i77rqLI0eOcOWVV3L99dcf9zof+MAHeOlLX8qv/uqvcuutt7JgwQI+9alPTdrrFUK8cKnR0VE5zCKEEEIIIYQQQkwz2UEXQgghhBBCCCFmAAnoQgghhBBCCCHEDCABXQghhBBCCCGEmAEkoAshhBBCCCGEEDOABHQhhBBCCCGEEGIGkIAuhBBCCCGEEELMABLQhRBCCCGEEEKIGUACuhBCCCGEEEIIMQNIQBdCCCGEEEIIIWYACehCCCGEEEIIIcQMIAFdCCGEEEIIIYSYASSgCyGEEEIIIYQQM4AEdCGEEEIIIYQQYgaQgC6EEEIIIYQQQswAEtCFEEIIIYQQQogZQAK6EEIIIYQQQggxA0hAF0IIIYQQQgghZgAJ6EIIIYQQQgghxAwgAV0IIYQQQgghhJgBJKALIYQQQgghhBAzgAR0IYQQQgghhBBiBpCALoQQQgghhBBCzAAS0IUQQgghhBBCiBlAAroQQgghhBBCCDEDSEAXQgghhBBCCCFmAAnoQgghhBBCCCHEDCABXQghhBBCCCGEmAEkoAshhBBCCCGEEDOABHQhhBBCCCGEEGIGkIAuhBBCCCGEEELMABLQhRBCCCGEEEKIGUACuhBCCCGEEEIIMQNIQBdCCCGEEEIIIWYACehCCCGEEEIIIcQM8P8DJYiYJ+oKtXUAAAAASUVORK5CYII=' width=1000.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"068885077e2041d1b051640a8cd297f7\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"w_range = np.array([-1, 7])\\n\",\n    \"b_range = np.array([1, -14])\\n\",\n    \"quad = plt_quad_logistic( x_train, y_train, w_range, b_range )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have:\\n\",\n    \"- examined the formulas and implementation of calculating the gradient for logistic regression\\n\",\n    \"- utilized those routines in\\n\",\n    \"    - exploring a single variable data set\\n\",\n    \"    - exploring a two-variable data set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/C1_W3_Lab07_Scikit_Learn_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Ungraded Lab:  Logistic Regression using Scikit-Learn\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab you will:\\n\",\n    \"-  Train a logistic regression model using scikit-learn.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Dataset \\n\",\n    \"Let's start with the same dataset as before.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"X = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])\\n\",\n    \"y = np.array([0, 0, 0, 1, 1, 1])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Fit the model\\n\",\n    \"\\n\",\n    \"The code below imports the [logistic regression model](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression) from scikit-learn. You can fit this model on the training data by calling `fit` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"LogisticRegression()\"\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"\\n\",\n    \"lr_model = LogisticRegression()\\n\",\n    \"lr_model.fit(X, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Make Predictions\\n\",\n    \"\\n\",\n    \"You can see the predictions made by this model by calling the `predict` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Prediction on training set: [0 0 0 1 1 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"y_pred = lr_model.predict(X)\\n\",\n    \"\\n\",\n    \"print(\\\"Prediction on training set:\\\", y_pred)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Calculate accuracy\\n\",\n    \"\\n\",\n    \"You can calculate this accuracy of this model by calling the `score` function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy on training set: 1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Accuracy on training set:\\\", lr_model.score(X, y))\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/C1_W3_Lab08_Overfitting_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Ungraded Lab:  Overfitting \\n\",\n    \"\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_Overfitting_a.png\\\"     style=\\\" width:250px; padding: 10px; \\\" >\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_Overfitting_b.png\\\"     style=\\\" width:250px; padding: 10px; \\\" >\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C1_W3_Overfitting_c.png\\\"     style=\\\" width:250px; padding: 10px; \\\" >\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab, you will explore:\\n\",\n    \"- the situations where overfitting can occur\\n\",\n    \"- some of the solutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from ipywidgets import Output\\n\",\n    \"from plt_overfit import overfit_example, output\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Overfitting\\n\",\n    \"The week's lecture described situations where overfitting can arise. Run the cell below to generate a plot that will allow you to explore overfitting. There are further instructions below the cell.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Output()\",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"6b3d7992b1ad49f9a313ec228ac5ddbf\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=800.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"b05f923a07e14eb4a798cd82a0ddf399\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"display(output)\\n\",\n    \"ofit = overfit_example(False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"In the plot above you can:\\n\",\n    \"- switch between Regression and Categorization examples\\n\",\n    \"- add data\\n\",\n    \"- select the degree of the model\\n\",\n    \"- fit the model to the data  \\n\",\n    \"\\n\",\n    \"Here are some things you should try:\\n\",\n    \"- Fit the data with degree = 1; Note 'underfitting'.\\n\",\n    \"- Fit the data with degree = 6; Note 'overfitting'\\n\",\n    \"- tune degree to get the 'best fit'\\n\",\n    \"- add data:\\n\",\n    \"    - extreme examples can increase overfitting (assuming they are outliers).\\n\",\n    \"    - nominal examples can reduce overfitting\\n\",\n    \"- switch between `Regression` and `Categorical` to try both examples.\\n\",\n    \"\\n\",\n    \"To reset the plot, re-run the cell. Click slowly to allow the plot to update before receiving the next click.\\n\",\n    \"\\n\",\n    \"Notes on implementations:\\n\",\n    \"- the 'ideal' curves represent the generator model to which noise was added to achieve the data set\\n\",\n    \"- 'fit' does not use pure gradient descent to improve speed. These methods can be used on smaller data sets. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have developed some intuition about the causes and solutions to overfitting. In the next lab, you will explore a commonly used solution, Regularization.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/C1_W3_Lab09_Regularization_Soln.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Optional Lab - Regularized Cost and Gradient\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- extend the previous linear and logistic cost functions with a regularization term.\\n\",\n    \"- rerun the previous example of over-fitting with a regularization term added.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from plt_overfit import overfit_example, output\\n\",\n    \"from lab_utils_common import sigmoid\\n\",\n    \"np.set_printoptions(precision=8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Adding regularization\\n\",\n    \"<img align=\\\"Left\\\" src=\\\"./images/C1_W3_LinearGradientRegularized.png\\\"  style=\\\" width:400px; padding: 10px; \\\" >\\n\",\n    \"<img align=\\\"Center\\\" src=\\\"./images/C1_W3_LogisticGradientRegularized.png\\\"  style=\\\" width:400px; padding: 10px; \\\" >\\n\",\n    \"\\n\",\n    \"The slides above show the cost and gradient functions for both linear and logistic regression. Note:\\n\",\n    \"- Cost\\n\",\n    \"    - The cost functions differ significantly between linear and logistic regression, but adding regularization to the equations is the same.\\n\",\n    \"- Gradient\\n\",\n    \"    - The gradient functions for linear and logistic regression are very similar. They differ only in the implementation of $f_{wb}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Cost functions with regularization\\n\",\n    \"### Cost function for regularized linear regression\\n\",\n    \"\\n\",\n    \"The equation for the cost function regularized linear regression is:\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})^2  + \\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2 \\\\tag{1}$$ \\n\",\n    \"where:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b  \\\\tag{2} $$ \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Compare this to the cost function without regularization (which you implemented in  a previous lab), which is of the form:\\n\",\n    \"\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})^2 $$ \\n\",\n    \"\\n\",\n    \"The difference is the regularization term,  <span style=\\\"color:blue\\\">\\n\",\n    \"    $\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$ </span> \\n\",\n    \"    \\n\",\n    \"Including this term encourages gradient descent to minimize the size of the parameters. Note, in this example, the parameter $b$ is not regularized. This is standard practice.\\n\",\n    \"\\n\",\n    \"Below is an implementation of equations (1) and (2). Note that this uses a *standard pattern for this course*,   a `for loop` over all `m` examples.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_linear_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost (scalar):  cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m  = X.shape[0]\\n\",\n    \"    n  = len(w)\\n\",\n    \"    cost = 0.\\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb_i = np.dot(X[i], w) + b                                   #(n,)(n,)=scalar, see np.dot\\n\",\n    \"        cost = cost + (f_wb_i - y[i])**2                               #scalar             \\n\",\n    \"    cost = cost / (2 * m)                                              #scalar  \\n\",\n    \" \\n\",\n    \"    reg_cost = 0\\n\",\n    \"    for j in range(n):\\n\",\n    \"        reg_cost += (w[j]**2)                                          #scalar\\n\",\n    \"    reg_cost = (lambda_/(2*m)) * reg_cost                              #scalar\\n\",\n    \"    \\n\",\n    \"    total_cost = cost + reg_cost                                       #scalar\\n\",\n    \"    return total_cost                                                  #scalar\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Regularized cost: 0.07917239320214275\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,6)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1]).reshape(-1,)-0.5\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"cost_tmp = compute_cost_linear_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(\\\"Regularized cost:\\\", cost_tmp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Regularized cost: </b> 0.07917239320214275 </td>\\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Cost function for regularized logistic regression\\n\",\n    \"For regularized **logistic** regression, the cost function is of the form\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} \\\\left[ -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\right] + \\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2 \\\\tag{3}$$\\n\",\n    \"where:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = sigmoid(\\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b)  \\\\tag{4} $$ \\n\",\n    \"\\n\",\n    \"Compare this to the cost function without regularization (which you implemented in  a previous lab):\\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\\\left[ (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)\\\\right] $$\\n\",\n    \"\\n\",\n    \"As was the case in linear regression above, the difference is the regularization term, which is    <span style=\\\"color:blue\\\">\\n\",\n    \"    $\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$ </span> \\n\",\n    \"\\n\",\n    \"Including this term encourages gradient descent to minimize the size of the parameters. Note, in this example, the parameter $b$ is not regularized. This is standard practice. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_logistic_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost (scalar):  cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m,n  = X.shape\\n\",\n    \"    cost = 0.\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_i = np.dot(X[i], w) + b                                      #(n,)(n,)=scalar, see np.dot\\n\",\n    \"        f_wb_i = sigmoid(z_i)                                          #scalar\\n\",\n    \"        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)      #scalar\\n\",\n    \"             \\n\",\n    \"    cost = cost/m                                                      #scalar\\n\",\n    \"\\n\",\n    \"    reg_cost = 0\\n\",\n    \"    for j in range(n):\\n\",\n    \"        reg_cost += (w[j]**2)                                          #scalar\\n\",\n    \"    reg_cost = (lambda_/(2*m)) * reg_cost                              #scalar\\n\",\n    \"    \\n\",\n    \"    total_cost = cost + reg_cost                                       #scalar\\n\",\n    \"    return total_cost                                                  #scalar\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Regularized cost: 0.6850849138741673\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,6)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1]).reshape(-1,)-0.5\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"cost_tmp = compute_cost_logistic_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(\\\"Regularized cost:\\\", cost_tmp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Regularized cost: </b> 0.6850849138741673 </td>\\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Gradient descent with regularization\\n\",\n    \"The basic algorithm for running gradient descent does not change with regularization, it is:\\n\",\n    \"$$\\\\begin{align*}\\n\",\n    \"&\\\\text{repeat until convergence:} \\\\; \\\\lbrace \\\\\\\\\\n\",\n    \"&  \\\\; \\\\; \\\\;w_j = w_j -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} \\\\tag{1}  \\\\; & \\\\text{for j := 0..n-1} \\\\\\\\ \\n\",\n    \"&  \\\\; \\\\; \\\\;  \\\\; \\\\;b = b -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} \\\\\\\\\\n\",\n    \"&\\\\rbrace\\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"Where each iteration performs simultaneous updates on $w_j$ for all $j$.\\n\",\n    \"\\n\",\n    \"What changes with regularization is computing the gradients.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Computing the Gradient with regularization (both linear/logistic)\\n\",\n    \"The gradient calculation for both linear and logistic regression are nearly identical, differing only in computation of $f_{\\\\mathbf{w}b}$.\\n\",\n    \"$$\\\\begin{align*}\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})x_{j}^{(i)}  +  \\\\frac{\\\\lambda}{m} w_j \\\\tag{2} \\\\\\\\\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)}) \\\\tag{3} \\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"* m is the number of training examples in the data set      \\n\",\n    \"* $f_{\\\\mathbf{w},b}(x^{(i)})$ is the model's prediction, while $y^{(i)}$ is the target\\n\",\n    \"\\n\",\n    \"      \\n\",\n    \"* For a  <span style=\\\"color:blue\\\"> **linear** </span> regression model  \\n\",\n    \"    $f_{\\\\mathbf{w},b}(x) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$  \\n\",\n    \"* For a <span style=\\\"color:blue\\\"> **logistic** </span> regression model  \\n\",\n    \"    $z = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$  \\n\",\n    \"    $f_{\\\\mathbf{w},b}(x) = g(z)$  \\n\",\n    \"    where $g(z)$ is the sigmoid function:  \\n\",\n    \"    $g(z) = \\\\frac{1}{1+e^{-z}}$   \\n\",\n    \"    \\n\",\n    \"The term which adds regularization is  the <span style=\\\"color:blue\\\">$\\\\frac{\\\\lambda}{m} w_j $</span>.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Gradient function for regularized linear regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_gradient_linear_reg(X, y, w, b, lambda_): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      dj_dw (ndarray (n,)): The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db (scalar):       The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m,n = X.shape           #(number of examples, number of features)\\n\",\n    \"    dj_dw = np.zeros((n,))\\n\",\n    \"    dj_db = 0.\\n\",\n    \"\\n\",\n    \"    for i in range(m):                             \\n\",\n    \"        err = (np.dot(X[i], w) + b) - y[i]                 \\n\",\n    \"        for j in range(n):                         \\n\",\n    \"            dj_dw[j] = dj_dw[j] + err * X[i, j]               \\n\",\n    \"        dj_db = dj_db + err                        \\n\",\n    \"    dj_dw = dj_dw / m                                \\n\",\n    \"    dj_db = dj_db / m   \\n\",\n    \"    \\n\",\n    \"    for j in range(n):\\n\",\n    \"        dj_dw[j] = dj_dw[j] + (lambda_/m) * w[j]\\n\",\n    \"\\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dj_db: 0.6648774569425726\\n\",\n      \"Regularized dj_dw:\\n\",\n      \" [0.29653214748822276, 0.4911679625918033, 0.21645877535865857]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,3)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1])\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"dj_db_tmp, dj_dw_tmp =  compute_gradient_linear_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(f\\\"dj_db: {dj_db_tmp}\\\", )\\n\",\n    \"print(f\\\"Regularized dj_dw:\\\\n {dj_dw_tmp.tolist()}\\\", )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"dj_db: 0.6648774569425726\\n\",\n    \"Regularized dj_dw:\\n\",\n    \" [0.29653214748822276, 0.4911679625918033, 0.21645877535865857]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Gradient function for regularized logistic regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_gradient_logistic_reg(X, y, w, b, lambda_): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"    Returns\\n\",\n    \"      dj_dw (ndarray Shape (n,)): The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db (scalar)            : The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m,n = X.shape\\n\",\n    \"    dj_dw = np.zeros((n,))                            #(n,)\\n\",\n    \"    dj_db = 0.0                                       #scalar\\n\",\n    \"\\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb_i = sigmoid(np.dot(X[i],w) + b)          #(n,)(n,)=scalar\\n\",\n    \"        err_i  = f_wb_i  - y[i]                       #scalar\\n\",\n    \"        for j in range(n):\\n\",\n    \"            dj_dw[j] = dj_dw[j] + err_i * X[i,j]      #scalar\\n\",\n    \"        dj_db = dj_db + err_i\\n\",\n    \"    dj_dw = dj_dw/m                                   #(n,)\\n\",\n    \"    dj_db = dj_db/m                                   #scalar\\n\",\n    \"\\n\",\n    \"    for j in range(n):\\n\",\n    \"        dj_dw[j] = dj_dw[j] + (lambda_/m) * w[j]\\n\",\n    \"\\n\",\n    \"    return dj_db, dj_dw  \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dj_db: 0.341798994972791\\n\",\n      \"Regularized dj_dw:\\n\",\n      \" [0.17380012933994293, 0.32007507881566943, 0.10776313396851499]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,3)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1])\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"dj_db_tmp, dj_dw_tmp =  compute_gradient_logistic_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(f\\\"dj_db: {dj_db_tmp}\\\", )\\n\",\n    \"print(f\\\"Regularized dj_dw:\\\\n {dj_dw_tmp.tolist()}\\\", )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"dj_db: 0.341798994972791\\n\",\n    \"Regularized dj_dw:\\n\",\n    \" [0.17380012933994293, 0.32007507881566943, 0.10776313396851499]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Rerun over-fitting example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": \"Output()\",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"76b039411fd54fd9b42e37142c313bd5\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\",\n      \"image/png\": 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\",\n 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' width=800.0/>\\n            </div>\\n        \",\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"version_major\": 2,\n       \"version_minor\": 0,\n       \"model_id\": \"459acf8b994541afa4beff34c9e8fa9c\"\n      }\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"display(output)\\n\",\n    \"ofit = overfit_example(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"In the plot above, try out regularization on the previous example. In particular:\\n\",\n    \"- Categorical (logistic regression)\\n\",\n    \"    - set degree to 6, lambda to 0 (no regularization), fit the data\\n\",\n    \"    - now set lambda to 1 (increase regularization), fit the data, notice the difference.\\n\",\n    \"- Regression (linear regression)\\n\",\n    \"    - try the same procedure.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have:\\n\",\n    \"- examples of cost and gradient routines with regularization added for both linear and logistic regression\\n\",\n    \"- developed some intuition on how regularization can reduce over-fitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/archive/.ipynb_checkpoints/C1_W3_Lab05_Cost_Function_Soln-Copy1-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Cost Function for Logistic Regression\\n\",\n    \"\\n\",\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- examine the implementation and utilize the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_common import  plot_data, sigmoid, dlc\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset \\n\",\n    \"Let's start with the same dataset as was used in the decision boundary lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])  #(m,n)\\n\",\n    \"y_train = np.array([0, 0, 0, 1, 1, 1])                                           #(m,)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will use a helper function to plot this data. The data points with label $y=1$ are shown as red crosses, while the data points with label $y=0$ are shown as blue circles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fig,ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"plot_data(X_train, y_train, ax)\\n\",\n    \"\\n\",\n    \"# Set both axes to be from 0-4\\n\",\n    \"ax.axis([0, 4, 0, 3.5])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Cost function\\n\",\n    \"\\n\",\n    \"In a previous lab, you developed the *logistic loss* function. Recall, loss is defined to apply to one example. Here you combine the losses to form the **cost**, which includes all the examples.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the cost function is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m} \\\\sum_{i=0}^{m-1} \\\\left[ loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) \\\\right] \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"where\\n\",\n    \"* $loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)})$ is the cost for a single data point, which is:\\n\",\n    \"\\n\",\n    \"    $$loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\tag{2}$$\\n\",\n    \"    \\n\",\n    \"*  where m is the number of training examples in the data set and:\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"  f_{\\\\mathbf{w},b}(\\\\mathbf{x^{(i)}}) &= g(z^{(i)})\\\\tag{3} \\\\\\\\\\n\",\n    \"  z^{(i)} &= \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)}+ b\\\\tag{4} \\\\\\\\\\n\",\n    \"  g(z^{(i)}) &= \\\\frac{1}{1+e^{-z^{(i)}}}\\\\tag{5} \\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-02'></a>\\n\",\n    \"#### Code Description\\n\",\n    \"\\n\",\n    \"The algorithm for `compute_cost_logistic` loops over all the examples calculating the loss for each example summing.\\n\",\n    \"\\n\",\n    \"Note that the variables X and y are not scalar values but matrices of shape ($m, n$) and ($𝑚$,) respectively, where  $𝑛$ is the number of features and $𝑚$ is the number of training examples.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_logistic(X, y, w, b):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes cost\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n)): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)) : target values\\n\",\n    \"      w (ndarray (n,)) : model parameters  \\n\",\n    \"      b (scalar)       : model parameter\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      cost (scalar): cost\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m = X.shape[0]\\n\",\n    \"    cost = 0.0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_i = np.dot(X[i],w) + b\\n\",\n    \"        f_wb_i = sigmoid(z_i)\\n\",\n    \"        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)\\n\",\n    \"             \\n\",\n    \"    cost = cost / m\\n\",\n    \"    return cost\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Check the implementation of the cost function using the cell below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"w_tmp = np.array([1,1])\\n\",\n    \"b_tmp = -3\\n\",\n    \"print(compute_cost_logistic(X_train, y_train, w_tmp, b_tmp))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output**: 0.3668667864055175\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example\\n\",\n    \"Now, let's see what the cost function output is for a different value of $w$. \\n\",\n    \"\\n\",\n    \"* In a previous lab, you plotted the decision boundary for  $b = -3, w_0 = 1, w_1 = 1$. That is, you had `w = np.array([-3,1,1])`.\\n\",\n    \"\\n\",\n    \"* Let's say you want to see if $b = -4, w_0 = 1, w_1 = 1$, or `w = np.array([-4,1,1])` provides a better model.\\n\",\n    \"\\n\",\n    \"Let's first plot the decision boundary for these two different $b$ values to see which one fits the data better.\\n\",\n    \"\\n\",\n    \"* For $b = -3, w_0 = 1, w_1 = 1$, we'll plot $-3 + x_0+x_1 = 0$ (shown in blue)\\n\",\n    \"* For $b = -4, w_0 = 1, w_1 = 1$, we'll plot $-4 + x_0+x_1 = 0$ (shown in magenta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"# Choose values between 0 and 6\\n\",\n    \"x0 = np.arange(0,6)\\n\",\n    \"\\n\",\n    \"# Plot the two decision boundaries\\n\",\n    \"x1 = 3 - x0\\n\",\n    \"x1_other = 4 - x0\\n\",\n    \"\\n\",\n    \"fig,ax = plt.subplots(1, 1, figsize=(4,4))\\n\",\n    \"# Plot the decision boundary\\n\",\n    \"ax.plot(x0,x1, c=dlc[\\\"dlblue\\\"], label=\\\"$b$=-3\\\")\\n\",\n    \"ax.plot(x0,x1_other, c=dlc[\\\"dlmagenta\\\"], label=\\\"$b$=-4\\\")\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"\\n\",\n    \"# Plot the original data\\n\",\n    \"plot_data(X_train,y_train,ax)\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.title(\\\"Decision Boundary\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can see from this plot that `w = np.array([-4,1,1])` is a worse model for the training data. Let's see if the cost function implementation reflects this.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"w_array1 = np.array([1,1])\\n\",\n    \"b_1 = -3\\n\",\n    \"w_array2 = np.array([1,1])\\n\",\n    \"b_2 = -4\\n\",\n    \"\\n\",\n    \"print(\\\"Cost for b = -3 : \\\", compute_cost_logistic(X_train, y_train, w_array1, b_1))\\n\",\n    \"print(\\\"Cost for b = -4 : \\\", compute_cost_logistic(X_train, y_train, w_array2, b_2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output**\\n\",\n    \"\\n\",\n    \"Cost for b = -3 :  0.3668667864055175\\n\",\n    \"\\n\",\n    \"Cost for b = -4 :  0.5036808636748461\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"You can see the cost function behaves as expected and the cost for `w = np.array([-4,1,1])` is indeed higher than the cost for `w = np.array([-3,1,1])`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you examined and utilized the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/archive/.ipynb_checkpoints/C1_W3_Lab05_Cost_Function_Soln-Copy2-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Cost Function for Logistic Regression\\n\",\n    \"\\n\",\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- examine the implementation and utilize the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_common import  plot_data, sigmoid, dlc\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset \\n\",\n    \"Let's start with the same dataset as was used in the decision boundary lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])  #(m,n)\\n\",\n    \"y_train = np.array([0, 0, 0, 1, 1, 1])                                           #(m,)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will use a helper function to plot this data. The data points with label $y=1$ are shown as red crosses, while the data points with label $y=0$ are shown as blue circles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fig,ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"plot_data(X_train, y_train, ax)\\n\",\n    \"\\n\",\n    \"# Set both axes to be from 0-4\\n\",\n    \"ax.axis([0, 4, 0, 3.5])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Cost function\\n\",\n    \"\\n\",\n    \"In a previous lab, you developed the *logistic loss* function. Recall, loss is defined to apply to one example. Here you combine the losses to form the **cost**, which includes all the examples.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the cost function is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m} \\\\sum_{i=0}^{m-1} \\\\left[ loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) \\\\right] \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"where\\n\",\n    \"* $loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)})$ is the cost for a single data point, which is:\\n\",\n    \"\\n\",\n    \"    $$loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\tag{2}$$\\n\",\n    \"    \\n\",\n    \"*  where m is the number of training examples in the data set and:\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"  f_{\\\\mathbf{w},b}(\\\\mathbf{x^{(i)}}) &= g(z^{(i)})\\\\tag{3} \\\\\\\\\\n\",\n    \"  z^{(i)} &= \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)}+ b\\\\tag{4} \\\\\\\\\\n\",\n    \"  g(z^{(i)}) &= \\\\frac{1}{1+e^{-z^{(i)}}}\\\\tag{5} \\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-02'></a>\\n\",\n    \"#### Code Description\\n\",\n    \"\\n\",\n    \"The algorithm for `compute_cost_logistic` loops over all the examples calculating the loss for each example and accumulating the total.\\n\",\n    \"\\n\",\n    \"Note that the variables X and y are not scalar values but matrices of shape ($m, n$) and ($𝑚$,) respectively, where  $𝑛$ is the number of features and $𝑚$ is the number of training examples.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_logistic(X, y, w, b):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes cost\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n)): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)) : target values\\n\",\n    \"      w (ndarray (n,)) : model parameters  \\n\",\n    \"      b (scalar)       : model parameter\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      cost (scalar): cost\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m = X.shape[0]\\n\",\n    \"    cost = 0.0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_i = np.dot(X[i],w) + b\\n\",\n    \"        f_wb_i = sigmoid(z_i)\\n\",\n    \"        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)\\n\",\n    \"             \\n\",\n    \"    cost = cost / m\\n\",\n    \"    return cost\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Check the implementation of the cost function using the cell below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"w_tmp = np.array([1,1])\\n\",\n    \"b_tmp = -3\\n\",\n    \"print(compute_cost_logistic(X_train, y_train, w_tmp, b_tmp))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output**: 0.3668667864055175\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example\\n\",\n    \"Now, let's see what the cost function output is for a different value of $w$. \\n\",\n    \"\\n\",\n    \"* In a previous lab, you plotted the decision boundary for  $b = -3, w_0 = 1, w_1 = 1$. That is, you had `w = np.array([-3,1,1])`.\\n\",\n    \"\\n\",\n    \"* Let's say you want to see if $b = -4, w_0 = 1, w_1 = 1$, or `w = np.array([-4,1,1])` provides a better model.\\n\",\n    \"\\n\",\n    \"Let's first plot the decision boundary for these two different $b$ values to see which one fits the data better.\\n\",\n    \"\\n\",\n    \"* For $b = -3, w_0 = 1, w_1 = 1$, we'll plot $-3 + x_0+x_1 = 0$ (shown in blue)\\n\",\n    \"* For $b = -4, w_0 = 1, w_1 = 1$, we'll plot $-4 + x_0+x_1 = 0$ (shown in magenta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"# Choose values between 0 and 6\\n\",\n    \"x0 = np.arange(0,6)\\n\",\n    \"\\n\",\n    \"# Plot the two decision boundaries\\n\",\n    \"x1 = 3 - x0\\n\",\n    \"x1_other = 4 - x0\\n\",\n    \"\\n\",\n    \"fig,ax = plt.subplots(1, 1, figsize=(4,4))\\n\",\n    \"# Plot the decision boundary\\n\",\n    \"ax.plot(x0,x1, c=dlc[\\\"dlblue\\\"], label=\\\"$b$=-3\\\")\\n\",\n    \"ax.plot(x0,x1_other, c=dlc[\\\"dlmagenta\\\"], label=\\\"$b$=-4\\\")\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"\\n\",\n    \"# Plot the original data\\n\",\n    \"plot_data(X_train,y_train,ax)\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.title(\\\"Decision Boundary\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can see from this plot that `w = np.array([-4,1,1])` is a worse model for the training data. Let's see if the cost function implementation reflects this.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"w_array1 = np.array([1,1])\\n\",\n    \"b_1 = -3\\n\",\n    \"w_array2 = np.array([1,1])\\n\",\n    \"b_2 = -4\\n\",\n    \"\\n\",\n    \"print(\\\"Cost for b = -3 : \\\", compute_cost_logistic(X_train, y_train, w_array1, b_1))\\n\",\n    \"print(\\\"Cost for b = -4 : \\\", compute_cost_logistic(X_train, y_train, w_array2, b_2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output**\\n\",\n    \"\\n\",\n    \"Cost for b = -3 :  0.3668667864055175\\n\",\n    \"\\n\",\n    \"Cost for b = -4 :  0.5036808636748461\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"You can see the cost function behaves as expected and the cost for `w = np.array([-4,1,1])` is indeed higher than the cost for `w = np.array([-3,1,1])`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you examined and utilized the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/archive/.ipynb_checkpoints/C1_W3_Lab09_Regularization_Soln-Copy1-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab - Regularized Cost and Gradient\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- extend the previous linear and logistic cost functions with a regularization term.\\n\",\n    \"- rerun the previous example of over-fitting with a regularization term added.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from plt_overfit import overfit_example, output\\n\",\n    \"from lab_utils_common import sigmoid\\n\",\n    \"np.set_printoptions(precision=8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Adding regularization\\n\",\n    \"<img align=\\\"Left\\\" src=\\\"./images/C1_W3_LinearGradientRegularized.png\\\"  style=\\\" width:400px; padding: 10px; \\\" >\\n\",\n    \"<img align=\\\"Center\\\" src=\\\"./images/C1_W3_LogisticGradientRegularized.png\\\"  style=\\\" width:400px; padding: 10px; \\\" >\\n\",\n    \"\\n\",\n    \"The slides above show the cost and gradient functions for both linear and logistic regression. Note:\\n\",\n    \"- Cost\\n\",\n    \"    - The cost functions differ significantly between linear and logistic regression, but adding regularization to the equations is the same.\\n\",\n    \"- Gradient\\n\",\n    \"    - The gradient functions for linear and logistic regression are very similar. They differ only in the implementation of $f_{wb}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Cost functions with regularization\\n\",\n    \"### Cost function for regularized linear regression\\n\",\n    \"\\n\",\n    \"The equation for the cost function regularized linear regression is:\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})^2  + \\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2 \\\\tag{1}$$ \\n\",\n    \"where:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b  \\\\tag{2} $$ \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Compare this to the cost function without regularization (which you implemented in  a previous lab), which is of the form:\\n\",\n    \"\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})^2 $$ \\n\",\n    \"\\n\",\n    \"The difference is the regularization term,  <span style=\\\"color:blue\\\">\\n\",\n    \"    $\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$ </span> \\n\",\n    \"    \\n\",\n    \"Including this term incentives gradient descent to minimize the size of the parameters. Note, in this example, the parameter $b$ is not regularized. This is standard practice.\\n\",\n    \"\\n\",\n    \"Below is an implementation of equations (1) and (2). Note that this uses a *standard pattern for this course*,   a `for loop` over all `m` examples.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_linear_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost (scalar):  cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m  = X.shape[0]\\n\",\n    \"    n  = len(w)\\n\",\n    \"    cost = 0.\\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb_i = np.dot(X[i], w) + b                                   #(n,)(n,)=scalar, see np.dot\\n\",\n    \"        cost = cost + (f_wb_i - y[i])**2                               #scalar             \\n\",\n    \"    cost = cost / (2 * m)                                              #scalar  \\n\",\n    \" \\n\",\n    \"    reg_cost = 0\\n\",\n    \"    for j in range(n):\\n\",\n    \"        reg_cost += (w[j]**2)                                          #scalar\\n\",\n    \"    reg_cost = (lambda_/(2*m)) * reg_cost                              #scalar\\n\",\n    \"    \\n\",\n    \"    total_cost = cost + reg_cost                                       #scalar\\n\",\n    \"    return total_cost                                                  #scalar\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,6)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1]).reshape(-1,)-0.5\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"cost_tmp = compute_cost_linear_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(\\\"Regularized cost:\\\", cost_tmp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Regularized cost: </b> 0.07917239320214275 </td>\\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Cost function for regularized logistic regression\\n\",\n    \"For regularized **logistic** regression, the cost function is of the form\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} \\\\left[ -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\right] + \\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2 \\\\tag{3}$$\\n\",\n    \"where:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = sigmoid(\\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b)  \\\\tag{4} $$ \\n\",\n    \"\\n\",\n    \"Compare this to the cost function without regularization (which you implemented in  a previous lab):\\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\\\left[ (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)\\\\right] $$\\n\",\n    \"\\n\",\n    \"As was the case in linear regression above, the difference is the regularization term, which is    <span style=\\\"color:blue\\\">\\n\",\n    \"    $\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$ </span> \\n\",\n    \"\\n\",\n    \"Including this term incentives gradient descent to minimize the size of the parameters. Note, in this example, the parameter $b$ is not regularized. This is standard practice. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_logistic_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost (scalar):  cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m,n  = X.shape\\n\",\n    \"    cost = 0.\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_i = np.dot(X[i], w) + b                                      #(n,)(n,)=scalar, see np.dot\\n\",\n    \"        f_wb_i = sigmoid(z_i)                                          #scalar\\n\",\n    \"        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)      #scalar\\n\",\n    \"             \\n\",\n    \"    cost = cost/m                                                      #scalar\\n\",\n    \"\\n\",\n    \"    reg_cost = 0\\n\",\n    \"    for j in range(n):\\n\",\n    \"        reg_cost += (w[j]**2)                                          #scalar\\n\",\n    \"    reg_cost = (lambda_/(2*m)) * reg_cost                              #scalar\\n\",\n    \"    \\n\",\n    \"    total_cost = cost + reg_cost                                       #scalar\\n\",\n    \"    return total_cost                                                  #scalar\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,6)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1]).reshape(-1,)-0.5\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"cost_tmp = compute_cost_logistic_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(\\\"Regularized cost:\\\", cost_tmp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Regularized cost: </b> 0.6850849138741673 </td>\\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Gradient descent with regularization\\n\",\n    \"The basic algorithm for running gradient descent does not change with regularization, it is:\\n\",\n    \"$$\\\\begin{align*}\\n\",\n    \"&\\\\text{repeat until convergence:} \\\\; \\\\lbrace \\\\\\\\\\n\",\n    \"&  \\\\; \\\\; \\\\;w_j = w_j -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} \\\\tag{1}  \\\\; & \\\\text{for j := 0..n-1} \\\\\\\\ \\n\",\n    \"&  \\\\; \\\\; \\\\;  \\\\; \\\\;b = b -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} \\\\\\\\\\n\",\n    \"&\\\\rbrace\\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"Where each iteration performs simultaneous updates on $w_j$ for all $j$.\\n\",\n    \"\\n\",\n    \"What changes with regularization is computing the gradients.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Computing the Gradient with regularization (both linear/logistic)\\n\",\n    \"The gradient calculation for both linear and logistic regression are nearly identical, differing only in computation of $f_{\\\\mathbf{w}b}$.\\n\",\n    \"$$\\\\begin{align*}\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})x_{j}^{(i)}  +  \\\\frac{\\\\lambda}{m} w_j \\\\tag{2} \\\\\\\\\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)}) \\\\tag{3} \\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"* m is the number of training examples in the data set      \\n\",\n    \"* $f_{\\\\mathbf{w},b}(x^{(i)})$ is the model's prediction, while $y^{(i)}$ is the target\\n\",\n    \"\\n\",\n    \"      \\n\",\n    \"* For a  <span style=\\\"color:blue\\\"> **linear** </span> regression model  \\n\",\n    \"    $f_{\\\\mathbf{w},b}(x) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$  \\n\",\n    \"* For a <span style=\\\"color:blue\\\"> **logistic** </span> regression model  \\n\",\n    \"    $z = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$  \\n\",\n    \"    $f_{\\\\mathbf{w},b}(x) = g(z)$  \\n\",\n    \"    where $g(z)$ is the sigmoid function:  \\n\",\n    \"    $g(z) = \\\\frac{1}{1+e^{-z}}$   \\n\",\n    \"    \\n\",\n    \"The term which adds regularization is  the <span style=\\\"color:blue\\\">$\\\\frac{\\\\lambda}{m} w_j $</span>.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Gradient function for regularized linear regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_gradient_linear_reg(X, y, w, b, lambda_): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      dj_dw (ndarray (n,)): The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db (scalar):       The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m,n = X.shape           #(number of examples, number of features)\\n\",\n    \"    dj_dw = np.zeros((n,))\\n\",\n    \"    dj_db = 0.\\n\",\n    \"\\n\",\n    \"    for i in range(m):                             \\n\",\n    \"        err = (np.dot(X[i], w) + b) - y[i]                 \\n\",\n    \"        for j in range(n):                         \\n\",\n    \"            dj_dw[j] = dj_dw[j] + err * X[i, j]               \\n\",\n    \"        dj_db = dj_db + err                        \\n\",\n    \"    dj_dw = dj_dw / m                                \\n\",\n    \"    dj_db = dj_db / m   \\n\",\n    \"    \\n\",\n    \"    for j in range(n):\\n\",\n    \"        dj_dw[j] = dj_dw[j] + (lambda_/m) * w[j]\\n\",\n    \"\\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,3)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1])\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"dj_db_tmp, dj_dw_tmp =  compute_gradient_linear_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(f\\\"dj_db: {dj_db_tmp}\\\", )\\n\",\n    \"print(f\\\"Regularized dj_dw:\\\\n {dj_dw_tmp.tolist()}\\\", )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"dj_db: 0.6648774569425726\\n\",\n    \"Regularized dj_dw:\\n\",\n    \" [0.29653214748822276, 0.4911679625918033, 0.21645877535865857]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Gradient function for regularized logistic regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_gradient_logistic_reg(X, y, w, b, lambda_): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"    Returns\\n\",\n    \"      dj_dw (ndarray Shape (n,)): The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db (scalar)            : The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m,n = X.shape\\n\",\n    \"    dj_dw = np.zeros((n,))                            #(n,)\\n\",\n    \"    dj_db = 0.0                                       #scalar\\n\",\n    \"\\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb_i = sigmoid(np.dot(X[i],w) + b)          #(n,)(n,)=scalar\\n\",\n    \"        err_i  = f_wb_i  - y[i]                       #scalar\\n\",\n    \"        for j in range(n):\\n\",\n    \"            dj_dw[j] = dj_dw[j] + err_i * X[i,j]      #scalar\\n\",\n    \"        dj_db = dj_db + err_i\\n\",\n    \"    dj_dw = dj_dw/m                                   #(n,)\\n\",\n    \"    dj_db = dj_db/m                                   #scalar\\n\",\n    \"\\n\",\n    \"    for j in range(n):\\n\",\n    \"        dj_dw[j] = dj_dw[j] + (lambda_/m) * w[j]\\n\",\n    \"\\n\",\n    \"    return dj_db, dj_dw  \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,3)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1])\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"dj_db_tmp, dj_dw_tmp =  compute_gradient_logistic_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(f\\\"dj_db: {dj_db_tmp}\\\", )\\n\",\n    \"print(f\\\"Regularized dj_dw:\\\\n {dj_dw_tmp.tolist()}\\\", )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"dj_db: 0.341798994972791\\n\",\n    \"Regularized dj_dw:\\n\",\n    \" [0.17380012933994293, 0.32007507881566943, 0.10776313396851499]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Rerun over-fitting example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"display(output)\\n\",\n    \"ofit = overfit_example(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the plot above, try out regularization on the previous example. In particular:\\n\",\n    \"- Categorical (logistic regression)\\n\",\n    \"    - set degree to 6, lambda to 0 (no regularization), fit the data\\n\",\n    \"    - now set lambda to 1 (increase regularization), fit the data, notice the difference.\\n\",\n    \"- Regression (linear regression)\\n\",\n    \"    - try the same procedure.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have:\\n\",\n    \"- examples of cost and gradient routines with regression added for both linear and logistic regression\\n\",\n    \"- developed some intuition on how regularization can reduce over-fitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/archive/C1_W3_Lab05_Cost_Function_Soln-Copy1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Cost Function for Logistic Regression\\n\",\n    \"\\n\",\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- examine the implementation and utilize the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_common import  plot_data, sigmoid, dlc\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset \\n\",\n    \"Let's start with the same dataset as was used in the decision boundary lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])  #(m,n)\\n\",\n    \"y_train = np.array([0, 0, 0, 1, 1, 1])                                           #(m,)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will use a helper function to plot this data. The data points with label $y=1$ are shown as red crosses, while the data points with label $y=0$ are shown as blue circles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fig,ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"plot_data(X_train, y_train, ax)\\n\",\n    \"\\n\",\n    \"# Set both axes to be from 0-4\\n\",\n    \"ax.axis([0, 4, 0, 3.5])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Cost function\\n\",\n    \"\\n\",\n    \"In a previous lab, you developed the *logistic loss* function. Recall, loss is defined to apply to one example. Here you combine the losses to form the **cost**, which includes all the examples.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the cost function is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m} \\\\sum_{i=0}^{m-1} \\\\left[ loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) \\\\right] \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"where\\n\",\n    \"* $loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)})$ is the cost for a single data point, which is:\\n\",\n    \"\\n\",\n    \"    $$loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\tag{2}$$\\n\",\n    \"    \\n\",\n    \"*  where m is the number of training examples in the data set and:\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"  f_{\\\\mathbf{w},b}(\\\\mathbf{x^{(i)}}) &= g(z^{(i)})\\\\tag{3} \\\\\\\\\\n\",\n    \"  z^{(i)} &= \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)}+ b\\\\tag{4} \\\\\\\\\\n\",\n    \"  g(z^{(i)}) &= \\\\frac{1}{1+e^{-z^{(i)}}}\\\\tag{5} \\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-02'></a>\\n\",\n    \"#### Code Description\\n\",\n    \"\\n\",\n    \"The algorithm for `compute_cost_logistic` loops over all the examples calculating the loss for each example summing.\\n\",\n    \"\\n\",\n    \"Note that the variables X and y are not scalar values but matrices of shape ($m, n$) and ($𝑚$,) respectively, where  $𝑛$ is the number of features and $𝑚$ is the number of training examples.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_logistic(X, y, w, b):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes cost\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n)): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)) : target values\\n\",\n    \"      w (ndarray (n,)) : model parameters  \\n\",\n    \"      b (scalar)       : model parameter\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      cost (scalar): cost\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m = X.shape[0]\\n\",\n    \"    cost = 0.0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_i = np.dot(X[i],w) + b\\n\",\n    \"        f_wb_i = sigmoid(z_i)\\n\",\n    \"        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)\\n\",\n    \"             \\n\",\n    \"    cost = cost / m\\n\",\n    \"    return cost\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Check the implementation of the cost function using the cell below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"w_tmp = np.array([1,1])\\n\",\n    \"b_tmp = -3\\n\",\n    \"print(compute_cost_logistic(X_train, y_train, w_tmp, b_tmp))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output**: 0.3668667864055175\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example\\n\",\n    \"Now, let's see what the cost function output is for a different value of $w$. \\n\",\n    \"\\n\",\n    \"* In a previous lab, you plotted the decision boundary for  $b = -3, w_0 = 1, w_1 = 1$. That is, you had `w = np.array([-3,1,1])`.\\n\",\n    \"\\n\",\n    \"* Let's say you want to see if $b = -4, w_0 = 1, w_1 = 1$, or `w = np.array([-4,1,1])` provides a better model.\\n\",\n    \"\\n\",\n    \"Let's first plot the decision boundary for these two different $b$ values to see which one fits the data better.\\n\",\n    \"\\n\",\n    \"* For $b = -3, w_0 = 1, w_1 = 1$, we'll plot $-3 + x_0+x_1 = 0$ (shown in blue)\\n\",\n    \"* For $b = -4, w_0 = 1, w_1 = 1$, we'll plot $-4 + x_0+x_1 = 0$ (shown in magenta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"# Choose values between 0 and 6\\n\",\n    \"x0 = np.arange(0,6)\\n\",\n    \"\\n\",\n    \"# Plot the two decision boundaries\\n\",\n    \"x1 = 3 - x0\\n\",\n    \"x1_other = 4 - x0\\n\",\n    \"\\n\",\n    \"fig,ax = plt.subplots(1, 1, figsize=(4,4))\\n\",\n    \"# Plot the decision boundary\\n\",\n    \"ax.plot(x0,x1, c=dlc[\\\"dlblue\\\"], label=\\\"$b$=-3\\\")\\n\",\n    \"ax.plot(x0,x1_other, c=dlc[\\\"dlmagenta\\\"], label=\\\"$b$=-4\\\")\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"\\n\",\n    \"# Plot the original data\\n\",\n    \"plot_data(X_train,y_train,ax)\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.title(\\\"Decision Boundary\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can see from this plot that `w = np.array([-4,1,1])` is a worse model for the training data. Let's see if the cost function implementation reflects this.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"w_array1 = np.array([1,1])\\n\",\n    \"b_1 = -3\\n\",\n    \"w_array2 = np.array([1,1])\\n\",\n    \"b_2 = -4\\n\",\n    \"\\n\",\n    \"print(\\\"Cost for b = -3 : \\\", compute_cost_logistic(X_train, y_train, w_array1, b_1))\\n\",\n    \"print(\\\"Cost for b = -4 : \\\", compute_cost_logistic(X_train, y_train, w_array2, b_2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output**\\n\",\n    \"\\n\",\n    \"Cost for b = -3 :  0.3668667864055175\\n\",\n    \"\\n\",\n    \"Cost for b = -4 :  0.5036808636748461\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"You can see the cost function behaves as expected and the cost for `w = np.array([-4,1,1])` is indeed higher than the cost for `w = np.array([-3,1,1])`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you examined and utilized the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/archive/C1_W3_Lab05_Cost_Function_Soln-Copy2.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Cost Function for Logistic Regression\\n\",\n    \"\\n\",\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- examine the implementation and utilize the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from lab_utils_common import  plot_data, sigmoid, dlc\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset \\n\",\n    \"Let's start with the same dataset as was used in the decision boundary lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[0.5, 1.5], [1,1], [1.5, 0.5], [3, 0.5], [2, 2], [1, 2.5]])  #(m,n)\\n\",\n    \"y_train = np.array([0, 0, 0, 1, 1, 1])                                           #(m,)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will use a helper function to plot this data. The data points with label $y=1$ are shown as red crosses, while the data points with label $y=0$ are shown as blue circles.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fig,ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"plot_data(X_train, y_train, ax)\\n\",\n    \"\\n\",\n    \"# Set both axes to be from 0-4\\n\",\n    \"ax.axis([0, 4, 0, 3.5])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Cost function\\n\",\n    \"\\n\",\n    \"In a previous lab, you developed the *logistic loss* function. Recall, loss is defined to apply to one example. Here you combine the losses to form the **cost**, which includes all the examples.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Recall that for logistic regression, the cost function is of the form \\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m} \\\\sum_{i=0}^{m-1} \\\\left[ loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) \\\\right] \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"where\\n\",\n    \"* $loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)})$ is the cost for a single data point, which is:\\n\",\n    \"\\n\",\n    \"    $$loss(f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}), y^{(i)}) = -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\tag{2}$$\\n\",\n    \"    \\n\",\n    \"*  where m is the number of training examples in the data set and:\\n\",\n    \"$$\\n\",\n    \"\\\\begin{align}\\n\",\n    \"  f_{\\\\mathbf{w},b}(\\\\mathbf{x^{(i)}}) &= g(z^{(i)})\\\\tag{3} \\\\\\\\\\n\",\n    \"  z^{(i)} &= \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)}+ b\\\\tag{4} \\\\\\\\\\n\",\n    \"  g(z^{(i)}) &= \\\\frac{1}{1+e^{-z^{(i)}}}\\\\tag{5} \\n\",\n    \"\\\\end{align}\\n\",\n    \"$$\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name='ex-02'></a>\\n\",\n    \"#### Code Description\\n\",\n    \"\\n\",\n    \"The algorithm for `compute_cost_logistic` loops over all the examples calculating the loss for each example and accumulating the total.\\n\",\n    \"\\n\",\n    \"Note that the variables X and y are not scalar values but matrices of shape ($m, n$) and ($𝑚$,) respectively, where  $𝑛$ is the number of features and $𝑚$ is the number of training examples.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_logistic(X, y, w, b):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes cost\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n)): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)) : target values\\n\",\n    \"      w (ndarray (n,)) : model parameters  \\n\",\n    \"      b (scalar)       : model parameter\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      cost (scalar): cost\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m = X.shape[0]\\n\",\n    \"    cost = 0.0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_i = np.dot(X[i],w) + b\\n\",\n    \"        f_wb_i = sigmoid(z_i)\\n\",\n    \"        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)\\n\",\n    \"             \\n\",\n    \"    cost = cost / m\\n\",\n    \"    return cost\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Check the implementation of the cost function using the cell below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"w_tmp = np.array([1,1])\\n\",\n    \"b_tmp = -3\\n\",\n    \"print(compute_cost_logistic(X_train, y_train, w_tmp, b_tmp))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output**: 0.3668667864055175\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Example\\n\",\n    \"Now, let's see what the cost function output is for a different value of $w$. \\n\",\n    \"\\n\",\n    \"* In a previous lab, you plotted the decision boundary for  $b = -3, w_0 = 1, w_1 = 1$. That is, you had `w = np.array([-3,1,1])`.\\n\",\n    \"\\n\",\n    \"* Let's say you want to see if $b = -4, w_0 = 1, w_1 = 1$, or `w = np.array([-4,1,1])` provides a better model.\\n\",\n    \"\\n\",\n    \"Let's first plot the decision boundary for these two different $b$ values to see which one fits the data better.\\n\",\n    \"\\n\",\n    \"* For $b = -3, w_0 = 1, w_1 = 1$, we'll plot $-3 + x_0+x_1 = 0$ (shown in blue)\\n\",\n    \"* For $b = -4, w_0 = 1, w_1 = 1$, we'll plot $-4 + x_0+x_1 = 0$ (shown in magenta)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"# Choose values between 0 and 6\\n\",\n    \"x0 = np.arange(0,6)\\n\",\n    \"\\n\",\n    \"# Plot the two decision boundaries\\n\",\n    \"x1 = 3 - x0\\n\",\n    \"x1_other = 4 - x0\\n\",\n    \"\\n\",\n    \"fig,ax = plt.subplots(1, 1, figsize=(4,4))\\n\",\n    \"# Plot the decision boundary\\n\",\n    \"ax.plot(x0,x1, c=dlc[\\\"dlblue\\\"], label=\\\"$b$=-3\\\")\\n\",\n    \"ax.plot(x0,x1_other, c=dlc[\\\"dlmagenta\\\"], label=\\\"$b$=-4\\\")\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"\\n\",\n    \"# Plot the original data\\n\",\n    \"plot_data(X_train,y_train,ax)\\n\",\n    \"ax.axis([0, 4, 0, 4])\\n\",\n    \"ax.set_ylabel('$x_1$', fontsize=12)\\n\",\n    \"ax.set_xlabel('$x_0$', fontsize=12)\\n\",\n    \"plt.legend(loc=\\\"upper right\\\")\\n\",\n    \"plt.title(\\\"Decision Boundary\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can see from this plot that `w = np.array([-4,1,1])` is a worse model for the training data. Let's see if the cost function implementation reflects this.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"w_array1 = np.array([1,1])\\n\",\n    \"b_1 = -3\\n\",\n    \"w_array2 = np.array([1,1])\\n\",\n    \"b_2 = -4\\n\",\n    \"\\n\",\n    \"print(\\\"Cost for b = -3 : \\\", compute_cost_logistic(X_train, y_train, w_array1, b_1))\\n\",\n    \"print(\\\"Cost for b = -4 : \\\", compute_cost_logistic(X_train, y_train, w_array2, b_2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected output**\\n\",\n    \"\\n\",\n    \"Cost for b = -3 :  0.3668667864055175\\n\",\n    \"\\n\",\n    \"Cost for b = -4 :  0.5036808636748461\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"You can see the cost function behaves as expected and the cost for `w = np.array([-4,1,1])` is indeed higher than the cost for `w = np.array([-3,1,1])`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you examined and utilized the cost function for logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/archive/C1_W3_Lab09_Regularization_Soln-Copy1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab - Regularized Cost and Gradient\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Goals\\n\",\n    \"In this lab, you will:\\n\",\n    \"- extend the previous linear and logistic cost functions with a regularization term.\\n\",\n    \"- rerun the previous example of over-fitting with a regularization term added.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from plt_overfit import overfit_example, output\\n\",\n    \"from lab_utils_common import sigmoid\\n\",\n    \"np.set_printoptions(precision=8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Adding regularization\\n\",\n    \"<img align=\\\"Left\\\" src=\\\"./images/C1_W3_LinearGradientRegularized.png\\\"  style=\\\" width:400px; padding: 10px; \\\" >\\n\",\n    \"<img align=\\\"Center\\\" src=\\\"./images/C1_W3_LogisticGradientRegularized.png\\\"  style=\\\" width:400px; padding: 10px; \\\" >\\n\",\n    \"\\n\",\n    \"The slides above show the cost and gradient functions for both linear and logistic regression. Note:\\n\",\n    \"- Cost\\n\",\n    \"    - The cost functions differ significantly between linear and logistic regression, but adding regularization to the equations is the same.\\n\",\n    \"- Gradient\\n\",\n    \"    - The gradient functions for linear and logistic regression are very similar. They differ only in the implementation of $f_{wb}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Cost functions with regularization\\n\",\n    \"### Cost function for regularized linear regression\\n\",\n    \"\\n\",\n    \"The equation for the cost function regularized linear regression is:\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})^2  + \\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2 \\\\tag{1}$$ \\n\",\n    \"where:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b  \\\\tag{2} $$ \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Compare this to the cost function without regularization (which you implemented in  a previous lab), which is of the form:\\n\",\n    \"\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{2m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})^2 $$ \\n\",\n    \"\\n\",\n    \"The difference is the regularization term,  <span style=\\\"color:blue\\\">\\n\",\n    \"    $\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$ </span> \\n\",\n    \"    \\n\",\n    \"Including this term incentives gradient descent to minimize the size of the parameters. Note, in this example, the parameter $b$ is not regularized. This is standard practice.\\n\",\n    \"\\n\",\n    \"Below is an implementation of equations (1) and (2). Note that this uses a *standard pattern for this course*,   a `for loop` over all `m` examples.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_linear_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost (scalar):  cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m  = X.shape[0]\\n\",\n    \"    n  = len(w)\\n\",\n    \"    cost = 0.\\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb_i = np.dot(X[i], w) + b                                   #(n,)(n,)=scalar, see np.dot\\n\",\n    \"        cost = cost + (f_wb_i - y[i])**2                               #scalar             \\n\",\n    \"    cost = cost / (2 * m)                                              #scalar  \\n\",\n    \" \\n\",\n    \"    reg_cost = 0\\n\",\n    \"    for j in range(n):\\n\",\n    \"        reg_cost += (w[j]**2)                                          #scalar\\n\",\n    \"    reg_cost = (lambda_/(2*m)) * reg_cost                              #scalar\\n\",\n    \"    \\n\",\n    \"    total_cost = cost + reg_cost                                       #scalar\\n\",\n    \"    return total_cost                                                  #scalar\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,6)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1]).reshape(-1,)-0.5\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"cost_tmp = compute_cost_linear_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(\\\"Regularized cost:\\\", cost_tmp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Regularized cost: </b> 0.07917239320214275 </td>\\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Cost function for regularized logistic regression\\n\",\n    \"For regularized **logistic** regression, the cost function is of the form\\n\",\n    \"$$J(\\\\mathbf{w},b) = \\\\frac{1}{m}  \\\\sum_{i=0}^{m-1} \\\\left[ -y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) \\\\right] + \\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2 \\\\tag{3}$$\\n\",\n    \"where:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) = sigmoid(\\\\mathbf{w} \\\\cdot \\\\mathbf{x}^{(i)} + b)  \\\\tag{4} $$ \\n\",\n    \"\\n\",\n    \"Compare this to the cost function without regularization (which you implemented in  a previous lab):\\n\",\n    \"\\n\",\n    \"$$ J(\\\\mathbf{w},b) = \\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\\\left[ (-y^{(i)} \\\\log\\\\left(f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right) - \\\\left( 1 - y^{(i)}\\\\right) \\\\log \\\\left( 1 - f_{\\\\mathbf{w},b}\\\\left( \\\\mathbf{x}^{(i)} \\\\right) \\\\right)\\\\right] $$\\n\",\n    \"\\n\",\n    \"As was the case in linear regression above, the difference is the regularization term, which is    <span style=\\\"color:blue\\\">\\n\",\n    \"    $\\\\frac{\\\\lambda}{2m}  \\\\sum_{j=0}^{n-1} w_j^2$ </span> \\n\",\n    \"\\n\",\n    \"Including this term incentives gradient descent to minimize the size of the parameters. Note, in this example, the parameter $b$ is not regularized. This is standard practice. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_cost_logistic_reg(X, y, w, b, lambda_ = 1):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the cost over all examples\\n\",\n    \"    Args:\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"    Returns:\\n\",\n    \"      total_cost (scalar):  cost \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m,n  = X.shape\\n\",\n    \"    cost = 0.\\n\",\n    \"    for i in range(m):\\n\",\n    \"        z_i = np.dot(X[i], w) + b                                      #(n,)(n,)=scalar, see np.dot\\n\",\n    \"        f_wb_i = sigmoid(z_i)                                          #scalar\\n\",\n    \"        cost +=  -y[i]*np.log(f_wb_i) - (1-y[i])*np.log(1-f_wb_i)      #scalar\\n\",\n    \"             \\n\",\n    \"    cost = cost/m                                                      #scalar\\n\",\n    \"\\n\",\n    \"    reg_cost = 0\\n\",\n    \"    for j in range(n):\\n\",\n    \"        reg_cost += (w[j]**2)                                          #scalar\\n\",\n    \"    reg_cost = (lambda_/(2*m)) * reg_cost                              #scalar\\n\",\n    \"    \\n\",\n    \"    total_cost = cost + reg_cost                                       #scalar\\n\",\n    \"    return total_cost                                                  #scalar\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,6)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1]).reshape(-1,)-0.5\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"cost_tmp = compute_cost_logistic_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(\\\"Regularized cost:\\\", cost_tmp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Regularized cost: </b> 0.6850849138741673 </td>\\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Gradient descent with regularization\\n\",\n    \"The basic algorithm for running gradient descent does not change with regularization, it is:\\n\",\n    \"$$\\\\begin{align*}\\n\",\n    \"&\\\\text{repeat until convergence:} \\\\; \\\\lbrace \\\\\\\\\\n\",\n    \"&  \\\\; \\\\; \\\\;w_j = w_j -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j} \\\\tag{1}  \\\\; & \\\\text{for j := 0..n-1} \\\\\\\\ \\n\",\n    \"&  \\\\; \\\\; \\\\;  \\\\; \\\\;b = b -  \\\\alpha \\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b} \\\\\\\\\\n\",\n    \"&\\\\rbrace\\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"Where each iteration performs simultaneous updates on $w_j$ for all $j$.\\n\",\n    \"\\n\",\n    \"What changes with regularization is computing the gradients.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Computing the Gradient with regularization (both linear/logistic)\\n\",\n    \"The gradient calculation for both linear and logistic regression are nearly identical, differing only in computation of $f_{\\\\mathbf{w}b}$.\\n\",\n    \"$$\\\\begin{align*}\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial w_j}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)})x_{j}^{(i)}  +  \\\\frac{\\\\lambda}{m} w_j \\\\tag{2} \\\\\\\\\\n\",\n    \"\\\\frac{\\\\partial J(\\\\mathbf{w},b)}{\\\\partial b}  &= \\\\frac{1}{m} \\\\sum\\\\limits_{i = 0}^{m-1} (f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}) - y^{(i)}) \\\\tag{3} \\n\",\n    \"\\\\end{align*}$$\\n\",\n    \"\\n\",\n    \"* m is the number of training examples in the data set      \\n\",\n    \"* $f_{\\\\mathbf{w},b}(x^{(i)})$ is the model's prediction, while $y^{(i)}$ is the target\\n\",\n    \"\\n\",\n    \"      \\n\",\n    \"* For a  <span style=\\\"color:blue\\\"> **linear** </span> regression model  \\n\",\n    \"    $f_{\\\\mathbf{w},b}(x) = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$  \\n\",\n    \"* For a <span style=\\\"color:blue\\\"> **logistic** </span> regression model  \\n\",\n    \"    $z = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$  \\n\",\n    \"    $f_{\\\\mathbf{w},b}(x) = g(z)$  \\n\",\n    \"    where $g(z)$ is the sigmoid function:  \\n\",\n    \"    $g(z) = \\\\frac{1}{1+e^{-z}}$   \\n\",\n    \"    \\n\",\n    \"The term which adds regularization is  the <span style=\\\"color:blue\\\">$\\\\frac{\\\\lambda}{m} w_j $</span>.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Gradient function for regularized linear regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_gradient_linear_reg(X, y, w, b, lambda_): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"      \\n\",\n    \"    Returns:\\n\",\n    \"      dj_dw (ndarray (n,)): The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db (scalar):       The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m,n = X.shape           #(number of examples, number of features)\\n\",\n    \"    dj_dw = np.zeros((n,))\\n\",\n    \"    dj_db = 0.\\n\",\n    \"\\n\",\n    \"    for i in range(m):                             \\n\",\n    \"        err = (np.dot(X[i], w) + b) - y[i]                 \\n\",\n    \"        for j in range(n):                         \\n\",\n    \"            dj_dw[j] = dj_dw[j] + err * X[i, j]               \\n\",\n    \"        dj_db = dj_db + err                        \\n\",\n    \"    dj_dw = dj_dw / m                                \\n\",\n    \"    dj_db = dj_db / m   \\n\",\n    \"    \\n\",\n    \"    for j in range(n):\\n\",\n    \"        dj_dw[j] = dj_dw[j] + (lambda_/m) * w[j]\\n\",\n    \"\\n\",\n    \"    return dj_db, dj_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,3)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1])\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"dj_db_tmp, dj_dw_tmp =  compute_gradient_linear_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(f\\\"dj_db: {dj_db_tmp}\\\", )\\n\",\n    \"print(f\\\"Regularized dj_dw:\\\\n {dj_dw_tmp.tolist()}\\\", )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"dj_db: 0.6648774569425726\\n\",\n    \"Regularized dj_dw:\\n\",\n    \" [0.29653214748822276, 0.4911679625918033, 0.21645877535865857]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Gradient function for regularized logistic regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_gradient_logistic_reg(X, y, w, b, lambda_): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the gradient for linear regression \\n\",\n    \" \\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (m,n): Data, m examples with n features\\n\",\n    \"      y (ndarray (m,)): target values\\n\",\n    \"      w (ndarray (n,)): model parameters  \\n\",\n    \"      b (scalar)      : model parameter\\n\",\n    \"      lambda_ (scalar): Controls amount of regularization\\n\",\n    \"    Returns\\n\",\n    \"      dj_dw (ndarray Shape (n,)): The gradient of the cost w.r.t. the parameters w. \\n\",\n    \"      dj_db (scalar)            : The gradient of the cost w.r.t. the parameter b. \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m,n = X.shape\\n\",\n    \"    dj_dw = np.zeros((n,))                            #(n,)\\n\",\n    \"    dj_db = 0.0                                       #scalar\\n\",\n    \"\\n\",\n    \"    for i in range(m):\\n\",\n    \"        f_wb_i = sigmoid(np.dot(X[i],w) + b)          #(n,)(n,)=scalar\\n\",\n    \"        err_i  = f_wb_i  - y[i]                       #scalar\\n\",\n    \"        for j in range(n):\\n\",\n    \"            dj_dw[j] = dj_dw[j] + err_i * X[i,j]      #scalar\\n\",\n    \"        dj_db = dj_db + err_i\\n\",\n    \"    dj_dw = dj_dw/m                                   #(n,)\\n\",\n    \"    dj_db = dj_db/m                                   #scalar\\n\",\n    \"\\n\",\n    \"    for j in range(n):\\n\",\n    \"        dj_dw[j] = dj_dw[j] + (lambda_/m) * w[j]\\n\",\n    \"\\n\",\n    \"    return dj_db, dj_dw  \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the cell below to see it in action.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.seed(1)\\n\",\n    \"X_tmp = np.random.rand(5,3)\\n\",\n    \"y_tmp = np.array([0,1,0,1,0])\\n\",\n    \"w_tmp = np.random.rand(X_tmp.shape[1])\\n\",\n    \"b_tmp = 0.5\\n\",\n    \"lambda_tmp = 0.7\\n\",\n    \"dj_db_tmp, dj_dw_tmp =  compute_gradient_logistic_reg(X_tmp, y_tmp, w_tmp, b_tmp, lambda_tmp)\\n\",\n    \"\\n\",\n    \"print(f\\\"dj_db: {dj_db_tmp}\\\", )\\n\",\n    \"print(f\\\"Regularized dj_dw:\\\\n {dj_dw_tmp.tolist()}\\\", )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"dj_db: 0.341798994972791\\n\",\n    \"Regularized dj_dw:\\n\",\n    \" [0.17380012933994293, 0.32007507881566943, 0.10776313396851499]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Rerun over-fitting example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"display(output)\\n\",\n    \"ofit = overfit_example(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the plot above, try out regularization on the previous example. In particular:\\n\",\n    \"- Categorical (logistic regression)\\n\",\n    \"    - set degree to 6, lambda to 0 (no regularization), fit the data\\n\",\n    \"    - now set lambda to 1 (increase regularization), fit the data, notice the difference.\\n\",\n    \"- Regression (linear regression)\\n\",\n    \"    - try the same procedure.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have:\\n\",\n    \"- examples of cost and gradient routines with regression added for both linear and logistic regression\\n\",\n    \"- developed some intuition on how regularization can reduce over-fitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
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    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/deeplearning.mplstyle",
    "content": "# see https://matplotlib.org/stable/tutorials/introductory/customizing.html\nlines.linewidth: 4\nlines.solid_capstyle: butt\n\nlegend.fancybox: true\n\n# Verdana\" for non-math text,\n# Cambria Math\n\n#Blue (Crayon-Aqua) 0096FF\n#Dark Red C00000\n#Orange (Apple Orange) FF9300\n#Black 000000\n#Magenta FF40FF\n#Purple 7030A0\n\naxes.prop_cycle: cycler('color', ['0096FF', 'FF9300', 'FF40FF', '7030A0', 'C00000'])\n#axes.facecolor: f0f0f0 # grey\naxes.facecolor: ffffff  # white\naxes.labelsize: large\naxes.axisbelow: true\naxes.grid: False\naxes.edgecolor: f0f0f0\naxes.linewidth: 3.0\naxes.titlesize: x-large\n\npatch.edgecolor: f0f0f0\npatch.linewidth: 0.5\n\nsvg.fonttype: path\n\ngrid.linestyle: -\ngrid.linewidth: 1.0\ngrid.color: cbcbcb\n\nxtick.major.size: 0\nxtick.minor.size: 0\nytick.major.size: 0\nytick.minor.size: 0\n\nsavefig.edgecolor: f0f0f0\nsavefig.facecolor: f0f0f0\n\n#figure.subplot.left: 0.08\n#figure.subplot.right: 0.95\n#figure.subplot.bottom: 0.07\n\n#figure.facecolor: f0f0f0  # grey\nfigure.facecolor: ffffff  # white\n\n## ***************************************************************************\n## * FONT                                                                    *\n## ***************************************************************************\n## The font properties used by `text.Text`.\n## See https://matplotlib.org/api/font_manager_api.html for more information\n## on font properties.  The 6 font properties used for font matching are\n## given below with their default values.\n##\n## The font.family property can take either a concrete font name (not supported\n## when rendering text with usetex), or one of the following five generic\n## values:\n##     - 'serif' (e.g., Times),\n##     - 'sans-serif' (e.g., Helvetica),\n##     - 'cursive' (e.g., Zapf-Chancery),\n##     - 'fantasy' (e.g., Western), and\n##     - 'monospace' (e.g., Courier).\n## Each of these values has a corresponding default list of font names\n## (font.serif, etc.); the first available font in the list is used.  Note that\n## for font.serif, font.sans-serif, and font.monospace, the first element of\n## the list (a DejaVu font) will always be used because DejaVu is shipped with\n## Matplotlib and is thus guaranteed to be available; the other entries are\n## left as examples of other possible values.\n##\n## The font.style property has three values: normal (or roman), italic\n## or oblique.  The oblique style will be used for italic, if it is not\n## present.\n##\n## The font.variant property has two values: normal or small-caps.  For\n## TrueType fonts, which are scalable fonts, small-caps is equivalent\n## to using a font size of 'smaller', or about 83%% of the current font\n## size.\n##\n## The font.weight property has effectively 13 values: normal, bold,\n## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as\n## 400, and bold is 700.  bolder and lighter are relative values with\n## respect to the current weight.\n##\n## The font.stretch property has 11 values: ultra-condensed,\n## extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This\n## property is not currently implemented.\n##\n## The font.size property is the default font size for text, given in points.\n## 10 pt is the standard value.\n##\n## Note that font.size controls default text sizes.  To configure\n## special text sizes tick labels, axes, labels, title, etc., see the rc\n## settings for axes and ticks.  Special text sizes can be defined\n## relative to font.size, using the following values: xx-small, x-small,\n## small, medium, large, x-large, xx-large, larger, or smaller\n\n\nfont.family:  sans-serif\nfont.style:   normal\nfont.variant: normal\nfont.weight:  normal\nfont.stretch: normal\nfont.size:    8.0\n\nfont.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\nfont.sans-serif: Verdana, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\nfont.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive\nfont.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy\nfont.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n\n\n## ***************************************************************************\n## * TEXT                                                                    *\n## ***************************************************************************\n## The text properties used by `text.Text`.\n## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\n## for more information on text properties\n#text.color: black\n\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/lab_utils_common.py",
    "content": "\"\"\"\nlab_utils_common\n   contains common routines and variable definitions\n   used by all the labs in this week.\n   by contrast, specific, large plotting routines will be in separate files\n   and are generally imported into the week where they are used.\n   those files will import this file\n\"\"\"\nimport copy\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import FancyArrowPatch\nfrom ipywidgets import Output\n\nnp.set_printoptions(precision=2)\n\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0'\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\nplt.style.use('./deeplearning.mplstyle')\n\ndef sigmoid(z):\n    \"\"\"\n    Compute the sigmoid of z\n\n    Parameters\n    ----------\n    z : array_like\n        A scalar or numpy array of any size.\n\n    Returns\n    -------\n     g : array_like\n         sigmoid(z)\n    \"\"\"\n    z = np.clip( z, -500, 500 )           # protect against overflow\n    g = 1.0/(1.0+np.exp(-z))\n\n    return g\n\n##########################################################\n# Regression Routines\n##########################################################\n\ndef predict_logistic(X, w, b):\n    \"\"\" performs prediction \"\"\"\n    return sigmoid(X @ w + b)\n\ndef predict_linear(X, w, b):\n    \"\"\" performs prediction \"\"\"\n    return X @ w + b\n\ndef compute_cost_logistic(X, y, w, b, lambda_=0, safe=False):\n    \"\"\"\n    Computes cost using logistic loss, non-matrix version\n\n    Args:\n      X (ndarray): Shape (m,n)  matrix of examples with n features\n      y (ndarray): Shape (m,)   target values\n      w (ndarray): Shape (n,)   parameters for prediction\n      b (scalar):               parameter  for prediction\n      lambda_ : (scalar, float) Controls amount of regularization, 0 = no regularization\n      safe : (boolean)          True-selects under/overflow safe algorithm\n    Returns:\n      cost (scalar): cost\n    \"\"\"\n\n    m,n = X.shape\n    cost = 0.0\n    for i in range(m):\n        z_i    = np.dot(X[i],w) + b                                             #(n,)(n,) or (n,) ()\n        if safe:  #avoids overflows\n            cost += -(y[i] * z_i ) + log_1pexp(z_i)\n        else:\n            f_wb_i = sigmoid(z_i)                                                   #(n,)\n            cost  += -y[i] * np.log(f_wb_i) - (1 - y[i]) * np.log(1 - f_wb_i)       # scalar\n    cost = cost/m\n\n    reg_cost = 0\n    if lambda_ != 0:\n        for j in range(n):\n            reg_cost += (w[j]**2)                                               # scalar\n        reg_cost = (lambda_/(2*m))*reg_cost\n\n    return cost + reg_cost\n\n\ndef log_1pexp(x, maximum=20):\n    ''' approximate log(1+exp^x)\n        https://stats.stackexchange.com/questions/475589/numerical-computation-of-cross-entropy-in-practice\n    Args:\n    x   : (ndarray Shape (n,1) or (n,)  input\n    out : (ndarray Shape matches x      output ~= np.log(1+exp(x))\n    '''\n\n    out  = np.zeros_like(x,dtype=float)\n    i    = x <= maximum\n    ni   = np.logical_not(i)\n\n    out[i]  = np.log(1 + np.exp(x[i]))\n    out[ni] = x[ni]\n    return out\n\n\ndef compute_cost_matrix(X, y, w, b, logistic=False, lambda_=0, safe=True):\n    \"\"\"\n    Computes the cost using  using matrices\n    Args:\n      X : (ndarray, Shape (m,n))          matrix of examples\n      y : (ndarray  Shape (m,) or (m,1))  target value of each example\n      w : (ndarray  Shape (n,) or (n,1))  Values of parameter(s) of the model\n      b : (scalar )                       Values of parameter of the model\n      verbose : (Boolean) If true, print out intermediate value f_wb\n    Returns:\n      total_cost: (scalar)                cost\n    \"\"\"\n    m = X.shape[0]\n    y = y.reshape(-1,1)             # ensure 2D\n    w = w.reshape(-1,1)             # ensure 2D\n    if logistic:\n        if safe:  #safe from overflow\n            z = X @ w + b                                                           #(m,n)(n,1)=(m,1)\n            cost = -(y * z) + log_1pexp(z)\n            cost = np.sum(cost)/m                                                   # (scalar)\n        else:\n            f    = sigmoid(X @ w + b)                                               # (m,n)(n,1) = (m,1)\n            cost = (1/m)*(np.dot(-y.T, np.log(f)) - np.dot((1-y).T, np.log(1-f)))   # (1,m)(m,1) = (1,1)\n            cost = cost[0,0]                                                        # scalar\n    else:\n        f    = X @ w + b                                                        # (m,n)(n,1) = (m,1)\n        cost = (1/(2*m)) * np.sum((f - y)**2)                                   # scalar\n\n    reg_cost = (lambda_/(2*m)) * np.sum(w**2)                                   # scalar\n\n    total_cost = cost + reg_cost                                                # scalar\n\n    return total_cost                                                           # scalar\n\ndef compute_gradient_matrix(X, y, w, b, logistic=False, lambda_=0):\n    \"\"\"\n    Computes the gradient using matrices\n\n    Args:\n      X : (ndarray, Shape (m,n))          matrix of examples\n      y : (ndarray  Shape (m,) or (m,1))  target value of each example\n      w : (ndarray  Shape (n,) or (n,1))  Values of parameters of the model\n      b : (scalar )                       Values of parameter of the model\n      logistic: (boolean)                 linear if false, logistic if true\n      lambda_:  (float)                   applies regularization if non-zero\n    Returns\n      dj_dw: (array_like Shape (n,1))     The gradient of the cost w.r.t. the parameters w\n      dj_db: (scalar)                     The gradient of the cost w.r.t. the parameter b\n    \"\"\"\n    m = X.shape[0]\n    y = y.reshape(-1,1)             # ensure 2D\n    w = w.reshape(-1,1)             # ensure 2D\n\n    f_wb  = sigmoid( X @ w + b ) if logistic else  X @ w + b      # (m,n)(n,1) = (m,1)\n    err   = f_wb - y                                              # (m,1)\n    dj_dw = (1/m) * (X.T @ err)                                   # (n,m)(m,1) = (n,1)\n    dj_db = (1/m) * np.sum(err)                                   # scalar\n\n    dj_dw += (lambda_/m) * w        # regularize                  # (n,1)\n\n    return dj_db, dj_dw                                           # scalar, (n,1)\n\ndef gradient_descent(X, y, w_in, b_in, alpha, num_iters, logistic=False, lambda_=0, verbose=True):\n    \"\"\"\n    Performs batch gradient descent to learn theta. Updates theta by taking\n    num_iters gradient steps with learning rate alpha\n\n    Args:\n      X (ndarray):    Shape (m,n)         matrix of examples\n      y (ndarray):    Shape (m,) or (m,1) target value of each example\n      w_in (ndarray): Shape (n,) or (n,1) Initial values of parameters of the model\n      b_in (scalar):                      Initial value of parameter of the model\n      logistic: (boolean)                 linear if false, logistic if true\n      lambda_:  (float)                   applies regularization if non-zero\n      alpha (float):                      Learning rate\n      num_iters (int):                    number of iterations to run gradient descent\n\n    Returns:\n      w (ndarray): Shape (n,) or (n,1)    Updated values of parameters; matches incoming shape\n      b (scalar):                         Updated value of parameter\n    \"\"\"\n    # An array to store cost J and w's at each iteration primarily for graphing later\n    J_history = []\n    w = copy.deepcopy(w_in)  #avoid modifying global w within function\n    b = b_in\n    w = w.reshape(-1,1)      #prep for matrix operations\n    y = y.reshape(-1,1)\n\n    for i in range(num_iters):\n\n        # Calculate the gradient and update the parameters\n        dj_db,dj_dw = compute_gradient_matrix(X, y, w, b, logistic, lambda_)\n\n        # Update Parameters using w, b, alpha and gradient\n        w = w - alpha * dj_dw\n        b = b - alpha * dj_db\n\n        # Save cost J at each iteration\n        if i<100000:      # prevent resource exhaustion\n            J_history.append( compute_cost_matrix(X, y, w, b, logistic, lambda_) )\n\n        # Print cost every at intervals 10 times or as many iterations if < 10\n        if i% math.ceil(num_iters / 10) == 0:\n            if verbose: print(f\"Iteration {i:4d}: Cost {J_history[-1]}   \")\n\n    return w.reshape(w_in.shape), b, J_history  #return final w,b and J history for graphing\n\ndef zscore_normalize_features(X):\n    \"\"\"\n    computes  X, zcore normalized by column\n\n    Args:\n      X (ndarray): Shape (m,n) input data, m examples, n features\n\n    Returns:\n      X_norm (ndarray): Shape (m,n)  input normalized by column\n      mu (ndarray):     Shape (n,)   mean of each feature\n      sigma (ndarray):  Shape (n,)   standard deviation of each feature\n    \"\"\"\n    # find the mean of each column/feature\n    mu     = np.mean(X, axis=0)                 # mu will have shape (n,)\n    # find the standard deviation of each column/feature\n    sigma  = np.std(X, axis=0)                  # sigma will have shape (n,)\n    # element-wise, subtract mu for that column from each example, divide by std for that column\n    X_norm = (X - mu) / sigma\n\n    return X_norm, mu, sigma\n\n#check our work\n#from sklearn.preprocessing import scale\n#scale(X_orig, axis=0, with_mean=True, with_std=True, copy=True)\n\n######################################################\n# Common Plotting Routines\n######################################################\n\n\ndef plot_data(X, y, ax, pos_label=\"y=1\", neg_label=\"y=0\", s=80, loc='best' ):\n    \"\"\" plots logistic data with two axis \"\"\"\n    # Find Indices of Positive and Negative Examples\n    pos = y == 1\n    neg = y == 0\n    pos = pos.reshape(-1,)  #work with 1D or 1D y vectors\n    neg = neg.reshape(-1,)\n\n    # Plot examples\n    ax.scatter(X[pos, 0], X[pos, 1], marker='x', s=s, c = 'red', label=pos_label)\n    ax.scatter(X[neg, 0], X[neg, 1], marker='o', s=s, label=neg_label, facecolors='none', edgecolors=dlblue, lw=3)\n    ax.legend(loc=loc)\n\n    ax.figure.canvas.toolbar_visible = False\n    ax.figure.canvas.header_visible = False\n    ax.figure.canvas.footer_visible = False\n\ndef plt_tumor_data(x, y, ax):\n    \"\"\" plots tumor data on one axis \"\"\"\n    pos = y == 1\n    neg = y == 0\n\n    ax.scatter(x[pos], y[pos], marker='x', s=80, c = 'red', label=\"malignant\")\n    ax.scatter(x[neg], y[neg], marker='o', s=100, label=\"benign\", facecolors='none', edgecolors=dlblue,lw=3)\n    ax.set_ylim(-0.175,1.1)\n    ax.set_ylabel('y')\n    ax.set_xlabel('Tumor Size')\n    ax.set_title(\"Logistic Regression on Categorical Data\")\n\n    ax.figure.canvas.toolbar_visible = False\n    ax.figure.canvas.header_visible = False\n    ax.figure.canvas.footer_visible = False\n\n# Draws a threshold at 0.5\ndef draw_vthresh(ax,x):\n    \"\"\" draws a threshold \"\"\"\n    ylim = ax.get_ylim()\n    xlim = ax.get_xlim()\n    ax.fill_between([xlim[0], x], [ylim[1], ylim[1]], alpha=0.2, color=dlblue)\n    ax.fill_between([x, xlim[1]], [ylim[1], ylim[1]], alpha=0.2, color=dldarkred)\n    ax.annotate(\"z >= 0\", xy= [x,0.5], xycoords='data',\n                xytext=[30,5],textcoords='offset points')\n    d = FancyArrowPatch(\n        posA=(x, 0.5), posB=(x+3, 0.5), color=dldarkred,\n        arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n    )\n    ax.add_artist(d)\n    ax.annotate(\"z < 0\", xy= [x,0.5], xycoords='data',\n                 xytext=[-50,5],textcoords='offset points', ha='left')\n    f = FancyArrowPatch(\n        posA=(x, 0.5), posB=(x-3, 0.5), color=dlblue,\n        arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n    )\n    ax.add_artist(f)\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/plt_logistic_loss.py",
    "content": "\"\"\"----------------------------------------------------------------\n logistic_loss plotting routines and support\n\"\"\"\n\nfrom matplotlib import cm\nfrom lab_utils_common import sigmoid, dlblue, dlorange, np, plt, compute_cost_matrix\n\ndef compute_cost_logistic_sq_err(X, y, w, b):\n    \"\"\"\n    compute sq error cost on logicist data (for negative example only, not used in practice)\n    Args:\n      X (ndarray): Shape (m,n) matrix of examples with multiple features\n      w (ndarray): Shape (n)   parameters for prediction\n      b (scalar):              parameter  for prediction\n    Returns:\n      cost (scalar): cost\n    \"\"\"\n    m = X.shape[0]\n    cost = 0.0\n    for i in range(m):\n        z_i = np.dot(X[i],w) + b\n        f_wb_i = sigmoid(z_i)                 #add sigmoid to normal sq error cost for linear regression\n        cost = cost + (f_wb_i - y[i])**2\n    cost = cost / (2 * m)\n    return np.squeeze(cost)\n\ndef plt_logistic_squared_error(X,y):\n    \"\"\" plots logistic squared error for demonstration \"\"\"\n    wx, by = np.meshgrid(np.linspace(-6,12,50),\n                         np.linspace(10, -20, 40))\n    points = np.c_[wx.ravel(), by.ravel()]\n    cost = np.zeros(points.shape[0])\n\n    for i in range(points.shape[0]):\n        w,b = points[i]\n        cost[i] = compute_cost_logistic_sq_err(X.reshape(-1,1), y, w, b)\n    cost = cost.reshape(wx.shape)\n\n    fig = plt.figure()\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n    ax = fig.add_subplot(1, 1, 1, projection='3d')\n    ax.plot_surface(wx, by, cost, alpha=0.6,cmap=cm.jet,)\n\n    ax.set_xlabel('w', fontsize=16)\n    ax.set_ylabel('b', fontsize=16)\n    ax.set_zlabel(\"Cost\", rotation=90, fontsize=16)\n    ax.set_title('\"Logistic\" Squared Error Cost vs (w, b)')\n    ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n\n\ndef plt_logistic_cost(X,y):\n    \"\"\" plots logistic cost \"\"\"\n    wx, by = np.meshgrid(np.linspace(-6,12,50),\n                         np.linspace(0, -20, 40))\n    points = np.c_[wx.ravel(), by.ravel()]\n    cost = np.zeros(points.shape[0],dtype=np.longdouble)\n\n    for i in range(points.shape[0]):\n        w,b = points[i]\n        cost[i] = compute_cost_matrix(X.reshape(-1,1), y, w, b, logistic=True, safe=True)\n    cost = cost.reshape(wx.shape)\n\n    fig = plt.figure(figsize=(9,5))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n    ax = fig.add_subplot(1, 2, 1, projection='3d')\n    ax.plot_surface(wx, by, cost, alpha=0.6,cmap=cm.jet,)\n\n    ax.set_xlabel('w', fontsize=16)\n    ax.set_ylabel('b', fontsize=16)\n    ax.set_zlabel(\"Cost\", rotation=90, fontsize=16)\n    ax.set_title('Logistic Cost vs (w, b)')\n    ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n\n    ax = fig.add_subplot(1, 2, 2, projection='3d')\n\n    ax.plot_surface(wx, by, np.log(cost), alpha=0.6,cmap=cm.jet,)\n\n    ax.set_xlabel('w', fontsize=16)\n    ax.set_ylabel('b', fontsize=16)\n    ax.set_zlabel('\\nlog(Cost)', fontsize=16)\n    ax.set_title('log(Logistic Cost) vs (w, b)')\n    ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n\n    plt.show()\n    return cost\n\n\ndef soup_bowl():\n    \"\"\" creates 3D quadratic error surface \"\"\"\n    #Create figure and plot with a 3D projection\n    fig = plt.figure(figsize=(4,4))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\n    #Plot configuration\n    ax = fig.add_subplot(111, projection='3d')\n    ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n    ax.zaxis.set_rotate_label(False)\n    ax.view_init(15, -120)\n\n    #Useful linearspaces to give values to the parameters w and b\n    w = np.linspace(-20, 20, 100)\n    b = np.linspace(-20, 20, 100)\n\n    #Get the z value for a bowl-shaped cost function\n    z=np.zeros((len(w), len(b)))\n    j=0\n    for x in w:\n        i=0\n        for y in b:\n            z[i,j] = x**2 + y**2\n            i+=1\n        j+=1\n\n    #Meshgrid used for plotting 3D functions\n    W, B = np.meshgrid(w, b)\n\n    #Create the 3D surface plot of the bowl-shaped cost function\n    ax.plot_surface(W, B, z, cmap = \"Spectral_r\", alpha=0.7, antialiased=False)\n    ax.plot_wireframe(W, B, z, color='k', alpha=0.1)\n    ax.set_xlabel(\"$w$\")\n    ax.set_ylabel(\"$b$\")\n    ax.set_zlabel(\"Cost\", rotation=90)\n    ax.set_title(\"Squared Error Cost used in Linear Regression\")\n\n    plt.show()\n\n\ndef plt_simple_example(x, y):\n    \"\"\" plots tumor data \"\"\"\n    pos = y == 1\n    neg = y == 0\n\n    fig,ax = plt.subplots(1,1,figsize=(5,3))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\n    ax.scatter(x[pos], y[pos], marker='x', s=80, c = 'red', label=\"malignant\")\n    ax.scatter(x[neg], y[neg], marker='o', s=100, label=\"benign\", facecolors='none', edgecolors=dlblue,lw=3)\n    ax.set_ylim(-0.075,1.1)\n    ax.set_ylabel('y')\n    ax.set_xlabel('Tumor Size')\n    ax.legend(loc='lower right')\n    ax.set_title(\"Example of Logistic Regression on Categorical Data\")\n\n\ndef plt_two_logistic_loss_curves():\n    \"\"\" plots the logistic loss \"\"\"\n    fig,ax = plt.subplots(1,2,figsize=(6,3),sharey=True)\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n    x = np.linspace(0.01,1-0.01,20)\n    ax[0].plot(x,-np.log(x))\n    #ax[0].set_title(\"y = 1\")\n    ax[0].text(0.5, 4.0, \"y = 1\", fontsize=12)\n    ax[0].set_ylabel(\"loss\")\n    ax[0].set_xlabel(r\"$f_{w,b}(x)$\")\n    ax[1].plot(x,-np.log(1-x))\n    #ax[1].set_title(\"y = 0\")\n    ax[1].text(0.5, 4.0, \"y = 0\", fontsize=12)\n    ax[1].set_xlabel(r\"$f_{w,b}(x)$\")\n    ax[0].annotate(\"prediction \\nmatches \\ntarget \", xy= [1,0], xycoords='data',\n                 xytext=[-10,30],textcoords='offset points', ha=\"right\", va=\"center\",\n                   arrowprops={'arrowstyle': '->', 'color': dlorange, 'lw': 3},)\n    ax[0].annotate(\"loss increases as prediction\\n differs from target\", xy= [0.1,-np.log(0.1)], xycoords='data',\n                 xytext=[10,30],textcoords='offset points', ha=\"left\", va=\"center\",\n                   arrowprops={'arrowstyle': '->', 'color': dlorange, 'lw': 3},)\n    ax[1].annotate(\"prediction \\nmatches \\ntarget \", xy= [0,0], xycoords='data',\n                 xytext=[10,30],textcoords='offset points', ha=\"left\", va=\"center\",\n                   arrowprops={'arrowstyle': '->', 'color': dlorange, 'lw': 3},)\n    ax[1].annotate(\"loss increases as prediction\\n differs from target\", xy= [0.9,-np.log(1-0.9)], xycoords='data',\n                 xytext=[-10,30],textcoords='offset points', ha=\"right\", va=\"center\",\n                   arrowprops={'arrowstyle': '->', 'color': dlorange, 'lw': 3},)\n    plt.suptitle(\"Loss Curves for Two Categorical Target Values\", fontsize=12)\n    plt.tight_layout()\n    plt.show()\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/plt_one_addpt_onclick.py",
    "content": "import time\nimport copy\nfrom ipywidgets import Output\nfrom matplotlib.widgets import Button, CheckButtons\nfrom matplotlib.patches import FancyArrowPatch\nfrom lab_utils_common import np, plt, dlblue, dlorange, sigmoid, dldarkred, gradient_descent\n\n# for debug\n#output = Output() # sends hidden error messages to display when using widgets\n#display(output)\n\nclass plt_one_addpt_onclick:\n    \"\"\" class to run one interactive plot \"\"\"\n    def __init__(self, x, y, w, b, logistic=True):\n        self.logistic=logistic\n        pos = y == 1\n        neg = y == 0\n\n        fig,ax = plt.subplots(1,1,figsize=(8,4))\n        fig.canvas.toolbar_visible = False\n        fig.canvas.header_visible = False\n        fig.canvas.footer_visible = False\n\n        plt.subplots_adjust(bottom=0.25)\n        ax.scatter(x[pos], y[pos], marker='x', s=80, c = 'red', label=\"malignant\")\n        ax.scatter(x[neg], y[neg], marker='o', s=100, label=\"benign\", facecolors='none', edgecolors=dlblue,lw=3)\n        ax.set_ylim(-0.05,1.1)\n        xlim = ax.get_xlim()\n        ax.set_xlim(xlim[0],xlim[1]*2)\n        ax.set_ylabel('y')\n        ax.set_xlabel('Tumor Size')\n        self.alegend = ax.legend(loc='lower right')\n        if self.logistic:\n            ax.set_title(\"Example of Logistic Regression on Categorical Data\")\n        else:\n            ax.set_title(\"Example of Linear Regression on Categorical Data\")\n\n        ax.text(0.65,0.8,\"[Click to add data points]\", size=10, transform=ax.transAxes)\n\n        axcalc   = plt.axes([0.1, 0.05, 0.38, 0.075])  #l,b,w,h\n        axthresh = plt.axes([0.5, 0.05, 0.38, 0.075])  #l,b,w,h\n        self.tlist = []\n\n        self.fig = fig\n        self.ax = [ax,axcalc,axthresh]\n        self.x = x\n        self.y = y\n        self.w = copy.deepcopy(w)\n        self.b = b\n        f_wb = np.matmul(self.x.reshape(-1,1), self.w) + self.b\n        if self.logistic:\n            self.aline = self.ax[0].plot(self.x, sigmoid(f_wb), color=dlblue)\n            self.bline = self.ax[0].plot(self.x, f_wb, color=dlorange,lw=1)\n        else:\n            self.aline = self.ax[0].plot(self.x, sigmoid(f_wb), color=dlblue)\n\n        self.cid = fig.canvas.mpl_connect('button_press_event', self.add_data)\n        if self.logistic:\n            self.bcalc = Button(axcalc, 'Run Logistic Regression (click)', color=dlblue)\n            self.bcalc.on_clicked(self.calc_logistic)\n        else:\n            self.bcalc = Button(axcalc, 'Run Linear Regression (click)', color=dlblue)\n            self.bcalc.on_clicked(self.calc_linear)\n        self.bthresh = CheckButtons(axthresh, ('Toggle 0.5 threshold (after regression)',))\n        self.bthresh.on_clicked(self.thresh)\n        self.resize_sq(self.bthresh)\n\n #   @output.capture()  # debug\n    def add_data(self, event):\n        #self.ax[0].text(0.1,0.1, f\"in onclick\")\n        if event.inaxes == self.ax[0]:\n            x_coord = event.xdata\n            y_coord = event.ydata\n\n            if y_coord > 0.5:\n                self.ax[0].scatter(x_coord, 1, marker='x', s=80, c = 'red' )\n                self.y = np.append(self.y,1)\n            else:\n                self.ax[0].scatter(x_coord, 0, marker='o', s=100, facecolors='none', edgecolors=dlblue,lw=3)\n                self.y = np.append(self.y,0)\n            self.x = np.append(self.x,x_coord)\n        self.fig.canvas.draw()\n\n#   @output.capture()  # debug\n    def calc_linear(self, event):\n        if self.bthresh.get_status()[0]:\n            self.remove_thresh()\n        for it in [1,1,1,1,1,2,4,8,16,32,64,128,256]:\n            self.w, self.b, _ = gradient_descent(self.x.reshape(-1,1), self.y.reshape(-1,1),\n                                                 self.w.reshape(-1,1), self.b, 0.01, it,\n                                                 logistic=False, lambda_=0, verbose=False)\n            self.aline[0].remove()\n            self.alegend.remove()\n            y_hat = np.matmul(self.x.reshape(-1,1), self.w) + self.b\n            self.aline = self.ax[0].plot(self.x, y_hat, color=dlblue,\n                                         label=f\"y = {np.squeeze(self.w):0.2f}x+({self.b:0.2f})\")\n            self.alegend = self.ax[0].legend(loc='lower right')\n            time.sleep(0.3)\n            self.fig.canvas.draw()\n        if self.bthresh.get_status()[0]:\n            self.draw_thresh()\n            self.fig.canvas.draw()\n\n    def calc_logistic(self, event):\n        if self.bthresh.get_status()[0]:\n            self.remove_thresh()\n        for it in [1, 8,16,32,64,128,256,512,1024,2048,4096]:\n            self.w, self.b, _ = gradient_descent(self.x.reshape(-1,1), self.y.reshape(-1,1),\n                                                 self.w.reshape(-1,1), self.b, 0.1, it,\n                                                 logistic=True, lambda_=0, verbose=False)\n            self.aline[0].remove()\n            self.bline[0].remove()\n            self.alegend.remove()\n            xlim  = self.ax[0].get_xlim()\n            x_hat = np.linspace(*xlim, 30)\n            y_hat = sigmoid(np.matmul(x_hat.reshape(-1,1), self.w) + self.b)\n            self.aline = self.ax[0].plot(x_hat, y_hat, color=dlblue,\n                                         label=\"y = sigmoid(z)\")\n            f_wb = np.matmul(x_hat.reshape(-1,1), self.w) + self.b\n            self.bline = self.ax[0].plot(x_hat, f_wb, color=dlorange, lw=1,\n                                         label=f\"z = {np.squeeze(self.w):0.2f}x+({self.b:0.2f})\")\n            self.alegend = self.ax[0].legend(loc='lower right')\n            time.sleep(0.3)\n            self.fig.canvas.draw()\n        if self.bthresh.get_status()[0]:\n            self.draw_thresh()\n            self.fig.canvas.draw()\n\n\n    def thresh(self, event):\n        if self.bthresh.get_status()[0]:\n            #plt.figtext(0,0, f\"in thresh {self.bthresh.get_status()}\")\n            self.draw_thresh()\n        else:\n            #plt.figtext(0,0.3, f\"in thresh {self.bthresh.get_status()}\")\n            self.remove_thresh()\n\n    def draw_thresh(self):\n        ws = np.squeeze(self.w)\n        xp5 = -self.b/ws if self.logistic else (0.5 - self.b) / ws\n        ylim = self.ax[0].get_ylim()\n        xlim = self.ax[0].get_xlim()\n        a = self.ax[0].fill_between([xlim[0], xp5], [ylim[1], ylim[1]], alpha=0.2, color=dlblue)\n        b = self.ax[0].fill_between([xp5, xlim[1]], [ylim[1], ylim[1]], alpha=0.2, color=dldarkred)\n        c = self.ax[0].annotate(\"Malignant\", xy= [xp5,0.5], xycoords='data',\n             xytext=[30,5],textcoords='offset points')\n        d = FancyArrowPatch(\n            posA=(xp5, 0.5), posB=(xp5+1.5, 0.5), color=dldarkred,\n            arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n        )\n        self.ax[0].add_artist(d)\n\n        e = self.ax[0].annotate(\"Benign\", xy= [xp5,0.5], xycoords='data',\n                     xytext=[-70,5],textcoords='offset points', ha='left')\n        f = FancyArrowPatch(\n            posA=(xp5, 0.5), posB=(xp5-1.5, 0.5), color=dlblue,\n            arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n        )\n        self.ax[0].add_artist(f)\n        self.tlist = [a,b,c,d,e,f]\n\n        self.fig.canvas.draw()\n\n    def remove_thresh(self):\n        #plt.figtext(0.5,0.0, f\"rem thresh {self.bthresh.get_status()}\")\n        for artist in self.tlist:\n            artist.remove()\n        self.fig.canvas.draw()\n\n    def resize_sq(self, bcid):\n        \"\"\" resizes the check box \"\"\"\n        #future reference\n        #print(f\"width  : {bcid.rectangles[0].get_width()}\")\n        #print(f\"height : {bcid.rectangles[0].get_height()}\")\n        #print(f\"xy     : {bcid.rectangles[0].get_xy()}\")\n        #print(f\"bb     : {bcid.rectangles[0].get_bbox()}\")\n        #print(f\"points : {bcid.rectangles[0].get_bbox().get_points()}\")  #[[xmin,ymin],[xmax,ymax]]\n\n        if hasattr(bcid,'rectangles'):\n            # Buttons has attribute rectangles\n            h = bcid.rectangles[0].get_height()\n            bcid.rectangles[0].set_height(3*h)\n\n            ymax = bcid.rectangles[0].get_bbox().y1\n            ymin = bcid.rectangles[0].get_bbox().y0\n\n            bcid.lines[0][0].set_ydata([ymax,ymin])\n            bcid.lines[0][1].set_ydata([ymin,ymax])\n\n        elif hasattr(bcid,'boxes'):\n            # Button has attribute boxes\n            h = bcid.boxes[0].get_height()\n            bcid.boxes[0].set_height(3*h)\n\n            ymax = bcid.boxes[0].get_bbox().y1\n            ymin = bcid.boxes[0].get_bbox().y0\n\n            bcid.lines[0][0].set_ydata([ymax,ymin])\n            bcid.lines[0][1].set_ydata([ymin,ymax])\n\n\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/plt_overfit.py",
    "content": "\"\"\"\nplot_overfit\n    class and assocaited routines that plot an interactive example of overfitting and its solutions\n\"\"\"\nimport math\nfrom ipywidgets import Output\nfrom matplotlib.gridspec import GridSpec\nfrom matplotlib.widgets import Button, CheckButtons\nfrom sklearn.linear_model import LogisticRegression, Ridge\nfrom lab_utils_common import np, plt, dlc, predict_logistic, plot_data, zscore_normalize_features\n\ndef map_one_feature(X1, degree):\n    \"\"\"\n    Feature mapping function to polynomial features\n    \"\"\"\n    X1 = np.atleast_1d(X1)\n    out = []\n    string = \"\"\n    k = 0\n    for i in range(1, degree+1):\n        out.append((X1**i))\n        string = string + f\"w_{{{k}}}{munge('x_0',i)} + \"\n        k += 1\n    string = string + ' b' #add b to text equation, not to data\n    return np.stack(out, axis=1), string\n\n\ndef map_feature(X1, X2, degree):\n    \"\"\"\n    Feature mapping function to polynomial features\n    \"\"\"\n    X1 = np.atleast_1d(X1)\n    X2 = np.atleast_1d(X2)\n\n    out = []\n    string = \"\"\n    k = 0\n    for i in range(1, degree+1):\n        for j in range(i + 1):\n            out.append((X1**(i-j) * (X2**j)))\n            string = string + f\"w_{{{k}}}{munge('x_0',i-j)}{munge('x_1',j)} + \"\n            k += 1\n    #print(string + 'b')\n    return np.stack(out, axis=1), string + ' b'\n\ndef munge(base, exp):\n    if exp == 0:\n        return ''\n    if exp == 1:\n        return base\n    return base + f'^{{{exp}}}'\n\ndef plot_decision_boundary(ax, x0r,x1r, predict,  w, b, scaler = False, mu=None, sigma=None, degree=None):\n    \"\"\"\n    Plots a decision boundary\n     Args:\n      x0r : (array_like Shape (1,1)) range (min, max) of x0\n      x1r : (array_like Shape (1,1)) range (min, max) of x1\n      predict : function to predict z values\n      scalar : (boolean) scale data or not\n    \"\"\"\n\n    h = .01  # step size in the mesh\n    # create a mesh to plot in\n    xx, yy = np.meshgrid(np.arange(x0r[0], x0r[1], h),\n                         np.arange(x1r[0], x1r[1], h))\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, m_max]x[y_min, y_max].\n    points = np.c_[xx.ravel(), yy.ravel()]\n    Xm,_ = map_feature(points[:, 0], points[:, 1],degree)\n    if scaler:\n        Xm = (Xm - mu)/sigma\n    Z = predict(Xm, w, b)\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    contour = ax.contour(xx, yy, Z, levels = [0.5], colors='g')\n    return contour\n\n# use this to test the above routine\ndef plot_decision_boundary_sklearn(x0r, x1r, predict, degree,  scaler = False):\n    \"\"\"\n    Plots a decision boundary\n     Args:\n      x0r : (array_like Shape (1,1)) range (min, max) of x0\n      x1r : (array_like Shape (1,1)) range (min, max) of x1\n      degree: (int)                  degree of polynomial\n      predict : function to predict z values\n      scaler  : not sure\n    \"\"\"\n\n    h = .01  # step size in the mesh\n    # create a mesh to plot in\n    xx, yy = np.meshgrid(np.arange(x0r[0], x0r[1], h),\n                         np.arange(x1r[0], x1r[1], h))\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, m_max]x[y_min, y_max].\n    points = np.c_[xx.ravel(), yy.ravel()]\n    Xm = map_feature(points[:, 0], points[:, 1],degree)\n    if scaler:\n        Xm = scaler.transform(Xm)\n    Z = predict(Xm)\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    plt.contour(xx, yy, Z, colors='g')\n    #plot_data(X_train,y_train)\n\n#for debug, uncomment the #@output statments below for routines you want to get error output from\n# In the notebook that will call these routines, import  `output`\n# from plt_overfit import overfit_example, output\n# then, in a cell where the error messages will be the output of..\n#display(output)\n\noutput = Output() # sends hidden error messages to display when using widgets\n\nclass button_manager:\n    ''' Handles some missing features of matplotlib check buttons\n    on init:\n        creates button, links to button_click routine,\n        calls call_on_click with active index and firsttime=True\n    on click:\n        maintains single button on state, calls call_on_click\n    '''\n\n    @output.capture()  # debug\n    def __init__(self,fig, dim, labels, init, call_on_click):\n        '''\n        dim: (list)     [leftbottom_x,bottom_y,width,height]\n        labels: (list)  for example ['1','2','3','4','5','6']\n        init: (list)    for example [True, False, False, False, False, False]\n        '''\n        self.fig = fig\n        self.ax = plt.axes(dim)  #lx,by,w,h\n        self.init_state = init\n        self.call_on_click = call_on_click\n        self.button  = CheckButtons(self.ax,labels,init)\n        self.button.on_clicked(self.button_click)\n        self.status = self.button.get_status()\n        self.call_on_click(self.status.index(True),firsttime=True)\n\n    @output.capture()  # debug\n    def reinit(self):\n        self.status = self.init_state\n        self.button.set_active(self.status.index(True))      #turn off old, will trigger update and set to status\n\n    @output.capture()  # debug\n    def button_click(self, event):\n        ''' maintains one-on state. If on-button is clicked, will process correctly '''\n        #new_status = self.button.get_status()\n        #new = [self.status[i] ^ new_status[i] for i in range(len(self.status))]\n        #newidx = new.index(True)\n        self.button.eventson = False\n        self.button.set_active(self.status.index(True))  #turn off old or reenable if same\n        self.button.eventson = True\n        self.status = self.button.get_status()\n        self.call_on_click(self.status.index(True))\n\nclass overfit_example():\n    \"\"\" plot overfit example \"\"\"\n    # pylint: disable=too-many-instance-attributes\n    # pylint: disable=too-many-locals\n    # pylint: disable=missing-function-docstring\n    # pylint: disable=attribute-defined-outside-init\n    def __init__(self, regularize=False):\n        self.regularize=regularize\n        self.lambda_=0\n        fig = plt.figure( figsize=(8,6))\n        fig.canvas.toolbar_visible = False\n        fig.canvas.header_visible = False\n        fig.canvas.footer_visible = False\n        fig.set_facecolor('#ffffff') #white\n        gs  = GridSpec(5, 3, figure=fig)\n        ax0 = fig.add_subplot(gs[0:3, :])\n        ax1 = fig.add_subplot(gs[-2, :])\n        ax2 = fig.add_subplot(gs[-1, :])\n        ax1.set_axis_off()\n        ax2.set_axis_off()\n        self.ax = [ax0,ax1,ax2]\n        self.fig = fig\n\n        self.axfitdata = plt.axes([0.26,0.124,0.12,0.1 ])  #lx,by,w,h\n        self.bfitdata  = Button(self.axfitdata , 'fit data', color=dlc['dlblue'])\n        self.bfitdata.label.set_fontsize(12)\n        self.bfitdata.on_clicked(self.fitdata_clicked)\n\n        #clear data is a future enhancement\n        #self.axclrdata = plt.axes([0.26,0.06,0.12,0.05 ])  #lx,by,w,h\n        #self.bclrdata  = Button(self.axclrdata , 'clear data', color='white')\n        #self.bclrdata.label.set_fontsize(12)\n        #self.bclrdata.on_clicked(self.clrdata_clicked)\n\n        self.cid = fig.canvas.mpl_connect('button_press_event', self.add_data)\n\n        self.typebut = button_manager(fig, [0.4, 0.07,0.15,0.15], [\"Regression\", \"Categorical\"],\n                                       [False,True], self.toggle_type)\n\n        self.fig.text(0.1, 0.02+0.21, \"Degree\", fontsize=12)\n        self.degrbut = button_manager(fig,[0.1,0.02,0.15,0.2 ], ['1','2','3','4','5','6'],\n                                        [True, False, False, False, False, False], self.update_equation)\n        if self.regularize:\n            self.fig.text(0.6, 0.02+0.21, r\"lambda($\\lambda$)\", fontsize=12)\n            self.lambut = button_manager(fig,[0.6,0.02,0.15,0.2 ], ['0.0','0.2','0.4','0.6','0.8','1'],\n                                        [True, False, False, False, False, False], self.updt_lambda)\n\n        #self.regbut =  button_manager(fig, [0.8, 0.08,0.24,0.15], [\"Regularize\"],\n        #                               [False], self.toggle_reg)\n        #self.logistic_data()\n\n    def updt_lambda(self, idx, firsttime=False):\n      # pylint: disable=unused-argument\n        self.lambda_ = idx * 0.2\n\n    def toggle_type(self, idx, firsttime=False):\n        self.logistic = idx==1\n        self.ax[0].clear()\n        if self.logistic:\n            self.logistic_data()\n        else:\n            self.linear_data()\n        if not firsttime:\n            self.degrbut.reinit()\n\n    @output.capture()  # debug\n    def logistic_data(self,redraw=False):\n        if not redraw:\n            m = 50\n            n = 2\n            np.random.seed(2)\n            X_train = 2*(np.random.rand(m,n)-[0.5,0.5])\n            y_train = X_train[:,1]+0.5  > X_train[:,0]**2 + 0.5*np.random.rand(m) #quadratic + random\n            y_train = y_train + 0  #convert from boolean to integer\n            self.X = X_train\n            self.y = y_train\n            self.x_ideal = np.sort(X_train[:,0])\n            self.y_ideal =  self.x_ideal**2\n\n\n        self.ax[0].plot(self.x_ideal, self.y_ideal, \"--\", color = \"orangered\", label=\"ideal\", lw=1)\n        plot_data(self.X, self.y, self.ax[0], s=10, loc='lower right')\n        self.ax[0].set_title(\"OverFitting Example: Categorical data set with noise\")\n        self.ax[0].text(0.5,0.93, \"Click on plot to add data. Hold [Shift] for blue(y=0) data.\",\n                        fontsize=12, ha='center',transform=self.ax[0].transAxes, color=dlc[\"dlblue\"])\n        self.ax[0].set_xlabel(r\"$x_0$\")\n        self.ax[0].set_ylabel(r\"$x_1$\")\n\n    def linear_data(self,redraw=False):\n        if not redraw:\n            m = 30\n            c = 0\n            x_train = np.arange(0,m,1)\n            np.random.seed(1)\n            y_ideal = x_train**2 + c\n            y_train = y_ideal + 0.7 * y_ideal*(np.random.sample((m,))-0.5)\n            self.x_ideal = x_train #for redraw when new data included in X\n            self.X = x_train\n            self.y = y_train\n            self.y_ideal = y_ideal\n        else:\n            self.ax[0].set_xlim(self.xlim)\n            self.ax[0].set_ylim(self.ylim)\n\n        self.ax[0].scatter(self.X,self.y, label=\"y\")\n        self.ax[0].plot(self.x_ideal, self.y_ideal, \"--\", color = \"orangered\", label=\"y_ideal\", lw=1)\n        self.ax[0].set_title(\"OverFitting Example: Regression Data Set (quadratic with noise)\",fontsize = 14)\n        self.ax[0].set_xlabel(\"x\")\n        self.ax[0].set_ylabel(\"y\")\n        self.ax0ledgend = self.ax[0].legend(loc='lower right')\n        self.ax[0].text(0.5,0.93, \"Click on plot to add data\",\n                        fontsize=12, ha='center',transform=self.ax[0].transAxes, color=dlc[\"dlblue\"])\n        if not redraw:\n            self.xlim = self.ax[0].get_xlim()\n            self.ylim = self.ax[0].get_ylim()\n\n\n    @output.capture()  # debug\n    def add_data(self, event):\n        if self.logistic:\n            self.add_data_logistic(event)\n        else:\n            self.add_data_linear(event)\n\n    @output.capture()  # debug\n    def add_data_logistic(self, event):\n        if event.inaxes == self.ax[0]:\n            x0_coord = event.xdata\n            x1_coord = event.ydata\n\n            if event.key is None:  #shift not pressed\n                self.ax[0].scatter(x0_coord, x1_coord, marker='x', s=10, c = 'red', label=\"y=1\")\n                self.y = np.append(self.y,1)\n            else:\n                self.ax[0].scatter(x0_coord, x1_coord, marker='o', s=10, label=\"y=0\", facecolors='none',\n                                   edgecolors=dlc['dlblue'],lw=3)\n                self.y = np.append(self.y,0)\n            self.X = np.append(self.X,np.array([[x0_coord, x1_coord]]),axis=0)\n        self.fig.canvas.draw()\n\n    def add_data_linear(self, event):\n        if event.inaxes == self.ax[0]:\n            x_coord = event.xdata\n            y_coord = event.ydata\n\n            self.ax[0].scatter(x_coord, y_coord, marker='o', s=10, facecolors='none',\n                                   edgecolors=dlc['dlblue'],lw=3)\n            self.y = np.append(self.y,y_coord)\n            self.X = np.append(self.X,x_coord)\n            self.fig.canvas.draw()\n\n    #@output.capture()  # debug\n    #def clrdata_clicked(self,event):\n    #    if self.logistic == True:\n    #        self.X = np.\n    #    else:\n    #        self.linear_regression()\n\n\n    @output.capture()  # debug\n    def fitdata_clicked(self,event):\n        if self.logistic:\n            self.logistic_regression()\n        else:\n            self.linear_regression()\n\n    def linear_regression(self):\n        self.ax[0].clear()\n        self.fig.canvas.draw()\n\n        # create and fit the model using our mapped_X feature set.\n        self.X_mapped, _ =  map_one_feature(self.X, self.degree)\n        self.X_mapped_scaled, self.X_mu, self.X_sigma  = zscore_normalize_features(self.X_mapped)\n\n        #linear_model = LinearRegression()\n        linear_model = Ridge(alpha=self.lambda_, normalize=True, max_iter=10000)\n        linear_model.fit(self.X_mapped_scaled, self.y )\n        self.w = linear_model.coef_.reshape(-1,)\n        self.b = linear_model.intercept_\n        x = np.linspace(*self.xlim,30)  #plot line idependent of data which gets disordered\n        xm, _ =  map_one_feature(x, self.degree)\n        xms = (xm - self.X_mu)/ self.X_sigma\n        y_pred = linear_model.predict(xms)\n\n        #self.fig.canvas.draw()\n        self.linear_data(redraw=True)\n        self.ax0yfit = self.ax[0].plot(x, y_pred, color = \"blue\", label=\"y_fit\")\n        self.ax0ledgend = self.ax[0].legend(loc='lower right')\n        self.fig.canvas.draw()\n\n    def logistic_regression(self):\n        self.ax[0].clear()\n        self.fig.canvas.draw()\n\n        # create and fit the model using our mapped_X feature set.\n        self.X_mapped, _ =  map_feature(self.X[:, 0], self.X[:, 1], self.degree)\n        self.X_mapped_scaled, self.X_mu, self.X_sigma  = zscore_normalize_features(self.X_mapped)\n        if not self.regularize or self.lambda_ == 0:\n            lr = LogisticRegression(penalty='none', max_iter=10000)\n        else:\n            C = 1/self.lambda_\n            lr = LogisticRegression(C=C, max_iter=10000)\n\n        lr.fit(self.X_mapped_scaled,self.y)\n        #print(lr.score(self.X_mapped_scaled, self.y))\n        self.w = lr.coef_.reshape(-1,)\n        self.b = lr.intercept_\n        #print(self.w, self.b)\n        self.logistic_data(redraw=True)\n        self.contour = plot_decision_boundary(self.ax[0],[-1,1],[-1,1], predict_logistic, self.w, self.b,\n                       scaler=True, mu=self.X_mu, sigma=self.X_sigma, degree=self.degree )\n        self.fig.canvas.draw()\n\n    @output.capture()  # debug\n    def update_equation(self, idx, firsttime=False):\n        #print(f\"Update equation, index = {idx}, firsttime={firsttime}\")\n        self.degree = idx+1\n        if firsttime:\n            self.eqtext = []\n        else:\n            for artist in self.eqtext:\n                #print(artist)\n                artist.remove()\n            self.eqtext = []\n        if self.logistic:\n            _, equation =  map_feature(self.X[:, 0], self.X[:, 1], self.degree)\n            string = 'f_{wb} = sigmoid('\n        else:\n            _, equation =  map_one_feature(self.X, self.degree)\n            string = 'f_{wb} = ('\n        bz = 10\n        seq = equation.split('+')\n        blks = math.ceil(len(seq)/bz)\n        for i in range(blks):\n            if i == 0:\n                string = string +  '+'.join(seq[bz*i:bz*i+bz])\n            else:\n                string = '+'.join(seq[bz*i:bz*i+bz])\n            string = string + ')' if i == blks-1 else string + '+'\n            ei = self.ax[1].text(0.01,(0.75-i*0.25), f\"${string}$\",fontsize=9,\n                                 transform = self.ax[1].transAxes, ma='left', va='top' )\n            self.eqtext.append(ei)\n        self.fig.canvas.draw()\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Optional Labs/plt_quad_logistic.py",
    "content": "\"\"\"\nplt_quad_logistic.py\n    interactive plot and supporting routines showing logistic regression\n\"\"\"\n\nimport time\nfrom matplotlib import cm\nimport matplotlib.colors as colors\nfrom matplotlib.gridspec import GridSpec\nfrom matplotlib.widgets import Button\nfrom matplotlib.patches import FancyArrowPatch\nfrom ipywidgets import Output\nfrom lab_utils_common import np, plt, dlc, dlcolors, sigmoid, compute_cost_matrix, gradient_descent\n\n# for debug\n#output = Output() # sends hidden error messages to display when using widgets\n#display(output)\n\nclass plt_quad_logistic:\n    ''' plots a quad plot showing logistic regression '''\n    # pylint: disable=too-many-instance-attributes\n    # pylint: disable=too-many-locals\n    # pylint: disable=missing-function-docstring\n    # pylint: disable=attribute-defined-outside-init\n    def __init__(self, x_train,y_train, w_range, b_range):\n        # setup figure\n        fig = plt.figure( figsize=(10,6))\n        fig.canvas.toolbar_visible = False\n        fig.canvas.header_visible = False\n        fig.canvas.footer_visible = False\n        fig.set_facecolor('#ffffff') #white\n        gs  = GridSpec(2, 2, figure=fig)\n        ax0 = fig.add_subplot(gs[0, 0])\n        ax1 = fig.add_subplot(gs[0, 1])\n        ax2 = fig.add_subplot(gs[1, 0],  projection='3d')\n        ax3 = fig.add_subplot(gs[1,1])\n        pos = ax3.get_position().get_points()  ##[[lb_x,lb_y], [rt_x, rt_y]]\n        h = 0.05 \n        width = 0.2\n        axcalc   = plt.axes([pos[1,0]-width, pos[1,1]-h, width, h])  #lx,by,w,h\n        ax = np.array([ax0, ax1, ax2, ax3, axcalc])\n        self.fig = fig\n        self.ax = ax\n        self.x_train = x_train\n        self.y_train = y_train\n\n        self.w = 0. #initial point, non-array\n        self.b = 0.\n\n        # initialize subplots\n        self.dplot = data_plot(ax[0], x_train, y_train, self.w, self.b)\n        self.con_plot = contour_and_surface_plot(ax[1], ax[2], x_train, y_train, w_range, b_range, self.w, self.b)\n        self.cplot = cost_plot(ax[3])\n\n        # setup events\n        self.cid = fig.canvas.mpl_connect('button_press_event', self.click_contour)\n        self.bcalc = Button(axcalc, 'Run Gradient Descent \\nfrom current w,b (click)', color=dlc[\"dlorange\"])\n        self.bcalc.on_clicked(self.calc_logistic)\n\n#    @output.capture()  # debug\n    def click_contour(self, event):\n        ''' called when click in contour '''\n        if event.inaxes == self.ax[1]:   #contour plot\n            self.w = event.xdata\n            self.b = event.ydata\n\n            self.cplot.re_init()\n            self.dplot.update(self.w, self.b)\n            self.con_plot.update_contour_wb_lines(self.w, self.b)\n            self.con_plot.path.re_init(self.w, self.b)\n\n            self.fig.canvas.draw()\n\n#    @output.capture()  # debug\n    def calc_logistic(self, event):\n        ''' called on run gradient event '''\n        for it in [1, 8,16,32,64,128,256,512,1024,2048,4096]:\n            w, self.b, J_hist = gradient_descent(self.x_train.reshape(-1,1), self.y_train.reshape(-1,1),\n                                                 np.array(self.w).reshape(-1,1), self.b, 0.1, it,\n                                                 logistic=True, lambda_=0, verbose=False)\n            self.w = w[0,0]\n            self.dplot.update(self.w, self.b)\n            self.con_plot.update_contour_wb_lines(self.w, self.b)\n            self.con_plot.path.add_path_item(self.w,self.b)\n            self.cplot.add_cost(J_hist)\n\n            time.sleep(0.3)\n            self.fig.canvas.draw()\n\n\nclass data_plot:\n    ''' handles data plot '''\n    # pylint: disable=missing-function-docstring\n    # pylint: disable=attribute-defined-outside-init\n    def __init__(self, ax, x_train, y_train, w, b):\n        self.ax = ax\n        self.x_train = x_train\n        self.y_train = y_train\n        self.m = x_train.shape[0]\n        self.w = w\n        self.b = b\n\n        self.plt_tumor_data()\n        self.draw_logistic_lines(firsttime=True)\n        self.mk_cost_lines(firsttime=True)\n\n        self.ax.autoscale(enable=False) # leave plot scales the same after initial setup\n\n    def plt_tumor_data(self):\n        x = self.x_train\n        y = self.y_train\n        pos = y == 1\n        neg = y == 0\n        self.ax.scatter(x[pos], y[pos], marker='x', s=80, c = 'red', label=\"malignant\")\n        self.ax.scatter(x[neg], y[neg], marker='o', s=100, label=\"benign\", facecolors='none',\n                   edgecolors=dlc[\"dlblue\"],lw=3)\n        self.ax.set_ylim(-0.175,1.1)\n        self.ax.set_ylabel('y')\n        self.ax.set_xlabel('Tumor Size')\n        self.ax.set_title(\"Logistic Regression on Categorical Data\")\n\n    def update(self, w, b):\n        self.w = w\n        self.b = b\n        self.draw_logistic_lines()\n        self.mk_cost_lines()\n\n    def draw_logistic_lines(self, firsttime=False):\n        if not firsttime:\n            self.aline[0].remove()\n            self.bline[0].remove()\n            self.alegend.remove()\n\n        xlim  = self.ax.get_xlim()\n        x_hat = np.linspace(*xlim, 30)\n        y_hat = sigmoid(np.dot(x_hat.reshape(-1,1), self.w) + self.b)\n        self.aline = self.ax.plot(x_hat, y_hat, color=dlc[\"dlblue\"],\n                                     label=\"y = sigmoid(z)\")\n        f_wb = np.dot(x_hat.reshape(-1,1), self.w) + self.b\n        self.bline = self.ax.plot(x_hat, f_wb, color=dlc[\"dlorange\"], lw=1,\n                                     label=f\"z = {np.squeeze(self.w):0.2f}x+({self.b:0.2f})\")\n        self.alegend = self.ax.legend(loc='upper left')\n\n    def mk_cost_lines(self, firsttime=False):\n        ''' makes vertical cost lines'''\n        if not firsttime:\n            for artist in self.cost_items:\n                artist.remove()\n        self.cost_items = []\n        cstr = f\"cost = (1/{self.m})*(\"\n        ctot = 0\n        label = 'cost for point'\n        addedbreak = False\n        for p in zip(self.x_train,self.y_train):\n            f_wb_p = sigmoid(self.w*p[0]+self.b)\n            c_p = compute_cost_matrix(p[0].reshape(-1,1), p[1],np.array(self.w), self.b, logistic=True, lambda_=0, safe=True)\n            c_p_txt = c_p\n            a = self.ax.vlines(p[0], p[1],f_wb_p, lw=3, color=dlc[\"dlpurple\"], ls='dotted', label=label)\n            label='' #just one\n            cxy = [p[0], p[1] + (f_wb_p-p[1])/2]\n            b = self.ax.annotate(f'{c_p_txt:0.1f}', xy=cxy, xycoords='data',color=dlc[\"dlpurple\"],\n                        xytext=(5, 0), textcoords='offset points')\n            cstr += f\"{c_p_txt:0.1f} +\"\n            if len(cstr) > 38 and addedbreak is False:\n                cstr += \"\\n\"\n                addedbreak = True\n            ctot += c_p\n            self.cost_items.extend((a,b))\n        ctot = ctot/(len(self.x_train))\n        cstr = cstr[:-1] + f\") = {ctot:0.2f}\"\n        ## todo.. figure out how to get this textbox to extend to the width of the subplot\n        c = self.ax.text(0.05,0.02,cstr, transform=self.ax.transAxes, color=dlc[\"dlpurple\"])\n        self.cost_items.append(c)\n\n\nclass contour_and_surface_plot:\n    ''' plots combined in class as they have similar operations '''\n    # pylint: disable=missing-function-docstring\n    # pylint: disable=attribute-defined-outside-init\n    def __init__(self, axc, axs, x_train, y_train, w_range, b_range, w, b):\n\n        self.x_train = x_train\n        self.y_train = y_train\n        self.axc = axc\n        self.axs = axs\n\n        #setup useful ranges and common linspaces\n        b_space  = np.linspace(*b_range, 100)\n        w_space  = np.linspace(*w_range, 100)\n\n        # get cost for w,b ranges for contour and 3D\n        tmp_b,tmp_w = np.meshgrid(b_space,w_space)\n        z = np.zeros_like(tmp_b)\n        for i in range(tmp_w.shape[0]):\n            for j in range(tmp_w.shape[1]):\n                z[i,j] = compute_cost_matrix(x_train.reshape(-1,1), y_train, tmp_w[i,j], tmp_b[i,j],\n                                             logistic=True, lambda_=0, safe=True)\n                if z[i,j] == 0:\n                    z[i,j] = 1e-9\n\n        ### plot contour ###\n        CS = axc.contour(tmp_w, tmp_b, np.log(z),levels=12, linewidths=2, alpha=0.7,colors=dlcolors)\n        axc.set_title('log(Cost(w,b))')\n        axc.set_xlabel('w', fontsize=10)\n        axc.set_ylabel('b', fontsize=10)\n        axc.set_xlim(w_range)\n        axc.set_ylim(b_range)\n        self.update_contour_wb_lines(w, b, firsttime=True)\n        axc.text(0.7,0.05,\"Click to choose w,b\",  bbox=dict(facecolor='white', ec = 'black'), fontsize = 10,\n                transform=axc.transAxes, verticalalignment = 'center', horizontalalignment= 'center')\n\n        #Surface plot of the cost function J(w,b)\n        axs.plot_surface(tmp_w, tmp_b, z,  cmap = cm.jet, alpha=0.3, antialiased=True)\n        axs.plot_wireframe(tmp_w, tmp_b, z, color='k', alpha=0.1)\n        axs.set_xlabel(\"$w$\")\n        axs.set_ylabel(\"$b$\")\n        axs.zaxis.set_rotate_label(False)\n        axs.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n        axs.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n        axs.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))\n        axs.set_zlabel(\"J(w, b)\", rotation=90)\n        axs.view_init(30, -120)\n\n        axs.autoscale(enable=False)\n        axc.autoscale(enable=False)\n\n        self.path = path(self.w,self.b, self.axc)  # initialize an empty path, avoids existance check\n\n    def update_contour_wb_lines(self, w, b, firsttime=False):\n        self.w = w\n        self.b = b\n        cst = compute_cost_matrix(self.x_train.reshape(-1,1), self.y_train, np.array(self.w), self.b,\n                                  logistic=True, lambda_=0, safe=True)\n\n        # remove lines and re-add on contour plot and 3d plot\n        if not firsttime:\n            for artist in self.dyn_items:\n                artist.remove()\n        a = self.axc.scatter(self.w, self.b, s=100, color=dlc[\"dlblue\"], zorder= 10, label=\"cost with \\ncurrent w,b\")\n        b = self.axc.hlines(self.b, self.axc.get_xlim()[0], self.w, lw=4, color=dlc[\"dlpurple\"], ls='dotted')\n        c = self.axc.vlines(self.w, self.axc.get_ylim()[0] ,self.b, lw=4, color=dlc[\"dlpurple\"], ls='dotted')\n        d = self.axc.annotate(f\"Cost: {cst:0.2f}\", xy= (self.w, self.b), xytext = (4,4), textcoords = 'offset points',\n                           bbox=dict(facecolor='white'), size = 10)\n        #Add point in 3D surface plot\n        e = self.axs.scatter3D(self.w, self.b, cst , marker='X', s=100)\n\n        self.dyn_items = [a,b,c,d,e]\n\n\nclass cost_plot:\n    \"\"\" manages cost plot for plt_quad_logistic \"\"\"\n    # pylint: disable=missing-function-docstring\n    # pylint: disable=attribute-defined-outside-init\n    def __init__(self,ax):\n        self.ax = ax\n        self.ax.set_ylabel(\"log(cost)\")\n        self.ax.set_xlabel(\"iteration\")\n        self.costs = []\n        self.cline = self.ax.plot(0,0, color=dlc[\"dlblue\"])\n\n    def re_init(self):\n        self.ax.clear()\n        self.__init__(self.ax)\n\n    def add_cost(self,J_hist):\n        self.costs.extend(J_hist)\n        self.cline[0].remove()\n        self.cline = self.ax.plot(self.costs)\n\nclass path:\n    ''' tracks paths during gradient descent on contour plot '''\n    # pylint: disable=missing-function-docstring\n    # pylint: disable=attribute-defined-outside-init\n    def __init__(self, w, b, ax):\n        ''' w, b at start of path '''\n        self.path_items = []\n        self.w = w\n        self.b = b\n        self.ax = ax\n\n    def re_init(self, w, b):\n        for artist in self.path_items:\n            artist.remove()\n        self.path_items = []\n        self.w = w\n        self.b = b\n\n    def add_path_item(self, w, b):\n        a = FancyArrowPatch(\n            posA=(self.w, self.b), posB=(w, b), color=dlc[\"dlblue\"],\n            arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n        )\n        self.ax.add_artist(a)\n        self.path_items.append(a)\n        self.w = w\n        self.b = b\n\n#-----------\n# related to the logistic gradient descent lab\n#----------\n\ndef truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n    \"\"\" truncates color map \"\"\"\n    new_cmap = colors.LinearSegmentedColormap.from_list(\n        'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n        cmap(np.linspace(minval, maxval, n)))\n    return new_cmap\n\ndef plt_prob(ax, w_out,b_out):\n    \"\"\" plots a decision boundary but include shading to indicate the probability \"\"\"\n    #setup useful ranges and common linspaces\n    x0_space  = np.linspace(0, 4 , 100)\n    x1_space  = np.linspace(0, 4 , 100)\n\n    # get probability for x0,x1 ranges\n    tmp_x0,tmp_x1 = np.meshgrid(x0_space,x1_space)\n    z = np.zeros_like(tmp_x0)\n    for i in range(tmp_x0.shape[0]):\n        for j in range(tmp_x1.shape[1]):\n            z[i,j] = sigmoid(np.dot(w_out, np.array([tmp_x0[i,j],tmp_x1[i,j]])) + b_out)\n\n\n    cmap = plt.get_cmap('Blues')\n    new_cmap = truncate_colormap(cmap, 0.0, 0.5)\n    pcm = ax.pcolormesh(tmp_x0, tmp_x1, z,\n                   norm=cm.colors.Normalize(vmin=0, vmax=1),\n                   cmap=new_cmap, shading='nearest', alpha = 0.9)\n    ax.figure.colorbar(pcm, ax=ax)\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Practice quiz - Cost function for logistic regression/Readme.md",
    "content": "![](/C1%20-%20Supervised%20Machine%20Learning:%20Regression%20and%20Classification/week3/Practice%20quiz:%20Cost%20function%20for%20logistic%20regression/ss1.png)\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Practice quiz - Gradient descent for logistic regression/Readme.md",
    "content": "![](/C1%20-%20Supervised%20Machine%20Learning:%20Regression%20and%20Classification/week3/Practice%20quiz:%20Gradient%20descent%20for%20logistic%20regression/ss1.png)\n"
  },
  {
    "path": "C1 - Supervised Machine Learning - Regression and Classification/week3/Readme.md",
    "content": "### Week 3 Solutions\n\n<br></br>\n\n\n- [Practice quiz: Cost function for logistic regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Practice%20quiz%20-%20Cost%20function%20for%20logistic%20regression)\n- [Practice quiz: Gradient descent for logistic regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Practice%20quiz%20-%20Gradient%20descent%20for%20logistic%20regression)\n- [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs)\n  - [Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab01_Classification_Soln.ipynb)\n  - [Sigmoid Function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab02_Sigmoid_function_Soln.ipynb)\n  - [Decision Boundary](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab03_Decision_Boundary_Soln.ipynb)\n  - [Logistic Loss](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab04_LogisticLoss_Soln.ipynb)\n  - [Cost Function](/C1%20-%20Supervised%20Machine%20Learning:%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab05_Cost_Function_Soln.ipynb)\n  - [Gradient Descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab06_Gradient_Descent_Soln.ipynb)\n  - [Scikit Learn - Logistic Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab07_Scikit_Learn_Soln.ipynb)\n  - [Overfitting](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab08_Overfitting_Soln.ipynb)\n  - [Regularization](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab09_Regularization_Soln.ipynb)\n  \n- [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/C1W3A1)\n\n  - [Logistic Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/C1W3A1/C1_W3_Logistic_Regression.ipynb)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/Readme.md",
    "content": "## Advanced Learning Algorithms\n\n- [Week 1](/C2%20-%20Advanced%20Learning%20Algorithms/week1)\n    - [Practice quiz: Neural networks intuition](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz%20-%20Neural%20networks%20intuition)\n    - [Practice quiz: Neural network model](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz%20-%20Neural%20network%20model)\n    - [Practice quiz: TensorFlow implementation](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz%20-%20TensorFlow%20implementation)\n    - [Practice quiz : Neural Networks Implementation in Numpy](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/e5d6103f4bdf732390bd85aeb453002f276d8bf3/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice-Quiz-Neural-Networks-Implementation-in-python)\n    - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs)\n      - [Neurons and Layers](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs/C2_W1_Lab01_Neurons_and_Layers.ipynb)\n      - [Coffee Roasting](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs/C2_W1_Lab02_CoffeeRoasting_TF.ipynb)\n      - [Coffee Roasting Using Numpy](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs/C2_W1_Lab03_CoffeeRoasting_Numpy.ipynb)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/C2W1A1)\n      - [Neural Networks for Binary Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/C2W1A1/C2_W1_Assignment.ipynb)\n  \n\n  <br/>\n\n- [Week 2](/C2%20-%20Advanced%20Learning%20Algorithms/week2)\n    - [Practice quiz : Neural Networks Training](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7e5505d650d56554edde4abebc51a2c7c7fb81fb/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Neural-Network-Training)\n    - [Practice quiz : Activation Functions](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/f2b84223545cc7c0062903cf4eac5c6fda53dc20/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Activation-Functions)\n    - [Practice quiz : Multiclass Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/80c14a835b066568b075410bb2e5e1220b4c3653/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-quiz-Multiclass-Classification)\n    - [Practice quiz : Additional Neural Networks Concepts](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/3bf176864d32d12eb2cb98ed4661e3ded627befa/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Additional-Neural-Network-Concepts)\n    - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs)\n        - [RElu](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs/C2_W2_Relu.ipynb)\n        - [Softmax](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs/C2_W2_SoftMax.ipynb)\n        - [Multiclass Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs/C2_W2_Multiclass_TF.ipynb)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/C2W2A1)\n      - [Neural Networks For Handwritten Digit Recogonition - Multiclass](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/C2W2A1/C2_W2_Assignment.ipynb)\n    \n\n<br/>\n\n- [Week 3](/C2%20-%20Advanced%20Learning%20Algorithms/week3)\n    - [Practice quiz : Advice for Applying Machine Learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/614fe817ac9b5fba6718512ba8c8a36b856a1cab/C2%20-%20Advanced%20Learning%20Algorithms/week3/Practice-Quiz-Advice-for-applying-machine-learning)    \n    - [Practice quiz : Bias and Variance](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-bias-and-variance)\n    - [Practice quiz : Machine Learning Development Process](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-machine-learning-development-process)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/C2W3A1)\n        - [Advice for Applied Machine Learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/C2W3A1/C2_W3_Assignment.ipynb)\n\n<br/>\n\n\n- [Week 4](/C2%20-%20Advanced%20Learning%20Algorithms/week4)\n    - [Practice quiz : Decision Trees](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-trees)\n    - [Practice Quiz : Decision Trees Learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-tree-learning)\n    - [Practice quiz : Decision Trees Ensembles](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-tree-ensembles)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/C2W4A1)\n        - [Decision Trees](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/C2W4A1/C2_W4_Decision_Tree_with_Markdown.ipynb)\n\n#### [Certificate of Completion](https://coursera.org/share/c9a7766b0c6eab27db2e955376d29bf7)        \n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/C2W1A1/.ipynb_checkpoints/C2_W1_Assignment-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Practice Lab: Neural Networks for Handwritten Digit Recognition, Binary\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize the hand-written digits zero and one.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Neural Networks](#2)\\n\",\n    \"  - [ 2.1 Problem Statement](#2.1)\\n\",\n    \"  - [ 2.2 Dataset](#2.2)\\n\",\n    \"  - [ 2.3 Model representation](#2.3)\\n\",\n    \"  - [ 2.4 Tensorflow Model Implementation](#2.4)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 2.5 NumPy Model Implementation (Forward Prop in NumPy)](#2.5)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"  - [ 2.6 Vectorized NumPy Model Implementation (Optional)](#2.6)\\n\",\n    \"    - [ Exercise 3](#ex03)\\n\",\n    \"  - [ 2.7 Congratulations!](#2.7)\\n\",\n    \"  - [ 2.8 NumPy Broadcasting Tutorial (Optional)](#2.8)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\\n\",\n    \"- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from autils import *\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tensorflow and Keras**  \\n\",\n    \"Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Neural Networks\\n\",\n    \"\\n\",\n    \"In Course 1, you implemented logistic regression. This was extended to handle non-linear boundaries using polynomial regression. For even more complex scenarios such as image recognition, neural networks are preferred.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Problem Statement\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize two handwritten digits, zero and one. This is a binary classification task. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. You will extend this network to recognize all 10 digits (0-9) in a future assignment. \\n\",\n    \"\\n\",\n    \"This exercise will show you how the methods you have learned can be used for this classification task.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_data()` function shown below loads the data into variables `X` and `y`\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- The data set contains 1000 training examples of handwritten digits $^1$, here limited to zero and one.  \\n\",\n    \"\\n\",\n    \"    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \\n\",\n    \"        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \\n\",\n    \"        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \\n\",\n    \"        - Each training example becomes a single row in our data matrix `X`. \\n\",\n    \"        - This gives us a 1000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\\n\",\n    \"\\n\",\n    \"$$X = \\n\",\n    \"\\\\left(\\\\begin{array}{cc} \\n\",\n    \"--- (x^{(1)}) --- \\\\\\\\\\n\",\n    \"--- (x^{(2)}) --- \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\ \\n\",\n    \"--- (x^{(m)}) --- \\n\",\n    \"\\\\end{array}\\\\right)$$ \\n\",\n    \"\\n\",\n    \"- The second part of the training set is a 1000 x 1 dimensional vector `y` that contains labels for the training set\\n\",\n    \"    - `y = 0` if the image is of the digit `0`, `y = 1` if the image is of the digit `1`.\\n\",\n    \"\\n\",\n    \"$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X, y = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.1\\\"></a>\\n\",\n    \"#### 2.2.1 View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints elements of the variables `X` and `y`.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first element of X is:  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  8.56059680e-06\\n\",\n      \"  1.94035948e-06 -7.37438725e-04 -8.13403799e-03 -1.86104473e-02\\n\",\n      \" -1.87412865e-02 -1.87572508e-02 -1.90963542e-02 -1.64039011e-02\\n\",\n      \" -3.78191381e-03  3.30347316e-04  1.27655229e-05  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  1.16421569e-04  1.20052179e-04\\n\",\n      \" -1.40444581e-02 -2.84542484e-02  8.03826593e-02  2.66540339e-01\\n\",\n      \"  2.73853746e-01  2.78729541e-01  2.74293607e-01  2.24676403e-01\\n\",\n      \"  2.77562977e-02 -7.06315478e-03  2.34715414e-04  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  1.28335523e-17 -3.26286765e-04 -1.38651604e-02\\n\",\n      \"  8.15651552e-02  3.82800381e-01  8.57849775e-01  1.00109761e+00\\n\",\n      \"  9.69710638e-01  9.30928598e-01  1.00383757e+00  9.64157356e-01\\n\",\n      \"  4.49256553e-01 -5.60408259e-03 -3.78319036e-03  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  5.10620915e-06\\n\",\n      \"  4.36410675e-04 -3.95509940e-03 -2.68537241e-02  1.00755014e-01\\n\",\n      \"  6.42031710e-01  1.03136838e+00  8.50968614e-01  5.43122379e-01\\n\",\n      \"  3.42599738e-01  2.68918777e-01  6.68374643e-01  1.01256958e+00\\n\",\n      \"  9.03795598e-01  1.04481574e-01 -1.66424973e-02  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.59875260e-05\\n\",\n      \" -3.10606987e-03  7.52456076e-03  1.77539831e-01  7.92890120e-01\\n\",\n      \"  9.65626503e-01  4.63166079e-01  6.91720680e-02 -3.64100526e-03\\n\",\n      \" -4.12180405e-02 -5.01900656e-02  1.56102907e-01  9.01762651e-01\\n\",\n      \"  1.04748346e+00  1.51055252e-01 -2.16044665e-02  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  5.87012352e-05 -6.40931373e-04\\n\",\n      \" -3.23305249e-02  2.78203465e-01  9.36720163e-01  1.04320956e+00\\n\",\n      \"  5.98003217e-01 -3.59409041e-03 -2.16751770e-02 -4.81021923e-03\\n\",\n      \"  6.16566793e-05 -1.23773318e-02  1.55477482e-01  9.14867477e-01\\n\",\n      \"  9.20401348e-01  1.09173902e-01 -1.71058007e-02  0.00000000e+00\\n\",\n      \"  0.00000000e+00  1.56250000e-04 -4.27724104e-04 -2.51466503e-02\\n\",\n      \"  1.30532561e-01  7.81664862e-01  1.02836583e+00  7.57137601e-01\\n\",\n      \"  2.84667194e-01  4.86865128e-03 -3.18688725e-03  0.00000000e+00\\n\",\n      \"  8.36492601e-04 -3.70751123e-02  4.52644165e-01  1.03180133e+00\\n\",\n      \"  5.39028101e-01 -2.43742611e-03 -4.80290033e-03  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -7.03635621e-04 -1.27262443e-02  1.61706648e-01\\n\",\n      \"  7.79865383e-01  1.03676705e+00  8.04490400e-01  1.60586724e-01\\n\",\n      \" -1.38173339e-02  2.14879493e-03 -2.12622549e-04  2.04248366e-04\\n\",\n      \" -6.85907627e-03  4.31712963e-04  7.20680947e-01  8.48136063e-01\\n\",\n      \"  1.51383408e-01 -2.28404366e-02  1.98971950e-04  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -9.40410539e-03  3.74520505e-02  6.94389110e-01\\n\",\n      \"  1.02844844e+00  1.01648066e+00  8.80488426e-01  3.92123945e-01\\n\",\n      \" -1.74122413e-02 -1.20098039e-04  5.55215142e-05 -2.23907271e-03\\n\",\n      \" -2.76068376e-02  3.68645493e-01  9.36411169e-01  4.59006723e-01\\n\",\n      \" -4.24701797e-02  1.17356610e-03  1.88929739e-05  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -1.93511951e-02  1.29999794e-01  9.79821705e-01\\n\",\n      \"  9.41862388e-01  7.75147704e-01  8.73632241e-01  2.12778350e-01\\n\",\n      \" -1.72353349e-02  0.00000000e+00  1.09937426e-03 -2.61793751e-02\\n\",\n      \"  1.22872879e-01  8.30812662e-01  7.26501773e-01  5.24441863e-02\\n\",\n      \" -6.18971913e-03  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -9.36563862e-03  3.68349741e-02  6.99079299e-01\\n\",\n      \"  1.00293583e+00  6.05704402e-01  3.27299224e-01 -3.22099249e-02\\n\",\n      \" -4.83053002e-02 -4.34069138e-02 -5.75151144e-02  9.55674190e-02\\n\",\n      \"  7.26512627e-01  6.95366966e-01  1.47114481e-01 -1.20048679e-02\\n\",\n      \" -3.02798203e-04  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -6.76572712e-04 -6.51415556e-03  1.17339359e-01\\n\",\n      \"  4.21948410e-01  9.93210937e-01  8.82013974e-01  7.45758734e-01\\n\",\n      \"  7.23874268e-01  7.23341725e-01  7.20020340e-01  8.45324959e-01\\n\",\n      \"  8.31859739e-01  6.88831870e-02 -2.77765012e-02  3.59136710e-04\\n\",\n      \"  7.14869281e-05  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  1.53186275e-04  3.17353553e-04 -2.29167177e-02\\n\",\n      \" -4.14402914e-03  3.87038450e-01  5.04583435e-01  7.74885876e-01\\n\",\n      \"  9.90037446e-01  1.00769478e+00  1.00851440e+00  7.37905042e-01\\n\",\n      \"  2.15455291e-01 -2.69624864e-02  1.32506127e-03  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.36366422e-04\\n\",\n      \" -2.26031454e-03 -2.51994485e-02 -3.73889910e-02  6.62121228e-02\\n\",\n      \"  2.91134498e-01  3.23055726e-01  3.06260315e-01  8.76070942e-02\\n\",\n      \" -2.50581917e-02  2.37438725e-04  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  6.20939216e-18  6.72618320e-04 -1.13151411e-02\\n\",\n      \" -3.54641066e-02 -3.88214912e-02 -3.71077412e-02 -1.33524928e-02\\n\",\n      \"  9.90964718e-04  4.89176960e-05  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The first element of X is: ', X[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first element of y is:  0\\n\",\n      \"The last element of y is:  1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The first element of y is: ', y[0,0])\\n\",\n    \"print ('The last element of y is: ', y[-1,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.2\\\"></a>\\n\",\n    \"#### 2.2.2 Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of X is: (1000, 400)\\n\",\n      \"The shape of y is: (1000, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of X is: ' + str(X.shape))\\n\",\n    \"print ('The shape of y is: ' + str(y.shape))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.3\\\"></a>\\n\",\n    \"#### 2.2.3 Visualizing the Data\\n\",\n    \"\\n\",\n    \"You will begin by visualizing a subset of the training set. \\n\",\n    \"- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \\n\",\n    \"- The label for each image is displayed above the image \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 64 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1)\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(y[random_index,0])\\n\",\n    \"    ax.set_axis_off()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3 Model representation\\n\",\n    \"\\n\",\n    \"The neural network you will use in this assignment is shown in the figure below. \\n\",\n    \"- This has three dense layers with sigmoid activations.\\n\",\n    \"    - Recall that our inputs are pixel values of digit images.\\n\",\n    \"    - Since the images are of size $20\\\\times20$, this gives us $400$ inputs  \\n\",\n    \"    \\n\",\n    \"<img src=\\\"images/C2_W1_Assign1.PNG\\\" width=\\\"500\\\" height=\\\"400\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $1$ output unit in layer 3. \\n\",\n    \"\\n\",\n    \"    - Recall that the dimensions of these parameters are determined as follows:\\n\",\n    \"        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \\n\",\n    \"            - $W$ will be of dimension $s_{in} \\\\times s_{out}$.\\n\",\n    \"            - $b$ will a vector with $s_{out}$ elements\\n\",\n    \"  \\n\",\n    \"    - Therefore, the shapes of `W`, and `b`,  are \\n\",\n    \"        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\\n\",\n    \"        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\\n\",\n    \"        - layer3: The shape of `W3` is (15, 1) and the shape of `b3` is: (1,)\\n\",\n    \">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (n,1) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention. \\n\",\n    \"               \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.4\\\"></a>\\n\",\n    \"### 2.4 Tensorflow Model Implementation\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statment below. \\n\",\n    \">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \\n\",\n    \"`tf.keras.Input(shape=(400,)),    #specify input shape`  \\n\",\n    \"We will include that here to illuminate some model sizing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a sigmoid activation to construct the network described above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED CELL: Sequential model\\n\",\n    \"\\n\",\n    \"model = Sequential(\\n\",\n    \"    [               \\n\",\n    \"        tf.keras.Input(shape=(400,)),    #specify input size\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        tf.keras.layers.Dense(25, activation=\\\"sigmoid\\\"),\\n\",\n    \"        tf.keras.layers.Dense(15, activation=\\\"sigmoid\\\"),\\n\",\n    \"        tf.keras.layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"my_model\\\" \\n\",\n    \")                            \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"my_model\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" dense (Dense)               (None, 25)                10025     \\n\",\n      \"                                                                 \\n\",\n      \" dense_1 (Dense)             (None, 15)                390       \\n\",\n      \"                                                                 \\n\",\n      \" dense_2 (Dense)             (None, 1)                 16        \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 10,431\\n\",\n      \"Trainable params: 10,431\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Expected Output (Click to Expand) </b></font></summary>\\n\",\n    \"The `model.summary()` function displays a useful summary of the model. Because we have specified an input layer size, the shape of the weight and bias arrays are determined and the total number of parameters per layer can be shown. Note, the names of the layers may vary as they are auto-generated.  \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"```\\n\",\n    \"Model: \\\"my_model\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"dense (Dense)                (None, 25)                10025     \\n\",\n    \"_________________________________________________________________\\n\",\n    \"dense_1 (Dense)              (None, 15)                390       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"dense_2 (Dense)              (None, 1)                 16        \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 10,431\\n\",\n    \"Trainable params: 10,431\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"As described in the lecture:\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"model = Sequential(                      \\n\",\n    \"    [                                   \\n\",\n    \"        tf.keras.Input(shape=(400,)),    # specify input size (optional)\\n\",\n    \"        Dense(25, activation='sigmoid'), \\n\",\n    \"        Dense(15, activation='sigmoid'), \\n\",\n    \"        Dense(1,  activation='sigmoid')  \\n\",\n    \"    ], name = \\\"my_model\\\"                                    \\n\",\n    \")                                       \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"from public_tests import * \\n\",\n    \"\\n\",\n    \"test_c1(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"L1 params =  10025 , L2 params =  390 ,  L3 params =  16\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"L1_num_params = 400 * 25 + 25  # W1 parameters  + b1 parameters\\n\",\n    \"L2_num_params = 25 * 15 + 15   # W2 parameters  + b2 parameters\\n\",\n    \"L3_num_params = 15 * 1 + 1     # W3 parameters  + b3 parameters\\n\",\n    \"print(\\\"L1 params = \\\", L1_num_params, \\\", L2 params = \\\", L2_num_params, \\\",  L3 params = \\\", L3_num_params )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's further examine the weights to verify that tensorflow produced the same dimensions as we calculated above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"[layer1, layer2, layer3] = model.layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"W1 shape = (400, 25), b1 shape = (25,)\\n\",\n      \"W2 shape = (25, 15), b2 shape = (15,)\\n\",\n      \"W3 shape = (15, 1), b3 shape = (1,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#### Examine Weights shapes\\n\",\n    \"W1,b1 = layer1.get_weights()\\n\",\n    \"W2,b2 = layer2.get_weights()\\n\",\n    \"W3,b3 = layer3.get_weights()\\n\",\n    \"print(f\\\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\\\")\\n\",\n    \"print(f\\\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\\\")\\n\",\n    \"print(f\\\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"W1 shape = (400, 25), b1 shape = (25,)  \\n\",\n    \"W2 shape = (25, 15), b2 shape = (15,)  \\n\",\n    \"W3 shape = (15, 1), b3 shape = (1,)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`xx.get_weights` returns a NumPy array. One can also access the weights directly in their tensor form. Note the shape of the tensors in the final layer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[<tf.Variable 'dense_2/kernel:0' shape=(15, 1) dtype=float32, numpy=\\n\",\n      \"array([[-0.08530456],\\n\",\n      \"       [-0.2747036 ],\\n\",\n      \"       [ 0.08510572],\\n\",\n      \"       [-0.12527409],\\n\",\n      \"       [-0.2926382 ],\\n\",\n      \"       [-0.34840912],\\n\",\n      \"       [ 0.21684825],\\n\",\n      \"       [-0.08979291],\\n\",\n      \"       [ 0.5360281 ],\\n\",\n      \"       [ 0.19300771],\\n\",\n      \"       [-0.44613487],\\n\",\n      \"       [ 0.1397686 ],\\n\",\n      \"       [-0.42860353],\\n\",\n      \"       [ 0.5345983 ],\\n\",\n      \"       [ 0.22546476]], dtype=float32)>, <tf.Variable 'dense_2/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(model.layers[2].weights)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following code will define a loss function and run gradient descent to fit the weights of the model to the training data. This will be explained in more detail in the following week.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.6348\\n\",\n      \"Epoch 2/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.4996\\n\",\n      \"Epoch 3/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.3573\\n\",\n      \"Epoch 4/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.2490\\n\",\n      \"Epoch 5/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.1787\\n\",\n      \"Epoch 6/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.1338\\n\",\n      \"Epoch 7/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.1047\\n\",\n      \"Epoch 8/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0848\\n\",\n      \"Epoch 9/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0708\\n\",\n      \"Epoch 10/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0600\\n\",\n      \"Epoch 11/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0520\\n\",\n      \"Epoch 12/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0456\\n\",\n      \"Epoch 13/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0405\\n\",\n      \"Epoch 14/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0365\\n\",\n      \"Epoch 15/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0332\\n\",\n      \"Epoch 16/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0304\\n\",\n      \"Epoch 17/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0280\\n\",\n      \"Epoch 18/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0260\\n\",\n      \"Epoch 19/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0243\\n\",\n      \"Epoch 20/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0228\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f2d2b00a650>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.BinaryCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X,y,\\n\",\n    \"    epochs=20\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To run the model on an example to make a prediction, use [Keras `predict`](https://www.tensorflow.org/api_docs/python/tf/keras/Model). The input to `predict` is an array so the single example is reshaped to be two dimensional.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" predicting a zero: [[0.01574531]]\\n\",\n      \" predicting a one:  [[0.98137283]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"prediction = model.predict(X[0].reshape(1,400))  # a zero\\n\",\n    \"print(f\\\" predicting a zero: {prediction}\\\")\\n\",\n    \"prediction = model.predict(X[500].reshape(1,400))  # a one\\n\",\n    \"print(f\\\" predicting a one:  {prediction}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output of the model is interpreted as a probability. In the first example above, the input is a zero. The model predicts the probability that the input is a one is nearly zero. \\n\",\n    \"In the second example, the input is a one. The model predicts the probability that the input is a one is nearly one.\\n\",\n    \"As in the case of logistic regression, the probability is compared to a threshold to make a final prediction.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"prediction after threshold: 1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print(f\\\"prediction after threshold: {yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 64 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Predict using the Neural Network\\n\",\n    \"    prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    if prediction >= 0.5:\\n\",\n    \"        yhat = 1\\n\",\n    \"    else:\\n\",\n    \"        yhat = 0\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{yhat}\\\")\\n\",\n    \"    ax.set_axis_off()\\n\",\n    \"fig.suptitle(\\\"Label, yhat\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.5\\\"></a>\\n\",\n    \"### 2.5 NumPy Model Implementation (Forward Prop in NumPy)\\n\",\n    \"As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_dense2.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Below, build a dense layer subroutine. The example in lecture utilized a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` is then applied to that result. This section will not utilize some of the matrix operations described in the optional lectures. These will be explored in a later section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: my_dense\\n\",\n    \"\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"### START CODE HERE ### \\n\",\n    \"    for i in range(units):\\n\",\n    \"        w = W[:,i]\\n\",\n    \"        z=np.dot(w,a_in) + b[i]\\n\",\n    \"        a_out[i]=g(z)\\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(a_out)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.54735762 0.57932425 0.61063923]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Quick Check\\n\",\n    \"x_tst = 0.1*np.arange(1,3,1).reshape(2,)  # (1 examples, 2 features)\\n\",\n    \"W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\\n\",\n    \"b_tst = 0.1*np.arange(1,4,1).reshape(3,)  # (3 features)\\n\",\n    \"A_tst = my_dense(x_tst, W_tst, b_tst, sigmoid)\\n\",\n    \"print(A_tst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"[0.54735762 0.57932425 0.61063923]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"As described in the lecture:\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):             \\n\",\n    \"        w =                            # Select weights for unit j. These are in column j of W\\n\",\n    \"        z =                            # dot product of w and a_in + b\\n\",\n    \"        a_out[j] =                     # apply activation to z\\n\",\n    \"    return(a_out)\\n\",\n    \"```\\n\",\n    \"   \\n\",\n    \"    \\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):             \\n\",\n    \"        w = W[:,j]                     \\n\",\n    \"        z = np.dot(w, a_in) + b[j]     \\n\",\n    \"        a_out[j] = g(z)                \\n\",\n    \"    return(a_out)\\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"test_c2(my_dense)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a three-layer neural network utilizing the `my_dense` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential(x, W1, b1, W2, b2, W3, b3):\\n\",\n    \"    a1 = my_dense(x,  W1, b1, sigmoid)\\n\",\n    \"    a2 = my_dense(a1, W2, b2, sigmoid)\\n\",\n    \"    a3 = my_dense(a2, W3, b3, sigmoid)\\n\",\n    \"    return(a3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can copy trained weights and biases from Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp,b1_tmp = layer1.get_weights()\\n\",\n    \"W2_tmp,b2_tmp = layer2.get_weights()\\n\",\n    \"W3_tmp,b3_tmp = layer3.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"yhat =  0  label=  0\\n\",\n      \"yhat =  1  label=  1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# make predictions\\n\",\n    \"prediction = my_sequential(X[0], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print( \\\"yhat = \\\", yhat, \\\" label= \\\", y[0,0])\\n\",\n    \"prediction = my_sequential(X[500], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print( \\\"yhat = \\\", yhat, \\\" label= \\\", y[500,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following cell to see predictions from both the Numpy model and the Tensorflow model. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 64 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"\\n\",\n    \"    # Predict using the Neural Network implemented in Numpy\\n\",\n    \"    my_prediction = my_sequential(X[random_index], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"    my_yhat = int(my_prediction >= 0.5)\\n\",\n    \"\\n\",\n    \"    # Predict using the Neural Network implemented in Tensorflow\\n\",\n    \"    tf_prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    tf_yhat = int(tf_prediction >= 0.5)\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{tf_yhat},{my_yhat}\\\")\\n\",\n    \"    ax.set_axis_off() \\n\",\n    \"fig.suptitle(\\\"Label, yhat Tensorflow, yhat Numpy\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.6\\\"></a>\\n\",\n    \"### 2.6 Vectorized NumPy Model Implementation (Optional)\\n\",\n    \"The optional lectures described vector and matrix operations that can be used to speed the calculations.\\n\",\n    \"Below describes a layer operation that computes the output for all units in a layer on a given input example:\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_VectorMatrix.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"\\n\",\n    \"We can demonstrate this using the examples `X` and the `W1`,`b1` parameters above. We use `np.matmul` to perform the matrix multiply. Note, the dimensions of x and W must be compatible as shown in the diagram above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1, 25)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = X[0].reshape(-1,1)         # column vector (400,1)\\n\",\n    \"z1 = np.matmul(x.T,W1) + b1    # (1,400)(400,25) = (1,25)\\n\",\n    \"a1 = sigmoid(z1)\\n\",\n    \"print(a1.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can take this a step further and compute all the units for all examples in one Matrix-Matrix operation.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_MatrixMatrix.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"The full operation is $\\\\mathbf{Z}=\\\\mathbf{XW}+\\\\mathbf{b}$. This will utilize NumPy broadcasting to expand $\\\\mathbf{b}$ to $m$ rows. If this is unfamiliar, a short tutorial is provided at the end of the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex03\\\"></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"\\n\",\n    \"Below, compose a new `my_dense_v` subroutine that performs the layer calculations for a matrix of examples. This will utilize `np.matmul()`. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED FUNCTION: my_dense_v\\n\",\n    \"\\n\",\n    \"def my_dense_v(A_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      A_in (ndarray (m,n)) : Data, m examples, n features each\\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (1,j)) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      A_out (ndarray (m,j)) : m examples, j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"### START CODE HERE ### \\n\",\n    \"    A_out = g(np.matmul(A_in,W) + b)\\n\",\n    \"    \\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(A_out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tf.Tensor(\\n\",\n      \"[[0.54735762 0.57932425 0.61063923]\\n\",\n      \" [0.57199613 0.61301418 0.65248946]\\n\",\n      \" [0.5962827  0.64565631 0.6921095 ]\\n\",\n      \" [0.62010643 0.67699586 0.72908792]], shape=(4, 3), dtype=float64)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_tst = 0.1*np.arange(1,9,1).reshape(4,2) # (4 examples, 2 features)\\n\",\n    \"W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\\n\",\n    \"b_tst = 0.1*np.arange(1,4,1).reshape(1,3) # (1, 3 features)\\n\",\n    \"A_tst = my_dense_v(X_tst, W_tst, b_tst, sigmoid)\\n\",\n    \"print(A_tst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"[[0.54735762 0.57932425 0.61063923]\\n\",\n    \" [0.57199613 0.61301418 0.65248946]\\n\",\n    \" [0.5962827  0.64565631 0.6921095 ]\\n\",\n    \" [0.62010643 0.67699586 0.72908792]]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    In matrix form, this can be written in one or two lines. \\n\",\n    \"    \\n\",\n    \"       Z = np.matmul of A_in and W plus b    \\n\",\n    \"       A_out is g(Z)  \\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for code</b></font></summary>\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"def my_dense_v(A_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      A_in (ndarray (m,n)) : Data, m examples, n features each\\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j,1)) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      A_out (ndarray (m,j)) : m examples, j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Z = np.matmul(A_in,W) + b    \\n\",\n    \"    A_out = g(Z)                 \\n\",\n    \"    return(A_out)\\n\",\n    \"```\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"test_c3(my_dense_v)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a three-layer neural network utilizing the `my_dense_v` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential_v(X, W1, b1, W2, b2, W3, b3):\\n\",\n    \"    A1 = my_dense_v(X,  W1, b1, sigmoid)\\n\",\n    \"    A2 = my_dense_v(A1, W2, b2, sigmoid)\\n\",\n    \"    A3 = my_dense_v(A2, W3, b3, sigmoid)\\n\",\n    \"    return(A3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can again copy trained weights and biases from Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp,b1_tmp = layer1.get_weights()\\n\",\n    \"W2_tmp,b2_tmp = layer2.get_weights()\\n\",\n    \"W3_tmp,b3_tmp = layer3.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's make a prediction with the new model. This will make a prediction on *all of the examples at once*. Note the shape of the output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"TensorShape([1000, 1])\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Prediction = my_sequential_v(X, W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"Prediction.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll apply a threshold of 0.5 as before, but to all predictions at once.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"predict a zero:  [0] predict a one:  [1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Yhat = (Prediction >= 0.5).numpy().astype(int)\\n\",\n    \"print(\\\"predict a zero: \\\",Yhat[0], \\\"predict a one: \\\", Yhat[500])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following cell to see predictions. This will use the predictions we just calculated above. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 64 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8, 8, figsize=(8, 8))\\n\",\n    \"fig.tight_layout(pad=0.1, rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i, ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20, 20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"   \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]}, {Yhat[random_index, 0]}\\\")\\n\",\n    \"    ax.set_axis_off() \\n\",\n    \"fig.suptitle(\\\"Label, Yhat\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can see how one of the misclassified images looks.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAEQAAABUCAYAAAA7xZEpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAJUElEQVR4nO2bv28jxxXHP7M/uD+4FH/p9MsGJJx99gExlBgH2ICDuEjhNl0KVynyZ6RKlTJNinQB0idp0ruK6ytytg82cKezIFInUeRql9zlLmdTCDNZ7f2AbWjJuwO/wIDUiEsOP3zz3ps3s6IoCtb6v4xVD+BV0xpIRWsgFa2BVLQGUtEaSEVrIBXVDkQI0RNC/FMIEQshHgshPv8R1/5WCPEfIcRUCPFFjcPUspbwGX8B5sA28Avg30KI+0VR/PcHXDsC/gzcBX5d1wCvqSiK2hrQ5ArGe6W+vwN/+pHv83vgizrHqlrdU+Y9IC+K4mGp7z7ws5o/9yerbiABEFb6JkCr5s/9yaobSARsVPo2gMuaP/cnq24gDwFLCHGn1Pdz4Ic41JWoViBFUcTAP4A/CiGaQohfAr/hyrEihDgQQhRCiIPnXS+EMIUQLlfR0BBCuEIIu84x1+61gR7wLyAGjoDPS//7FfAIsF9w7e+AotL+Vud4xSoLREKIPwBPi6L468oGUdFKgbyKWq9lKloDqWgNpKKXLu48z3tjHcxsNhPP619bSEXLWP6/VOUoV414Qohrj8vQSoEoAJVkTEsIgWEYVwnTkqCs3ELgxRZQ7l8WlJUBKYoCwzAwDAMhBLZtY9tXyxQpJVJKFosF8/mcxWKBEGIpUFYCRE0NwzCwbRvDMGg2m/i+D0CWZeR5rh8Xi4WGUTeUpQMp+wnDMGg0GliWRbfbpd/vA1dAsiwjjmOklMxmMxaLhQZTJ5SVWIiUEgDf99nd3SUIAj7++GM++ugjTNMkiiLSNOXk5IQvv/ySwWDAZDLh9PSULMsQQrxZQJSVNBoNut0unU6Hw8NDPvvsMyzLYjwekyQJ3333HScnJ0gpKYqCs7OzN2/KvExlUADNZpNer0ccx8xmMwyj/jzylQGyWCzIsgzTNPE8D9/32dvb4/DwkO3tbYQQfP3118Rx/Ey+cpNaORDlJFWoLYoC27axLAvf9+n1ekgpCYJgKRay0rXMizLUcp9hGJimiWmaSxnTyhd3VSjKWtTfpmli2zamaS4lU105EBVCqwu5ar7xxq5lyhZhWRZBENBqtXAcR6fxZV/xgmp8bVoqEPVl1JQoA3FdVwOpXrMMEEpLA1J2kq7rIoQgCALa7TbtdvuFQJTFlNtrnZiVnSWA53lsb2/jeR6Hh4d88skn9Ho99vf3sSzr2nVqFey6rm6e5+l1jdJNAlr6lLFtm83NTTqdDgcHB7z//vv0ej1ardYzFlAuCzQaDVzXpdFo6JJAHWl8rUDK815FErV+uXXrFt1uF9/3cV1X10LUder1CoZt2ziOg+M416zjprWUKaP8gBCCdrvN3bt32d/f591332VnZ4cgCIBnQ65pmjSbTQzDoNPp0Ol0yLIMKSXT6VQDv0krWVoeoqCULaTT6WgLsSzrWjSRUiKEwLIsLMvS1tFoNHTWWkfkqX3KSCmxLIt2u02z2WR7e5utrS22trawbZuLiwvCMNTNsix2d3fpdDoURcFsNiOOY8IwZDKZEIYhaZoC1FIXWQoQwzDo9/vs7u5y+/ZtDg4O2N/fJ0kSTk5OmM/nPHnyhKOjI5rNJp9++in9fh8pJWEYMh6POT8/Zzgccn5+rmusdYTfpUQZwzDwfZ8gCAiCQE+T+XxOmqYkSUIYhlxcXOjCsvqyqtic57l+XnXWN6kbB1IerGVZNBoN2u0277zzDh988AF7e3tsbm4SBAFRFDGdTomiiNFoxHA4ZD6fkyQJQghM08R1XXzfv9YUyDqcai0WoiKLcoatVos7d+5w7949Op0Om5ubOnoo/6CA5HlOkiTAlWU5jqMLRkEQMJvNKIpCv+amVWuUUUDKv7LrujpKLBYLkiRhNpuRpulzp4Tqk1LqLQlVpK5lzDf9hmVH2mq12NraYmdnh52dHZ2ym6apHeajR48YjUaMRiPyPNfvI4RgsVgQxzGXl5dMJhPG4zGTyUQ71TpUGxAhBK7r0u/36fV6urpuWRamaVIUBdPplKdPn3J2dsbl5aUuIwJ6UypNU6bTqW5xHNdaQbtxIOVwOJ/PiaII3/eJoog4jq8lV6oaZts2GxsbOI5Dr9fDcRzthzzPo9ls6p29ZrP5ei3uVHQQQhCGofYTx8fHHB8fs7Gxge/7OI6DbdsEQUCe59q/dDod2u02Ukps26bf7+N5Hnt7e7z99ts4jsPFxQXj8VjvD9+kaokyZQspigLf9/XeSqPR0L+u2tt1HEfXRlT1TFmI67rA1R5NEAQ6TMOzZcabUK2JmYoYaZoyn8/JsuyaqWdZph1ms9nUEUlZWJZlnJ+fE4ah9jNxHGvn+1qk7uUB5nmugSRJQpqmZFmmw2qSJJyfnzOZTNja2tKh2bZthBCkacr333/P6ekpT5480XDSNK0tytS+2i1Hjmp9tFxxVw7WsiwdYfI812F3Op3qIxKvVR7yPL2ocLy5ucm9e/eYTqe89dZb7O3taf8RhiHD4ZAHDx7w+PFjjo6OCMOQJEn0tHstF3flQZcLQFJK+v0+H374IVmW0Wq1aLVaCCGQUurjD1999RXffPONXvpnWaZ38+rQUmuqVTi2beP7Pnme43kejUZDJ2zz+ZzZbEaSJCRJoiNW3VralFGJWLl5nsetW7eQUuqELU1TBoMBw+GQ4+Nj7XRVUei134ZQKu/KKUdaLi6bpollWbpeenZ2xmg00iWCst947YFIKbm8vGQ8HgPoR8uydIhVeUoURTx9+pTT01MuLi6Yz+fXNr/rVq1A1K+Z5zmDwYAHDx7Q7/cxDINut0u322V7exvTNBkOhwyHQ8bjMffv3+fhw4eMRiOd/tdRDHqelhJlpJTEccx4PMYwDEajkXaq6uRhHMeMRiPG4zFnZ2fadygLeV60qkO1WwhcpfDD4RCAwWBAFEUEQUCn02FnZ+eahcRxzLfffstgMGA6nepTh8vSS28xu4nbQ9SKVO2+qaNStm3jeZ4+KhVFEVEUkec5l5eXJEnyTAXtJsG86PaQ2oFUVT4R5DiOPr2cJAnT6RQppXaw1YiyDCBL3+yWUl47sq22HFRfueL2Rt8eUt6iVDmFSraUygBWAQNegTNmLzsdtGwYsEQLUUt69bzaV+6vPl+mVra4e1nfKrXyKfOqaX2re0VrC6loDaSiNZCK1kAqWgOpaA2kov8B6S2cICBQS5IAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 72x72 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(1, 1))\\n\",\n    \"errors = np.where(y != Yhat)\\n\",\n    \"random_index = errors[0][0]\\n\",\n    \"X_random_reshaped = X[random_index].reshape((20, 20)).T\\n\",\n    \"plt.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"plt.title(f\\\"{y[random_index,0]}, {Yhat[random_index, 0]}\\\")\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.7\\\"></a>\\n\",\n    \"### 2.7 Congratulations!\\n\",\n    \"You have successfully built and utilized a neural network.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.8\\\"></a>\\n\",\n    \"### 2.8 NumPy Broadcasting Tutorial (Optional)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"In the last example,  $\\\\mathbf{Z}=\\\\mathbf{XW} + \\\\mathbf{b}$ utilized NumPy broadcasting to expand the vector $\\\\mathbf{b}$. If you are not familiar with NumPy Broadcasting, this short tutorial is provided.\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{XW}$  is a matrix-matrix operation with dimensions $(m,j_1)(j_1,j_2)$ which results in a matrix with dimension  $(m,j_2)$. To that, we add a vector $\\\\mathbf{b}$ with dimension $(1,j_2)$.  $\\\\mathbf{b}$ must be expanded to be a $(m,j_2)$ matrix for this element-wise operation to make sense. This expansion is accomplished for you by NumPy broadcasting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Broadcasting applies to element-wise operations.  \\n\",\n    \"Its basic operation is to 'stretch' a smaller dimension by replicating elements to match a larger dimension.\\n\",\n    \"\\n\",\n    \"More [specifically](https://NumPy.org/doc/stable/user/basics.broadcasting.html): \\n\",\n    \"When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimensions and works its way left. Two dimensions are compatible when\\n\",\n    \"- they are equal, or\\n\",\n    \"- one of them is 1   \\n\",\n    \"\\n\",\n    \"If these conditions are not met, a ValueError: operands could not be broadcast together exception is thrown, indicating that the arrays have incompatible shapes. The size of the resulting array is the size that is not 1 along each axis of the inputs.\\n\",\n    \"\\n\",\n    \"Here are some examples:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C2_W1_Assign1_BroadcastIndexes.PNG\\\"  alt='missing' width=\\\"400\\\"  ><center/>\\n\",\n    \"    <figcaption>Calculating Broadcast Result shape</figcaption>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The graphic below describes expanding dimensions. Note the red text below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C2_W1_Assign1_Broadcasting.gif\\\"  alt='missing' width=\\\"600\\\"  ><center/>\\n\",\n    \"    <figcaption>Broadcast notionally expands arguments to match for element wise operations</figcaption>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The graphic above shows NumPy expanding the arguments to match before the final operation. Note that this is a notional description. The actual mechanics of NumPy operation choose the most efficient implementation.\\n\",\n    \"\\n\",\n    \"For each of the following examples, try to guess the size of the result before running the example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(a + b).shape: (3, 1), \\n\",\n      \"a + b = \\n\",\n      \"[[6]\\n\",\n      \" [7]\\n\",\n      \" [8]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\\n\",\n    \"b = 5\\n\",\n    \"print(f\\\"(a + b).shape: {(a + b).shape}, \\\\na + b = \\\\n{a + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that this applies to all element-wise operations:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(a * b).shape: (3, 1), \\n\",\n      \"a * b = \\n\",\n      \"[[ 5]\\n\",\n      \" [10]\\n\",\n      \" [15]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\\n\",\n    \"b = 5\\n\",\n    \"print(f\\\"(a * b).shape: {(a * b).shape}, \\\\na * b = \\\\n{a * b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C2_W1_Assign1_VectorAdd.PNG\\\"  alt='missing' width=\\\"740\\\" >\\n\",\n    \"    <center><figcaption><b>Row-Column Element-Wise Operations</b></figcaption></center>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1]\\n\",\n      \" [2]\\n\",\n      \" [3]\\n\",\n      \" [4]]\\n\",\n      \"[[1 2 3]]\\n\",\n      \"(a + b).shape: (4, 3), \\n\",\n      \"a + b = \\n\",\n      \"[[2 3 4]\\n\",\n      \" [3 4 5]\\n\",\n      \" [4 5 6]\\n\",\n      \" [5 6 7]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1,2,3,4]).reshape(-1,1)\\n\",\n    \"b = np.array([1,2,3]).reshape(1,-1)\\n\",\n    \"print(a)\\n\",\n    \"print(b)\\n\",\n    \"print(f\\\"(a + b).shape: {(a + b).shape}, \\\\na + b = \\\\n{a + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is the scenario in the dense layer you built above. Adding a 1-D vector $b$ to a (m,j) matrix.\\n\",\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C2_W1_Assign1_BroadcastMatrix.PNG\\\"  alt='missing' width=\\\"740\\\" >\\n\",\n    \"    <center><figcaption><b>Matrix + 1-D Vector</b></figcaption></center>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"89367\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/C2W1A1/C2_W1_Assignment.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Practice Lab: Neural Networks for Handwritten Digit Recognition, Binary\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize the hand-written digits zero and one.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Neural Networks](#2)\\n\",\n    \"  - [ 2.1 Problem Statement](#2.1)\\n\",\n    \"  - [ 2.2 Dataset](#2.2)\\n\",\n    \"  - [ 2.3 Model representation](#2.3)\\n\",\n    \"  - [ 2.4 Tensorflow Model Implementation](#2.4)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 2.5 NumPy Model Implementation (Forward Prop in NumPy)](#2.5)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"  - [ 2.6 Vectorized NumPy Model Implementation (Optional)](#2.6)\\n\",\n    \"    - [ Exercise 3](#ex03)\\n\",\n    \"  - [ 2.7 Congratulations!](#2.7)\\n\",\n    \"  - [ 2.8 NumPy Broadcasting Tutorial (Optional)](#2.8)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\\n\",\n    \"- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from autils import *\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tensorflow and Keras**  \\n\",\n    \"Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Neural Networks\\n\",\n    \"\\n\",\n    \"In Course 1, you implemented logistic regression. This was extended to handle non-linear boundaries using polynomial regression. For even more complex scenarios such as image recognition, neural networks are preferred.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Problem Statement\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize two handwritten digits, zero and one. This is a binary classification task. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. You will extend this network to recognize all 10 digits (0-9) in a future assignment. \\n\",\n    \"\\n\",\n    \"This exercise will show you how the methods you have learned can be used for this classification task.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_data()` function shown below loads the data into variables `X` and `y`\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- The data set contains 1000 training examples of handwritten digits $^1$, here limited to zero and one.  \\n\",\n    \"\\n\",\n    \"    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \\n\",\n    \"        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \\n\",\n    \"        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \\n\",\n    \"        - Each training example becomes a single row in our data matrix `X`. \\n\",\n    \"        - This gives us a 1000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\\n\",\n    \"\\n\",\n    \"$$X = \\n\",\n    \"\\\\left(\\\\begin{array}{cc} \\n\",\n    \"--- (x^{(1)}) --- \\\\\\\\\\n\",\n    \"--- (x^{(2)}) --- \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\ \\n\",\n    \"--- (x^{(m)}) --- \\n\",\n    \"\\\\end{array}\\\\right)$$ \\n\",\n    \"\\n\",\n    \"- The second part of the training set is a 1000 x 1 dimensional vector `y` that contains labels for the training set\\n\",\n    \"    - `y = 0` if the image is of the digit `0`, `y = 1` if the image is of the digit `1`.\\n\",\n    \"\\n\",\n    \"$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X, y = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.1\\\"></a>\\n\",\n    \"#### 2.2.1 View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints elements of the variables `X` and `y`.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first element of X is:  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  8.56059680e-06\\n\",\n      \"  1.94035948e-06 -7.37438725e-04 -8.13403799e-03 -1.86104473e-02\\n\",\n      \" -1.87412865e-02 -1.87572508e-02 -1.90963542e-02 -1.64039011e-02\\n\",\n      \" -3.78191381e-03  3.30347316e-04  1.27655229e-05  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  1.16421569e-04  1.20052179e-04\\n\",\n      \" -1.40444581e-02 -2.84542484e-02  8.03826593e-02  2.66540339e-01\\n\",\n      \"  2.73853746e-01  2.78729541e-01  2.74293607e-01  2.24676403e-01\\n\",\n      \"  2.77562977e-02 -7.06315478e-03  2.34715414e-04  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  1.28335523e-17 -3.26286765e-04 -1.38651604e-02\\n\",\n      \"  8.15651552e-02  3.82800381e-01  8.57849775e-01  1.00109761e+00\\n\",\n      \"  9.69710638e-01  9.30928598e-01  1.00383757e+00  9.64157356e-01\\n\",\n      \"  4.49256553e-01 -5.60408259e-03 -3.78319036e-03  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  5.10620915e-06\\n\",\n      \"  4.36410675e-04 -3.95509940e-03 -2.68537241e-02  1.00755014e-01\\n\",\n      \"  6.42031710e-01  1.03136838e+00  8.50968614e-01  5.43122379e-01\\n\",\n      \"  3.42599738e-01  2.68918777e-01  6.68374643e-01  1.01256958e+00\\n\",\n      \"  9.03795598e-01  1.04481574e-01 -1.66424973e-02  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.59875260e-05\\n\",\n      \" -3.10606987e-03  7.52456076e-03  1.77539831e-01  7.92890120e-01\\n\",\n      \"  9.65626503e-01  4.63166079e-01  6.91720680e-02 -3.64100526e-03\\n\",\n      \" -4.12180405e-02 -5.01900656e-02  1.56102907e-01  9.01762651e-01\\n\",\n      \"  1.04748346e+00  1.51055252e-01 -2.16044665e-02  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  5.87012352e-05 -6.40931373e-04\\n\",\n      \" -3.23305249e-02  2.78203465e-01  9.36720163e-01  1.04320956e+00\\n\",\n      \"  5.98003217e-01 -3.59409041e-03 -2.16751770e-02 -4.81021923e-03\\n\",\n      \"  6.16566793e-05 -1.23773318e-02  1.55477482e-01  9.14867477e-01\\n\",\n      \"  9.20401348e-01  1.09173902e-01 -1.71058007e-02  0.00000000e+00\\n\",\n      \"  0.00000000e+00  1.56250000e-04 -4.27724104e-04 -2.51466503e-02\\n\",\n      \"  1.30532561e-01  7.81664862e-01  1.02836583e+00  7.57137601e-01\\n\",\n      \"  2.84667194e-01  4.86865128e-03 -3.18688725e-03  0.00000000e+00\\n\",\n      \"  8.36492601e-04 -3.70751123e-02  4.52644165e-01  1.03180133e+00\\n\",\n      \"  5.39028101e-01 -2.43742611e-03 -4.80290033e-03  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -7.03635621e-04 -1.27262443e-02  1.61706648e-01\\n\",\n      \"  7.79865383e-01  1.03676705e+00  8.04490400e-01  1.60586724e-01\\n\",\n      \" -1.38173339e-02  2.14879493e-03 -2.12622549e-04  2.04248366e-04\\n\",\n      \" -6.85907627e-03  4.31712963e-04  7.20680947e-01  8.48136063e-01\\n\",\n      \"  1.51383408e-01 -2.28404366e-02  1.98971950e-04  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -9.40410539e-03  3.74520505e-02  6.94389110e-01\\n\",\n      \"  1.02844844e+00  1.01648066e+00  8.80488426e-01  3.92123945e-01\\n\",\n      \" -1.74122413e-02 -1.20098039e-04  5.55215142e-05 -2.23907271e-03\\n\",\n      \" -2.76068376e-02  3.68645493e-01  9.36411169e-01  4.59006723e-01\\n\",\n      \" -4.24701797e-02  1.17356610e-03  1.88929739e-05  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -1.93511951e-02  1.29999794e-01  9.79821705e-01\\n\",\n      \"  9.41862388e-01  7.75147704e-01  8.73632241e-01  2.12778350e-01\\n\",\n      \" -1.72353349e-02  0.00000000e+00  1.09937426e-03 -2.61793751e-02\\n\",\n      \"  1.22872879e-01  8.30812662e-01  7.26501773e-01  5.24441863e-02\\n\",\n      \" -6.18971913e-03  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -9.36563862e-03  3.68349741e-02  6.99079299e-01\\n\",\n      \"  1.00293583e+00  6.05704402e-01  3.27299224e-01 -3.22099249e-02\\n\",\n      \" -4.83053002e-02 -4.34069138e-02 -5.75151144e-02  9.55674190e-02\\n\",\n      \"  7.26512627e-01  6.95366966e-01  1.47114481e-01 -1.20048679e-02\\n\",\n      \" -3.02798203e-04  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00 -6.76572712e-04 -6.51415556e-03  1.17339359e-01\\n\",\n      \"  4.21948410e-01  9.93210937e-01  8.82013974e-01  7.45758734e-01\\n\",\n      \"  7.23874268e-01  7.23341725e-01  7.20020340e-01  8.45324959e-01\\n\",\n      \"  8.31859739e-01  6.88831870e-02 -2.77765012e-02  3.59136710e-04\\n\",\n      \"  7.14869281e-05  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  1.53186275e-04  3.17353553e-04 -2.29167177e-02\\n\",\n      \" -4.14402914e-03  3.87038450e-01  5.04583435e-01  7.74885876e-01\\n\",\n      \"  9.90037446e-01  1.00769478e+00  1.00851440e+00  7.37905042e-01\\n\",\n      \"  2.15455291e-01 -2.69624864e-02  1.32506127e-03  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.36366422e-04\\n\",\n      \" -2.26031454e-03 -2.51994485e-02 -3.73889910e-02  6.62121228e-02\\n\",\n      \"  2.91134498e-01  3.23055726e-01  3.06260315e-01  8.76070942e-02\\n\",\n      \" -2.50581917e-02  2.37438725e-04  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  6.20939216e-18  6.72618320e-04 -1.13151411e-02\\n\",\n      \" -3.54641066e-02 -3.88214912e-02 -3.71077412e-02 -1.33524928e-02\\n\",\n      \"  9.90964718e-04  4.89176960e-05  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\\n\",\n      \"  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The first element of X is: ', X[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first element of y is:  0\\n\",\n      \"The last element of y is:  1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The first element of y is: ', y[0,0])\\n\",\n    \"print ('The last element of y is: ', y[-1,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.2\\\"></a>\\n\",\n    \"#### 2.2.2 Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of X is: (1000, 400)\\n\",\n      \"The shape of y is: (1000, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of X is: ' + str(X.shape))\\n\",\n    \"print ('The shape of y is: ' + str(y.shape))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.3\\\"></a>\\n\",\n    \"#### 2.2.3 Visualizing the Data\\n\",\n    \"\\n\",\n    \"You will begin by visualizing a subset of the training set. \\n\",\n    \"- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \\n\",\n    \"- The label for each image is displayed above the image \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 64 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1)\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(y[random_index,0])\\n\",\n    \"    ax.set_axis_off()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3 Model representation\\n\",\n    \"\\n\",\n    \"The neural network you will use in this assignment is shown in the figure below. \\n\",\n    \"- This has three dense layers with sigmoid activations.\\n\",\n    \"    - Recall that our inputs are pixel values of digit images.\\n\",\n    \"    - Since the images are of size $20\\\\times20$, this gives us $400$ inputs  \\n\",\n    \"    \\n\",\n    \"<img src=\\\"images/C2_W1_Assign1.PNG\\\" width=\\\"500\\\" height=\\\"400\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $1$ output unit in layer 3. \\n\",\n    \"\\n\",\n    \"    - Recall that the dimensions of these parameters are determined as follows:\\n\",\n    \"        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \\n\",\n    \"            - $W$ will be of dimension $s_{in} \\\\times s_{out}$.\\n\",\n    \"            - $b$ will a vector with $s_{out}$ elements\\n\",\n    \"  \\n\",\n    \"    - Therefore, the shapes of `W`, and `b`,  are \\n\",\n    \"        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\\n\",\n    \"        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\\n\",\n    \"        - layer3: The shape of `W3` is (15, 1) and the shape of `b3` is: (1,)\\n\",\n    \">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (n,1) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention. \\n\",\n    \"               \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.4\\\"></a>\\n\",\n    \"### 2.4 Tensorflow Model Implementation\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statment below. \\n\",\n    \">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \\n\",\n    \"`tf.keras.Input(shape=(400,)),    #specify input shape`  \\n\",\n    \"We will include that here to illuminate some model sizing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a sigmoid activation to construct the network described above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED CELL: Sequential model\\n\",\n    \"\\n\",\n    \"model = Sequential(\\n\",\n    \"    [               \\n\",\n    \"        tf.keras.Input(shape=(400,)),    #specify input size\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        tf.keras.layers.Dense(25, activation=\\\"sigmoid\\\"),\\n\",\n    \"        tf.keras.layers.Dense(15, activation=\\\"sigmoid\\\"),\\n\",\n    \"        tf.keras.layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"my_model\\\" \\n\",\n    \")                            \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"my_model\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" dense (Dense)               (None, 25)                10025     \\n\",\n      \"                                                                 \\n\",\n      \" dense_1 (Dense)             (None, 15)                390       \\n\",\n      \"                                                                 \\n\",\n      \" dense_2 (Dense)             (None, 1)                 16        \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 10,431\\n\",\n      \"Trainable params: 10,431\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Expected Output (Click to Expand) </b></font></summary>\\n\",\n    \"The `model.summary()` function displays a useful summary of the model. Because we have specified an input layer size, the shape of the weight and bias arrays are determined and the total number of parameters per layer can be shown. Note, the names of the layers may vary as they are auto-generated.  \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"```\\n\",\n    \"Model: \\\"my_model\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"dense (Dense)                (None, 25)                10025     \\n\",\n    \"_________________________________________________________________\\n\",\n    \"dense_1 (Dense)              (None, 15)                390       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"dense_2 (Dense)              (None, 1)                 16        \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 10,431\\n\",\n    \"Trainable params: 10,431\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"As described in the lecture:\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"model = Sequential(                      \\n\",\n    \"    [                                   \\n\",\n    \"        tf.keras.Input(shape=(400,)),    # specify input size (optional)\\n\",\n    \"        Dense(25, activation='sigmoid'), \\n\",\n    \"        Dense(15, activation='sigmoid'), \\n\",\n    \"        Dense(1,  activation='sigmoid')  \\n\",\n    \"    ], name = \\\"my_model\\\"                                    \\n\",\n    \")                                       \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"from public_tests import * \\n\",\n    \"\\n\",\n    \"test_c1(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"L1 params =  10025 , L2 params =  390 ,  L3 params =  16\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"L1_num_params = 400 * 25 + 25  # W1 parameters  + b1 parameters\\n\",\n    \"L2_num_params = 25 * 15 + 15   # W2 parameters  + b2 parameters\\n\",\n    \"L3_num_params = 15 * 1 + 1     # W3 parameters  + b3 parameters\\n\",\n    \"print(\\\"L1 params = \\\", L1_num_params, \\\", L2 params = \\\", L2_num_params, \\\",  L3 params = \\\", L3_num_params )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's further examine the weights to verify that tensorflow produced the same dimensions as we calculated above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"[layer1, layer2, layer3] = model.layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"W1 shape = (400, 25), b1 shape = (25,)\\n\",\n      \"W2 shape = (25, 15), b2 shape = (15,)\\n\",\n      \"W3 shape = (15, 1), b3 shape = (1,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#### Examine Weights shapes\\n\",\n    \"W1,b1 = layer1.get_weights()\\n\",\n    \"W2,b2 = layer2.get_weights()\\n\",\n    \"W3,b3 = layer3.get_weights()\\n\",\n    \"print(f\\\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\\\")\\n\",\n    \"print(f\\\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\\\")\\n\",\n    \"print(f\\\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"W1 shape = (400, 25), b1 shape = (25,)  \\n\",\n    \"W2 shape = (25, 15), b2 shape = (15,)  \\n\",\n    \"W3 shape = (15, 1), b3 shape = (1,)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`xx.get_weights` returns a NumPy array. One can also access the weights directly in their tensor form. Note the shape of the tensors in the final layer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[<tf.Variable 'dense_2/kernel:0' shape=(15, 1) dtype=float32, numpy=\\n\",\n      \"array([[-0.08530456],\\n\",\n      \"       [-0.2747036 ],\\n\",\n      \"       [ 0.08510572],\\n\",\n      \"       [-0.12527409],\\n\",\n      \"       [-0.2926382 ],\\n\",\n      \"       [-0.34840912],\\n\",\n      \"       [ 0.21684825],\\n\",\n      \"       [-0.08979291],\\n\",\n      \"       [ 0.5360281 ],\\n\",\n      \"       [ 0.19300771],\\n\",\n      \"       [-0.44613487],\\n\",\n      \"       [ 0.1397686 ],\\n\",\n      \"       [-0.42860353],\\n\",\n      \"       [ 0.5345983 ],\\n\",\n      \"       [ 0.22546476]], dtype=float32)>, <tf.Variable 'dense_2/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(model.layers[2].weights)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following code will define a loss function and run gradient descent to fit the weights of the model to the training data. This will be explained in more detail in the following week.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.6348\\n\",\n      \"Epoch 2/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.4996\\n\",\n      \"Epoch 3/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.3573\\n\",\n      \"Epoch 4/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.2490\\n\",\n      \"Epoch 5/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.1787\\n\",\n      \"Epoch 6/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.1338\\n\",\n      \"Epoch 7/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.1047\\n\",\n      \"Epoch 8/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0848\\n\",\n      \"Epoch 9/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0708\\n\",\n      \"Epoch 10/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0600\\n\",\n      \"Epoch 11/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0520\\n\",\n      \"Epoch 12/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0456\\n\",\n      \"Epoch 13/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0405\\n\",\n      \"Epoch 14/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0365\\n\",\n      \"Epoch 15/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0332\\n\",\n      \"Epoch 16/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0304\\n\",\n      \"Epoch 17/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0280\\n\",\n      \"Epoch 18/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0260\\n\",\n      \"Epoch 19/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0243\\n\",\n      \"Epoch 20/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0228\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f2d2b00a650>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.BinaryCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X,y,\\n\",\n    \"    epochs=20\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To run the model on an example to make a prediction, use [Keras `predict`](https://www.tensorflow.org/api_docs/python/tf/keras/Model). The input to `predict` is an array so the single example is reshaped to be two dimensional.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" predicting a zero: [[0.01574531]]\\n\",\n      \" predicting a one:  [[0.98137283]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"prediction = model.predict(X[0].reshape(1,400))  # a zero\\n\",\n    \"print(f\\\" predicting a zero: {prediction}\\\")\\n\",\n    \"prediction = model.predict(X[500].reshape(1,400))  # a one\\n\",\n    \"print(f\\\" predicting a one:  {prediction}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output of the model is interpreted as a probability. In the first example above, the input is a zero. The model predicts the probability that the input is a one is nearly zero. \\n\",\n    \"In the second example, the input is a one. The model predicts the probability that the input is a one is nearly one.\\n\",\n    \"As in the case of logistic regression, the probability is compared to a threshold to make a final prediction.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"prediction after threshold: 1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print(f\\\"prediction after threshold: {yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 64 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Predict using the Neural Network\\n\",\n    \"    prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    if prediction >= 0.5:\\n\",\n    \"        yhat = 1\\n\",\n    \"    else:\\n\",\n    \"        yhat = 0\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{yhat}\\\")\\n\",\n    \"    ax.set_axis_off()\\n\",\n    \"fig.suptitle(\\\"Label, yhat\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.5\\\"></a>\\n\",\n    \"### 2.5 NumPy Model Implementation (Forward Prop in NumPy)\\n\",\n    \"As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_dense2.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Below, build a dense layer subroutine. The example in lecture utilized a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` is then applied to that result. This section will not utilize some of the matrix operations described in the optional lectures. These will be explored in a later section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: my_dense\\n\",\n    \"\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"### START CODE HERE ### \\n\",\n    \"    for i in range(units):\\n\",\n    \"        w = W[:,i]\\n\",\n    \"        z=np.dot(w,a_in) + b[i]\\n\",\n    \"        a_out[i]=g(z)\\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(a_out)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.54735762 0.57932425 0.61063923]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Quick Check\\n\",\n    \"x_tst = 0.1*np.arange(1,3,1).reshape(2,)  # (1 examples, 2 features)\\n\",\n    \"W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\\n\",\n    \"b_tst = 0.1*np.arange(1,4,1).reshape(3,)  # (3 features)\\n\",\n    \"A_tst = my_dense(x_tst, W_tst, b_tst, sigmoid)\\n\",\n    \"print(A_tst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"[0.54735762 0.57932425 0.61063923]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"As described in the lecture:\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):             \\n\",\n    \"        w =                            # Select weights for unit j. These are in column j of W\\n\",\n    \"        z =                            # dot product of w and a_in + b\\n\",\n    \"        a_out[j] =                     # apply activation to z\\n\",\n    \"    return(a_out)\\n\",\n    \"```\\n\",\n    \"   \\n\",\n    \"    \\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):             \\n\",\n    \"        w = W[:,j]                     \\n\",\n    \"        z = np.dot(w, a_in) + b[j]     \\n\",\n    \"        a_out[j] = g(z)                \\n\",\n    \"    return(a_out)\\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"test_c2(my_dense)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a three-layer neural network utilizing the `my_dense` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential(x, W1, b1, W2, b2, W3, b3):\\n\",\n    \"    a1 = my_dense(x,  W1, b1, sigmoid)\\n\",\n    \"    a2 = my_dense(a1, W2, b2, sigmoid)\\n\",\n    \"    a3 = my_dense(a2, W3, b3, sigmoid)\\n\",\n    \"    return(a3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can copy trained weights and biases from Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp,b1_tmp = layer1.get_weights()\\n\",\n    \"W2_tmp,b2_tmp = layer2.get_weights()\\n\",\n    \"W3_tmp,b3_tmp = layer3.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"yhat =  0  label=  0\\n\",\n      \"yhat =  1  label=  1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# make predictions\\n\",\n    \"prediction = my_sequential(X[0], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print( \\\"yhat = \\\", yhat, \\\" label= \\\", y[0,0])\\n\",\n    \"prediction = my_sequential(X[500], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print( \\\"yhat = \\\", yhat, \\\" label= \\\", y[500,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following cell to see predictions from both the Numpy model and the Tensorflow model. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 64 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"\\n\",\n    \"    # Predict using the Neural Network implemented in Numpy\\n\",\n    \"    my_prediction = my_sequential(X[random_index], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"    my_yhat = int(my_prediction >= 0.5)\\n\",\n    \"\\n\",\n    \"    # Predict using the Neural Network implemented in Tensorflow\\n\",\n    \"    tf_prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    tf_yhat = int(tf_prediction >= 0.5)\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{tf_yhat},{my_yhat}\\\")\\n\",\n    \"    ax.set_axis_off() \\n\",\n    \"fig.suptitle(\\\"Label, yhat Tensorflow, yhat Numpy\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.6\\\"></a>\\n\",\n    \"### 2.6 Vectorized NumPy Model Implementation (Optional)\\n\",\n    \"The optional lectures described vector and matrix operations that can be used to speed the calculations.\\n\",\n    \"Below describes a layer operation that computes the output for all units in a layer on a given input example:\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_VectorMatrix.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"\\n\",\n    \"We can demonstrate this using the examples `X` and the `W1`,`b1` parameters above. We use `np.matmul` to perform the matrix multiply. Note, the dimensions of x and W must be compatible as shown in the diagram above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1, 25)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x = X[0].reshape(-1,1)         # column vector (400,1)\\n\",\n    \"z1 = np.matmul(x.T,W1) + b1    # (1,400)(400,25) = (1,25)\\n\",\n    \"a1 = sigmoid(z1)\\n\",\n    \"print(a1.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can take this a step further and compute all the units for all examples in one Matrix-Matrix operation.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_MatrixMatrix.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"The full operation is $\\\\mathbf{Z}=\\\\mathbf{XW}+\\\\mathbf{b}$. This will utilize NumPy broadcasting to expand $\\\\mathbf{b}$ to $m$ rows. If this is unfamiliar, a short tutorial is provided at the end of the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex03\\\"></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"\\n\",\n    \"Below, compose a new `my_dense_v` subroutine that performs the layer calculations for a matrix of examples. This will utilize `np.matmul()`. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED FUNCTION: my_dense_v\\n\",\n    \"\\n\",\n    \"def my_dense_v(A_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      A_in (ndarray (m,n)) : Data, m examples, n features each\\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (1,j)) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      A_out (ndarray (m,j)) : m examples, j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"### START CODE HERE ### \\n\",\n    \"    A_out = g(np.matmul(A_in,W) + b)\\n\",\n    \"    \\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(A_out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tf.Tensor(\\n\",\n      \"[[0.54735762 0.57932425 0.61063923]\\n\",\n      \" [0.57199613 0.61301418 0.65248946]\\n\",\n      \" [0.5962827  0.64565631 0.6921095 ]\\n\",\n      \" [0.62010643 0.67699586 0.72908792]], shape=(4, 3), dtype=float64)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_tst = 0.1*np.arange(1,9,1).reshape(4,2) # (4 examples, 2 features)\\n\",\n    \"W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\\n\",\n    \"b_tst = 0.1*np.arange(1,4,1).reshape(1,3) # (1, 3 features)\\n\",\n    \"A_tst = my_dense_v(X_tst, W_tst, b_tst, sigmoid)\\n\",\n    \"print(A_tst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"[[0.54735762 0.57932425 0.61063923]\\n\",\n    \" [0.57199613 0.61301418 0.65248946]\\n\",\n    \" [0.5962827  0.64565631 0.6921095 ]\\n\",\n    \" [0.62010643 0.67699586 0.72908792]]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    In matrix form, this can be written in one or two lines. \\n\",\n    \"    \\n\",\n    \"       Z = np.matmul of A_in and W plus b    \\n\",\n    \"       A_out is g(Z)  \\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for code</b></font></summary>\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"def my_dense_v(A_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      A_in (ndarray (m,n)) : Data, m examples, n features each\\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j,1)) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      A_out (ndarray (m,j)) : m examples, j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Z = np.matmul(A_in,W) + b    \\n\",\n    \"    A_out = g(Z)                 \\n\",\n    \"    return(A_out)\\n\",\n    \"```\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"test_c3(my_dense_v)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a three-layer neural network utilizing the `my_dense_v` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential_v(X, W1, b1, W2, b2, W3, b3):\\n\",\n    \"    A1 = my_dense_v(X,  W1, b1, sigmoid)\\n\",\n    \"    A2 = my_dense_v(A1, W2, b2, sigmoid)\\n\",\n    \"    A3 = my_dense_v(A2, W3, b3, sigmoid)\\n\",\n    \"    return(A3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can again copy trained weights and biases from Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp,b1_tmp = layer1.get_weights()\\n\",\n    \"W2_tmp,b2_tmp = layer2.get_weights()\\n\",\n    \"W3_tmp,b3_tmp = layer3.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's make a prediction with the new model. This will make a prediction on *all of the examples at once*. Note the shape of the output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"TensorShape([1000, 1])\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"Prediction = my_sequential_v(X, W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"Prediction.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll apply a threshold of 0.5 as before, but to all predictions at once.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"predict a zero:  [0] predict a one:  [1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Yhat = (Prediction >= 0.5).numpy().astype(int)\\n\",\n    \"print(\\\"predict a zero: \\\",Yhat[0], \\\"predict a one: \\\", Yhat[500])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following cell to see predictions. This will use the predictions we just calculated above. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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JqagrvvvsuXC4XnE4nSqUSfvSjH6FYLCIej6NWq6FarUKSJFitVmi1WkxMTGBqagpXrlzha3c8Hu9YyyaNE7IWTE9P49y5c/D5fJicnOSxY8DWelSr1RCJRFCpVJDL5VCpVLC+vo5MJoPZ2VnEYjEUCgWUy+WeOywwxmAwGHD8+HH09fVheHgYJpMJGo0G9Xq9M91GwIMTIgXJvfzyy/D5fNDpdNjY2MDnn3/eUx90K3IXgE6nw5EjRzAyMoLXXnsNfr8fwWAQyWQSH374IWZnZ1Gv11Gr1bpWXjTxydduNBrh8/lw7tw5eL1enDlzBlarFXa7nQfqyjeboaEh2O12LC0t4dq1awCAfD4PoPuUPZJVqVTCxsYGms0mZmZmoFar4XQ6USgUcO/evX0LmDvs0KY0MjKCr371qzAYDKhWq8jlcvjwww+RTqfxyiuvwOv1wm63cytWt8VLyS1QBoMBg4ODePXVV6HT6aBQKJDJZPDJJ58gkUhgc3MTtVoNCoUCKpUK09PTcLlcePHFF9Hf349isYhKpYJqtcrda52KJEkwGo3weDyYmprCq6++Cr/fz5UXcgeR2yiZTKJarXIZLC4uIpFIbIsPqlQqu6addzNarRajo6MYGBiAz+eDRqPhMuxY5YWQZ9HU63Wu0MjNc730YbdC8iC/q1qtBgC+iLTKqpvR6/UwGo3o6+vD+fPn4fF4uNLidDqh1WpRrVZRqVRQKBRQq9VgMBig0Wig0Whgs9kwNjaGF198EbOzs0gmk121GQEPUsLJxUEWKo/Hw5WXfD7PF5FeSCEnedjtdgQCAUxNTeErX/kKDAYDNjc3UalUMD8/j3w+j0gkgkajgc3NTZRKpa4N2JUfjgKBAE6dOoWpqSmoVCpsbm7ik08+QSQSwZ07d1AsFpHL5fiYUigUuH37Nlf6+vr6oFarcf78eajVapTLZRQKhY6KgSF5qNVqKJVKTE9P48KFC5icnER/fz9MJhOq1eq2uiSkoFFmlkajQaPR4DIwGo2IRqO4efMmVlZWsL6+jvX1dSgUiq7PpqXkCa1WC4/HA7fbjWq1ilKphFKpxGMR94PnqrxQJki9XuebcqcM+v2GBoHVaoXVauWyIcWFvrp9E2KMQa/Xc/PtW2+9BbfbjYmJCa6cNJtNpNNpVCoVxONxlMtluN1uGI1G6PV6mEwmjI2NoVQqoVwu4/r169tOAN0iOzoQKBQKGI1GWK1WHlPmdDqRzWZ55H8vQGuMxWLBzMwMTpw4gS996UvI5/N4//33EQ6H8Ytf/AKFQgF+vx8GgwGhUAi5XG5byn23IB8fjDH09/fj1VdfhcvlglKpRCgUwve+9z2k02lkMpkdY30ikQgYY8jn8/D7/Xjrrbdw5swZFItFxGIxhMNhpNPpjit7oVarodVqMT09ja9+9atwOp3w+XxoNBo8ZR54kGgCbCkvwIOChn6/HwqFAhMTEyiVSvjZz36GK1euoFwuY3l5uesPnDS2VCoVdDod3G43XC4XP1SS8rJfMWT7rrzIT4hkamSMcetLt36wT4o8qr1Wq20zPXY7rRH/g4ODOHv2LIaHhzEwMACj0QhJklAulxGLxVAsFrG4uIhMJsOD6MbHx+FyuTA6Ogqfzwez2YzBwUEEAgF4vV4UCgWk02kAna+8yE+E5ArweDxwuVxQq9VQqVRwOBzI5/Pc99xoNLZlRXQT8tO0wWBAf38/j1+Ix+MIh8P4/PPPkUgkUK1WYTQacfr0aXg8HthsNqjVap511G0bjiRJsFgssNvtGBwcxODgIIrFIq5cuYK7d+8in8+jUqk88pmLxSLPNCoWi3A6nTh+/Di34HTSOiVJEi8roNPpYDAYeIkB4IGlpdlsolwuY3V1FeVymccD0aHSbrfzIFW9Xo/h4WEolUqUSiXkcjlks1kkk8mDfNTngnwdMhgMz+0A8NwsL/V6HcViEWtra9yU1Csnwsel0WigVqtxbVWuwMjjFrplYSXIIqfX6xEIBHDy5En8/b//9+FwODA8PAwAKJVKXGlJJBL41a9+hXA4zANVL1y4gJGREajVajgcDthsNhgMBqytrWFgYADRaJQvJN0w7ujUo9PpYLFYMDAwgL6+Pmg0Gp4+TVYIvV7Ps0W6bewQFOBttVoxPj6Or3zlK6jX6wiFQrh79y5+/OMfo1gsYmhoCE6nE2+++SbGx8cBbM27GzducFN/t7jZaM1wOp0YGxvD5OQkJicnce3aNbz33nsIBoNIp9Oo1+vcutAKBa5SsgAlWHi9Xvj9ftRqNVy/fh21Wq0jEgloDSVrgcFggNlshkaj2RafSa8rFAq4desW4vE47ty5w1N+VSoVRkZG4HA48PLLL8Nms+Ho0aOYmJjgdZYWFhaQSqW6OoCX3IsqlQpmsxkWiwXZbHbb7/eL56a8UEEkv98Pr9eLYDDIc+S79YN9XMgCRdqryWTiiwadmLvREiOvO6HVajE+Po4zZ85gcnISLpcLBoMBtVoNpVIJq6urSKfT+OKLL5BMJrG4uIhkMol0Os3rURiNRl4RVKlUwmQywWKx8OBV+pvdgrzSJ1lcCHKx0c+7eX7RODIajQgEAnA4HFCpVEin07hx4waWlpZ4Jkg8HgcAXLlyBclkEpOTkzzOrFarwel0wmq1olqt8tiHTlV26d5tNhuGh4dhNBqRyWSQSCQQCoW4Mt9ubNB80el0MJlMsNvtcLlcfOxZrVYYjUZe+uJR73dYcLvdGBwchMfj4ZmKjDFuYcrn81xG169fRyaTwfr6OsrlMo9lyefzsFgscLvdUCqVsNlsMJlM6Ovrw/T0NFdgqtUqz0LqBNk8CeTmNxgM0Ov10Gq12yoRy//da55LthEtriaTCceOHYPX60U4HOanQaLbPtgngQrUOZ1OOJ1OqFQq7m5rVWC6BXo2q9XK/fG/8Ru/AavVCp/Px611kUgEH374IYLBIH784x8jlUqhUCjw+CkA2NjYQLVa5TEwNpuN120YGhpCoVDYZsHq9LFGY4Gqomq1Wmg0Gv57iv2hCqrFYpFf1+nP3gqNI6fTiZMnT2J4eBhqtRrRaBQ/+tGPEIlEeIExitX47ne/i76+PvzhH/4hjh49ir6+PrhcLgwNDcHn8yGRSHA3UqdCykt/fz/Onj0LnU6HYDCI5eVlzM7OolwuA0DbZyQXAG3SAwMDGB0d5Qcrn88Hh8PB6+UcZuSW68nJSZw/fx4TExO8tQZjDIlEAp9//jlWV1fx/vvvI51OY2NjA5VKBeVyme9XlB2q1+uh1+tRKBTw4osvoq+vD0eOHIHNZkOj0cDNmze5qw1oL+tOgzLYrFYrbDYbTzvPZDLPpaHnc63zQsGnjDFUKhUUi0Ue4d9tC+peIEkSj9UolUrbqjl2A1Ti3+fz4ciRIxgYGIDVaoVOp0Oj0UAmk8Hi4iI2NzcxPz+PaDTKffTyzBDGGC/kVygUUCwWufVK/tULyE/KBoMBRqMRBoOhK4tByhU4pVIJq9WKvr4+GAwGpNNppFIpxONxpNNp/uyU8RiLxSBJEm7evIlSqQS/3w+TyQSXy4WJiQkAQDKZ7Eh5kVwogN1ut8NmsyGbzSIUCiEUCm2rF9UOev7WIEyKGVGpVB3Vd40UOr1eD5vNBr1ez3tc0eFnbm4OwWAQkUiEp0VTbyeC4hMZY4jFYtwFV6lUePsJl8uFvr4+KJVKxOPxrjp80iGQLE4OhwM6nQ4qlYqPFRpjHZ0qTaXdyR8PAOl0mp+SKRq5ExeKvUTen4e+DwaDWF1dRTweR6FQ6ApZ0cA3m83Q6/V48cUX8eu//uvw+/3w+Xy8BPfCwgL+/b//99jc3ORR/BTMLE+1B8CrXkajUcTjcVgsFv77bgrAbKU1Lopq3lDdEp/PB7/fj0wms80X3Q3QM+t0Ouh0OgwNDeHFF19ErVbD/Pw85ubmsLS0hGKxyA9PtOksLi5yV6TL5cLv//7v48yZM5iamoLD4cDf/u3fYnV1dVs6eqdAcnG73QgEAhgdHcXg4CA+/PBD/OhHP0I4HOaWALmrcSdofqVSKeTzeYTDYcRiMTgcDhiNRm7xI9keVuQHHZofgUAANpsNKpUK+XwesVgMt27dwg9/+ENkMhmucND60SorivOZnZ1FPp/H0NAQBgcHodfr0d/fjyNHjuDChQu4desWlpaWHjpwdTrNZhM6nQ5TU1MYHh6GzWaDTqdDKpVCJBLhCsx+7Vn7ri6TdmY2m3k6YjeZzvYa+WbUbDZ5xDqZLA/zAvGkmM1mnl7ndDq5taRarSKRSCAajSIYDPJKuVT1k5BPBvJD07/dsDg8DfTc5FKjooadtgE/LlQ5lmIyaDNKpVLIZrO8SSchL/lOhcfC4TA2NzcRDAahVqu5C4msgPR3OgW6V5PJxFPnqaw/xXM8DbQBycdRJ8lFDikx9Dz1ep3XJqFNV56hR9fstAnLLQ20RlGnd4fDAYvF0lHWqcdFbvlUq9X8+UiG1Wq1cy0v5BfVarWYmprC+Pg47wJMwYTd9oE+LfIPmORWqVSwtLSEmzdv8jQ9ubWhUyHLydjYGI4dO4YTJ07wNMNms4lYLIbPP/8cN27cwKeffsobd+703HITOQVeUnpwpy6szwJjjCu9iUQCsVgM8XgclUrloG9tz5Gn2AcCAfT392NoaAj5fJ4355SfnAl5TRKyJLz33ntYX1/Hr//6r+P8+fNYWVnB4uIigsEgFhcXAXROvAJZXoaGhnDu3DmoVCrMzs5ibm4Oy8vLqNfrj/0sNIe0Wi13QVLtILly3AmZRnLocAhsHXxKpRKPjSKFlywtO6078vWoVCohnU7z/nOk8NpsNoyPjyMajfKg5lKpxK/vFuT1hGj9pvpJ5XJ53wwW+6K8tG4aKpUKJpOJ95yh4Cd5AFQvIjchku+YTLAU30K+e3nhpE5FXo+DapH4/X5YrVbeB6NYLCKVSmFjYwPRaJR31aYCUe3e02Aw8ABVADzKn6w2vYDc8kI1g7otVoqgZ9XpdLDZbDAajVCpVKjX69zN0e5a2sCou/3m5ibPSjObzfD7/bx6bCdB1gGDwQCLxYJyuYxcLsfjN+g1j4NceaGMEr1eDwB8DafYhk6Tk5ynzYyhcSQvZSG3SMgzcDr90Pm4UOmL/fYUPJcideSP9nq9iEajqNVqmJubw8LCAl8sehV50JPb7eaFs0KhELLZLG7fvo1bt24hm812fOwGbaAulwt2ux2nT5/G66+/Dq/Xy4Pa1tbW8Nlnn+EHP/gBj9GgNMZW5PEI5MP2eDyw2+1oNBoIh8PY2Njgwb67mX27BfmzkeJGXbapxks3PTtZUPx+P06fPo1AIIBms4lUKoW7d+8+skO0vHrqwsIC1tfXMTMzg0AgAJfLha9//ev4yU9+gjt37nREvALdH1m1XS4X+vv7+bMlk0muZDwq1oWgceNyuRAIBHhcx8bGBi+FL++H1CnI3UaSJPG2IkajERqN5okOO7u506hhLFmqOkk+TwvJ9Xkoa89FeaECNnq9nleyJDObfFE9rIvCfkLmNqpOSGZZkk8+n+dpwZ2OfHGlVF6KK6AMtFQqhWQyiUQigWKxuOuYkBebUigUvO+RyWSCVqvlrQHy+Tw3X/YS8hT7bkyzJ8jCYLPZoNVqeV2gfD6PUqn0yOemekpkQcjlcsjn81CpVHC73TxeAUDHzEGy4FIgM8WRPWlmp1xZMxqNfHPX6XSo1+vIZDI8+6/Tqje31tCSW0nkisazzBtSrju5VtCz0pGp0jRJ1Go1LBYLJiYmoNfrcf36dUSjUdy5cwfBYHCbb7HXoAmk0WgwNDSEsbExBAIBWK1W3L59G2tra0ilUiiVSl0RcEljQl6XhKwqzWYTkUgEV69exfz8PK/8uVuAIPXH8ng8sFgseP3113Hy5ElMTU3BbDYjl8shFothc3MTq6urKJVKPbWAyN1F3ai4yGNZbDYbBgYGoFarsbm5iXg8jmw2yxXWR80buQspm80iEonwoF2XywWHw4FCoYBUKnWoD1nymiyUvkpKHSmxj7r3VheIWq2GRqPB+Pg4Tp48Cb/fD7VajVgshhs3bvD6SvI4osOI3L3TbDZRLBaRyWR4U06j0ci9AzqdjneHfta5I/+b3Yp8zDzPtWZfV3PSOFUqFU+ro0BCqsnRDam/zwq5jaxWK/R6PdRqNUqlEi/J3W21cMisKDctUrxLPB5HJpNBrVbbVhBqp/dQqVTc3eb3+xEIBGAymcAY4wXJ5JHvvTTOKN6lNdumm6DPU61WQ6/X83o/NGeeJJ6OFt5yuYxMJsMPFRqNhncTptcdVuQKByVFyK1GtIa0mwPy35NsdTod7HY73G43tFot6vU6Ty3O5/PPfdN6FuheK5XKNquRvDcPye5x309wMOx7thH9S5r/ThtXryE3x1IvnhdeeAGDg4NQKpUoFosIBoNYX1/nhfzkaX2djkaj4ZkL1HcnnU7zzKpoNArg4aBK4EFaHsUGfe1rX8Po6CiOHTvGu8KGw2Gsr69jaWkJsVisqzfwVijif3V1FcvLy4jFYsjlctxS0U3QPKIAQbk15kk3VOpnQ92AtVothoaGAKCjyjuQC0SeyZlIJLCwsIB4PN52DSHXCVlClUolBgYG4HA4cPbsWbzwwgvIZDL47LPP8Nlnn+Hzzz9HOp3umNIE8vXk3r17vKdTf38/r5Zrt9tx9uxZrKysIBKJ8JYHOyEfX+RqolYc9Dt5kcxOkNGTQMYJss4972d8Lu0BWs1m8o242z7Qx4UWWpVKBb1eD5/PB7fbDWAr2DKbzXILRLdp95RZRUpso9FAsVhELpfjplzg4QwAcjlRGwW3243JyUlMTU3B7XZDr9cjmUzy2jjxeBz5fH6b66TbxxspL7lcjvd9ohT7bp1vFN8jV16e9FnptblcDuFweNu4kcc/dIL8WjeRSqXCrbiPsrrIC4lS3I/H44HX64XT6eS1lzY3N/nm3mnjSpIkpFIpHsRcLpe54kZrcTabhVKpfOwqxI+jpHTbOg48OIDLLZPPy032XJQXWgR2qrnQi1Csi9VqxcTEBKanpzE9PQ2r1co33rW1NR6rQXSL3CiQloJqtVotvF4vpqen8cYbb2Bubg6xWAzAVoqmTqeDx+OB0WjE4OAgbDYbTp8+DbfbjaNHj8JmsyEcDmNpaQmXLl3C7Ows4vE4r3HSK4qL/Pnk1s1ufG6yDkiShGg0itnZWV5LiooeKpVK3tPqUVYnei+NRsNdj+VyeVt7jsNuuZJnmlGjRLIumM1m7uLZLXNPqVTC5/PBYrHw/jwnTpyAy+WCTqfD4uIi3n//fVy5cgULCwu8Sm8njS9StJLJJEqlEtbW1hAMBuHz+XgvtFdffRVmsxlXr15FNpvlnaHlVj16L4VCgZGREYyNjWFwcBB2u53HGBUKBUSjUd42gGL4ukWJoXCQ4eFhBAIBXpsskUggkUjsu2L7XJQXwQPkgU1arRYDAwPo7+/ngWKbm5vIZDK8Y3I7s2WnUqvVeGxCtVqF0WiE0WiE1+vF+Pg4stks1+SpOFZfXx9sNhtmZmbgdDrx0ksvwel08jYA8/Pz2NjYwLVr1/Dpp5+iWCzyjavXaE1XbA147qTNph3yINtwOMxLsxuNRt4Zml73qOem16hUKp6hU6vVUKlUtnVM7gTq9Trvx9NoNLhVoV0Jf9qczWYzXC4Xb1Z58uRJOBwOzM3NIRqNYmFhAdeuXUMul+Pp0YddqZNDygPFw6VSKaRSKVgsFjDGYLFYMDo6imQyCZvNxmsGUcgDsN3tr1Qq4XQ6easBvV7PM9jIgl4sFre5rjt9/sljp9RqNex2O6xWK5rNJqrVKgqFAvL5/L7XcNu3InWNRgN6vZ4HUzqdTgDgAbudXtToaWhdID0eD06dOoWhoSFotVqUy2V89tln3OoSj8d5NcxukJW8D1Gj0cDa2hqWlpYwMDAAk8kEj8eD06dPQ6/Xo1Kp8MXUarXiyJEjMJlM6O/vh16v55Plzp07yGQy+OCDD7CwsIC7d+/ysvCdFEi4F8jr3vj9ftTrddhsNhgMBp4q2w3jiKAFlHrSuN1uJBIJ2O12fP3rX8f8/DxPDNgtbZp+ZrFYoNfrcfbsWbz44ouw2+3I5/Pc7dYJsqP7KxQK2+ocaTQaTE1NoVwuY25ublsMmRwqkV+r1eD3+zEwMIBSqYRgMIgPP/wQd+/e5fONFJfDLpOdkMdELS8v4/3338epU6dgt9uhVqths9kwOjqKd955B2tra3jvvfd4kT+KP1SpVBgaGoLNZsMLL7yA48eP8yaMVLxvaWkJH3/8MZaWlnitpW5AHjtULpcRiUSgUqkwODiIarWKXC6HXC6373v8vikvFMFtt9vhcDj4SahQKCCXy3VF6u/TIK8p4HQ6MT4+Dp/PB7VajWw2i7t372JxcRHRaJT7XTvpZNMOeo5CoYBarYZIJILNzU3eRNBut/MTYrFYhEql4mmfMzMzMBgMsFqtUCgUyOVyKBQKWF1dxcbGBi5fvoy5uTlkMhkUCoWH4mV6BTohOhwOVCoVmEwmXpejWw8MVIH6yJEjyGazMJvNePnll6HX6/GrX/0KjUaDb+itz0/rEPWhmZqawrlz53gjy5163BxW6MRPCkgqlUIsFuOBt6urqwCwTXmRz5NGo8EVNYfDAbfbjWQyiVwuh5s3b+LTTz/lcWSdZnFphZSXcDiMGzduwGq1YmZmBna7HXa7HT6fD2fPnoXVasXVq1f5GKJAZpVKhb6+PgQCAUxNTWF6ehpmsxlKpZI3lg2FQrh79y6i0SivbNxNZUGoP1gqlYLBYOAWJrJq7fcev6eSbD3ZaLVauN1u2Gw2PqnW19exvr6OarXasZr700ITxmazYXp6GjMzMxgfH4fBYEA8HkcwGMTCwgJWVlYeGVzXiZDGTo0Cl5aWeFn/wcFBKBQKHv9y+vRpruTpdDoYDAYoFArEYjFUKhXMz88jmUziww8/RDAYxPLyMjKZDE+Jpr/X7dAztgsW7FbrE7nHarUa8vk85ubm8JOf/AQjIyM4d+4cJicn8Vu/9VsIh8P4/PPPUSwWUS6Xt5n9qfDa2bNnMTg4iImJCTSbTe4euXv3bkeZ/GmOUbfjSqWC4eFhjIyMYHx8HBcvXkQul+OxYBTnQ9l7Z86cgdPphCRJ2NzcxKVLl7CxsYHZ2Vkkk0luET3scngUdP/pdJqnxVerVUxNTeHLX/4yAGBiYgImkwnhcBiJRIJ3hvZ4PDCZTDh16hS8Xi+Gh4dhMBi4W25jYwN37tzBrVu3sLa2hmKx2NGK3m6Qh0XeJoKKRMqVl46MeaGsEKvVynPrNzY2sLGxwYN5egmySNlsNhw/fhzHjh3D+Pg4b5dAyou88FO3wRjjfXeWlpaQy+UwPDyMfD7P06ep0zRlF9EkoW7TqVQKly9fxubmJt5//30Eg0HuFukmS9WT0Kq8HETRqOcNLYwUm7KwsIBKpYI33ngDX/7yl7krcnV1lffMouKHZPofGRmBw+HAr/3ar2F6epq7BxYXF/Gzn/0MmUyGZ5x0yriSpwOvrKzg3XffxcmTJzE2NgaLxYJoNIp79+7xIGUK0PV4PHj99ddhMBiwubnJ59fs7CwikQivPNwpcmgHjR2KL8xms1hZWcHrr7+OU6dO8aaKbrebdx+/ceMGGo0GJicnYbPZcOzYMTidTuh0um0p0sFgEJ9//jlu376N9fV1Pta6DRpn8l6FpLwUi8XOsrwAD9wiGo0GLpeLm+LW1tYQDod5wbBuChx8HEgu5AoZGBiAy+UCsJV9s7a2hvX1dZTL5W0dWrtRRvRcVJRubm4Oly5dgt/vx8jICE8fJ/MrBb6R+TqZTOLWrVt80aFJ0g0nwseFnpOUulgsBp1Ox5Xezc1NrK2tIZPJoFwud7WbVt7dNxKJYHZ2Fj//+c9hsVh4IPyrr76KSqXCi5LRGKR2EtVqFYuLi1hYWEAwGMTNmzd5qnknyo2UOkmSsLa2hi+++AJmsxl2ux0WiwXDw8N8U6UMK7VazdOfv/jiC0SjUT6G6GDQibJoBwXdVqtVpFIprKys4NKlSwgEAvzg1N/fz1uPNJtNuFwuHndHFptSqcQzi65fv87dRd2+JpH8GGMolUqoVCpIp9N8zHSM5YU0MZoMgUAAL730Emq1Gq5evYqNjQ1ks9meq3ZKp18qfuVyuTAxMQGv1wvGGAqFAubm5rCyssLjQbrhdLMb9GzZbBbZbBZXr15FuVzGzMwMV+6cTify+TzW1tb4v/F4HD//+c+RSqUQDoe5a42+OqWQ2F5BFqlisYjNzU2oVCq+2dJGHI/Hudm6W+ccffYUT1ev11EulzE2NoZ33nkHTqcTX/va13jFWXnAJil3t2/f5qnAN27cQDqdRjqd5llbnQR9xmSRoliwM2fOYHx8HH19fThy5Ah0Oh1MJhMqlQpCoRDS6TQuX76MUCiEH/7whwgGgzyOgVLvuw1aiygt/t69e/i7v/s7HDlyBF6vF3a7HWNjY1CpVDh16hQAbIt/ajabvNLwrVu3sLCwgI8//hiXL1/mCl83I193i8Uir7wcj8f3vTL1vtmyyIxN7qJIJMI7SlPQUy8hL0pHX7VaDZubm7waLLk/iG7caHYinU7zYMJGo8Eb7RWLRUQiERSLRcRiMW7ilafh9YqMWpFbXvL5PO7cuYNYLMYX1lu3biEUCnFlphfkRM9ZLpcRCoUgSRLPVltcXOTFtEgWlGZdLpexsrKCeDyOjY0N5HK5rojJI2tkuVxGMpnE0tISDAYDnE4n1tfXeYPUWq2GeDyOXC7HY8nkBTI7WQaPC8mqWCxifX0djDF8/PHHcDgcGBsb44oeNbms1Wp87EQiEWSzWdy7dw/BYBCRSGRbkHe3yk+hUKBSqXDrHMWUUdG//X7ufVFe5GWS6/U6nxThcJgH8vSa8gJsyYV6pVDn45s3b2JpaQmffPIJL5zUK9AJOBQK8cj8Dz74gKeSU+Q6naTJRUKKYC+OIeDBYkjBz9FoFD/5yU+g0Wjwk5/8BAB4YSy526NbF1GCToHZbBbpdBqLi4u4du0atFotbDYbL5svt7zk83nuliSXLaXDdvr4ImUul8shm80iGo3i2rVr0Ov1vD6SSqVCo9FAqVRCtVpFPp/ndWLk7Vy6HTpsZ7NZ3LhxA8vLy1hYWIDb7cbp06dhtVoxPDwMtVrN46KoGGYwGNzWs09eYbZb5xzNtWKxiMuXL3P3Ix0INBrNvhfk23PlhTHGi9XEYjFcvnwZmUwGkUgEmUxmW7XLbv1gd4PkQtkPVMY9FArxRbSbAyx3gzYSeXdaCsClRZQsdoKHoXElt0bRRvw4zfi6FcqEoBiynYK56fdUAbVb5UUuRnm35FbruHy+9eI6BGxfi7LZLABgcXERRqMR6XSal3GoVqvc4pBMJlEoFPgBi+i2MdQK7fWUZUSWuucVX8faDVKj0fhEI7i1DDtp9pS619rzYK8esFAoHMgoeVL5ANubWcn7QDxOD42n5aDkYzKZHls+NHZax2PrGNmPSZHP5w9EPjqdbs92iNbMor20tpTL5UM/fnZitzG1E3I5PanMDmr8GAyGJ5ZPO5k8iwzaUSwWn7t8nmXstM4jch+2Nv2kflqPWrPacVBj51nnFrC92F/rz/fK6tJOPntqeZGbs4EHAWMAtllbul0jbQeZ+uVWFpoY9P9epHWwP8uC0GvIF5CdFJheRi6Xdht2r7jWgN1lAfTG8z8KWovooP2og6V87PSy/FqNFx3nNpLTixkgjwOZauX08qAnen3yPy1yubUuIL2MGE8PI2TyeIi969HspKy0/m4/2RflRUyORyNkJNgPxLgSCAS9QNuYF4FAIBAIBILDRvdWQhMIBAKBQNCVCOVFIBAIBAJBRyGUF4FAIBAIBB2FUF4EAoFAIBB0FEJ5EQgEAoFA0FEI5UUgEAgEAkFHIZQXgUAgEAgEHcWeKS+MsX/CGLvMGKswxr79hNe+yRh7jzGWYYyt7NU9HSYYYw7G2A8YYwXG2Cpj7Hef4FotY+wvGWNZxliYMfZH+3mvB8Ezyue3GWMfM8aKjLFf7uNtHghi7LRHrD3tEfJpj5hf7Tms8tnLCrubAP4MwDsA9E94bQHAXwL49wD+mz28p8PEnwOoAvACOAXgbxhj1yVJuv0Y134LwASAIQA+AO8xxu5IkvR3+3SvB8GzyCcJ4P8F4AiAL+3XDR4gYuy0R6w97RHyaY+YX+05nPKhJl179YWtSfLtp7z2KwBW9vqeDvoLgPH+hz8p+9l3APzfHvP6TQBvy77/FwD+14N+rsMiH9k1/wjALw/6eQ6TbLp97LQ8q1h7hHye9LnE/OpQ+YiYl+fDJIC6JElzsp9dB3DsURcyxuwA/Pdf/0TXdhBPLZ8eQIwdgWD/EPOrPYdWPkJ5eT6YAGRbfpYBYH7Ma+n1T3ptp/As8ul2xNgRCPYPMb/ac2jlI5SX50MegKXlZxYAuce8ll7/pNd2Cs8in25HjB2BYP8Q86s9h1Y+Qnl5PswBUDHGJmQ/OwngkQFPkiSlAITuv/6Jru0gnlo+PYAYOwLB/iHmV3sOrXz2MlVaxRjTAVACUDLGdIwxlez3EmPs4i7XKu5fq976lukYY5q9ureDRpKkAoDvA/hTxpiRMfYKgG9gK/AJjLHh+/IZ3uUt/grAnzDG7IyxIwD+EMC39//Onw/PKh/GmPL++FEBUNwfP+rndPv7ihg7j0asPe0R8tkdMb/ac6jls4dRyd8CILV8fev+7waw5Tdz7nLtxR2u/eVBR1rvcdS2A8APsZV6uAbgd2W/ew3ACgD1LtdqsZWumAUQAfBHB/08h0w+f7DD+Pn2QT/TIZFNL4wdsfYI+TyLfMT86kD5sPt/YF9hjP0+gGOSJP3xvv+xDoQx9icAYpIk/euDvpfDiJDP7gjZtEesPe0R8mmPmF/tOUj5PBflRSAQCAQCgWCvEAG7AoFAIBAIOgqhvAgEAoFAIOgohPIiEAgEAoGgo2jbmNFms3VEQEw6nWYH8XfNZnNHyCeXyx2IfPR6fUfIp1QqHYh8LBZLR8gnm80eiHwMBkNHyKdYLAr5tOEg5GO1WjtCNplM5kDGTjfIZy+7Sguegd0Cpxk7kLF9aGmVk5DP4yGXm5CZQCDodJ678kKLaLPZBAAoFIqeX0xlOfHb5EL0unyABzIi+ZBMSE5CRjsjH1eSJIlxJRAIuoIDs7yITWeLVkuC2Fx2hzG2TT6C9rRaW+iLvhc8QFimtiA59LIMBHvDflvJn5vy0mw2wRiDSqWCUqmE0WiEQqFAPp9HrVbbZn3oFeiZFQoFtFotVCoVDAYDAKBYLKJer6NarXLZ9eKCQvJRq9VQqVQwmUxgjKFWq6HZbPLxA4gFV06j0eCyUygUMJvN0Gg0KJfLqNVqaDQaaDQaAHpbbjQHG40Gms0mlEoltwb3ilzIoim3bKpUqp6SAUHPK5cJzSWC/q9UKsEY4//2Oq1WXl4J9/442ut59VwtL4wxaDQaqNVqmEwmqFQqVKvVbQtpL6JQKKDT6aDRaGCz2fjPK5UK6vU6X1B6FcYY1Go1NBoNrFYrVCoVyuUy6vU6KpUKHzs0UXod+elZflgwGo3IZDLbNmzBA1qtUwIBIcbEk7Pfc2lflRfSXmlztlqtuHjxIpxOJ4aHhwEA3/ve9zA/P49CoYBqtdoTiwfJRaPRwGg0wuPx4NVXX4XL5cLRo0fBGMOVK1cQjUbx4YcfIhQKcW0W6J2JRHLS6/UYHByE3+/HP/gH/wA2mw2ZTAb5fB4//elPsbq6is3NTWQymZ6OoSKFRKFQwGKxQKfT4dixY3C73Thx4gR8Ph/ee+89XL16FdFoFJFIBEqlEkql8qBv/blDc0mj0UCpVMLv98NisSAWiyGTyaBSqXT9ekRKrEajgd1uh0ajgcPhQLPZxNraGorFIrc6dKsMCHq+ZrOJZrMJlUoFs9kMg8GAQCAApVLJ5aDT6QAAsVgMxWIR8XgchUKBv1evrEE0hxqNBj8oqVQqWK1W6HQ6eL1e6HQ65HI5lMtlxONxpNPpPdvL9t3yQh+4VquFzWbDsWPH0N/fj6mpKQDA+++/j9XVVZRKpZ6YJMB2d5HBYIDL5cKpU6cQCARw/vx5bupfXV3FzZs3EY1G+QfeC/IhSHlRKpVwOp0YHBzEG2+8Aa/Xi1QqhVQqhaWlJZTLZSQSiW1myl6FFgWDwQCLxYLx8XEMDQ3h1VdfxdDQEILBINbW1pDNZrk7sleVF1pwtVotvF4vPB4P6vU6SqUS6vV6TxwWJEmCUqmEzWaDwWBAf38/Go0GYrEYKpXKto2mm5GvG3JXvtVqxcjICDQaDer1OnfBkjU4nU4jl8tx5aXX1iD52FCpVNx7YDKZMDw8DIvFgkQigXw+j3K5jGw2y9f1Z5XRvigvdHNGoxFerxdOpxOnTp2C2+3G0aNHYTKZUK1WUSwWuQ++F1wjcqVFrVajr68Pr7/+OgYGBnDixAnYbDaoVFsfyeTkJOx2Oz744APE43Ekk0kUi8WePSnTiYjcaCaTCUqlEjMzM9Dr9QiFQgiHwwd9mwcCWVxIyTObzXjzzTfh9/tx5MgROBwOaLVaZLNZvgD3yuK6G+TCfvHFFxEIBDA2NgaHw4FqtYpYLAaFQtG1mxBtOGQR7+vrw9tvvw2Xy4WRkRFUKhWUy2Wsra0hGAwin8/zWKBuQ25xaTQaUKvVMBqN6O/vx6uvvgqn04kTJ05sU15MJhMAYHl5Gel0Gvfu3UM8HkcqlUIul0MsFkM8Hu/KIHC5ckbu6OHhYZjNZgwODsJoNCIQCPB/DQYDMpkMisUiPvroI9y6dQvhcBjhcHhb5ujTWDj3TXmRJAlarRZ9fX0YHBzEl770JdhsNrhcLigUCqRSKRQKBR6z0K0LxU4oFApoNBq4XC6cPXuWL546nY5/oAMDA7BarXC5XDCbzUin09wl0EvIx4QkSTy422AwQK1WY2RkBCqVCr/61a8O8C4PDnkKOZlsPR4Pzp07h+HhYXi9Xuj1epTLZeTzedTr9Z4OMJRbU9RqNY4cOYKjR49iaGgIVqsV165d2yafbrU60HjR6XRwOBw4d+4c/H4/pqamUCgU8Mknn6BUKiESiXDXfzcjD3EwGo3w+/14+eWX4fP5MDMzA7Vaza0FlFQxMDCAdDoNp9OJSCSC9fV1RKNR1Ot1xONxAA8SVbppvtFeTYrc5OQk9x5YLBauvLhcLuh0OhQKBW51of/TQZP2NLlb7nHZU+WFFlK68dHRUXz1q1+FVqtFKBRCNBpFKBTiptpSqYRqtco3pG6HTshOpxPj4+M4duwYjh49CrvdDqVSuc36pFQqodFo0N/fj2QyiVqtxrNs6vV6102IRyEPMJVnppElqpdkQdCCq9VqEQgE4HA48Prrr8Pv9+PYsWOw2+2o1+vI5/NYWVlBPB7H4uIiQqEQCoUCd0/2CnLLp9PphM1mw5EjR3Ds2DE0m02Uy2VUKpVtluBeGFe0eZB1RalUQqvVQqfTcQtUtyFXTiVJ4oqcy+XC2NgYjhw5grGxMVit1odkUK1WAWy5ZpVKJY4ePcpj8mKxGHw+H0ZGRhAOh7GysoJqtYpSqbTt73YatNaQZYqUFbfbjRdeeIErLTqdDmazGWq1mu9parUajDEcP34cNpsNY2NjmJqaQiwWQzAYRKFQQDqdfuKM4z1XXshdNDw8jJMnT+JrX/saUqkUfvjDH6JYLPJso+HhYUiStC2jpts3ZJKP1WrFiRMncPToUUxPT0Or1T6Ujkf+w0AggGKxiGQyiWq1inQ6jWq12lOm/50GNfnilUol1Gp1T23CwHaLi0ajweDgIAYGBvDrv/7r8Pv98Pl8UCqVWF1dRT6fx+LiIpaXl7G0tIRwOIxSqdSTSh/FUDkcDvh8PkxOTuLo0aNYWlpCLBbjh4O98Ml3AnIXEikv5E7S6XRgjHWl8kLI55DBYIDH48GRI0cwNTWFsbExaDSabYdrSZK2KS+0kQOAz+dDPB7H0NAQotEorl27hnQ6jXw+3/ExneSy12q1MJlMGBkZwTvvvAOfz4cTJ07wbFn585HcKJD3+PHjmJ6exvj4OI4cOYLZ2Vl88cUXiEajyOVyvFwB8HhK3r64jYxGIwYGBmAymbC+vo5wOIybN2+iVqthYmKC1+yggDlSWrp1ktBzkULidrsxOTmJ/v7+XYvS0cY8NTUFm80Go9GIUCiEmzdvYmVlBcViEaVSqesLt5EplxYKlUq1TXGTb+LdOn5akWeJmEwm+P1+nD9/Hj6fD2azGQqFArFYDJIkoV6vQ6/XY3x8HE6nE5lMBul0GolEgp8GewlaHE0mE6xWK9RqNYCtzJGVlRUkEgleY6mbD1O03lKZilYLeLfX3ZK7D5VKJVdkh4eHce7cOfh8PgDYVk6gdZ+Sx8sA4MkXOp0OTqcTjUYDhUIB6+vryGazDx1QOwEaB6TMBgIBHDt2DKOjoxgfH4fVaoVGo4FCoeBxiZSlptVqoVAoeBwnHZZcLhcYY9Dr9bDb7bhz5w4ikQhKpRKKxeJjK3n7orxYLBZMTU1Bp9Nhbm4Oy8vL+Oijj7alJNIDUx2Kbo57kccAUerd6dOn4XA4HjJJ0sZMGuupU6fQbDYxPT2NZDKJ73//+yiXy4hEIsjlcnxAdJvc5EqJQqGAzWbjm02rkid3t3Xa4vCkyOVC7qKJiQm8/fbbcDgcMJvNaDabCAaDqFQqcLlcMBgMOHnyJJRKJQ+Wq1QqiMfjXa34tiIfK1arlQcyA0AwGMSdO3cQCoV4gGq3B8aT8lKpVPgXrcO9orzQ5zw4OIiLFy9idHQUFy5c4Ifq1qBS+Xyh35GsrFYrbDYbt15RuYIrV65gdnaWuyTp/ToBUkj0ej23snzta1+Dz+fD0aNHoVart9WNqtfrSCQSqFarcDqd0Ov12+aSSqWC3+/n8VXlchk/+clP8OmnnwIACoXCY4+7PVVe6APO5XK4d+8eV0wikQiq1Sr0ej0/OZfLZTSbTVSrVZ6W2Ckf6JMgfy6bzYahoSEMDAzAbrfDaDRue12j0UA+n4ckSVxWJC+LxQKFQoHp6Wk0Gg3cvHmTT4ZqtdpV8qPBq9Vq4XK5EAgEMD09jf7+fmi12m3PSdYsKmJH13eLLOSQdUmtVkOv1yMQCODFF19Ef38/zGYzGo0Grl69inw+j42NDVSrVdjtdhgMBkxNTcHlcsHtduPUqVOoVqsIhULbNqlulBkh36wondPlckGSJOTzee5/z+fzB3ynzw8KWtbpdDAYDHyjkf++m2k2mzCbzbBYLOjv78fw8DA8Hs+2zCqyXpZKJaytraFSqfB9zWKxQK/Xw2QycSsDKTxUD8btdnPrRKPR4MrLYYfWGoPBAIPBgNHRUZw4cQJjY2MIBAKwWCwAgHK5zEMaMpkMSqUSlpaWUK1WMTk5CYfDAbfbzTOMa7UaNBoNX6u1Wi03ZDzpQWrPlRc63f385z/nfrJarYZSqQS9Xg+NRgOVSoVcLsfTpakYVLfRmio3MDCAN998E0ePHkVfXx//sGigVKtVrK+vo16vw+/3w2Aw8OAnt9sNl8sFrVaLM2fO4K//+q+Ry+WQTCZ55HY3yFB+6jObzTh27BjGx8fxla98hacBywe5RqOBTqeDyWSC0WjkxcWA7pCHHMq2MplM/BT0O7/zO7BYLDAajQiHw/gP/+E/YH19nVteqNDWN7/5TZw6dYoHy5XLZdy7d4+nxXabrHaC3NQ6nQ5DQ0M87i6dTmNxcRG3bt1COp3uiXgyiv2hNcZut8NisXCLQ7fLgCwKDocDo6OjOHbsGF544QW+PxGNRgPFYhGRSAQ/+MEPkEgkYDQaodVqcezYMbhcLoyPj8PlcnGrMGMMjUYDZrMZw8PDWFlZgV6v5+8FHP61iVLHrVYrhoaG8Nprr+Eb3/gGbDYbfD4fX4symQxu377N51A6ncb169dRqVTw9ttvY2RkBKdOnYJGo0EymUQ2m4XVauUKHcXKyJWXA7G8EPQhyU1JpNjo9XrodDrUajVe4p1cA91mwpbHuqjValgsFng8Hh7BTn7CarWKVCqFTCaDmzdvcrcQBYPp9Xq+mFB6Wl9fH44cOYKlpSXE4/GuqMArv3/yiQYCAfT19cFms21TXOi1FGin1+uh1+u70oontxqQIjs9PY3h4WEYDAY0Gg0sLy9jc3MT4XAY8Xicp0UDQL1eRzqdRiaTgc/n40WkKDW/XC53nczkkLtIqVTCZDLBZrPB7/fD6/Uik8kgmUwimUzyPlnd6IZ9FPS89Xqdt92oVCpdOy7oM6aDgM1mg1ar5W54spIUCgWsrKwgHA5jfX0dyWQSWq2Wx0rZbDYwxlAul+FyubiFnDHGM/2o6CFZZA67O46su1qtFj6fDxMTE+jv74fNZoNer0ez2UShUEAwGEQ8HsetW7eQyWSwsbGBXC7H3UaLi4solUpQq9VIpVKIx+PIZDIYHh7mc1Gv1z/1fe6L8kKuDOCBCV+pVPKaAjabDZFIBKlUivc26jbFBXiwaJpMJm6apHLtZJ5tNpvIZrO4fv061tfX8e/+3b9DOp1GX18fTCYTJiYmePE6pVKJCxcu4MiRI3jhhRfg9/vxs5/9DAsLC6hWq11hcaBUVqVSCbfbjZdeegkDAwPo7++HTqfb5pMHtgIvKf3c5XKhWq1yv2kny0EOHQLohHzq1Cl885vf3BbI/aMf/QjBYBC3b99GJpPh1xYKBajVaqytrcFms6Gvrw/9/f3weDyw2+18/HWLrHaC5KdWqzEwMICBgQGcOXMGg4OD+E//6T9hcXER9+7dQzQa5WOvVymVSsjn88jlcjwDpBuVOToIUvzh8PAw30ibzSZqtRqSySQ2NzfxN3/zNwgGg/j444/53GKM4aOPPoJOp8M777yD6elpnDt3DlNTU9wVks/nsba2hkgkgmKxiEqlwq89rMit3kajEWfPnsU777yD/v5++Hw+7kJbXV3F3/3d32FjYwO//OUvkc/nUSwWucWGMcYVvevXr8Pr9SIcDiOdTuPtt9/GW2+9BZ/PB6vV+tTy2Lf2AK3aJSkwWq0WWq2Wd0xu3Yy6Dbl2T/5P2oSpCFQ8HsfCwgI2NzeRTCaRy+WgVquRz+ehVqu560ilUmFiYgKVSgVqtRp2ux1WqxUGgwGMMVQqlUM9MR4FjQN6ZpvNxl1F5XKZ+1UBwOl0QqlU8nFULpd5HFU3IVf+qZaC3++H2+2GQqHgtZOCwSAPxJWn+ZICXavVtv1Obsnrdug5tVot3G43PzxUq1Ukk0lEIhFufeplKL6D5lS3xdIRtMaoVCpYLBa4XK5t8YcKhQKNRoO75alGGXkKSCYUFhGNRmGxWDAxMYFyucwP6vKElE5xw9H6aTQaeaiCy+WCyWQCYwylUgnhcBgbGxtYXV1FOBxGNptFqVTi9ZFI2aUyKNFolLuY8vk8V+KAQ9rbSG7ep8VXrVbDZrPBYrHw2iVkmuwmWt0fo6OjeOmll3D69GkMDQ1BkiSUy2UsLy/jr//6rxEMBvH5558jn88jmUyiXq+jUCiAMYbZ2Vkwxrg27/F44PF4tgX/Dg4OIhqNIp1Ob/u7nYbcwnD06FEcPXoUR44cgVqtxtLSEnK5HG7evAmVSoVvfOMbcLlcSKfTCIfD2NzcRDAY5BVkuwF5AS2VSoXJyUl89atfxejoKEZGRrC2toaf/vSnWFlZwUcffcSbm8rrt1CqJ1XYpSDvXukoTesPWX0vXLiAvr4+bl24fv06bt++jVQq1TEbzH5A6zWNk2w2y9N7u0kmNKfsdjvsdjsmJiZw9OhRHvBO+1SlUsHi4iLm5ubw6aefPlRfi5SXWq2G27dvIxgM8iwar9cLk8nE68bY7XZey+uwu+IoFmhoaAgnT57EqVOnMDk5CYVCgVqthpWVFfz4xz/G0tISfv7zn6NUKvFnarVakjI8NzcHSdoqXkvWLYPBwIOcnzZkZN8bMwIPTj40MFQqFWq1WtdX1qVBTrErFosFarUa5XKZ+wapDk4ymUS5XOaWKNLw6V+K0ibtH3gQS0PVDDsdGicGg4EvApT6WyqVeN8QtVrN46iA7k2PpoVWr9fDYrHA5/MhEAjAarWiWq0im80iGAwiGo3yPmGtiit9r1QqeSBit8qrFZIfxdqZTCZ4vV643W6Uy2WUSiVkMhne8+mwbijPA7n1u5utcXK3iNfrhd1uh8lk4tkvVCitWCwinU5vK2lPc6m1VIPci0A/A7bWZ4PBAJ1Ox7NGDzN0UGKMwWazwePxwGKxQKvV8sD+ZDLJrby5XG5b6nfr/JGnUEuSBJPJBJfLxWNnSCGieKAnXZeem/JCmywF7FJJYIp36caFgxQ1v9+Po0ePwu/3Q6lUIpVK4dq1a7hx4wbee+893uNpp8WD/i+v/yL/oLthI5K7i7RaLSYmJvD3/t7fQ39/P9xuNzKZDFKpFCKRCFZWVqDRaFCpVLiiY7FYeI2FbDaLYrF46BeKRyH/fMfHx3H27FmcP38eFy9eRDwex7Vr13Dz5k28//77yGaz3GXYWkiLFFyKl6G6JkB3b1LAg41Fp9NheHgYY2NjeO2112Cz2fDjH/8YKysr3PTdrWvQbtAYkdcNAh5k7xmNRhiNxq5pD0DPS4fB6elpvPbaazhx4gQcDgeArfFSKBSQTCaxurqK2dlZrK+v8/o3tLET8uJ0VIeKDqj1ep03eKSkC1rjD6M86VmcTidMJhOOHz+OCxcuoL+/H0qlEolEAnNzc/jss894jAspLjutI3TgliQJFosFBoMBX/nKV/DCCy/g+PHjGBgY4IkpdHB/0ho4z015oZMfWQkomvswfpB7AZnRVCoVPzlTue1yuYxoNMqjr8vl8kNKCkGLB5nW5JtTvV7ncuwGN4BcVuQaI2tBoVDYVma7NQ29mzYeudtRqVTCZrPxRZCyiyKRCHcVUmYfXSN/D+pgLq8oS8pvt8UHtUKWPKq34fF4YDabeYdtqjJMVs1uG0ePAwVY0loi73PUull3Mq3zw2QyweFwwGQy8TlBZT2y2SzS6TRP7d1tnsgzHo1GI3Q63baePvJD+2HPpiVXll6v5wdBm80GnU4HACgWi4jH40gkEkilUo90fzHG+JyiNHyqpUO9/GgfTKfT2/awQ6O80MZrMBh4apRGo+HNqrqxhwgNaqo/4nK54PP5YDAY0Gw2EQ6H8atf/YrXdAEeaK+7md6omaVOp4Ner0etVkMqlUI0GsXm5iZvtNeJ0AJqsVgwMjKC0dFRDA4Owmw2AwDy+Tzu3LmDeDyOUqnETbz0u0QigWg0ilgsBuBhBbATITOrXq/np0SdTodgMIhbt27hxz/+MUKhEEql0kPZeqSYULC4xWLBqVOn8MYbb0ClUnGXCZ12Ol1WrdDzazQaXsfjN37jN+DxeJBKpRAMBnHlyhXMz893dZXqdlA6cDabRSqV4tYnm83GA051Ol1XuKPlkHKh1+thMBigUqm2KTaZTAZ37tzhsS75fJ534JYfDOSHg0AggMnJSV7SgWRWr9e5O5dcI4cRmi8KhQKDg4MYHR3FxMQEBgcHoVAoUK/XEYlE+JzJZrPcUr6TJYrqKU1MTMDhcOD06dM8w4+CmiORCC5fvoxf/OIXWF5eRjwef2LX7XN3G8nrCXRrNDtBMT4ajQZ6vZ7XBiiXy4jH48jlctuefzfFBXhQz4SqyVIcCG1E8k2oU+VJGSEUnEwLC9UNIneQfBxRgBm9rlsUFwBcYbVYLHC73ahUKshkMkgkEtjc3EQikXio90orNG6oomyhUODjRd49uZugeUOHJqvVioGBAVitVh4UT6fIXox1kW/CVNZC3itNflDqhvkEPFxzS24RISjLqFAoIJvNcnfGbt4Beg+bzQaHw8F7rxFUL6darfJ1Cji86zPFZ1KGJxXWo70mnU6jUCg8NGdareAajQZarRYejwd+vx8jIyMYGhqC2+2G0WjkrrlQKITFxUVEo1GuCzzJAXxflRd5LIPX64XD4eDZDmR+qtfrXTNBdoJM160xK2Rt2A25Zq9UKnH8+HEMDQ1hZmYGfr8fwWAQoVAI8Xh8WxBvJ8qR3CPZbBbz8/Pw+XxYXV3lKYwqlYoX9hsdHYXNZoPBYIAkSbyc9/DwMN/QqW5Jp1qigK3PX6fT8WqUVqsVCwsLuHnzJm7evImlpaVtqfGt7iIKVB0cHER/fz8cDgfUajWi0Sg2Njawvr7O518njpndkC+kVCdpYmICfr8fjUYDP/3pT7GxsYH5+XlEo1HU6/VtG06vQNkyZH1ZX1/n1YdNJhPvdr+ysoJ0Os3Xrk4dKzQuLBYLzGYzXC4X770jV+CpJYnJZOIHQ7LMycsLaLVaTE1Nwe124/XXX8f09DT8fj+AB5bkSCSC+fl5nil5GEtZkFzo0Njf34+xsTGeKEHPK1f2SF6057Q2zz1x4gTcbjfefvttDA0NccUul8vh9u3buHLlCq5du4bFxUXcuXPnqeuTPTe3EaVJUcBUqVTqWrfR49DuxCu3uMirqg4PD8PpdPK4B4qXIX91p8qRTPZUxyWTySCXy/HaC2S5o9RDm83GrVharRZGoxEmkwkmkwmZTKajF1ng4S7kZG2rVqsIh8NcQdspiJCgeCu73Q632w2tVotms8kztijIt1tjzugE6HK5eH8n6vlEG3KxWOyILJD9hOJdcrkcb0hJaeWZTIans8qV4k6cW3T/1MtJp9Pxirq0FtOcIQv3TsG1NK50Oh18Ph/6+/vR39+PQCAAk8kEADwOMZPJIBwOI5VK8ayawwZ9nrTWmEymhwL7aX1ujbmUo1AoeEYfFRWdmprC0NAQn1+xWAzhcBhLS0u4ffs2l438vZ+EfVdeyPdMZqNcLsdNUOQ761Z/s7w4GJlkScO12Ww8+JQ0V+CB4kL1SiYnJ+H1enHx4kUcP34cLpcL5XIZS0tL+OijjzA/P9/xJfHlAchUwVEefV6r1RCJRFCv1zE0NASTycRdSqurq7zGC9UN6lQ5tEKKqyRJqFQqPF6D3B1y5EG4arUao6OjcLlceOuttzAxMQEAmJ2dxXvvvYfPPvsMKysr26wu3SAzmjtarRZms5nXqnC5XIjH44jFYlhaWsLa2tpD9XB6FSrWt7y8DKVSycu5U8Cu3GLcydDnTL2FqI6NyWTa1mNOpVLBbDajr68PZ8+e5W4jcl1rNBpuWTh37hwvX2CxWHgsXiQSwfr6Oj7//HP87Gc/4+vSYTxgyuMzjUYjBgYGuOWl0WjwL7IEm81mXiCUklDIzXT8+HE4HA6cOHGCJwdsbm5ibm4O0WgU9+7dw8bGBv+ijNGn3f+fi9tIpVLB4XDAarWiXC5vy51v9Tt2E/JIfrnmTdkfcu22FVJGvF4vhoaGMD4+jsnJSV75kqryxuNxPik6WY4UQEinFvoCwIv20cZMvvhGo8F9p3SS7qZy5nQSJMVO7ndu3Uzkaa8KhQJut5uffiYnJ7G2toZQKIS5uTlcv36dl/I+7FkQTwq5qc1mMxwOBwKBADdZJ5NJxGIxJJPJQ7mRPG9ontTrdSQSCTgcDp4BSmOv3Wm7k6DPmpoEU3E1UuDllm6dTgeLxYKhoSFee4sSJHQ6HWZmZhAIBHDq1Cm43e5t1juFQoFcLofNzU0sLCzg+vXrT1XD5HlDLkOr1QqXy8X7nsmzgrVaLfR6PcxmMw+GN5vNGBgYgNPpxIsvvgi3243x8fFtLse5uTksLi7i7t27WFtbQ6FQQLFY5FltT33Pe/j825Bv3AB4ZV1KSetmaDKQWT4Wi2FtbQ19fX2w2+3o6+vDl770JczNzSGRSCCXyyGVSgEA71ja19cHq9WKt956C8PDw/D5fGg0Grhx4wbm5+dx6dIlrK6udnSWUTvkAbk2mw1vvPEGr3lC7QLy+TyuXr2KW7duYX19Hfl8vqMtUK3QQksbrVKphEaj4aZuOhGSe8RgMGBwcBA2mw0vv/wyX1ipLwv18KEMim6LNSPLEy2k4+PjGB8fRy6Xw0cffcRjoii1vFuU3L2E3Pgej4dnAOp0Ou6e7lR50X1Tm5F0Oo1EIsHbbFBgKrmDqNs2ueSpualKpcLRo0d5KrHcekfzqdFo8EMmlcI/7Fa+er2OcrmMdDqNeDwOj8fD4wobjQb6+/vxyiuvYHp6GjMzM9BoNLw1jd/vh8FggM/n4xlqpVIJX3zxBRYXF3H58mWsr6/zGlzt3N1Pwr4oL/LCRzTgjUYjDAYDisUid3N0M4wxnrueTqcRiUR4x1Gn04nTp09Do9HgypUriMfj3IVG8RtTU1Pw+Xw4d+4c78LZaDSwuLiIjz/+GHfv3kUoFOqqBZiepTX9zmAw4OTJkwDAawSQT3l+fh63bt3igcvdEMMgd6NR5U5SXsikT3OMSpo7HA7etNHr9eKVV16BzWbD5uYmYrEYbt++jRs3bvD06mc99RxGaM0xm804cuQIRkZGEAgEeMGxjY0NHuvTTTVM9gK56wTYOmxSoTWtVtvxmaE0pyjDjlogyN3MkiRxi4LdbsfAwACfY7VaDblcjlvDqT/dTlC4AFncKeniMO558uxf6qKdyWRgt9u3BShT8kSlUsGxY8e4e02r1cLr9fLno8ykSqWCe/fu8WKaoVCIF23dKy/BnikvFL1OWQ6U7mo2mzE2NsabEjYaDZhMJhw9ehQmkwmhUAiFQmGvbuNQQVlFq6uruHTpEqrVKtxuN4CtSoZTU1P4xje+wcu8M8bgcDhgMBi4hcFisaBQKODevXsIh8O4dOkS7t69i0QiseNm3y2Qz52sDS6XC8CDLtKbm5uIRqM8eJWy1roJWjhp03C73Th16hS3PDG21bPHbrfjzJkzvBCUVqtFIpFAJBLB559/jlAohNnZWcRiMVQqlUN/CnwaaN1RKpWw2+0YHh6GwWDA7OwsVlZWsLi4yGtJdNs4eVZa1xE6bJbL5W0V0Ulh7uSxQxtyKBTCjRs3+FiRx5cB2wtfkvIh38zlxUHp31AohGQyidu3b+PWrVvY2Njg1x1W6BnJS3D37l2eUSRPGCB3WrVahU6n43Kq1+sIhUK8GKQkSUgkEkin04jFYkilUtzSstf1lPZEeZFrrnSj5D+jJnIulwtWq5WnvR49epT7ortNeSF5kClybW2NlymfmpqCw+FAf38/rFYrvF4vyuUyV0aoqqHX64VKpcLa2hpSqRQ+//xz3LhxA7Ozs1hbW+tapQV4sFjQxKdsK8o6KpVKiMfj2NjY4F24uzHlnlyvtGF4PB6cOnUKFouFKy/Uo+XLX/4yTyuvVCq4du0awuEwfvnLX2JpaYlnpnWrxQV4UE3YarViaGgIxWKR+9uXl5d5tdRuGiP7AWNblVYpQ5Q2r05GPkYA8CauIyMjyGQyMBqNvCKsPPsIeODyoWqztM9Vq1XUajUUi0VUKhXcuXMH8/PzmJubw/z8PNLp9KE/KMhDHKrVKubm5lAsFuF0OuFwOOB0OmE0Gvlhsl6v8yKp1OiVMobsdjskSUIoFEIikUA8HuctgOTxU3vFnigvZHGxWCw8NXN8fJwXh/J4POjr64NSqUQkEkEqleKl3uWLc7dBCgYpJ7Ozs/j5z3+O/v5+nDhxAjqdjjfucrvd3NxIJs16vY4bN24gGo3i9u3bWFtbQz6f72qLC2MMhUIBq6urqNVq3K9qs9l4deJMJoO5uTmsra3xlOFug7GtlvK5XA6rq6u4evUql8Po6Cg/5VCAKlUZ3tzc5N23KZiZShJ0m3Inh+RA3XxVKhWq1So2NjYQDoe3ud+6de48DbRZ0+ZFcqISBHa7HU6nk3es73Tocy8UCohEIrh58yb0ej38fj+Gh4dhMpngdDp5erj8YN6amZfJZJDP53k2DbkmqeT9To1SDyNyNw5ZJz/77DMkk0neKZssU9RUmApmlkolrryQVyEYDCKbzWJjYwP5fH7fYqX2THmp1+uw2+04fvw4jhw5gosXL8JqtcLn8/Eqofl8HrOzswiHw0in0/zBgM6tH9AOilOg6OpSqYTl5WUcO3YM9XodPp+PKzFutxvVahWrq6vI5XJYXl5GMpnEL3/5S6yuriIYDCKTyQDYvZVAN8AYQzabxd27d5HJZHgqHtUImpubQzgcxpUrV7CxsYF0Os0nRycsFI+DPK2zUqlgdnYWJpMJJ0+exPj4OAYGBvDyyy/zU08ymcSNGzcQiUTwy1/+EtFoFHNzc7xuByktnX563g15OwCbzcb71ZTLZczPzyMUCvGskdaS5oIt+VFBNioiarFY+Ibk8XgQDAYPZczGk0KffTqdRjqdxscff4yVlRUcO3YM58+fR39/P+x2+0MZVq1Buc1mE8lkks85qluSSCS2VdOVl8A4zNB9bm5u8uKnn332GUZGRjAyMsIrExcKBR70nkwmUSqVEIvFoFAo4Pf7oVAoEA6HeexMtVrdt4zGPXMbKRQKlMtlpFIprK2t4dq1azwvnBrulUol3Lp1i78mlUo9dXW9ToIGLqW6rq6u4sqVK7Db7YhEIrx9QK1WQzgcRrFYRCgU4kW1dpJTN8qLnimXy2FpaQmpVAqlUgkWi4XXJVlaWuIyTCaTqFarXSkL4IF5OhKJ8EqU5XIZWq0WBoOBBxFms1ksLi4inU5jc3OTW6O62dIihzaaRqOBUqmEQqGAXC6HWq0Gm82GQqEAjUbDLXTdeFB6Wkhu+XwesVgMN27c4Fk49Xodm5ubXT3PCoUCotEoz5IhV7RKpeKuDmpVYrVawRhDPp9HuVzGnTt3kEgksLq6ing8zssPtCo8nQYVkQWAjY0N7m5WKBSoVqvcXUTGB8p4pd5yuVyOt0TYTxmwdhqhzWZ7LHWRzNfUuZZKTJM5EgCvJxCPx3kKmbw427OQTqcPZJSYzebHVqfl2SG0+VAxLfInUiEk6jVCfUfkG9HTDIZcLncg8tHr9U913CBlmJRejUbD0+xJmy8UCo/s6/O4lEqlA5GPxWJpKx8aMxSnQtUvSTbNZpP73akzKxXVepbx0ko2mz0Q+RgMhsdef4Ct+klarRYXLlzAP/yH/xClUgkrKyvY2NjAj370Ix4bBeztxlIsFg+1fB4HxhgsFgteeuklOJ1OjI6OQqlU4m/+5m94vNDTukEOQj5Wq/WxZEO1pciFb7FY4PP5tvWSs1gsPImCMYaVlRVkMhksLCwglUohEonwTKQnlU8mkzmQsfMo+ZAStpN+0NrLaCfr0l5ZwtvJZ08DdunkQ5VSKf2KXkO55BTI2g3R608KnXSo4iIpcFS7g34u/32vIc+yAcDTGyVJ4qn29H23jx15Fl+5XEYmk+EuIHlVYjoM7JQt0SuQ+zoWi+Hq1auo1WqIxWKIx+O80JZgZyRJQrVa5dmfZO6n6rDdHuhMa26hUEA8HudJJyqViruvKe15c3MTxWKRW1tojwM619rSjieZN3RwAvZfFntieWllN40N2B+3RydYXgh5j5Cdfi6Xy27/f1I6zfIil81uPVX2chwdVssLsdOY2Ulx26vx0spht7y0QpYpgpS8/VJeusHyIi/aJ/+SHyKelsNseZHT+pyt6ePyInTyitbPMtcOq+WFaPfZPw9Fbd8tL8CDlCuyFDxKeelV5EFgwO6K3n5tRJ3AbjKSa/W9iny87JQB0WtjpRVSVOg03M3B7XsJzTl5XBCAPXU/Hkbk2UT07+Mqa70w5w7zs+2Z8tKaRy/YncdRTA7zoHketJNRr8lGvkiKQ0F7dssSETwaeUZar8hvpzgNQWew712lBTsjJsnjIeS0HSGPx0PI6dkQ8hMcdtrGvAgEAoFAIBAcNoSPRyAQCAQCQUchlBeBQCAQCAQdhVBeBAKBQCAQdBRCeREIBAKBQNBRCOVFIBAIBAJBRyGUF4FAIBAIBB2FUF4EAoFAIBB0FHumvDDGHIyxHzDGCoyxVcbY7z7BtVrG2F8yxrKMsTBj7I/26r4OC4yxf8IYu8wYqzDGvv2E177JGHuPMZZhjK3szx0eHEI27RHyaY9Ye9ojxk97xPhpz2GVz15aXv4cQBWAF8DvAfhXjLFjj3nttwBMABgC8CaAf8YY++oe3tthYBPAnwH4y6e4tnD/un+6p3d0eBCyaY+QT3vE2tMeMX7aI8ZPew6lfPakwi5jzAggBWBGkqS5+z/7DoCgJEn//DGu3wTwB5Ik/eT+9/8CwIQkSd985ps7ZDDG/gxAvyRJf/AU134FwF9IkjS81/d1GBCyaY+Qz8OItefxEePnYcT4ac9hls9eWV4mAdTp4e5zHcAjtTPGmB2A//7rn+hagUDQ84i1R/AsiPHTnkMrn71SXkwAsi0/ywAwP+a19PonvVYgEPQ2Yu0RPAti/LTn0Mpnr5SXPABLy88sAHKPeS29/kmvFQgEvY1YewTPghg/7Tm08tkr5WUOgIoxNiH72UkAtx91oSRJKQCh+69/omsFAkHPI9YewbMgxk97Dq189kR5kSSpAOD7AP6UMWZkjL0C4BsAvgMAjLFhxpjEGBve5S3+CsCfMMbsjLEjAP4QwLf34t4OC4wxFWNMB0AJQMkY0zHGVLLfS4yxi7tcq7h/rXrrW6ZjjGmex30/D4Rs2iPkszti7Xk0Yvzsjhg/7TnU8pEkaU++ADgA/BBbqXVrAH5X9rvXAKwAUO9yrRZb6XhZABEAf7RX93VYvrCVMia1fH3r/u8G7j+7c5drL+5w7S8P+pmEbIR8DsOXWHvE+BHjp/fksyep0o+CMfYnAGKSJP3rff9jHQhj7PcBHJMk6Y8P+l4OG0I27RHyaY9Ye9ojxk97xPhpz0HK57koLwKBQCAQCAR7hehtJBAIBAKBoKMQyotAIBAIBIKOQigvAoFAIBAIOgpVu19ardaOCIjJZDLsIP6uwWDoCPkUi8UDkY/RaOwI+RQKBTF+2iDGT3sOavyYzeaOkE8ul3vu8rFYLB0hm2w2eyBjpxv29rbKy/NEHjjM2IF8noIORZIkMWYET8SjEhXEeBIIno7WubVfc+nAlBd6QPm/tAnJvwRbNJvNbZu0kM8WQi5bNJtNANgmB8HOtK459L183REKsUDwZNBcal2LFArFtu/3iueuvOx24pFvOmLReBghm90RY6d3n/tJ2e1UuJP8ek2Babc29zrCUvd4MMa4stLKXs+n56q8SJKERqOxzcKi1WqhVCqh0+mgVCpRrVZRq9VQr9dRr9cB9O7AkJ8KLRYLdDodl02j0UCj0UCz2USj0TjgO33+NJtNMMZgNpuhVqu5TKrVas+NG4VCAZ1OBwAol8vbxo3gAXK5KJVKKJVKmEymbeOnVCqhUqlsu67bx5HcEkXrM7D13Eqlkv+/F5GPGZINWXuVSiWXUa/KB3iglKjVaiiVShiNRqhUKpTLZdTrdf5vx1teCIVCAYVCAa1WC41GA6PRCI1Gg2KxiHK5jFKpxDehXkS+gCgUChiNRphMJj4QKpUKKpUKX3R7EcYYDAYD9Ho9SqUSarUaGo0GarVazywmtIgajUb+PSn+QoHZGVpoVSoVPxSUy2W+KdGhgMzfvUavzJ2nZTcr3W6/62bk4R4qlQpqtRo2mw0qlQr5fB61Wm3bXNpL68u+Ky/0oTabTajVani9XhgMBvT19cFkMmFgYAAWiwV+vx8WiwWLi4vY3NzE1atXcf36da7k9NqgUCgUUCqVGB0dhcPhwMWLFzE6Oop0Oo18Po8bN27gxo0byGQyiMViPRPrQYNfr9fDZDLhN37jNzA8PIzbt28jFArhzp07WFtb4yfrboYWDo/Hg9/7vd+DRqPBysoKkskkPvroIySTyZ6cO63I/fAqlQoGgwHnz5+H1+vFuXPn4Ha7kc1mUSqVMDs7i/X1dSwuLmJpaYkrNEB3bkxyBVer1cLv90Oj0UChUKDZbCIUCqFYLD4UG9Tt0JihA7ZarYbb7YZOp4PdbodSqUQ8Hke5XEYkEkEul9sWM7Wb66TboMOSVquFx+OB1+vFb/3Wb8Hn8yESiSCbzeLTTz/FysoKQqEQEonEnq3Nz8XyIjfVOhwOWCwWjI+Pw263Y3JyEi6XC8PDw7Db7XC5XFyBaX2PXpg0BGmyPp8P/f39uHDhAk6ePIloNIp0Oo1isYjV1VXuJqBruhm5lq/VamE0GnHixAkcP34c9XodSqUSKysraDQaPbV4WCwWXLhwAQaDATabDaFQCF988YWwvGC7S4TmlF6vx/j4OEZGRnDx4kUMDAwgkUggn8/DaDTCaDQil8thZWUFALrWstm6bmg0Gng8Huj1eiiVStTrdaRSKRSLxYO8zQOFrHR6vR5utxsWiwX9/f1Qq9VYXV1FOp1GOp1GNpvlrwd6a78iF77VaoXH48Err7yCsbExrK+vI5lMIplMolKpIJ1Oc4VwL9gX5aU1mt9kMmFiYgIulwuvvPIKHA4HAoEAjEYjnE4n9Ho9jEYj1Go1BgcHYTabEY/Hkc1mEQqFsLa2xrXZbh8QjUYDSqUSdrsdNpsNr776Ko4cOYKhoSHo9Xp4PB7YbDaYTCZUKhU0Go2ul4mcRqMBtVqNqakp9PX1ob+/H06nE2q1epu/vtuRZ8ooFAqYTCZYrVaMjY1Br9dDp9PxhaKXxgcht/gqlUqYzWaYzWacO3cOXq8Xb775Jvx+PxwOB5efVqvFqVOnMDg4CABIp9OIx+MIhUJdGRBOLjKn04nz58/D7Xbj9OnT0Gg0WFpaQjKZxMbGBrLZLOr1Ot+kuhlycWg0GlitVjidTpw9exY2mw1TU1Mwm83wer1Qq9XY3NxELpfDzZs3sbm5iWAwiFQqhUwmg2w229VZszsdCmw2G2w2G/+5y+Xic87tdqNWqyGTyfC4zWeVzb5ZXuRpU3q9HhMTExgYGMDFixfhdrtht9uh0WigUqmgUCh4oKXH44HD4cD6+jrC4TAkScLKygpXXrpVo6X0TFpsLRYLXC4XTpw4gVOnTsFqtUKr1fLATL1ez2M8egWSD2MMAwMDGBkZ4achlUr1UIper0AB72azGX19fQC23CO9osjtBo0Xsra4XC688MILGBwcxOnTp+F0OnmwJSl8BoMBIyMjWFtbw+zsLOr1OoLB4Lbg1W6BlBeDwYAzZ85gYGAAFy5c4DKh9YasML0CKf1msxk+nw8XLlyAx+PB9PQ0jEYjPB4PVCoVotEo8vk8rFYr1tfXcePGDSwtLfFNGniwrnfjmtQa2G0ymWA2m7nL0Wq1gjGGI0eOwGaz4fbt25idnUWxWES1Wn3mg9WeKi/yk6BarYbdbsfY2Bj6+vrw5ptvwu12w+fzwWg0otlsolQqoVwuo9FoQKfTcUVGo9FgZGQEjUYDGo0G8XgcuVwOiUQCQHduTnLZabVajIyMYGBgAC6Xi1ulGGNIp9PI5XJIJpMol8uo1WoAulMmhNxdZDQaYbPZMDw8jNHRUeh0OtTrdWSzWSQSCVQqla497ciRK7uFQgGzs7Nwu91Qq9VIp9M9G7DbGmNns9ngcrlw/vx5+P1+vPjii3A4HGg2m4jH4wgGg8jn8zwQfnx8HH19fbBYLDzGbC9N3YeNZrMJrVaL8fFxuFwuhEIh5HI5fPDBB1hfX0c8Hu+J7D35+qvX6+H3+3H+/HkMDg7i6NGjsFqt/JCUSCR4YoAkSRgZGYHX64XH40E0GsXVq1dx7do1ZDIZJJNJAOgaC2hrMVmz2YzR0VF4PB585Stfgc/ng8ViAQA+bhwOBzQaDY4cOYJEIoGFhQXk83n+fk8rlz1XXuiko9Vq4fV68eqrr2JwcBBvvPEGzGYzLBYL34QrlQpisRgqlQrfpHU6HdRqNR8QtVoNS0tLCAaDiMfjz/zAhxUaFHLlbWxsDC6XCyaTiT9vOp1GOBxGMpnkGVndJoudoIWF4jqGhoYwMjICrVaLRqOBbDbLfavdutHshCRJyOfzmJ+fRzKZhNfrRSaT6UnlpdVdrVar4XA4MDQ0hLfeegt9fX04ffo0dDod1tfXkU6ncfXqVYRCIWSzWVQqFSiVSm7uHh4exurqatdmHdF6rdPpMDo6CpPJhOvXr2NjYwMff/wxVldXH0oD7ta1ptVK5/V68fLLLyMQCGB6ehparRbA1oYciURQLpeh0+mg0WgwPDzMvQuFQgEajQaZTAYrKys8maKbvAbyGFaz2YwzZ85gcHAQX/7yl+F0Ovn6Swdru90Oh8OBqakpZLNZ5HI5LC0tbXu/p5HLnigv8o2XMoqmp6cxNDSE48ePw+VywWAwcBdQsVjE4uIicrkc4vE4qtUqXnjhBQwMDMDhcPD0acYYvF4vpqamwBjD4uIij2voloFA0GLr8/ngcrkwPT3NFxSi2WxiaWkJ169fx9LSEnK5HHcbdZMsdoLGGJn2LRYLLBYLGo0GCoXCNuWlFywvcmjeUZZILz17K5Q63np6DgQCfGEtl8uIRqOIRqO4ceMGD/IGwF0BFLxqtVqh0+ke8vF3C2TBq9frqNVq2+pItcZMddNzE3LXh0ql4t6CyclJjI2N8ZioZrOJSqWCYrGI27dvI5vNor+/H2azGXq9Hnq9ngf2joyM4Pz58zCZTNyqtxeWhoOktXaUxWLB8PAwBgYGcPbsWXi9XhiNxh0PjnSd1WpFIBCAxWI5XNlGFOSk0+kwMjKCd999F4FAAGfPnuW+01wuhzt37iAajeKDDz5ANBpFMplEo9GAVquFVquFwWCA1WrlA2JwcBBnzpxBrVbDpUuXUK1WUa1WAXTPZCKtX61WY2xsDAMDA3jhhRcwMjICvV6/zUVw+/Zt/O3f/i1WVlaQTqd5bn03I491MRgMMJvNsNvtsNvtqFQqKBQKiMfjiEajKJfLPbWBy03dOp2up569FYrh0Gq1sFgsGBoawrvvvguPx4PJyUl+eq5UKggGg1hbW8NHH32E2dlZXi/o1VdfRSaTgUajQX9/Pz941Wo1vu4A3bP2AOAbs0ql4usrBej2gvWOFF7K7jx37hwmJycxMzPDYwzr9Try+TwSiQQ+/fRThMNhvPjii+jr64Pb7YbNZoNOp4NWq8WxY8fg9/thMpkQiUQQi8VQKBT4GtaJCox8DWaMweFw4KWXXsLw8DC+/OUvw2w2Q6vVgjHGFV/5tcCW+2hkZAROp5PHKD6LVfOZlBf5wFYoFPB4PBgdHcXMzAxPhVYqlcjn87h16xaSySQuXbqEZDKJ5eVlZLNZrpEuLS1Bp9MhGo3CZrMhEAigr68P9XodKpWKx8N02of+JKjVarhcLrjdbhgMBmi1Wh54mUql+OTJZDJ7EvDUKdCE0Wg0PMPIZrNBr9cjFAohFoshm82iWq32REZEK+ROM5lMsFgsfM70ihzkp2eqxzEzM4OJiQn4fD5oNBrcu3cPwJapu9FooF6vQ6fTwWg0wmAwAAAv/Fir1bZZszQaTVe6jmhe1et1JBIJVKtVaDQaGAwGKJXKrldc5Bl7VqsVXq8XExMTOHLkCAKBAK/4nslkkM/ncffuXcRiMSwuLiKTySAYDEKSJIyPj2/L+tRqtdzKMDMzg4WFBWxubnKFsJOQe1UokcTr9WJsbAwzMzPw+Xw8qPtxxstejqlnVl7o5KdUKjE5OYmvf/3rGB8fx8svv4xms4lisYhIJILvfve72NjYwBdffMH9y2SWVCqVvJANWRLeeecdvPXWW6hWq3wT78YyzK21S0ZGRnhhOgpsbjQaWF9fx+bmJlZWVnhxpF7YoOT+VaPRiJmZGYyMjPCihmtra5ibm+NWl15yGdHYIXO30+nkZm6tVtszsT9yOWg0GoyNjeE3f/M30dfXh6mpKUQiEfzt3/4t8vk8n2eUAeFwOOByuRCPx1EsFnkSgdFo5NZfcnlTvZNuGl/kRlteXobT6YTFYoHdbodKdWDF158LrfFRfr8fL730EmZmZvDlL3+Zu2BzuRyvO/b9738f4XAYq6urXPlNpVKYmZnBwMAAn290kJiZmYFer4fFYsHNmzf52CI6ZRzR3KJSJq+//jrGx8fx1a9+FXq9HhqNhr+uXfYrWW/2KkN2T0YoBeQMDg7ySP1KpYJMJoP5+Xmsrq7y4CUKMpVrYJIkIZPJ8JQrhUKBcDiMaDSKarUKg8HATwPdtiDTYmqz2eD3+xEIBPhpUZIkfhIMBoNYWFhAIpHgGVq9slHTuNDpdPD5fPD5fNzEHYvFEAqFUCqVeqZYH0GBgCqVCiaTibcI6CVzPyEvkjU0NIT+/n4YDAZEo1EEg0GsrKzwQmJkSVGpVEilUtyKqdFoeNsAeSfcbh5P5DLR6XQ8nowOAd0+hiRJgsFg4OvK8PAwPB4PtFot6vU6wuEw4vE4bt26hXA4jEgkgmQyyS28iUQCKpUKGxsb/OBgNpu3xee5XC44HA7YbDauKHaS24hcOzabDT6fD6Ojo7zGFvUllEPxQa3sx1h6auVFbnKbnJzESy+9hJMnT+Ls2bNoNBqIRqO4ffs2vvOd7yAcDuPWrVt8kQDwUJxGKBRCKBTiH2wgEIDf74fP58PQ0BAcDgdvTCj/+50yCHZDoVDAZrPhpZdewuDgIF566SUe/NRoNJDL5ZDL5fDxxx/jk08+wcrKCjKZDG+b0M3ILXs6nQ42mw1nzpzByMgIlEolMpkM7ty5g88//xzxeJzXyOkF5BYpCk51OBw8i6+XFBhSMEZGRvD666/j5MmTOHfuHCKRCH71q19hfn4ev/jFL5DJZLi112g0cpd2rVbj7SbMZjM/KNH7dzN0qna5XPB4PDCbzVyx69ZnJwsAFVIbGhrCuXPn8Oabb/JeV6FQCJ9++imWl5fxgx/8AOl0GqlUaluCBGXBejwexONxXLhwAdPT09wCYbFYeNzL6OgozxLtJNcRNbsNBAL40pe+hBMnTuDdd9/dZkigiuby/Wg3BebA3UZ0E2Qy8vv9GBkZgcfjgU6nQyKRwPLyMlZXV3k/Ayqotlul3NZupgC60tJCyN1tOp0OXq8XXq8XJpMJer2eP3epVEI2m+WTR97xtldOR0qlElarFXa7HWazGTqdDul0GplMhte96ZWU8VbIKkVB8ZSp163zhpAfnnQ6Ha8+PTw8DIfDgXq9jlwuh/X1dd6fp1KpbDvwUHFMGmNUNJP892T17PZikDSGaL3t5gOAPIYDAGw2G/r7++HxeHgdF6optrKygrW1NWQyGRQKhW0ZWAB4nZdoNAqj0chrmlHwN8mTDl7U/6gTIDlR3JfdbkdfXx+cTidPDCAPikKhQKPRQD6fR7PZ5PVdTCbTNvfjXvd9eirlhRSNvr4+BAIBvPHGG/jGN77B/WKrq6v4t//232JjYwO3bt1CvV7nZtndPjz5giIPDNLr9SiXyzwCvhs2a7kGSsX8KFXc6XTCYDCAMYZarYbNzU0e67K2tsbbB/SK4kIVmo8ePYqxsTE+Jj755BOsrq5ibm4OoVCIj7FOWRz2AnpWlUrFlTulUolSqdTVCow8XqHZbMLlcmFkZAQvvfQS3n33XVSrVayuruLGjRv48Y9/jHg8jlKpBAB8YyYLLikllOlIGw8px6lUCtlstus7lVNSRC9AZSlUKhWOHDmCd955h9cVi8fjmJ2dxY0bN/D973+fj4GdrAuNRgONRgPXrl3DwsIC9Ho9AGBsbAxjY2N8nbfZbBgfH0ez2cSVK1cO6rGfCJpbHo+HV3q/cOEC7HY7gO2uaXKH3b17F4VCga/Fx48fh9PpBLBdcdmrefRUo5X+uMVi4dqYyWRCqVRCIpFANBrF5uYmr+FCadTtbpp+ZzKZeCEyKmhXr9e5NtctGzZNIEr5dTqdsFqtPAiXNNh0Oo1oNIpCodAT1XQJuWXKaDTC7/fD4/HwUuU0znpNLjshXxg0Gg0P1iV5dIN7tR0WiwU+n48HuZfLZWxubnIzfS6X23HdkLueSXZkgajVasjlcigWi13f14eCkUulEu8R1q3QOKCgWqfTybM7qWZUJBLhDXDz+Tw/rO8W/1QqldBsNhGJRLCxscGtFEqlklsurFYrjEZjR4whuQfEZDIhEAjwmkcUvE40Gg0Ui0VkMhmsr6+jUCjw2jetc46CdQ8kYJduhrT0U6dO4eLFixgfH+dF5D7++GNcv34dN27cQLVa5YvBbh8aLSD0uhMnTmBqagovv/wypqenEQ6Hsba2xn3Tcp9jJwyEnaBndjqduHjxIsbGxjA1NQWbzcY351KphFwuhy+++AK3bt3C5uYmt7p0OxQkZjAY4HK5MDU1ha997WvweDwAgFgshhs3bmB2dpYH0JG5v9cg/3qtVkOz2eRxG6QEd+oceRxIuZ2YmMDbb7+N4eFhMMawsrKC7373u1hfX0cwGORWE3kQbqtCR72hyBWeTCZx584dBINBHsBK46ubZEqWutnZWaTTaUxPT3dkSu/jII91mZqawtGjR3HhwgUcO3YMpVIJ6+vruHXrFn7yk59gY2ODNxGkg0Dr507jqVKpoFqt4tKlS5ibm+OBvG63G4FAAFarFZOTk8hms4d+jZJbU8h68u6772J8fByBQIAfrIGtdTqTyeDGjRsIBoP4/ve/j1qtht/8zd+ExWLhByl631KphHQ6jXK5vCcxeU+tvGi1WpjNZrjdbmi1WpTLZSSTSayuriISifCiPLt9WHLTLwBepM7r9WJoaAgul4sXaCuVSryDcqdPKrIoqNVqbrmifk9knWo2m8jn87yrLbVQoOu7afHcDXpOg8EAo9EIl8sFm82GRqOBSqXCy0yT1aUXkU9+Wgxo0en2MUKWS4qHoiD3crmMTCbzkOW3XVkBxhh0Oh3vLM0Y4+tZsVh8KKupW6BnoZIWZEGg33XTs7bWJKOwBLvdDr1ej2KxiHQ6jUQiwS128jCFnWQhlx9t5NVqFZFIBJFIhHd2B9Bxhyv6/I1GI5xOJ8xmM8/SI+tJrVZDsVjkHbUp4aZer/N4H7n1t1qtolAo8Diz56a8kNYKPEiN7uvrg8fj4QV7PvroI/z0pz9FJpPZtsm2fvByDZgCoCYnJzEwMIC33noL58+fh8ViQbPZRCqVwvz8PD8BdbLvmT54p9OJ4eFhHD9+HO+88w6cTidvvkgLyaVLl7C+vo7Lly9jYWGBV47t1jgGOTQuVCoVLBYLrFYrTCYTdDod90HH43HE4/FtMUC9Cllf6KubM43k6xClp05NTWFmZgbxeBw3btzAzZs3effanQIE5QcnMu1PT09jZmYGfr8fjDFEo1HcunULoVCoJ5RBsnxTmrB8s+2GEgTy9GWNRoPR0VGcOnUKfr8fKpUKkUgEly5dwq1bt3D9+nV+WHyc9ZZeUygUUCgU8PnnnyOdTuP111+H3+9HMpnExsYGYrFYRx6+6XOnhBulUolyuYxgMIjFxUX8x//4HxGNRhGPx3kfPvmaTIpQKBTCzZs3EQqFuEXrWebWU8W8UBsAymygXiGkccrL98tvrPUEQxUxNRoN3G43BgYG0N/fD7/fj0ajgXK5zPsfUUZJJ374BC2k1PiLapZYLBaeOk7PHQ6HsbGxwX323XYSehSk/KrV6m0ZNJVKBaVSiRd8kpesFvQOlGVkt9thtVphNpsRi8UQi8WQSqWQy+XathFptSI7nU74fD7e6LNQKPDmp70wvqifEbni6Jm74bnl+w4VMjSZTLDb7dBqtdzSHYlEEI/HeUo9BTA/Sgb0ewprSKfTCIVCSKfTfD3PZrMPWfEOM/I4up0sRhQTlkgk+D4lV25aryFrprxA7bOOradSXshMRmW2Nzc3ce3aNaysrCCfz3PzNbGTpYUi+1977TX09/fj3LlzGBgYQF9fHxQKBebm5jA7O4vLly/j5z//ObLZbEc2ISQ3EMVwmM1mnDp1Cr/927+Nvr4+uFyubRtzKBRCOBzGJ598gvn5eSQSCe5+66TnflZIcSGrCxXt29jYwPr6OqLRKFKpVM+4SXZCXlKAqsF2M3KLCWMMY2NjOH36NAYHB6FUKpFIJHDz5k0sLy/zQo6tcSpkribT9vDwMLxeL86fP49z584hFovh5s2bmJ+fx/r6OqrValdt5K1QzMudO3dQKBRw8eJFvrnr9Xpuyev0Z6dDL/VFo8Jx1WqVFwC9evUqIpEIgGfbY9RqNQwGA9RqNSRpq7XLzZs3sba2dugP3/KAZoPBALfbDa/XC4vFwvdvquN26dIlzM/P83jM6elp+P1+DA4Owuv18uBvefzdXo6jZ24PQG4O+WmnXa0A0uaoM/DY2BgmJiZ41T6lUsmrFy4uLvJ6MXJTeCdNJHk9Copz8Xq9mJyc5IX3SNGTJIl3R6aifa3lpDtFc39a5J8xbcrUOwMAr3lDcVA7VXnsNUhWCoWiJ2KiaBGkmDGTyQRJklAoFHiWiLxuFCH/nuTkcDjg8/l4QUx5JdVut3jSszUaDSSTSZ5qT640lUrVNZlH9HlrNBq+pmg0GlSrVVQqFZ7VSVWYdwt52O296bUUz0h1Xur1OorFImKxGA+n6ARUKhWvukzxmMCDPb9cLiMSiSCRSPCmnm63Gz6fj2dW0Xq0b/f4LBfTqZeCeqgqLJ1syBdPKcFUjM1iseDFF1+Ex+PBmTNneGVHpVKJubk5BINB/OpXv8IHH3yAVCq1LYisk5ArLWq1mmdRHT16FH19fdDpdFxWtVqNdyxdXV3FxsYG0un0NqtLpwz8p4XkRRPH5/Ph7NmzGBgYgEajQTabxZ07d/jJuhd6O7VDbkUgf7tWq+3KcSIP8NPr9dBqtRgYGMD4+Dj0ej0PtFxaWkI8Ht8xTZOgJrIWiwVvvPEGJicnYbPZkMlkcP36dXz88cdYXFzkFodujTOjjUipVMLpdMJut/NNSp5h0y0KsSRJ25QXiu+hjuGVSoWX5Hjc9wMeBMtTmY/jx4/j5Zdfhk6nw2effYbr169jdnYWhULhIa/EYYTukcJDdDod1Go19yJQ8gwl2FBl4q9//evwer0YHh6GwWDgVZpJrrlcDplM5qFCq0/LM1cloiZwFI0stzSQu4QUHLPZjMHBQXg8Hly4cAF9fX0YGhqCyWRCtVpFrVZDJBLB3bt3cefOHdy+fZsvHp14AmrdjP1+P2ZmZjA8PAyr1co103q9jmq1inw+j5WVFd5xmzboXujwStDE0Wq1sFgsGBgYgM/n47U3wuEw79Daq+4iOXRAoEKONAe7EXou2oCsVivcbjdUKhWvNZFIJHin+p3i7chKZbFY4HQ6MT4+jqNHj0Kn06FYLCIYDGJ2dpYrQE9yAu9UKA5Pr9fzpIFuQ17Ph0p9kMWWAt0p7udx3qv1eyqmSd2kJycnEYlEsLKywt3cdKDvhL2MLLmtsqL1Ru5JmJqagsvlwgsvvACn0/mQJZyUw3K5jHK5vGfV0J+ptxHh9/u5f5QWDnIN+Xw+GAwGeDweGAwGrqyMj4/DZDLxFDOqZfLxxx/j1q1bCAaDHe9rpkFNFUBPnDiBs2fPwmw289dQifKFhQWsra3h2rVrPLOqVywuBCl7er0efX19/GRtsVh4EGU4HEY4HO7orLO9gMYFKS7RaJQHv3fj5rMT8rWh9ZAjj4+hEu0GgwFjY2NwOBw4deoU3G43pqamYLFYcOvWLWxsbODOnTuIRCIolUode2h6EshtlMlkeCXhw24ZeBroc6R03WKxyNdYi8XCO4xLkoR4PA4AbUMf5O/pdrthMplw+vRpjI+Po7+/H81mExsbG/jggw8QDoc7qtAhY4xbiSgeiHoM0kG8v78f77zzDo+JojpJrQ1Nm80m78pNVvO96s33TL2NaIGgKoX5fH5bOpjT6cTMzAwMBgOcTidvIEd9WBhjvG/EvXv3eJO9u3fvbssi6YQPfCdIebHZbBgZGcH4+DgmJiYAgGufCoUCtVoNGxsbWFlZweLiIsLhME/j7DXItOt0OuHxeHj3UsouSiQSvFx3p46LvYLGV61WQyqVgtFohNfr7cjYsCdFbnWTKxmtCyL9TKPRwGw2Y3p6Gn19fbh48SI8Hg9sNhsYY1hfX8f169exsrKCZDLZE2UJSH4Ut1gsFrtW8aVnrVarvM9VtVrl2YxGoxEWi2VbRd3Wa1stcWTFs9vtcLvdOHHiBE6fPs03dCqmSW4W4PFSrw8Sej5y9cRiMWxubvKWNdQDjArwyWUjzyKSHyI2Nzdx7949rK6uIhwOc2/Mc8s2ki8MpKHPzc3B6XRidHQUIyMjmJiY4H4u8gH6/X7uO6PUMwqQKpfLuHr1KsLhMD799FOsrKzwhwMO/we9GzTwKdBpfHwcZ8+eRX9/P58ASqUSlUoFuVwOa2tr+OijjxAMBnlzq27eeB4FBb1pNBpeuI+6a5Npt1v88M+CPEDQYrHwxnKdOm8eBX3epVKJZzyEQiGetTc+Po433ngD6XQa4XCYB3ybTCb09/fDYrHgyJEjfF4mk0l88cUXSCQS+Pjjj7G8vIxkMtlT44pOx2SNoODLbqsXRHOC1hAq6+Hz+XjdrXPnzmFlZaWt+4gxxvcyj8cDo9HIm4Hq9XrejDgUCmFubo5nvXXaIZxksLa2hsuXL4Oxra7t1L9QkqRdC4RSH8KNjQ2kUil89NFHmJ2dxcbGBh9Tz91tRDEa2WwW2WwW8/PzMBgMsNvtOHnyJP8gAfA8ebKwAODmyVKphEgkglQqhffeew/z8/O4d+8eYrHYttTXTvqw5ZDyRhlVIyMjOH36NFwuF18MyOISjUaxsrKCTz75BLFYjGv+vZYaLYc2ZPqiOgxUUVd+OuxVGRGMMV7MT94WoNuQPxPV+aEgXbfbzZvfUYXdUCgErVbL3QG0PtntdtTrddy9exeRSAS/+MUvMD8/j6WlJSQSiW2HtG6UYyukvJTLZZ7BR3F43aK8yD9HqsxNdcmcTicsFguGhoZw/vx52Gw2pNNp1Go1HrxLFgRyQVqtVuj1ekxPT8PpdGJwcBA2mw13797F6uoqb49DrqlOCvomWdHnv76+DgBwOBw4f/48DAYDj6ujOkoEGSeq1SpvObGxsYFPPvkEt2/f5nVw9ipW8YndRvI/Gg6HoVar4fP54PV6eQ49fVjVahXZbJb/S/0jCoUCVldXkclkcPfuXYTD4W0VZDt90aBnGBgYwOjoKCYmJnj5cmofXqlUEI1GcfnyZSwtLSGZTG6rkdPpMnhWaMGoVCrI5/O4ceMGlpeXedfSblhU94p6vY5sNssVGFLwus06RWOCDlHr6+u4evUqj6mj02GpVEIgEODXaDQabrlbXFxEoVDAlStXEIvFMD8/z9efTj4wPSs0pyhjZmhoCPl8HhsbGzz+gehkGdG9b2xs4Pr169xq2Ww2MTg4yNvUyBsItrqN1Go1D3JWKpVYWlpCuVzGwsICL5VfKpX2LDD1ICBlq1gsIhQKYWFhAV988QX6+vpw9OjRbYXo5Gt1qVTCzZs3EY/H8dlnnyEUCiEWi+2LtfypYl7ki0c0GoXFYoHJZMLIyAhMJhM3XZfLZayvryObzWJ1dRWpVArXr1/nJf9pQanVal3Tkp1iVZRKJSYnJ/Hyyy/j+PHjGBwc5DEKcnfRL37xC94Bt1Kp9ISv/XFQKBRoNpvcSvfBBx8gGAwim83yAmOdujDsJXRKTqVSvBs7+Z7ptNhtcqL1Z3FxEYlEAmazGf39/fB4PDh69ChX/ilGisZQNpvFtWvXEIvF8OGHHyIejyOdTvMGsr1kcZFD6xIAHrw6MTHBq8Xm8/mOl0nr/S8tLSGXy0GtVsNms8Hv92Nqagrj4+M4d+7ctphO+XtI0laDwWq1yuOjPvnkE9y7dw9ra2uIxWIAtmc3dSIU2kDtWEipPXnyJMbGxriSR89Yr9d5P7D3338fS0tL/IAgD1beS3k8tbbAGOM+r7W1NXzxxRfY3NzE5uYmlEolT1+MRCJceysUClheXkaxWEQ+n+cmyk79gHdCrqWHw2HcvXsX1WqVB+GShkrWp7W1NV7LptMXiL2AMgKi0Sjm5ubw05/+FMlkEmtra0gkEh19mtlraDHI5/O4fPkylpeXsbCwgHw+j2w22/Hu13Ywxnjp/nv37vFy7y6Xiz93tVrldSVSqRQKhQIWFxeRyWR4ocNuVO6eFIpfqFQqyGQyUKvVcDqdCAQCMBgMXZU4QJ91oVDgY0ej0SAQCCCRSECv13NLTLVa5dl88uqysVgMxWIRa2trSKfTWF5eRjQafaigaCePK1LU6Dmy2SyWlpYAgKfUUzwiZazRunPv3j2Ew2EUCoUdi0Xu2T22e2Or1dr2r8qLsGk0Gh6xTdaDWq3Gzfz0INSPZi/7Z2QymQMZJQaDYUf50OJps9m4JUreGlzekfN5NOsqFosHIh+j0fhEo5bGolqt5kWkzGYzqtUq4vH4tkC6vVwYCoXCoRo/TwLNP5vNxuXWbDa3uUKelcM6fqiOlNls5nWmqBM9AF6ArF6vo1KpoFarPZRNshcu2oMaP2az+ZnGjzyOQ61WY2RkBH/2Z38Gv9+P9fV1xONx/MVf/AXu3Lmz7fT8pPLK5XLPXT4Wi+WRY0eSJD52hoaGMDk5icHBQZw6dQq1Wg2ZTAblcplbD8i6cPfuXaRSKYTDYeTzeRSLRZ5iLs9Gehyy2eyBjJ1H7e2EvFaZSqXiMa5kqSTDA2U91mo1xGIxbv0Fnk2Ra7e374mfptFo8B4g8jQo+jlt1t0SAPYo6BnJsiQ3SQMPukuTbIDeM1XvBGn7NBFae2h1axrns0LutUqlwsdcL6WS0yGpUqlsU9iazSYfNxQs2C29evYKuazy+Ty++OILOBwOJBIJ5HI5ZLPZrl2zKei0UCggHo9Dp9PxWBWqK0XWO1qDqJAqVbSWp0ET3Ta2aF8n5W23Oi0kt9akin27r2exvBA7+Qcf+kMtH+hefsCHzfIip51snpdp8bCenNshLzIGPFmvkSelky0vROs420tX7GEdP/LnbR0vxKPWnb0YT51qeWmFglHlMpFvRE/rYjuMlpfWsUMuWHlfvtZ6ZvSvPJ4M2D6GnlQ+h93yQrTKYKc9bac1ej+9KnseIbtTGutePkwnIR/8u038XpLH4yKfIL1QcG0vaLeodCtyq8HjPP9+HqA6HZprxWIRwIODlfyA1U3ykscmAg+s4U9yfa/ua7tZVeQxMsD+y2RPlBe64d2qwvbSByun04O2Dgr54BfKy+PRTUHvT8rTzDMxnraz2/rdC3J61nW6V2REPCqA+3nJY08tL73wIQqeL2JMCR6FGCN7Qy/KsRef+Vk4TPJqG/MiEAgEAoFAcNjoXVuzQCAQCASCjkQoLwKBQCAQCDoKobwIBAKBQCDoKITyIhAIBAKBoKMQyotAIBAIBIKOQigvAoFAIBAIOgqhvAgEAoFAIOgo9kx5YYz9E8bYZcZYhTH27Se89k3G2HuMsQxjbGWv7ukwwRhzMMZ+wBgrMMZWGWO/+wTXahljf8kYyzLGwoyxP9rPe33eiLHTHiGf9gj5tEesPe0R8mnPYZ1fe2l52QTwZwD+8imuLdy/7p/u4f0cNv4cQBWAF8DvAfhXjLFjj3nttwBMABgC8CaAf8YY++p+3OQBIcZOe4R82iPk0x6x9rRHyKc9h3J+7ZnyIknS9yVJ+iGAxFNc+5kkSd8BsLRX93OYYIwZAfxnAP5bSZLykiR9COB/B/B/eMy3+M8B/AtJklKSJN0F8D8D+IN9udkDQIyd9gj5tEfIZ3fE2tMeIZ9Hc1jnl4h5eT5MAqhLkjQn+9l1AI/U7hljdgD++69/omsFAkHPI9ae9gj5dChCeXk+mABkW36WAWB+zGvp9U96rUAg6G3E2tMeIZ8ORSgvz4c8AEvLzywAco95Lb3+Sa8VCAS9jVh72iPk06EI5eX5MAdAxRibkP3sJIDbj7pQkqQUgND91z/RtQKBoOcRa097hHw6lL1MlVYxxnQAlACUjDEdY0wl+73EGLu4y7WK+9eqt75lOsaYZq/u7aCRJKkA4PsA/pQxZmSMvQLgGwC+AwCMseH78hne5S3+CsCfMMbsjLEjAP4QwLf3/86fD2LstEfIpz1CPrsj1p72CPk8mkM7vyRJ2pMvbKWMSS1f37r/uwFs+RWdu1x7cYdrf7lX93YYvgA4APwQW6ljawB+V/a71wCsAFDvcq0WW+lmWQARAH900M+zx7IRY0fIR8hn/+Qj1h4hn2eRz6GcX+z+H9hXGGO/D+CYJEl/vO9/rANhjP0JgJgkSf/6oO/lsCHGTnuEfNoj5NMesfa0R8inPQc5v56L8iIQCAQCgUCwV4iAXYFAIBAIBB2FUF4EAoFAIBB0FEJ5EQgEAoFA0FGo2v3SYDB0REBMsVhkB/F3LRZLR8gnm80eiHyMRmNHyKdQKByIfEwmU0fIJ5/PH4h8zGZzR8gnl8uJ8dOGgxg/Yu9qTzeszW2VF8HBIw+oZuxAxnnHsVMQupCdQCAQdA9CeTmkNJtNAA/q8DDGtn0JduahWgAyeXWz3BhjXGmTJImPH/qdGDcCgeB5Il+DAOz5GiSUl0MIbbqtm6+gPa1Wql5QWgh6doVCAcYYVCoVGGOo1+toNpvyolECQVseZ5z0wpwSPButY2Svx8y+Ki/yEyBtxArFVoywGPwPI7cWqNVqKJVK6PV6KJVKlMtlVKtVvhkBQoZyaIPWaDRQq9XQarXQarWoVqsol8toNBqoVqsAulNujUYDKpUKDocDVqsVFy9ehM1mw82bNxGLxbC8vIxkMimU4Rbq9fo2S51KpeIKoNya1e3Q88vl0Q6lUgmlUtmT40lu4ZTTemDqlbGzE4wxGAwGqFQqvjZXKhXUajX++2dFWF4OGTQBtFot1Go1rFYrVP9/9v7zN7I0TfBDfye890EGfdAlyUymLdtdVd3VXdNWOzOanZHBagUJEPRF0KcFJFxd7AUG0gJX/4CwEAQsFhoII0HQ7EiYO9M1M93VVV22y6RnJr1neO/9/ZD1vnXIzGQ6MskInh9AMJMRhzznidc872MNBgqFAnq9nkqlQr1eP3MLxpMQC67RaMRqteJwOHA4HBSLRTlxxGLSa7JTK71WqxWXy8X58+fp6+ujUChgMBiIRCLyPRrfod5kNNnsl8Fh4+UsKi1qnqSYnGXFRYwbi8WC2Wym1WrRbrdpNptHuncdi/IiLC4mkwmHw4Fer8doNNJut8nlcvusBxoP5CVOzn6/H5fLxfe+9z2CwSDj4+M4HA42NzdJp9N89dVXLC0tUS6XKZVK6HQ6ac06i4hxZLFYMJlMvPnmm8zNzTE2Nsbo6Cg3btzg448/JhKJcO/ePaA3F95Op4NOp8PlchEMBpmammJ0dBSj0UgsFiMWixGJRHruuZ+XdruNXq9nbGwMh8OBxWLBaDQSiURIpVI0Gg0ajUbPy0tsNHq9HovFwujoKFarVVp8D27C4v/RaJRYLEaj0aBarfa8nATiYKnX66WVThweqtUqjUZj32HirKHey1577TVGRkYolUpUq1W+/vprlpeXpexelGNVXnQ6HQ6HA4PBgM1mo16vUy6XpRmp3W6fyQ/4IGKwC3n5/X4uXbrE6OgoFy5cwO12s7i4KBeMnZ0d6vX6mY9jUD+/yWTCZrMxOTnJa6+9xuzsLLOzsxiNRtbW1iiVSnLcHcXEOW2oLS8Oh4NgMMjAwACtVku6kjT2oygKfr+fQCCAy+XCZDJRqVTI5/O0Wq0zZZERysvw8DAul0tafA/GkYk5BJDL5fa5UM6CnBRF2eeaFgdHsZ+1Wq0zN3YOIsZCOBxmbm6OTCZDqVRicXGRVqt1ZIftI13FxQcmlJWhoSHeeustnE6nNGH/+te/JplMEolEaDab0r981hAbb7vdxmAw4Ha78fl8/OIXv2BwcJBXXnmFQCCAxWKh1WoRCoXweDwsLy+TzWZZXl4mn8+fSdnBdxYXocVfu3aNiYkJ3nzzTebn53G5XFQqFarVKtVqlWazCfTuYqK2vLjdbiwWCwaDgWKxSCqVolaryff1qgyeBiEnn8+Hy+XiRz/6ERMTExQKBSqVCru7u2xtbZ30bR476jgfl8vFhQsXCIVC/OxnP8Pn82G32/cp+WLMiM35/fffJx6PA1AoFPa9p9s4mKmnVtjUa7TX68XpdPK9731Pjh8R09Fut9nb2yOZTLK+vs7m5ibNZvNIYzxOO8JgIQ5LoVCI/v5+yuWyHCNHyZEfQTudjgw0DQaDXLt2Db/fz9jYGKlUisXFRdrtNvF4XD7sWUWY2IxGI06nk1AoxGuvvcbo6CgzMzPY7Xby+TyNRgOfz4der2dkZIS9vT0SicQ+rR/OxgSB/QuMTqfDZDJx7tw5rl27xvnz5wmHw9TrdfnV625K9edvs9lwOByYTCZ0Oh2VSoVCoSAtdWdljDwOsea43W6CwSBXr17lwoULrK+vk0wmsdvtZ8aiKTZmq9XKuXPnCIfDvPPOOwSDQSwWC3q9Xr5XuFobjQbNZpOVlRU+++wzGVPWS+NK/fmr3SB2u51gMMjrr7/O6OgoHo9HxnQ0m01WV1fZ3d2l1WoRi8VkksVZCOJVu8qcTiderxefz4fX62VnZ2efNeqoOBb7ebvdplaryYXT5XLh9/uxWCy89tpr9Pf3s7e3R7lcPpMbr1gIHA4HPp8Pv9/Pq6++yuDgINPT0wQCAQwGA61Wi3w+L32GjUaDQCDAj3/8Y0wmk1QC9/b2ejKO40kYDAbC4TB+v5/Z2VlmZmbwer3o9XrS6TR7e3ssLy+zurpKOp3etxh3O2IhFLEbNpuNYDDI3Nwc4XAYi8VCvV5nd3eX1dVVCoVCz20yz4rIpjEajYyOjjI6OkpfXx8ul4tsNisVmFKp1LNlCtRZn16vl/HxcUZGRnj77bfp7+/HZrMBD7KwWq0WpVKJRqNBsVikXq/j8Xiw2+0ytqMX6HQ62O12XC4XNpsNj8dDs9mUFttyuYzT6eT73/8+AwMDzM/PSwVPlCTodDpYrVaGhobI5XLs7u6SSqWoVqv7/k4vI9ai6elphoeHGRwcxO12k06n5RokMtSOgmNTXkSKarFYlJYDn88n3SEffPAB8Xh8n5bbawvFQQ76j202G+Pj44yNjfGzn/1MBlparVaZ2pvP58lkMkSjUYrFIq+88gpTU1M0Gg3y+Tx3795lZ2dnXxr6WUAEGY6MjBAOh5mdneXcuXPYbDZ0Oh2ZTIbl5WWpvDSbTam8dPs4E/cvlBej0YjH46G/v5+5uTlGR0cxm800m0329vZYW1uTJ+SzqsCIeI1WqyX98VNTU/T19eF0OvcpL+VyGYPB0HOxUerPX6fT4ff7eeONNwiHw7z99ts4nU5MJhOKosgMkWw2S6lUIhqNUigUmJqakskXvbIZC+VlYGBAJknU63XS6bT87vf7+clPfsLg4CBTU1PY7Xa53orA3UAgQL1eZ2tri4WFBVqtFolEYp+senXuqQ9S09PTTE1NEQqFHqm8nMqYF/HBiEWiWCwSiURwOBxUKhXMZjN+v59ms8ns7CwGg4GdnR2KxeJR3sapRSweFosFp9NJOBzmhz/8IaFQiJGREaxWK9lsVqa3isFgsVjI5XJEIhHGx8cplUoA2Gw2zGZzzywiT0Jtwh0YGMDj8fD6668zOTnJwMAAJpNJukjW19e5f/8+0Wi0p9xGB6sHm81meSgYGRmR1gSDwSBrdojrzjJCXiaTCavVisfjkVY6YSkW9YB60eIC37l+bDYbbrebsbExLl++TCgUkm4iMU7E4WllZYV4PE4mk6FarRIMBhkcHDzhJzka1AqF2+1mcnKSkZERrly5QrPZJJfLUa/XyeVyOBwOhoeH8Xg8tNttKpUKpVJJvq9arUqrVCgU4vvf/z5LS0sAZLNZYrFYT85B9VokPCzhcJixsTGKxSKlUolIJEIikaBarR5pjOuRHy2E1i6sBpubm9hsNorFotx0bDYb165dw+PxyAc8CxYYsfmaTCaCwSCzs7P8yZ/8CR6PB7fbTb1eZ21tjXq9LgPmjEYjdrudRCLBysoKU1NTjIyMoCgKXq8Xi8XSk5PiUQizv8FgYGJigpGREd577z3m5uaw2WwYjUai0SjRaJSFhQW++eYbmZklqs52K2olRAQIGo1GLBYLoVCIH//4x4yMjDAzM4PD4dhXlE8DmdkosrECgQDBYBCj0Uir1aJarVIul2k2mz27/gjlxW63MzY2xszMDO+8846MkVK7IhuNBuVymVu3brGyskKtVqPVanHu3Ll9v69bUW+6Iutsfn6eubk53nvvvYdcZnq9nkAgIGttiYN5sVjk/v37JJNJrl69yuTkJKOjowwPDzMwMADA+vq69DL0IiJu0+fzEQqFmJ2dZWJigqWlJeLxOBsbG+zs7MjCq0fFsa3mOp2OVqtFoVCgUChQKpWwWCw4HA5sNhuBQIBCoSA1/uMI6DktqLOwzGYzoVCIV155hdnZWZxOJ3q9nlQqRT6f55tvvqFSqciFtVgsUqvVWFpakrVeSqUSJpOJvr6+fQpMryp/6lRyr9eL2+2WgblerxeTySR98gsLCywvL7O4uMje3h75fL4nMtrU/Yl0Oh0GgwGPxyPruQwNDeHz+SgWizSbTaxWa9c/81EiEgmcTicej4dAIIDP55MZaYlEgkQiQa1W64nxokbMH6vVit1uZ3h4mCtXrjA9PY3ZbN4XtyEsUKurq6RSKZaXl9ne3mZ0dBSfz4fD4ZDy6ab1+nEZRSJVfmpqiomJCYLBIPBg/zIajftcHPV6nUajwdLSErlcjo2NDbLZLLu7uxQKBQYGBhgcHMRut2O32xkaGmJ6epparYbFYpG1g8T99BJGo1Fa5ZxOJxaLhUgkIl3Wx/G8x6K8iHiEZrNJMpkkHo+TSqUwmUwyGHVsbEzWNRGm216t+6JePFwuF/Pz8/zZn/0Z/f39BAIBSqUSGxsbbG9v81d/9Vdks1mmpqawWCzs7e1RKBRYXl4mk8nw2muvcf78eWw2G+fOnWNjYwO32y1PjtB7E0OdlTUyMsLAwAA/+clPmJ6eJhQKYTKZ2NraIhaL8Y//+I98/PHHJBIJ4vE4Op2u6wN1hdIi0sKtVitOp5PJyUn+8A//kP7+fi5evIhOpyOZTMqYjl4bB8+Luu5UX18fw8PDhMNhmbmXyWRYXV1ldXWVWq12pEGFpwHx/DabjcHBQS5fvsy//+//+9LNITboZrNJoVAgnU7zd3/3d2xsbHDjxg3y+TwXLlzg8uXL9PX1Sfl0myv2YJ0agHA4zLVr13j11Vd5/fXXZWkKeLAhCzdjq9UilUqRSqX4+7//ezY2Nrh9+zbJZFJm8o2MjDAxMYHX62VoaEha+hRF4dNPP5WWnF6KOxOyNJvNMmMtFAphs9lYWFjgiy++IJlMHsuB4NiUF0D6BqvVKrVaTX7I6t4hvXbKUSM+WBGkJFLGR0ZGpL9dWAfW1tZknYBCoSDNbMLSUqlUZAZAq9WSVizhctLpdDIWppdQBxiaTCb6+/sZHBzE5/PhdDrlGNva2mJtbY3d3V2y2SyVSuWkb/2FEYGAbrcbj8eDzWaTpzqv1ysD4hRFYWVlhU6nQy6Xw2QyMTg4iNls7tm59TyI3k+BQAC73Y7JZJJZaSJWqlcPUPDdIUBRHvROMxqN+ywuwgWSSCTY2toiGo1Kl6vYxLvV9aq2fuv1ejweDw6Hg+npac6dOyfjfprNJolEAr1eL+ePXq+nXC5z//594vE4m5ubRCIR8vm8jJMCKJfL5HI5WYjVbDbj8XhwOp0YjcaHiv51O+q12Ww2MzQ0xODgINVqVVbTFwk7XWN5gQdmt2azSTablUGo6tRoYWnpNu39WVAH6NpsNubn5/mDP/gDaeZPJpP87ne/I5FIcP36dZLJJBsbG5RKJba3t/edFESRMaG8uN1uRkZGGB8fZ3Jykp2dHaLRKEDPKITqiW4wGHA6nVy9epVwOEw4HKavr49kMkk+n+e3v/0tn332GRsbG8RiMela6UbUpxmLxcLVq1e5du2aNMt6PB6Ghoao1WqkUimi0Sh/8zd/Q6VSwWAwEAgEmJ+fx2azdZ15/zixWCzMzs7KsWO1WllaWuLOnTvs7u5SLpePNBviNCHWkkajQavV2tdYsdFokE6n2d3d5S//8i/Z2dlheXmZUqkkWwU4nU7cbreMjek2xD7jdDqx2Wy8/vrrzMzMcO3aNa5du4bFYsFqtbK3t8ft27exWq0MDg5iMBgwmUxEo1H+8i//ku3tbVZWVqQ7X1j0hOt/Y2MDv99PpVLBarUSDofp7+/H4XBIl1EvzUehCLvdbt58801GRkZIp9Nks1m2t7eJRqPUarVjCYI/ttVd+JhtNhtWq1X2ngF6WmFRo1ZevF4vwWCQoaEhbDYbiURC1iFJp9PEYjFZkE5o8urfI1B3lBbpnEcdCHWaED2y/H4/oVCIoaEhBgYGMBgMNBoNGaugzog4mJLerairMLdaLRqNBvV6nVKpRDKZpFgssru7SyQSYXt7m2azKdM11fFPvaLMPi+iaKYoTBcIBKTFLpvNkkqlej64WZyShcVFWAIAGo0G8XicWCxGPB4nkUhQLpdpNBqy9omw+In6U92YxSYyYnw+H0NDQ1KJtdvtwIOYlmw2y8bGBlarlUajIS0woj9YMpmkUqnsq3Mj5mmlUpGZRyJTVN17rpfmoHhmvV6P3W6XXgCHw0EsFpPW70ajcWzWzGOr89JsNnG5XExMTDAxMcHQ0BB9fX0ykFftNupVxMbT39/PhQsXeO211/jBD37A0tISf/u3f8va2hp///d/T61Wk2mtYsIcXBTEgiHkJU5OQnnpNTkK2dXrdbxeL++88w7hcJgf//jHBAIBdDodqVSK3/72tywtLXHjxg22t7dlbAx072Ih7rvZbFKr1YjFYiwvLxONRtnd3ZUFIEUtG5H5YLPZ+P73vy/HgrA+detp+aiwWq0yDfb111+nv7+fQqFANBplcXGRxcVFCoVCzyp56ixHr9eL1+vF4/FgtVqBB6m8n376KRsbGywsLJBKpeTmKzJIRFE/QFat7qZKxGIuzM7OMj09zR/8wR9w7do1zGazTIzIZDLcvn2bv/7rv8ZoNNLf3y97GImaWqLvlfidgDxYxONxVldXmZmZ2XcA7UZF72kQB0tRM2lkZASPx8Onn37K2toa6XSaSqWyL0zkKDlWu7qoQyGsLkLTV6d79lKxIzVC0RD9ZkRdElGyfW9vj2g0Si6Xkz5BtYlf/UEftLw8znLVK4FgB7OzPB4Pg4ODDAwM4HK5sFqtJBIJGem/s7NDoVCQZlnoXsVFjdh0yuUyqVSKcrlMuVyW8Qm5XI5EIkGlUqFSqcisPVFgTCwuIqPkLCHmnzgZCn+82LTj8bg0b4sMrV4YM49DxK04HA7ZMbrVapHJZGSV7mg0KmPrhCxMJtO+L1Htu1KpyPTp0yw3sZaI4NtgMMjIyAh+vx+73S7vvVKpkEqlSKfTpNNpdDqd7GlkMpkol8vSkvC4jbjZbFKv1x+ynPcyIoNPZKF1Oh0KhQK5XE7W1zquuknHtqIJzV2YG61WqyyoJkouiyI/3aTBPw3ieUQRucuXL/Pv/Xv/Hjabjd3dXe7fv89HH31ENpuVQYLC2vKoD/lRUfLqvyWsMqd5EXkaDj6fx+ORKdHvvfcefX19eDweWq0Wn376Kevr6/zDP/wDa2tr+6L4u10OgmazSbPZZHNzk52dHakMi89cWDjVG0ipVJLxZaKc+8DAABaL5YSf5uUhDkfC0jA1NcWf/dmfyWKQ9XqdTz/9lNXVVZaXl2VWWi9aL9VKXDAYZGZmhqGhISwWC/F4nM8//5y1tTXef/99stmszLYSc0ls3iL2RTQdXF5eZmdn59QrfepeVh6Phx/84Af86Ec/wmazSUt3q9ViY2ODzz77jDt37hCLxWi1Wuzu7gJIRaZer/fcGHlexBwzm82Mj4/LGLx6vc7S0hL3798nn88fa/mOY1NehBXh4ElQ/Fto771U/VSg1vYtFov0tYuS/rlcTpbdftKzqzOWOp2OjHNptVpysPSSti/8qCJAd3h4mOHhYYLBIG63WxaPEi6UdDpNPp+XmTm9gpg/Qtl/1Dg5aK0DZC0JodSI9501xOZrsViw2+309fXh8/lkbEI0GiUSicgDlHA19iJmsxmz2YzX65W9nIQc9vb2ZLq4usmietMR7n2dTketVpMduMXBS7zvtKJeN91uN36/X84tsY6KCuaZTEa6xQ7OnyetL0LR69X4w4OItdrlcuF0OuVhK5fLScsLHN/YONY6L7VaTVbYjUajtNttgsEg5XKZtbU1WbtEXRjqNE+Cp0VMfNFvZnBwkP7+fpaXl7l16xarq6uyqJp6Qhx8djG5AOkuCQaD+P1+SqUSCwsLLC4usra2Ri6X6/rePeJ53W434XCY8+fP88//+T+XKeaNRoObN28SiUT46KOP2NzclM/dS+MH9pcuf1IGjDqoV1g0RbGxeDxOJBKRgcwHrZy9Ii81wiJlNBrp6+sjFArR39+PyWTim2++IRKJ8Pvf/57NzU3K5XLPxwSNj48zNzfH66+/zk9/+lN0Op0ssvbRRx8Rj8cpl8u0Wq2H4n7EYVN8Fy5LkWnTTRZz8WzCkqIoCoVCgXg8zu3bt/nHf/xHmdqrfv/jUM8lnU5Hf38/MzMzBINBeZhS/45eGmPq2mXT09P09/fLg/nKyoqsyizS8Y+DY7W8tNtt6acX1QnVfY9EnIIwe/fSyRkeWF5sNhsWiwWj0Uiz2SSdTj/03PBoxUX8XJT0FkWlTCYT2WyWTCYjLQ8iHa1bUT+v1Wqlr6+PwcFBwuEwTqcTg8FAtVolEomws7MjI//VPuhufv4n8bjxof6/sNKo6wGJ4F5xCjrsd/YSwuUhToWiZkcqlSIWi5FKpchms/K9vSwLp9PJwMCAVOJKpRKpVIpcLkcsFiOTyTzWSicsFsIaU6/X92WRdBPqz1ldmE9kCYmCc0KxedJ+pLaKC0ux1+vFarU+5N5Vv7/bUWeuiZhEh8MhFVvxddwHymMP2DWZTDKVV0wAdddpdfO4XkN8cOIkKNLw4vG4TGcVC4NAnR6rKA8ahtntdt577z1mZmaYnp6m3W5z7949vvjiC1ZWVshkMl1dXEu4FG02G06nk8uXL/Nnf/ZnDA8P09fXR7Va5c6dO+zs7PB//p//J7u7u7IejjBdngXEuFAjFknxWqPRkApLuVyWwXNiromyBeLULA4TvUKn05GHhrGxMd577z2GhoYwmUzk83kWFhbY3NykUCjITuPdOm8OQ11AzOfzMTk5STAYRK/Xk0wm+fjjj1lYWCASiVCpVPZt1upYmbGxMaanp2Wdku3tbW7dukUkEqHZbPZEBWvY74I9mE30uPeLjM/h4WH8fj9XrlzhlVdekaUcRDC0aB9QrVbltd2KOpN4dHSUubk5JicnMZvN3Lx5k93dXUql0iOteEfNsdZ5ERNCfMgibkNthuxVxUWgVtiEZlqpVB65YRw05+t0OmlxCYfDzMzMYLfbqdVqxONx2fFVWF26NaNELJZGoxGXy0V/fz/nzp3D7/djsVhkjMLOzg6Li4vEYjEKhYLsMN3Ni8HzoK7dIpQXYF82X7PZfCj2RVTCtFqt8uePqivUrag3XVHXZXR0lEAgAECtVpM1gQ7WwunFdUhsyDabTWZZdTodisWiLCAmStYfdJ2JzVl0CjaZTLTbbQqFAolEQsbrddPcU2e5wn43kl6vl9bxJz2TuE7UywkEAoRCIUKhkKwboygK1WpV9qwTcTTdPtaEDEUxzEAggMfjAaBQKMiYoZfxjMey24kHFLnyooGg0+mUi+1ZMPULRIdf0X9IFDESVhn4rvicSA+enp7G6/XKuhQ+n496vc4XX3zB8vIym5ubbGxsUK/Xu/b0KJRXYZqemZnhe9/7HhcuXGBkZESeEjc3N/nNb37Dzs6OXDiBnjjxPQl1jQjREdnn8zE2NobJZNqXNQEPqsi6XC70ej2NRoOJiQkcDgdOp5NEIsHIyAjBYJDNzU22traIRCJsbm4CTw5IPO2IdcftdjM7O8vFixe5ePEizWaTr7/+mu3tbW7fvk0sFts3b7p5M3kU4nmElU1UZu50OqysrHD79m0+/vhjWc/l4DpkMBgYGRnB5/Nx9epVLly4gN1up1wuS3d/t7ipxR5Tq9UolUrEYjG2trbw+/2yarBer+eVV16RQbv3799/rEIv+vGZTCamp6fx+XxcunSJwcFBxsbG8Hg8dDod8vk8q6ur/O53v2NhYWFfLE03yO0wOp2O7FMoUs5LpRJLS0usra3JjLXj5lgtL+KDFp2kD3Y/PguoLS/ipCviD9Qbk9rPajabGRwcZHBwkGvXrjE8PEwikaBQKLC0tMTHH39MqVSiXC53fRl8kSZusVhkMb/x8XE8Ho+0MCUSCZaWlmQL+nq93nPZRU9C1EwSNYPm5uZk2XZA+uobjYaM72g2m/tOzYFAgOnpaYaGhrBardTrdQqFQtedoA9DKHmiw+/AwADpdJqtrS3W19eJRqMkk8mebQMgEJZMofC63W5KpZKs7L2+vk6lUnkou0is26I43dDQEENDQ8ADy5UIBD/uTJKjRrhTc7kc6XQau90uux+Lhq8XL17E4XCQSqUeGSMmXNSiu/bly5cZGBjg0qVLDA0Nyb5JYm2Ox+MsLS2xt7cnA5y7RV5PwmAw4HA4ZAymUAyj0ai0MB33sx77rnewEJ3a4tIrH+SjUC8GYhEZHh7m1Vdfxe12k81mZQ0Ci8XCwMCALKYlOk+LeJdsNstXX33FxsaGrKgq+pN0owzVbg69Xs/8/Dzz8/NcvnyZa9euSf/63t4ev/nNb9jY2GB1dVXWDejW535WRAn3cDjM+Pg4oVCIcDhMIBBgfHxcmvZbrRbVapVGo0Eul5OB8iKVXq/XEwgEZOBquVwmn8+TzWapVqs9MxdF4GRfXx9XrlxhcHCQvb09dnZ2uH79Oru7u/J5exkxv0wmkwwi9fv9snmr2WzG5/PJprnwnUtxdHQUj8fDq6++Sl9fH0NDQ+j1em7fvs3u7i737t0jHo/LooinXZbi/kS23b1796Ti6nK5pMsoFApx7do1wuEwExMTj3XrK4qCx+ORsnK5XHK9ErEey8vLrKyscPPmTW7fvr2v3kk3ow7UtdvthEIhnE4n8XhcxvZEIhEajcZLORi8tCP7o5SXs4LojxEIBJiZmaHRaHDv3j2p3Xu9Xi5duoTX6+X8+fO43W4mJiYwm81sbGyQTqdZWlri5s2bxGIxyuWy3Pi7EbXyYjQaGR8f5/vf/74MSBaVKpPJJF9//bXMLqpWq9Li0svjRy0fi8XC+Pg4b775JmNjY8zNzeFyuejr65P1kkTRx1qtRjQapVqtSt+z2GBcLhfAvhpLpVJJdg3udtT1gbxeL5OTk9hsNpLJpGw0GI/HD62Q2ksI076wvHg8HqrVqlRSRKdj0W9OVEq9ePEiwWCQt956i2AwKDf4jY0Nbt26JcsTiIDM0474nEXMydbWFjqdjunpaaampmRdFtGlvlarMTU19dh4TBGHaDQapQxFtqfIrF1ZWeH3v/+9LGNxnIXaXhbqZxDrks/nw2azkclk9vWXO86qumpemvIiHkQEElarVRn70YsIs71IEW82m3g8HmZmZnA6nbhcLvkh2+12wuGwLMEtTgiVSoUbN24Qj8dZXFwknU5Ll0k3TgS1NQogHA4zMjLCtWvXmJ+fx+fz0Ww2SSaTLC8vc+/ePe7fv086nZZWKujuReBpEPIRTeSmpqa4du2atJxkMhlWV1fJZrOsra1RrVZl9oz4rg6ofFQ22+7uLtFolGKx2PXyFNbdUCjExMQE58+fZ2RkhFwux/Xr19nY2CASiZDP53vKdH8YImA0n8+TTCaJRCJ0Oh2GhoZQlAfF+0RavXi/sCY4HA5GRkawWCxEIhGy2Sw3btyQa1E3VvQW9xqPx+l0Oty4cYN2u83Y2Bijo6MyYFdYYh5nKel0OtJ9lkgkaDabLC8vy807m82ytbXF1tYWqVSqJxQXgfqAYLVa8Xg80m0NyBAR0W4Djve5X7ryIuI+qtUqtVptX/BuryFSwkXVU5fLRSAQYHR0lNnZWeA7c63YuLe3t8lkMty/f18WY9vb25NdXnshNVFM6LGxMV577TUZFCjigpLJJNevX+f+/fssLi7uM2334jh5FJ1OB6fTSSgU4ty5c1y9epV6vU6tVmN3d5fPP/+czc1NPv30U0qlknRDqlOgD9tg1K93u0zF84p01dnZWYaGhigUCrJhp7DciXigXkbEsKiVl2g0Krvah0Ih5ufnH3mdsESYzWYajQbffPONtLpcv359X0HIbqPT6ZBIJCgWi3g8HpmxGAwGZXCzsFapEc8qsviEhWV7e5tsNsvHH3/M2toaGxsbxGKxfXGNBz0N3e4+Em4ji8Ui67sIA4RoBVQul1/KvbwU5UUM9mq1yvLysiw0Jsy43b4ZH0QMVBEgt7KywldffUV/fz8jIyPAg1O1KCJWKpXY29ujUChw9+5d0uk0N2/eJJVKkUqlZOpZNy4Y8HAKeCAQwGazMTExwdTUFH6/H4PBQD6fJ5VKsb6+zo0bN9jb25N1gLr12Z8H8ayi8/GtW7dwu93S1bO7u8utW7fIZDL7ypmrTd0Hvx/2d7oZEWAqXGMDAwOycWc0GpV1leBsZKcJFEWh0WhQqVRIp9Ps7Oyg1+vp6+uTSgrwSMtAvV5na2tLWq7W19fJZDJdreiq3UedTkfWiYIH67SI7zmovKjbsIgmqDs7O7KTe6FQkJYX0XFavV71itIC38W8iIxY4UGw2WxUKhWZlCOU5652G6nrUQjl5fr167KxVywW68kATPEs+XyeUqnErVu30Ol0XLx4UVb8DAQC1Go1kskk+XyeTz/9lFgsxscff0wymWRra4tKpSJNmdD9qawiwFRkgczPz3Pp0iX6+vowGAyUSiW2tra4d+8en3zyiYzJENf20hg5DPGsot/MZ599Jl08oqry5ubmY60nZ0VO6tLser0er9crU8j39vbY2Njg3r17FItFgK51tz4r6o26Xq8TjUZZXl6WTfRErR9gn2tRZKuVy2Vu3rzJzs4OH3zwAevr67INR7fOQ7F2inivQqHA4uIie3t7LC4uMj09zZUrV+TaLDbger1OJpMhn89z8+ZN6aoV9W5Ed21hFe/VmE51DSWRgOL1enG73bK6ruhY/rL2qWNVXkS1wnq9Tj6fp9FoEIvFSCQSsrx0r33IasTmImJWRK0XYXITjRozmQx37twhm83KlGh1jEcv0el0KJfLZDIZ7t69S6vVwul0yjokOzs73L9/Xwai9vL4eBKimmUymQSQcWKisjCczQB4NWJBFVYVYdaPRCLs7u5Sq9W6Lq33qEkmk7J2SafTwW63y2BLkYUESEUnk8lw+/ZtIpEIiURC9j3q9nF20BrQ6XTI5XLs7OzIcAabzYbb7ZbKS6PRkF3aV1dXpTVdzEW1Zbjb5XMYwhAhQiGi0Si/+93vsNvtWCwWUqkUe3t7ZLPZfW1vjvWeDjNn2Wy257J1tVotWWNifn6eiYkJ/sP/8D+kUqnwF3/xF+zs7LC0tESxWDwSq0u5XD6REeNyuQ6Vj1pbFTVvPB6PNLupTZKitkCj0Thya1Q+nz8R+djt9g7wkBnV4XBgNpulmVHtVqxUKrLXyEF303FRKpVORD4Oh+Ox40csnuoaQeoF8mW6QIrF4onIx+l0PtX8MpvNWCwW3nrrLf7JP/knxONxFhYW2N3d5bPPPqPZbB5rLaRCoXDqxo8a0VVaxLz4/X7C4TCDg4O88cYbGI1G2u02+XxeWvk+/PBDGYBaq9VeqK7SSYyfp927RKV3YY1SZ2AB0hollBh12fujUFhOau8Sa/OzoK6ab7VapeIrXJQi3ucoOWxtPrau0jqdjlarRSaTYXd3l6+++oparUYsFpOpdr2qpaoRm5CoxZHP52XEtjhZC6WlG9rLPw8Hn0cMcJG2KxDZWeoCUb0mi6NAk8l3iPnVbDZJJBLcvXtXrjmpVEq+5ywjDkn5fB6j0ShrAomGjGItKpfL3L9/n0wmI/thvay015NGeAjEWqw+PIgmpwczY3tdJgdRx+8Ir4EYG0JGLzM+8VgsL2qE1UFt1hWnyaPitFpeBI8Knjz4IR9nzMJJW14O8rig0oNBbi9rEpxGy8tp4rRaXgRinIj+aQezro6b0255gYfrbIkTs9oiJTZq8f2oOM2WFzVPCqo9jvWomywvah5VKf84lNyXbnlRI1xIZ9lHL7R42L9hPyoq/azIRTz/4xS6Xoz30TgexBhqNBoywBvOVpD3YRxsQ6L+2UHU69BZld+jNmWNhzlpOR278iImQC8V63keDsrhUa+fFc6aoqbxcjirm+2T0Obb06HJ6ekR1jv1/182L71I3VlHk8MDNDloaGhodC8nvYYfGvOioaGhoaGhoXHa0AILNDQ0NDQ0NLoKTXnR0NDQ0NDQ6Co05UVDQ0NDQ0Ojq9CUFw0NDQ0NDY2uQlNeNDQ0NDQ0NLoKTXnR0NDQ0NDQ6Co05UVDQ0NDQ0Ojqzgy5UVRFJ+iKP9OUZSSoiibiqL8s2e41qwoyr9RFCWvKEpUUZR/cVT3dVrQ5HM4mnwejyabw1EU5b9WFOUrRVFqiqL822e89keKonygKEpOUZSN47nDk0WTz+Fo8+twTqt8jrLC7v8E1IF+4Arw/1MU5Wan07n7FNf+OTANjAEh4ANFURY6nc6vjvD+ThpNPoejyefxaLI5nD3gXwE/A6zPeG0J+DfAXwL/7yO+r9OCJp/D0ebX4ZxO+agb5D3vF2D/9uHOqX72F8D/+JTX7wE/Vf3/fwD+96O4t9PwpclHk48mm5ciq38F/NvnvPYPgI2TfgZNPi9dJtr86lL5HJXb6BzQ7HQ6S6qf3QQuPOlCRVG8wMC373+ma7sITT6Ho8nn8Wiy0dA4PrT5dTinVj5Hpbw4gPyBn+UA51NeK97/rNd2C5p8DkeTz+PRZKOhcXxo8+twTq18jkp5KQKuAz9zAYWnvFa8/1mv7RY0+RyOJp/Ho8lGQ+P40ObX4Zxa+RyV8rIEGBRFmVb97DLwxICeTqeTASLfvv+Zru0iNPkcjiafx6PJRkPj+NDm1+GcXvkcYWDP/86DiHQ78BYPzEMXvn0tDHSA8GOu/R+BDwEvMPvtA//8pIOVjjjwSZOPJh9NNscjHwNgAf6/PAgmtAAG1esd4N3HXKv79v2/ADa//bfppJ9Jk89LlY82v7pQPkf5gD7gr3mQWrcF/DPVa+8AG4DxMdeaeZCOlwdiwL846Q/sGAaAJh9NPppsjkc+f/7tAqr++vNvXxv59tn9j7n23Udc+9uTfiZNPi9VPtr86kL5KN/+gWNFUZR/CSQ6nc7/fOx/rAvR5HM4mnwejyabw1EU5Z/z4JT43530vZxGNPkcjja/Duck5fNSlBcNDQ0NDQ0NjaNC622koaGhoaGh0VVoyouGhoaGhoZGV6EpLxoaGhoaGhpdxaGNGR0OR1cExBSLReUk/q4mn8NxuVxdIZ98Pn8i8nG73V0hn1wudyLysVqtXSGfSqWiyecQTkI+2tw6HJvN1hXyKZfLj5XPUXaVlijKg7/3pGDgp32fhoaGhsbxoyjKU6/Hz/LebuRRzyb2LI2T50iVl0d9sAdyvtHpdI99r4aGhsZJcHCdUhRFrlVnhUetye12+0FNjW9fUxRl3/t6UYER46Ddbu/7+WEy0HgY9XyCh+X2ohyp8nLwRnU6HQaDYd/rjUZDvk+9UBy8XkPjURwcI9rC8WIcnLNnGbG4qjfrs85Z3agPU14PykAbL4/nOMfOsbiN4IHG3tfXx6uvvorVasXhcFAoFPjoo4/I5XJSiVEPEE2JeVhbPWunv0chTkHtdptmsykXC0VR0Ov1+75rPB0PVatULdZnSY5irpnNZgwGAyaTCYPBQKVSoVwu73tPr/OoU7LT6cRoNNJqtWi321QqFer1+kPv73bEPNDpdOj1ekwmE06nc99caDQatNttyuWyXIfUFhqdTnem5s5hKIqCxWLBYDDItblSqVCr1Y7sbxyL8iIGtcViYXh4GJfLRSAQIJ1O880331CpVOSHr36/hsaTeJzpVkPjeVEUBZPJhNlsxmq1YrFYAKjVatJtcpZQHwwcDgdWq5VGo0Gr1aLVau2znvcaOp0Ok8mEzWbD6/Wi1+sBaLVa1Ot1ms0mrVYLnU5Hq9Wi0+nQbDYfcjGdZcRhyGw2YzKZMBqN6PV6KcOjGjtHorwctJiID1R82e12vve971GpVCgWi+zu7vLZZ5+RTqelNntwU3qUuamXJsyjejXodDpsNpucMPBgAW21Wid4pyeHONWYzWb8fj92u53x8XHMZjPVapVms0kkEqFUKpFOp6lUKmdeoVHPwce5hBRFwWg0YjQa5YZdrVYpFAq022053npZlkI2JpMJk8nED3/4Q6anpxkcHMTv9/Pb3/5WWonT6XTPK8vCXdZut9Hr9fj9ftxuN3/6p3/KxMQE+XyecrnMr3/9a27cuEG1WqVarfaEXDqdDnq9Hr1ez8jICJcvX2ZwcJA33nhDrsXNZpNsNkulUmFtbY1isUixWKRWq7G2tkYymaRQKFAul+XvOouIvd9kMnHlyhWGhobo6+vDZrPx4YcfcuvWLarVKrVa7YXHzgsrL48L0hUnllarhclkYmxsjFarxYULF3A4HNy9e5dCobDPAtPtk+B5OGi6F+ZrsZiIE89ZRq/X4/F48Pv9XLx4EZvNJhcORVFIp9OUSiVp5j+L4wj2K/cHFX21X77T6WAwGLBardjtdtxut9ycgDMx3oQ89Ho9ZrOZ6elpXnvtNSYnJxkYGCAWi3Hz5k1qtdqZWp/Es9rtdnw+H6+++ipXrlwhlUpRKBRYWlri3r17+6wv3SwX9TMYDAa8Xi9zc3NMTU3x3nvvYbFYaLfb1Ot1KQOfz0c2myWbzVIul+VaVKlUaLfb6HS6MxkHo3ajKYrC8PAwMzMzhMNh3G43q6urLC0t0Ww2j8R99ELKy6MsLuoTX7lcZmNjA7PZTCwWw2azMT8/z+joKIqiEI/HWVtbo1Ao0Gg0aDabZDIZORjq9XpPWWCEbAwGA2azGYfDweDgIFarFZ/Ph8Viob+/H6PRCEC9XueTTz4hGo2SyWSkVv8o61QvIZReu91Of38/IyMj/PEf/zFer5f+/n4ZBN5qtXjllVfIZrP8H//H/8GNGzdkbEyvyuYgYi6ozdYGg4H+/n7MZrP8mXCBCL/zhQsXuHjxIj6fj1AoxN27d/m7v/s7yuWyjGmA7t6YDkNsLk6nE7fbzcTEBHNzcwCk02lyuRzFYlGuQb0qB9j/GYu543A48Hg82Gw2zGbzmThItdttCoUC6+vrdDodhoaGsFqtmEwm9Ho9NpsNj8fDxYsXqdfrVKtVGo0GQ0NDRKNR7ty5w/r6OslkknQ6DZyNYF7xjAaDAaPRyNDQED6fj4sXLzI9PU0wGJR7f6VS4f79+ywtLb3w331u5eVxH4haeanVasRiMTweD+l0Gr1ez/j4uNROU6kULpeLZDJJsVik0WigKIr0rVar1Z4KghJaqU6nw2Kx4PP5mJmZwe12Ew6HcTgcDA8PYzKZ6HQ6lMtldnd3qVar5PN5ms0mQM8HpwrXhdFoZHBwkJmZGX7+85/j9XrlRiI25lwuRz6f55NPPuHOnTs0Go0TvvuXz0HrnaIo+P1+HA6HfE+pVJLzq91uEw6Heeutt6RyaDQa+d3vfifHmHAf9CpCXmJDGhgYYGRkhGg0us+Sd5bGk3rttlgs2O12GbMglBdhUe/WA+RBhIVbfJXLZSKRCAaDgfX1dZlsYrPZGB8fx2q14vf70el0cp3yer0kk0l0Op38uVBeoPcVGHUZFLPZTDgcJhQKMTExQTgcxul0YjKZGB8fly7+5eXlFx5Dz628POoPu91ufD4fZrMZm80mI7MjkQh/8zd/Q19fH2+//TYul4v+/n4CgQAOh0OecEQMQzqd5t69eywsLFAqlchmsw/FxHTT5BETw2w2Y7FYGB0d5ZVXXiEYDDI3NycXUJPJhMvlkpaFSqXC9PQ08GBg2O12KadGoyE3o16ZGEJOItJ/enqaP/qjP2JkZASr1Uomk+Fv/uZvyGazMl7j0qVLuN1urly5gs1m4+uvv5Zafa8uGgctnSaTCZ/Ph91uZ2xsDLfbLeUCDyxU9+/fJxaLAQ/m0Kuvvsrc3BwOhwO3243dbkev10uTd68ixoTNZsNqtXLlyhXC4TDBYBCAZDLJ+vo60Wh0n+WlVzm4lhqNRqxWK5OTk4yPj+NwOGi1WiwuLnLv3j02Njaku79XMiGFQt9oNCiXy+Tzefb29rh9+zY2m43+/n5cLhder5dqtUqpVKLT6eD3+zGbzbhcLiwWC2+++SbhcJgvv/wSi8VCPB4nEon01BqtRljpdDodLpeLN954g2AwKPe2cDiMx+PBbDaj0+mk8re1tcXXX39Ns9mUVt7nkc9zKS+PUx6E+VWcZBKJBF9++SXRaJS//du/pa+vj1AoxOjoKOfPn8fpdBIOh/fFyKRSKfL5PO+//z6lUond3V0ymcxzP+BpQDyf2GRmZmb45S9/SV9fH5OTkzLGRT3IdTod1WqVqakpFEWhVqthMpke8rP2kmUKHsjKaDTi8XiYnJzkj//4j3E6neh0Ovb29vjLv/xL1tfX0el0OBwO/qv/6r9ifn6ey5cvc+HCBTKZDCsrK/uyRHpJPgK1f9loNDIwMEAwGOSdd96hv7+fN954A6/XKwPo3n//fe7fv4/P58PhcHDt2jVmZ2flmBOB4r2yIT0OYfm02Wy43W45bgKBAJ1Oh2QyyerqKtFolHw+37Mbz0HEXDEYDNLKMD09jd1ul8rLZ599xubmJsVisefGinBV12o18vm8DGNwOByUy2X8fr88RO3s7NBsNlEUBY/Hg8vlwmw209fXR7PZxGg0UqlU0Ol07O7uHlozphsRyp6YS3q9HqfTyQ9+8APC4TCvvPIKHo9HpknDA/mGw2GGhoakcletVve5qJ+V51JeDp78RHrh+Pg4b775JiaTCYvFIlPJ6vW6DHz74osvWF9fJ5VK4Xa7pUlJTAaRnjc+Ps6rr76KyWQiGo3KOgPqDem0nxCFfOx2u8yUmZubY2ZmhuHhYRwOh3QBHTT7i4ExMTGB0+kkGAySz+eJx+PkcjlWVlZYW1uTgWK9sMCKzdjpdMpAL5vNRrlc5uuvv2Z9fZ10Oi2zHDqdDtevXyeTyXDlyhVCoRDDw8NMT0+TSCRIJBJAbygvB7PSDAYDdrudUChEIBDg1Vdfxe/3c+nSJbmgGgwG4vG4VHSNRiNer5dgMIjD4ZBj7CwhXLaTk5OEQiGmpqYYHR2lXq+zu7vL8vIyd+/eJR6P96zlTo16LTcYDPj9fvr6+hgZGWFoaIhGo0EmkyGVSpFIJOTc6wXEeiPWHI/Hw9jYGK+++io2m41gMIjFYsHr9e5zF9lsNiqVCplMhkqlQn9/vzxgGQwGQqEQFy5coFarsbm5Sa1Wk8HwvTDfWq0Wer1eBvwLpWRmZoaBgQEsFosM/xAWOnXxRxEo/6KyeKGAXfHhi/iN8+fP84tf/IJarUYul6NarcrYlXQ6TTab5Ve/+hUOh4PV1VV8Pp+01NjtdoxGI+FwmL6+Pubm5vB6vbRaLZleVS6Xu+okJHyiLpeL4eFhXn/9dX72s58RCAQYGxuTH95BH7L4kHU6HRcuXJC/p9VqsbOzQyKR4De/+Q3lclmmCPfCQivk4PP5uHbtGjMzMzgcDhKJBH/1V3/F5uYm8XhcLgTVapWPPvqI27dvMzExwaVLlzh37hyFQoFbt24Rj8e7Xiawf4MRNSbMZjPBYJDXX3+dkZERfvKTn+D1emXMlMiQiMfjxONxqtUqRqORvr4+xsfH8Xg8+4prnfaDwIsink8EXl66dImJiQkuXrzI8PAwS0tLMsPo888/J5/Py+t6YQwdhljHDQYDAwMD8gAQDodJp9Mkk0mi0SixWExaFNTrcDeOHbHWiLo1NpuNiYkJXnvtNf7j//g/xuVyEQwGZQyLmE+1Wk0Wr0skEjQaDYxGo3Trm81mxsbGsFgs1Go1lpaWSKfTFAqFnrDAqDOIXS4XAwMD/PCHP2RkZIRr167h9Xpl3RuRJCAsMGrDg9pF/bzz64WVF+Fzd7vdMrApm82yuLjIxsYGpVJJprTCg8DBZrPJ6uoq8XicfD6Pw+GQlS1FoJxerycYDDI4OMj4+DjJZJLt7e19C/lpRcjFaDTKGJdLly4xNTVFX18fFotFBgMWi0VZjVBYnlqtFtFolFqtJqsUOp1OmaFkMBiYnZ2lXC6ztrZGvV6nXq93daqwiAkyGo0Eg0HGx8fR6/V8+OGHbG9vs7m5SSKReMhaUCqVUBSFWCzG7u4uNpuNyclJdnZ2ut6ldlCpFfEpQkkJhUJcuXKFQCCAx+PBarXKBTaRSFAoFPj666/Z29uTBwnhfjQYDOh0OorFIrlcjlgsRqlU6tkYj06nI626brebwcFBBgcHMRqNNJtNdnd35ZokKqh200HpKNDr9fh8PmmZM5lM7O7usre3RzKZpFKp0Gq1ul4uwnqpKIq0XIbDYebm5pienpZrrQjWFuU8RIE6m82Goigy0eT27dsYjUampqYYGhpCp9Ph9/sZGxvj8uXLrK+vE4/HpScCunONFoh5NDExwfDwMOfPn6evrw+j0UitVpNxUdFolGq1ytjYGF6vF6/Xi91uly5bkbn2vArMkVhe7HY7fX19+P1+XC4XCwsL/PrXv2Zvb49UKkWr1ZJBqNlslk6nIwOZbDabTA3W6XT89Kc/5ZVXXuHy5ctcunSJRCLB7u4ui4uL7OzsdEUarNoU6XA4uHr1Kv/kn/wTBgcHCYfDFAoFYrEY2WyWjY0NmVUjCmaVSiV++9vfkk6nGRgYwOl0Mjs7K2Us0jvPnz/PRx99RDabJZVKUSwWAbouS0TIy+12EwwGOXfuHK+99hpLS0v863/9r4lGoywuLsrFxGAwyEUgl8tRKpVYWlrCbrczMDBAOBxmY2ODTz/9dJ87rts4WN01GAwyPz/P9PQ0b7/9Nj6fj/HxcYxGIwaDgVarRbFYpFQq8dVXXxGJRKTFamBgAK/XK9t1CAUmlUpx69YtlpaWyGQysv5CN8rrcYgxoNfrCQQC9Pf3Mzc3x+TkJGazmUqlwu3bt/niiy9YXl4ml8sBvWHif1pEBl84HGZiYkKWbrh79y43b95kc3OTfD7/UBxDt6EeC0ajkatXr/L2229LK5zFYsHpdNJqtWQl+Gq1us/l4fF4cDgcLC4ukkqluH79OpFIhH/6T/+pzOAbHR0FwOl08umnn3L37l1ZnA26c36pU6KDwSBvvvkmExMTvPfeezI2Kp/P8/HHH7O5uclXX31FJpPhl7/8JefPn+fKlSv4fD48Hg/9/f00Gg1isdjJWF7Eh2mxWHC73ZhMJur1OpVKhVwuR7lcfuwAFz8X6XdCKRF1KMTPRUGbbipJLcyIo6OjDA0NMTY2ht/vp9PpsLe3RywWY3FxkXw+z+7u7kOWp0KhwMrKiiyEZLfbKZfLBAIBJiYmCIVCtNttfD6fnCiKohCJRLqyTLU6Nmh0dBSn00k+nyedThOPx8lkMvLUos46UwetZrNZ4vE4oVAIp9OJ1WqVPVlE+m+3oA6eVBQFu92O1WplYmKC8+fPMzo6Sn9/Pw6HQ1oOkskk5XKZvb098vk8t27dIpFISLei+H1q/3Oj0SCfzxOJRMhkMrKOh1rGvYLYnEdHRxkeHpap5CJoMB6PE41Ge8YFexgH3T3CjG8ymfB4PDidTur1Ovl8nlQqRTKZPNKeNKcBj8eDx+NheHhYhiqIWA2xBu/s7FCr1SgUCvuUFzHn1tbWiMfjxGIxUqmU/Lc4zAvXitvtxu12o9PpjrQ8/stEbajw+/0MDQ0xNTUlrZeNRoPt7W2SySQrKytsbW1Jy5SohyP2JnWs0YvwwsqLMDWOj49jt9vJZrMkk0l2d3elJUB9ghH/VveMUPc8KJVKFItFKpUK1WpVmrSz2azcwE7riUgoYCJA90c/+hHvvPMOY2NjjI6Osrq6yhdffMGtW7f4u7/7O6rVKpVKhaGhIbmgfP3116TTaRYXF/dF9Yu4oB//+MdcuXKF+fl5zp8/z6VLl6jVanzzzTcsLy9Tr9e7rry7SFMcGBjgnXfewWKxyNTMxcVF6Wc/+LmL52u322xtbdFut5mdnSUUCskCW7VabV8zx25AWFysVqtUWiYmJnj11Vd57733cDgc+Hw+KbdcLsfnn39OLBbj008/JZlMsrS0tK92klhIbTYbJpOJZrNJsVhka2uLr776is3NTblxn9b59byIz93pdPLjH/+YiYkJZmZmcDqd3L17l0gkwp07d7h582ZPxCU8C+LzNhqNOBwOGaibTqep1+ssLS3tW4u6ZQ49CrUVdnJykrm5OX7wgx/wgx/8QL6nWCzKdPm/+7u/k0kSItYMvjs45fN5arWadKktLCxgsVgwm80MDw/LoqPiAJtIJMjlcl17wGy1Wvj9ft58800uXrzIL37xC2w2GzqdjmQyyfvvv8/m5ibvv/8+sViMZrOJwWCgUCjIdi6AtGa96KHyhZQXYWHweDzyxCuCS9UD5aCf76DmKcx4wopjt9sxGAzyd4naJt2isbpcLmkV6e/vR6fTkU6n2dvbY2VlhZ2dHbLZLPV6XZ5+9/b26HQ6coALbVX43sVE2dnZkT57EQwlYo2EotNNVTBFloO6gmW9XicSiZBMJmXw12Ht6RVFoVgsyuJi9XodvV6Py+Uin8/LuJjTzkGLSzAYxO/3MzExweTkJIODgzItU1EUWbI8Go2ytrZGIpEgEomQzWZlrJnIgBAKj3DTlkolqtWqzCQpFAr77qUb5PU0iM3ZZrPhcrnw+/14vV65vqRSKSKRCMVikWaz2XMpwI9CvY4Ki1R/fz+Dg4P09fXh8Xik20RYptTu+m5Zh9Wo55ZoN9Lf3y89Bo1GQ86HtbU1NjY22NnZoVAokM1m5fOrZSA2YHFgzGazRKNR2SNMp9NhtVrxeDzSjbS8vHxiMnhexBptNpvx+XyMjIwQCoWwWCx0Oh2i0SjRaJStrS12dnZkdiN8Z6RQHzSFrMXe9rw8d6p0p9PB6/Xicrm4cOECb7/9tiyepjaxHbzu4AAQg0pEao+MjHDu3Dl8Pp80bYuN/DRPGnUa67lz55ienuby5cvMzs5y584dPv/8c37/+9/zD//wD1QqFVlDQqfTkclk+M1vfkOn86DOjTDfi1ggQDZC+/LLL1lcXMRqtTIzM0O73aa/vx+PxyP7cKg/g9OMsDA4HA4cDgdDQ0MMDw9z7949PvjgAxKJxBMDBEUsVSQSIZVKsbGxwd7eHhaLhenpaTY2NkilUsDp35DFiUwoo2+//TbXrl3bVzBMBLqVy2Wi0ShffPEFGxsb/PVf/7V0M4oTjQgYt1gsTE1NcfHiRQYHB3E4HDIt+M6dO9y9e1e6lro9GFMgyg2IWh2jo6OMj48zOTnJ8PAwAPl8nq+//prbt2+/kO+9G1Gn3Pf19fGLX/yCcDjMG2+8gdPp5ObNm7IliQj2htM/hx6H2DtsNhs2m42pqSleeeUVBgcH0el0lMtlYrEYN27c4N/9u39HPB7n/v37h64/4neKebu+vk4mk2FyclJm93k8HmZmZvijP/ojvvrqK7788suuWZ/hu3hEYaC4du0av/zlL/H5fOj1euLxOL/61a/Y2triV7/6FalUal/DTr1evy9OSrRgEC7al668yIsNBkwmkyyhfLAvyqM4qICIm3c4HDidTrxeLx6PB6PRKPtHPCoL4jTVeVGnYYpaGqFQCLvdLjM69vb2iMfjcnNR33ur1ZLautDiH6X4dToPyleLASAagYnW449yrZx2hFYveqiIOBURiAuHL5hiHAi5CSud8N/bbLZTM04eh7g/8fn5/X58Ph/Dw8MMDw/L07Ber6fRaFAoFGT1zo2NDba3t2VPMOF+FYF1IhOpv79fdncFZDkD0Qm318rgq2VqNBoJBAIyzsVsNsvaG5lMRrpIoHs35+dF1L3p6+sjGAzKSsvFYpFMJiNPyN2y2T4OMR6sVqvM2BP1WxRFkeU8EokEe3t7ZLNZGaj7OHfZwf2oXq9Lq4NQaETjT5fLhd1u7zoZCgVElPsIhUL4/X5MJpO0+gqLi2juKsaKyWTCbDZjt9txuVyyzYQIFTmRmBe1Cc5isUjfvNAon2WzEMK5cOGC9OufP39emnO3t7dZX18/1X2OxIclLAgXL17ke9/7Hm63m1QqxcLCAr/5zW9k6ph4ZnGtcB2pf9dBhCYrItZFFpbdbsfr9cpif+ouuKcd4Ra02Wz73I7VapVkMvlUyot4XaSLi4U2EAhw/vx56WM+rUqder6IefTzn/+cq1evcv78ecbGxmTDMxFPduvWLf7v//v/lpYmEVQo+hGJtE6DwcCf/MmfcPnyZc6fP08oFJIpoKlUal8ZfOitnlliHpnNZgKBAG+99RZjY2MMDg5is9lYXl4mGo2yvLzMysqKLFlwVhCWF4vFgsfj4fz58wwNDaHX6ymXy3zzzTcsLS0RiUQolUpd7U4T80FRFEZHR5mYmGB+fp7Z2VkZ+hCJRPjoo4+4e/cu9+/fly75p1l7xPwVSotYg8WXcNtardaX8bhHgjruzmKxcOXKFf7kT/6E4eFhhoaGpKVlc3OTX/3qV+TzebmOiLEVCATw+XxcunSJ1157DavVKtfno9jLX8jyAt8teE97M+oPWywwojdCMBjE5XLJhxQCESlr6t9/mjZo8RzC8iJ868KCIAo9FYvFx963MMs+aYFQVyKG71q5q7tNi3vqFsTmrL53UVPhaRER7EKr1+v1skaOeP20oh4/JpOJvr4+RkdHZe8vkTFVLBaJxWLs7OywtrZGPp/f51qD75RcMRb7+voYGhqSZQzEiadcLstWEwf9+d2OetMQcVShUIhgMIjJZAKQ5QVETNSjrJ29hjruQH06tlqtOJ1ObDab7O8j1qxqtdoTTTrF/Be1skRvKyET0RZAWLQVRdn3+tMg1h91Vg3sL8rWTWOs03lQq8xut+Pz+RgaGsLj8cjEmq2tLba3t0mlUrLGmEBRHnRs9/l8uN1uXC6X/J3q97wIL6y8qBWRR/1czaOUD+F6GhgYYHJyUp6+t7e3+eyzz1hZWaFarT50/Wmj0+nItgiiuN5nn33GvXv3uHHjBltbW9IE+SgOKi0H/anw4PmHhob21akQm/ZB5aVbUVsinuVZhBIsFl51s8/TjthIRPEmcTIUtVvS6TTpdJqvvvqKX//61+zu7rK5ufmQOV+YZPV6vXQ9+f1+2RxNURQajQa1Wo3t7W2++eYb4vF4129MBxHKr8/n4+rVq8zMzPD9739f9npKpVJ88sknrKyssLm5SS6X60qX6/Mi5ON0OmWxvmAwiM1mk9a4hYUFlpeXqVQqT2WB6BbUB+2D+9GLZLOqLS29gHiO/v5+zp07x4ULFzh37pwsynfjxg1+/etfk81m9xWhFRgMBq5cucLMzAwjIyPYbDaZoHJUMnrh2XqY3+pRH+ZBZUecEEUwovDr53K5fZHb3TIo1AFKwo8qUsWeFFfwqAmgri1gNptl00vRYM9kMvXUpBE8zwKiKMq+uJeDit9pR8Rn2Gw26QbsdDrUajWKxSLxeJy1tTX29vYol8sPuQiFFc5kMuH1eqXlRlRvBuTvymazsgZMLyi9AnWsi8ViIRQK0d/fj8/nw+l0yhpU0WhUukROezLAUXHwYCACSt1uN1arFb1eLy1S2WyWQqGwL2C1F8aJOOyJr6P43A/uZY/LsOmmjFnxWTudThnnYrfbAchkMiSTSVmDS6yzaquvyWQiEAgwMDAgYz+bzSaVSuW5wksexZG0BxCmemFZUGu26v4pB69TFAWfzyfrxExNTVEul1lcXOT69et8/PHHFAqFU38iEs8qNoa9vT02NjYwGAyMjY2xuLiIw+Gg0Wg8VKTooPInToCiWaXFYsFkMjExMUEwGOSNN97Y17Qwl8uxtbUl+9d0e3n3FxnQ6hbt4v/dslgIhOlZWIx0Oh35fJ7NzU1WV1e5f//+vhR6+M4sbbPZmJmZIRAI8Ad/8AcMDg4yPz8vCyQWi0V+//vfc//+fb766iui0WhXVKx+GoTi2mq1cDqdMubpxz/+MYODg1itVgqFAr/61a/Y3t6W3e5FVkgvyOBpEBZil8vF1NQUv/zlL6WLslgs8s0337C+vk4sFpNu7oNrdzci9qR0Os3W1haRSIR4PC6roBuNRlnY8mk52Ch4ZGSEkZERhoeHpRKjKArZbJY7d+6wsbFx6stYiOexWCwYjUauXLnCP/2n/5RQKIROp5PPsr6+Tq1W21f/RlTSF27qixcvykax7Xab27dvs7i4yN27d6VRAp7/YPnCyovw8alNbgfTo9TvF4hNxuFwyAwjt9stqzrGYrF9FWPVC/XB33VaEL1jRG0AnU63r+eTOnJfrcDB/vgfobyIQC+LxcLQ0BBDQ0NMT08zNzeHw+EAHpykM5kMhUJBplifVdSnQ3Ha6abNWZwKRckBURG3Xq/Lqp/qFHv1eBFp0QMDAwwMDDA/P8/IyAgej0e2nKhWq0QiEZaWlohGo7KHWLf54g9DbM6BQIC+vj7ZVwWgUqnIGh6xWEyeGnvp+Q9DrDmi/ojX65UVu0Uzz2g0yt7enrRIqYO4T+Oa+7SIdaFcLss4p2q1KuNaRFaQODQ+yX2tfl3seaLyrMvlknIT9XISiYRsjdMNCGWur6+PyclJ2c9J/SzqXk0CsecFAgGCwaCMM2u1WiQSCVZXV2VDS3gxi/hzKS/iDxaLRVqtFhsbG9y7dw+v10tfXx/T09O899577O7u8uWXX1KpVCgWi3KhMBqNDAwM4Ha7+elPfyqtLlarlWg0yu3bt4lEItKK8Ki04dOEOugLYG1tDafTyfT0NBMTE7TbbQYHB0kmk+zt7ck0RHECEtcLpcVkMhEMBrFarYyMjOByueRGJHobJZNJ7t+/z+3bt/nkk09kcaBubpwmzI0iyFYd3P04xOui+JooxX3//n2+/PJLtre3T/3mJO6tUqmg1+vZ2tpiaWlJlrH3+XyMjY1x/vx53nzzTVnzRyi3ojKqy+Vibm4Ov99POBzG7XaTTqcpl8ssLCwQiUT45JNPuHPnDtlstqfipER3e4fDwczMDD/96U8ZGxtjfHycer3ON998w87ODl988QWRSOQhl0gvo3ZftNttTCYTfr+fwcFBmXEjSrpfv36dra2tfdmdTzMPTzPqQ02lUiGbzcpEENGTp7+/nzfeeAOLxcLe3h6ZTEZW7VYfFOC75AphqTl37hz9/f28/fbbXLhwgdHRUcxms2xxcv/+fT777LN9ls7TOuaEUiZcPqK3nlD8EokEd+7cIRaLSeVFxNk5nU48Hg8/+tGPZE0lj8cjyzgsLCzw1Vdfsbe3dyRyeCHlRZiN0uk0kUhE5nT39fUxMzODwWDg/v37ALI/BDzYpLxeL8FgkLm5OWZmZvD5fBgMBvL5vKxA+6hT0WmeRMLUL9ojzMzM0N/fz+zsLDabjb29PdxuN9lsVhZSCwaD0kIl3ESivLTT6WRychKv14vP55NZWCITa3Nzk6WlJW7cuCEDVKF7G8qJE8yzKBtiPIisCavVitlsplAosL6+Li0Vp3WxgO/mkyjIKHo6+f3+fafkgYEBxsfHKZfL5HI5jEYjPp8Pl8vF/Pw8LpeLcDiMy+WSQbqlUolUKsXS0hJra2vcv3+fzc1Naek7zXJ5WoTyIg4Dw8PDzM/Py9iwZDLJ1tYW6+vrrK+vk0wmu6pQ2FGiTt31eDz09fXRaDRYXl5mZ2eHnZ0dIpHIQ7LpBQUGkL33arXavtg4p9PJ+Pg42WyWkZERFEVhc3PzkZWFhWyEdWJkZITJyUnm5+e5ePEiVqsVg8EgK2CLekyibMNpHnNCeXE4HASDQdxuNxaLRQb6FwoFWcX7YPyqqKEjirQGAgFsNpvsnSbkoK5A/CK8kNtIbKSRSISFhQVZ4EhoseFwGL/fT7FYJJFIyNx5k8nE2NgYbrebubk5gsEgsViMlZUVbt68yd27d0kmk12zuB7M9rh//z7JZBK9Xk86nSYYDDI5OcnQ0BDnz5+nVCqRTqdluWX1Zi02cJHmK8y5IvJfXUl2bW1N/v9FSy2fFIryoEaLGNAulwuXyyU7u+ZyuUdmAIhJJp57aGiIgYEBDAYD0WiUZDJJJpM59TFA6nsT8VD37t2Tm7Hwx4tKoH6/Xy7AIs5F1MmxWCzSkre7u0upVOLXv/41m5ubrKyskEwmSaVSPbVpq13Vw8PDvPLKK1y6dIkLFy7I+h0bGxt8/PHH7O3tSfcqdEcQ94twcNMVAd2i6uvAwACNRoNEIsHvfvc72Tm62Wyeemvls/Io6/j169e5ePGibBfhdrtlNdxUKsXVq1ep1+uyyrBQ6kKhEDabjcHBQZxOpzykjo2NYbfbKZVKJBIJbt26xe9+9zs2NjaIRqNdUQhRjBWhvDgcDgwGg9zb7HY7wWAQeBC4K3obejwe3n33XQYGBrh48SJ9fX10Oh2y2Sw3btxgcXGR5eVl8vm8bN/yonJ4YeVF9AfZ2NhgampK9joS/Xe8Xi+VSoVkMilL3ovFWCy6ZrOZpaUluSFvbGzIZnzdhPiAt7e3iUajeDwems0mb731FpcuXZL9IWq1GqVSSbo51AuFMMWJirGiRPfu7i7RaJTFxUW2t7flV61WkxkjYkB0ywlJKKciCh0euH/sdrvs+Puo+CD4LlhOaPDBYJBwOCybhAnTcDdt1KK2zdbWFo1Gg3PnzjExMYHL5cLr9Ur3kIjnEeXdxUlazJdqtcrq6iqRSITPPvuMpaUl2ffpKOssnDQHYw6CwSAXLlxgZmaG0dFR8vm8tCjcvn2bRCIha7r0UkG+p0EdUyfc0IFAgGazSTab5fbt22xvb1MqlR46FXfLenIYastLq9UiEomwsrJCKBSSPa3E/PJ6veTzeaampmTBzHw+z+3bt2m1WszPz+Pz+ZiYmJA1hETPMZPJJC2nS0tLfPrpp6RSKTKZDNAdhSA7nY48CFksFvR6vSzHITJeRQq9iC8bHh7mBz/4AUNDQ4TDYaxWK9VqlWKxyMrKCt988408UKldcC/CCykv4gYymQyrq6v4/X75NTo6KiOwW60WAwMDcgLp9XqZPlUoFEgmk9y5c4eFhQW2tra6tmjUQQVkZWWFXC5HPp9na2uLQCAgW4ibTCYURWFvb09eI3rWNBoNqbSI0+L29jbZbFY2bhSNGkW0dzcG1YnxI5ribW9vs7a2htls5g//8A9ZXV2V5etFx2P4rpmc0WhkeHgYj8fD7OwsAwMD7O7uynH0otHsLwuhcAplTlS9/fTTTykUCszOzso+VvBd7IKwWul0OhmsnclkyGaz/OY3v2FnZ4f19XXS6fS+tOrTLo+nRSivLpcLt9vN+Pg4Fy5coL+/n1qtRiqV4vbt29LqJKpY95pV4XGoa0W1220GBgYYGxvjypUrvPXWW7hcLhnAWiwW5WGhF1HH/XQ6HdbX1ykWi7J0vc/nY3BwUMZuiAbBjUZDBveGw+F9lhfR1FGn08lYGuGCW1lZ4d69e0QiEdkgVX0fpxWxBu3t7WEwGBgdHeXcuXOYzWYsFgtjY2P80R/9kXQfiXY4brebqakp7Ha7bLdx7949EokEX375JUtLS+RyuSN9/hdWXkQqWLFYlKWWp6en5YIyPDz8yCJYohvn8vKyDAL65ptv5MItMpbUnPaJpZ4grVaL1dVVVldX2d3d5c6dO0xPT3Px4kVZq6XZbEpFTdSByWQyVCoVtre3KRQKLC8vyw2pUqnI7C6RZXJUWuxJIBRZUe1VbLahUIhf/vKXXL9+nc8//xxABlgCUvm12+3Mz88zNDTE6OgoLpeLGzdu8Pnnn8sTZDe5HoUSE4/HicfjmEwm9vb2qNfrshGaqBArEHNCmLbX1taIxWJ88MEHbG1tyUZpInNJ/K1eQCgvdrudUCjE2NgYc3NzmEwmGW9w9+5dNjY2SCaTVCoVuUadJYRF2O/3c+3aNV555RXeeustqtUqu7u7MvtGHBC6IbPzeRFjZnNzk62tLTweD16vVwbdippjOp2OwcFBALnmCnejGEPCUprL5aR3IZfLcffuXW7dusXy8jKxWEwmqXQDYm6IuJbZ2Vmi0ajsAzUyMsLg4KCspwXItHCh7N29e5dEIsFvf/tb1tbWuHfvHtFo9MgLQb5whV2RltpsNkkkEty9e5dcLkej0cDj8TA2NiZTz8T7O50H/XyElppMJllbW5MaWzf30RCoNwihpQrlxOFwSJOtaH1Qq9VoNptSiRGNwUSJbnXdD3X0fC8gfPGpVIqbN29SqVQYGhrC6/VK//PS0hLNZhObzbYvG0s0G9za2pIWLnU7iW6UkU6no9PpkE6nabfbXL9+XaatHlToBcIKs7e3Ry6XI5FIyCZp3WCqfhEOprUWCgWZsSV6GD0qbqrXETIRgaUDAwMyPkPEuvz+979nbW2NVColkyq61ZL7tKifLRaLcevWLVmUT6zN6rmmrmcGyBgQ0TA4EomQz+eJxWKk02m2t7fZ2dmRcY/dNPfE/iwUk/v37/P3f//3+P1+BgYG5FgS7iNRh6xarXL79m1yuRwLCwsyy0qUZDgOa+dzKy8H4w9arRa7u7vs7e2xuLjI4uKirCPwOOWlVquxvLxMNpuV1XS70eJykIMfUjabJZPJsLa2JlM6RUCqcP0Ik636dKy2qvSawqJGTPBYLMYnn3xCvV7n0qVL9PX18V/8F/8FmUyGTz75hFarRV9fH1arleHhYUwmkzwhfPzxx1y/fl2mQHarRUp96k0mkzLw+M6dO9JS9bjrRBZavV4nk8k8UeHpFdT1puCBG/vmzZssLCxw48YNaYXrto3kRRGbroghGxsb49q1a7hcLmq1Gjs7O7z//vvs7e1J94Z63nTbuvs0HFTKtra2yOVyLC8vs7i4SDAYZGJiQvZbe9zvaLfbsnns8vIy8Xic3d1dmcnWzXNPUR4UXK3Vanz99desrKzQ19fH4OAgbrebUChEIBBgfn4et9uNx+OhVCrx/vvvs7W1xeLiIrlcjng8LhMLjmMtPjLJqgeFsBiIUsDiJKlGmNySySTlcllGuPci6g1Jp9PJzqPqRl5CRuqsI/X1vb7oCstBPp+X2SF9fX2cP38eQMZQiZoDIo5D+FUjkYgcR90uL3UMjJhP2Wz2ic8k3vsiPVq6CTGfRFO9paUlfv3rX5NKpVhZWWF3d3ef1fKsoXZjNxoNdnd3+fzzz2U8x8bGBltbW7Lg2FlEZDoK5T+TyZDP59Hr9U/s+ZXP52UWkrrBZy/MPbH2iPRo8W+bzUYymcTj8ZDNZmX2UaFQ4N69e8Tjcdl25Ljd9sph2rXD4ThU9T54U+q8b/EhHtb7CPZ3pX5cc8InUSwWT2SnepJ8HsejsmeOc7M9Kfm4XK6nlo+ifFfeXXR+HR8f5xe/+AXDw8O8++67ss5NoVDgH//xH9nZ2ZGBqaLOzfOcrvP5/InIx+12P1E+Yqw8aR4J1Ba6oxpTuVzuRORjtVqfSj5GoxGDwYDT6cTtdsssB1GZ+Lg3k0ql0jXy8Xq90s1frVaJx+MPja2jdhmdhHyeZm7Bwy1ERFzZYXNHbOzqWBh1HKJ4z9NwUnPLZrM9lXzE2iO+hHzUZRxsNptMuFCXpjgKV1G5XH7sL3jh9gDw6A9KnbKr3qgfd436/71orlRz2LOfZcSG2263qVarZDIZlpaWyGQyMs282WzKirGiTHW1Wj31xZ+el25KfT8JxHgRZdjhu2J/wprQi+PiaVFvtMKCJ/5fr9fl5n1WZSTko87Meto6QEJ2B+dnL8pSvX8LS6ZoLyIyHYXB4mWFOByLQ0745p9kdjvL9OIAfxHEoBfBcLVaja2tLXZ3d/dF66uL06k7wx4WD9LtPOsJ5qyNLTEOhNsRHu6FdhYRzy42lkajQT6f33eAVG8yZ+kAKVCvG2oF5lno5VgqtXzUAcytVuuRdaNeZlbVkSgvRzXQz8qE0TgccRoS5m14UHgNHh4jZ6Vmx1l4xhflYNaRxn4e5X58lJzO6jr8vFbOszbWDlqr1MUiXybdFwqtcSY4mG2lofEktLFyOE9jnTyriovgrCkiz8tpSIrQlBcNDQ2NHuRZFZGzrrhodBeHZhtpaGhoaGhoaJw2NDurhoaGhoaGRlehKS8aGhoaGhoaXYWmvGhoaGhoaGh0FZryoqGhoaGhodFVaMqLhoaGhoaGRlehKS8aGhoaGhoaXYWmvGhoaGhoaGh0FUemvCiK8l8rivKVoig1RVH+7TNe+yNFUT5QFCWnKMrGUd3TaUKTz+PRZHM4mnwOR1EUn6Io/05RlJKiKJuKovyzZ7jWrCjKv1EUJa8oSlRRlH9xnPd6EmjyORxtfh3OaZXPUVpe9oB/Bfyb57i29O11/80R3s9pQ5PP49FkcziafA7nfwLqQD/wnwD/WlGUC0957Z8D08AY8CPgv1UU5efHcZMniCafw9Hm1+GcSvkcWXuATqfzVwCKorwKDD/jtb8Hfq8oyh8c1f2cNjT5PB5NNoejyefxKIpiB/4UmO90OkXgY0VR/h/gPwX+X0/xK/4z4D/vdDoZIKMoyv8C/OfAr47pll8qmnyejDa/Due0ykeLedHQ0OhmzgHNTqezpPrZTeCJlgVFUbzAwLfvf6ZruwhNPho9iaa8aGhodDMOIH/gZznA+ZTXivc/67XdgiYfjZ5EU140NDS6mSLgOvAzF1B4ymvF+5/12m5Bk49GT6IpLxoaGt3MEmBQFGVa9bPLwN0nXfhtHEfk2/c/07VdhCYfjZ7kKFOlDYqiWAA9oFcUxaIoikH1ekdRlHcfc63u22uND/6rWBRFMR3VvZ0GNPk8Hk02h6PJ5/F0Op0S8FfAf68oil1RlLeAPwb+AkBRlPC38gk/5lf8r8C/VBTFqyjKLPBfAv/2+O/85aDJ58lo8+twTq18Op3OkXzxIKWuc+Drz799bYQHflf/Y6599xHX/vao7u00fGny0WSjyefY5OMD/poHaZlbwD9TvfYOsAEYH3OtmQepnHkgBvyLk34eTT4vXT7a/OpC+Sjf/oFjRVGUfw5c6HQ6/92x/7EuRJPP49FkcziafA5HUZR/CSQ6nc7/fNL3chrR5HM42vw6nJOUz0tRXjQ0NDQ0NDQ0jgotYFdDQ0NDQ0Ojq9CUFw0NDQ0NDY2uQlNeNDQ0NDQ0NLqKQ3sbWSyWrgiIqVarykn8XafT2RXyKRQKmnwOQZPP4ZyUfBwOR1fIp1gsavI5hJOQj9Vq7QrZVCqVExk7vSCfI2vMqPF8HAyYVpQTGcsaGhpnkCclbGjr0cNoa/bTo5bVUcvpVCgvqpxw+YBnYUCon1t81+keePLOwvNraGicDOpN5eAaBA/WH0VRHlqXzzJCFu12m06nI9dq0OTzKIScBDqd7kjldCqUF+BMTpTDnrPT6ZwZOTwPjzsxajJ7NGdVXgfXlMMsDb0uC3j4+Z/2ZKytR98hFDvxb41Ho5bNccjpxJUXRVHQ6XSYzWb0ej2NRoNms/mQ1tZLCIuLwWDAarXKydButymVSrTbbfk+9UTR+E52zWZzX7VFodXr9foj1/C7nU6nQ6vVknOq3W7vk5derz/pWzxSxPOKZ4XvLJqPW1eEPHQ6Xc+OHzFX2u22HAMGgwGdTofJZJLP3+l0qNVqtNttOc/OspVBrMNCVg6HA71eT7lcptls0mg0aLfbZ04uj0PIy2q1YjAY5B5WrVap1+tH9ndOXHkRi4XFYsFsNsvNW0yyXh4QQmkTi0i73aZer9NsNmm1Wid9e6eag5a6Xh4nR4WQmZDXWZHZwed8mufuZUuD+Ox1Oh1Go1EeooTSJuaVUFzO8lp00O1hNBpxuVwYjUYAqeSJPatXx8yzoiiK3NOFTJrNJrVaTb7+opyY8iI0/0AggMvl4o033mB4eJjPP/+chYUFCoUCxWJRTrJeQpxoXC4Xk5OTBINBLl68SLvdZmlpiVwux8LCAvl8nlqtRqvVOlObzUFkLwtFwWg0YjKZGB4exmazYbPZ0Ov1FAoFqtUq8XicbDa779R9FuWmVuz0ej1DQ0O4XC4cDgdWq5VCoUC5XCabzZJIJHrCyik2Ebvdjs/nw2az4fV6aTab5HI5dDodPp9v32lQbDqFQoFarUYqlSKfz9NsNmk2m/Jw1c2oLb1GoxGfz8fIyAhOp5NgMIjFYiEQCKDX61EUhWazSTQapVQqsbGxQTabJR6PUygU5O87S+tRq9XCaDQSDAYJBAL8B//Bf0BfXx8LCwvE43E++eQT1tbWetZi9ywIZddoNPKTn/yEiYkJSqUStVqN3/3ud9y9e/fI3EknoryorSpOpxO/38/58+eZm5tjZ2eHjY0NKpWKVHB6DfH8YkKMjIzw2muv0el0MJlMxONxdnZ2qNVq1Ot1TaNXYTAYMJvNDA0N4fV6cbvdUmaFQoFKpUI+nz/Tp0U1Qvn3+Xz09/cTDAZxOp2k02lSqRTtdlt+73ZlT8wrk8kkD0VDQ0M0Gg2i0Sh6vZ7h4WFMJhMGw4OlTzx3IpGgWCxSq9Uol8v7XCa9gl6vx2w24/P5OHfuHG63m5GREex2OwMDA1ImjUaDjY0NOY+EslssFntKHk/Dwb0qGAzy5ptvEg6HsdvtbG1tcfv27TMnl8PodDro9XpmZ2e5evUqqVSKYrHInTt3jnQvO1G3kcFg4Ny5c0xMTBAMBjEYDDSbTUqlEs1ms2e1e51Oh16vp1qtsrGxIU88LpeL73//+2QyGXZ2djAYDNRqNarVas/K4kmoLQh2u53Lly8TCAR477336Ovrw+l0YjAYSCQS5PN5fvWrX1Gr1cjn8+Tz+TN7GhKLhNvtxm63873vfY/5+Xn6+vrweDzEYjFisRhfffUV6XSaarVKsVgEuk95ERuMz+ejr6+Pqakp3nrrLVwuF4ODgzSbTRKJBHq9nv7+fkwmk4zzabVatFotMpkMpVKJlZUV9vb22N3dZXd3l1wuRzKZ7EoLsLAq2e127HY7k5OTzM/PMzw8zMWLF7FYLDidToxGIzabTca7tFotAoEAtVqNcDhMLpfjq6++Ynt7m42NDaLRqHRtn5V1Sa/X43a78fl8OJ1OnE4nAwMDANjt9hO+u9OBOtbF4XDg9XrxeDysrKywtbVFPp/fZ0V/UU5MeRFBYCMjI5w7dw6Px4Ner6fZbFKtVqXy0osIU369XicajeLxeFAUBYfDweDgIMVikQ8//JBiscjOzk5PBy8/DWKBtFqtTE1NMTY2xltvvcXg4KB0GyWTSfL5PIuLiywtLVGtVns+ZupxqBcIu92O1+vl4sWLfO9736O/vx+v1ys36HQ6zRdffNHVY0woL06nk/Hxcebn5/nRj36Ey+UiFArRarVIp9PodDqCwSBGo1EqIiI5IJfLUS6XGRwcZHt7m7t378r3JBKJk3y850YoImazGY/Hw/T0NO+++y6Dg4PMzc3J4HaBGAOdTodQKESn02FsbIxyuYyiKHi9XsrlMul0WgbNQ/cpu8+DXq/HbrdLt6vVasXn89FsNrFYLCd9eyeOes2xWCxSgXE4HBSLRXZ3d/dZ7rpOeRGLjF6vZ2RkBL/fz6VLl5ifnyeTycjTz1mI81BnF5VKJYrFIpVKBYPBgM1mY3R0lHq9zurqKrlc7kymkovxYrFY6O/vZ2BggFdffZXh4WEcDgedTkea+Dc3N0kkEqRSKer1uhw/Zw11LQqDwcDMzAzhcJihoSEcDoeM+q/VanLjFu6ibkMd42K325mfn+enP/0pg4ODuN1uOp0OW1tbtFotGSiYzWb31VKyWCwyBkan0zE4OIjH48Hr9TI+Ps7NmzdptVoUi0VSqRTAqbfAqGtG6fV6BgcHmZmZYW5ujunpaRwOh3xfu92m1WpRLpelFUocpPR6vcwYuXjxIgMDA9hsNoaGhlheXmZlZUVeA723LqmzjBwOB/Pz84yPj+NwOGi326ysrLC+vk4mkznpWz0VCJftzMwMAwMD2O12Go0Gu7u7LC8vy7izrnUbtVotDAYDw8PDjIyMcPHiRebn5/n888/Z2dmhVCqdiTgPRVFotVpScSmVSlSrVblgjI6O0m63ZWS7WKh7WSYHUccGDQ8PEw6HuXbtGoODgxiNRjqdDpVKhVqtxvb2NltbW1J5EbI6S/ISqP30586d49KlSwwPD+NyuWQabL1ep9FodHVskNh4bTYb/f39zM/P87Of/QyTyYTZbCabzUrlRWTziVg6dcKAzWbD7XZjsVgYHh7GbDYzNjZGPp/HYrGwu7tLJBIhmUw+lDZ8mhFp8KFQiPn5eebm5piampIKh5CBCGgWab9iDTIajVgsFiwWCxcuXJDWrZGREQAZl9eLVnL1QVEoL3Nzc0xOTkrlZW1tjVu3bpFOp0/4bk8WdckKEQoilLxGo8He3h5LS0s0m80jLcvw0pQXMRiMRiN2u52pqSnC4TAWi4VarcbGxgZ37twhkUjsCxzstUkhEJuy1WqVcQlWq1W+LsxuIriw0Wic4N2eDOLzN5vNBINBgsGgrAek0+lotVqsrKyQTCb54osv2NjYYGNjg0KhQL1eP1PxLkLZFxtWX18fbrebUCiE3+/HZDLtW2QikQg3b95kfX1dZtp0m6yEtSQQCDA9PS3diLVajUQiwcbGBv/wD/9AvV7HYDDss9S1Wi0ZA+NwOAgGgzgcDs6dO8fQ0BAADoeDcDjMm2++yb1799jZ2ZF1qMTfP42Iz7ivrw+fz8fU1BTnzp0jEAjQarVoNpv7apTk83mWl5cpl8ukUil0Oh3hcBin0ymtnDabDYPBQDAYBGB9fZ319XUSiQQ7Ozty7PUiog6OWIMAqtUqiUSCvb09qtXqCd/hyXHQRe3xeBgbG2NkZIRkMkksFiOTyUgr71HyUpQX9QOaTCacTicXL17k3LlzWK1WSqUS9+7d47PPPiMSidBqtXoiRfFxiJOxwWDA7/fj9/ulAiNedzgcuFwuzGazDGTudWuUGvXJx2w2Mzo6yvDwsDRji4KGd+/eZXFxkQ8++ICVlZV9Y61Xx89B1G0m9Ho9FouFyclJ+vr6GB0dJRQKYbFY5LjrdDqsr6/z4YcfsrW1RTabBU6/O+QgYsMcGBjg6tWrjI+P43Q6qVar7O7ucuPGDf63/+1/k+7YdrstlRdhdRAp5ENDQ/h8Pn7+859jt9txu914vV5mZ2dxuVxYrVa++OILyuWyTBk+jXNR/RmHQiGmp6e5dOkSV69exWKx0G63qVar5HI5qtWqTJX/+OOPSafTrK6u0ul0mJubIxAI8MMf/pDh4WH6+vpkTF4oFCIajRKNRllYWGBzc1O6qHoJ9TpiMpkYHBxkYGCATqdDqVRiZ2eH9fV1Geh+VhEHcbfbTSAQYGZmhtHRUelNSSQS8jB5lGvMS7O8iM3a5XLJtM2+vj5KpRK5XI54PE48HqdWq52ZE7OoWyIKRaknf7cGTx4F6mc3GAxYLBb8fr+s0QEPYhcKhQKbm5vS2tKtsRtHgVhAvF4vLpeLCxcuMDAwQH9/v8zIEotuuVwmmUzK9OBuQ3zOQuEPh8NMTU3hcrkoFArs7u7yzTffsLy8LN0aagVPXaRPKDM6nY58Pi/TXsPhMOFwmFarJdercDhMMpmkWCye+rEmAtw9Hg9OpxObzUa1WiWTyZBKpdjY2KBUKpFMJslms6ytrVEsFkkmkwDSoilSzYX7SGzmQuGLx+M4HI6ejX1RB+qKrFChwIqKsWfpUPk49Ho9wWCQgYEBgsEgHo+HXC7H7u4upVLpWP7mS7O8tFotTCYToVCI0dFR6Re7desWe3t7rK6usrm5+dAm3qsI37kouiYUGPGaerE9i4haAWazGbfbLYNOrVYr7XabSCRCLBbjxo0b3Llzh3w+LzehbrMgvCjqrJLh4WEGBwf52c9+xujoKIFAALPZDDzY9JPJJPF4XJr9q9Vq1y2+asvC2NgYr776Km+99RaVSoVYLMbt27f5v/6v/4tMJiOzzkQMkNiABPl8nlwuRywWAx4oxTdu3OCdd96h1WrJvzE9Pc0rr7wi16nTGCt0cL3weDwMDQ3JzWR5eZlbt26xtrbGp59+KjeXRqMhg9yFvDY2NjCbzVgsFmKxGB6PR9ZUMhqNMsYom81y584dWfAQjr4B30khYjh8Ph9er1e6XlOpFIlEQiowp3EsvCzEPmU2m5mcnCQcDjM+Po7P5yMSiXDnzh1yudyx7GUvRXkRFga73c7Y2JiMdWm1WkQiEel3h7NtcVDTy/E+T0KYvkWKp8fjweVyYbPZaLVa1Ot1dnd32dnZIZfLySDMsygzoQQ7HA7cbjeTk5OygJ/D4ZCZNCJId2dnh5WVFSKRyL6svm5CfM4OhwOfzydjw/L5POl0mnQ6TT6flyc+9bh4VKsA9UGhUCgQi8XY29tja2sLi8VCOBxGr9fvK253mhE9eJxOJ16vV9ZwaTQa5HI5UqkU8XicYrG4L8tInS4vFGJR3G97exun0ylT7UWsnrDqNBqNrlOCD0OMCTG31HOpzoPA/gAAcdNJREFU0WhIi57oodUrz/0sqCstq0Mgms2mLPgokieOg2OfiWIAmM1m+vv7+fnPf87Y2BgOh4Nyuczvf/97rl+/TiwWO3Obj5ggYgKoFbfDFtxeR9SQELEbYkP2eDzU63Wy2Swff/zxvuJHYsE+S7ISSp7VamV4eJihoSH+8A//UP5b9KvpdDqyaN+HH37IBx98ICsSd1OsgjquRwTbTk1N0dfXJ6vA3rt3TxaaazQa+1xEj0JY6sTvjsViRKNRjEaj9NNfuXJFWgGNRuOpHGPq9UPUIRkaGmJqagq/349Op6NUKrG9vc3a2hp3797d16BTPJOwWorvN27cYGFhAZvNRjqd5p133iEYDOLz+WRqeSAQ2Oea6xXLpyhzPzQ0xMDAAFarFb1eL0MdKpWKVNrg7K3TAuFam52dZXR0lEKhQCaTIZvNSuX4OGRzrMqLOnjSbDZjtVoJBoP4/X7K5TLVapV0Ok0mk6Fer5+ZD1892IXbSCzIOp1OBtWVy2WZ3XAWtXt1TJBwq4modVF5uBcXzWdBuNc8Hg9+v1/GvIhNVsgmn8/LOjjZbLYrMyTUm7OIgwqFQlitVhqNhrSaiLRfEfj/NIi5JTKRqtWqLNsgXu+W+ScUMtHLSJRlaDQaMk5DnSp9cPMV3zudjuyYLKxaIktJrOnCtWQymU7seY8adX0Xs9ks55Ver6fdbpPNZmVV6rO6NqvXXLPZLPuIuVwuWfpDrdwdx/w5dsuLCNT1+XwMDg5y4cIF+vr6+PTTT9ne3mZ5eZnNzU3ZBO2sDAJRrE+Umrbb7VgsFvR6PbVajZ2dHdbW1mThvrMkG/huATkY0CwWXuGnP8tuRmG1s1gsshjd4OAgfr9fFkGEB71qFhcXWVxcZGVlRTZi7AYXiBpxghPB/q+88gpvv/02BoOBdDrN2toan3/+OXt7e1LpeFarklhkhYtFFLfrBtSWKaPRKA9EzWaTSqUiT8SiYu7B+LBHudTEGNrd3cVoNHLx4kVpsbPb7bhcLrxeL/l8ft89dCtiU9br9bJ+0NWrV2VPrGq1yv3791leXiYej1MqlR6KozorCNd+f38/IyMjzM3N4ff7+eijj9je3mZvb0+Oi+M4WL6Uo6o4Gbrdbpn1kEqliMVi8nRz1gqwAVJrFeZotUlaWBbEyafbF4UX4TCt/SzLRaQnip4+op6LWEw7nQ7FYpFsNivjOIrFonRTQneZusXGInryOJ1OXC4XiqJIU346nX6hDCox1kSMi1r5Oe0ZRgKLxSJrs4gKwurT76NcaY9SXMT3Tqcj06vL5TK1Wk0qv6KQ3UFFuJvnpdryYjKZZDd2YfHN5XJks1lpVejGuXQUiNInotu2sL5lMhkSiYQMZj6usXDsbiMAp9PJ1atXZZ+McrnMZ599xtLSEolEoufruhxEnJhFV+lgMIjX68XpdMria6VSiXw+T61Wo9FonDnLi5rDMq/OkkzUi6RITbxy5QojIyO8++67+Hw+WejQYDBQr9e5desW29vbvP/++9y5c4dKpdKVsVTqqsFer5ehoSH6+/sJBAKsrKywurrK8vIyCwsL0tr7PAjZer1exsbG8Hq9ALLFgDrG4bQg7kesoyLjQ9TbEFYYu91OIBAgHo9LpeRJiDU5Ho9TqVTY3t4mGo3KWBe3243H4+mZ5oRqd4jVapX9sfx+vzwIrK+vs7q6+siA8F5HvQYJxUW05BDd2a9fv87S0hKZTOZYFbtjU17EABDmNzEAxCKQTqdlKffTthi8LA6r8/KoDAANDWHSFj2wgsEgfX19+5RfEfDcaDRIp9NEo1FSqRSZTKYrFZeDiBOxmDfiRCwORqKo2PMgNiJRI0UUdqvX6zJeRLzvtGIymaQb2mQyyXVYxME8T4B2o9GQrThqtZp0ewsXVS8dPNUFVU0mExaLBaPRKDMdy+UypVKpayxxR426zIfdbqe/vx+fz0etVqNYLMo+hcddFf5YlBd1w7TBwUFmZ2f5wQ9+gMvlolQqkUgk2N7elr0xzqLP8DBLgtoffZa0+kehtrpoShyypkIwGGRoaIiZmRn6+/ux2+2YTCa5kafTaXK5HLdv32ZpaYlkMtkzdXAOGwcvUh9JLMqKojAyMsJrr71Gf38/uVyOaDTKvXv3iMVip37Tqtfrsldao9GQCow6EFed4XjY+iIsNCKhQlif1KUJemmNEhY+Eafp8/lkmYZ8Pi8VZFEPp9vn0rOirinl9/sZGBjg3LlzWCwWlpeXiUQirKyssL29/UIW0KfhWCVvMpkIBALydOjxeKhUKuRyORmNfNoXguPisBgOdWv6s4o69kCtxPWC5eB5UBcuFJYFm82Gy+XCbrfL07SwQIhaHslkknQ6LS0GvcJRzw3x+9T9xvr6+jCZTBSLRfL5PNlslmKxeOrm5cG5ILKKRKaiWGPVtWrEvBI87pnEz4X1Rn2dOKSqq+v2CqI5pdVqlTITFk3xvGc11gUeyMfhcMjYM4vFQjqdliUYKpXKsWUZCY5ULVJHalssFiYmJvjTP/1TRkdH6evro1gsygZ6wmXUa5r70yKsK8L0LU44ItZFpJCfpSws9UIpKuuOjo4yODj4UPCkeN9Z4OAmUa1WKRaLNJtNHA4HVqtVmmzv3r1LOp3myy+/JJlMsrW1JReTXhpHamXuca8/y7OKTf7ixYuymu7k5CSLi4v87ne/4+bNm6yurkol8DSOPXFPsViMZrPJzs4OsVgMn88ns0Jef/11dDodS0tLFAoF2SkbHlZg1HFGExMTDA4Oyjgag8FALBZjZ2eH1dVVEokE8HC6dbdwMJ7M5XIxPT3N+Pg4Ho8Hk8lEIpGg0Wh0RXPO40AtI4/Hw9WrV5mcnMTpdJLP5/nqq6/Y2NigXC7LPe04OXKbjvpk6PF4mJqaYmBgALPZTD6fJxKJsLOzI81uZ81lpB4AwoQvNhVRV0H417uxdPtRIGQjKuw6nc59GTRiUT1rVjuh5IvnB+SpUAR57+zsyLLcyWSSSqUilZ7TuOE+D0KRU8eDqeM6nme+iHHl8/kIh8MMDAzgcrloNBpsbW0RiUTI5/N0Op1TW6hO3FO5XEav18vChCJTxmq1EgqF6O/vJxgMoigKmUzm0IwQsZmLUhd+vx+n0ymV5Xw+vy/9+rRZpZ4HsX95vV48Ho/sZC9KNBy0vJwFDhZQtVgs9Pf34/f7ZTp+LBYjEonI4pDHzbFYXkR1x5GRESYmJjCbzaytrbG1tcWNGzfY3t6WJ8GzxEHtXjQdNJvNcuKLgLhKpSKbxp3GhfK4UAeDiToLfr9f9ufJZrOyP484CfWSReFRiPYIotCjKAo1NjaG2+0GYGlpiVgsxm9/+1sSiQTxeFwW0eoFJU8otIqikE6npZIWj8fR6/UMDw8zOTkp++3EYrEnFi5Ux34I98DU1BTXrl3D5/NRKBRIJBJsbW2RSqVO9Waltl6XSiVqtRr37t3DZrNx5coVXC6X7C137do1jEYj6+vr/OM//qM8VIric/BdX5+JiQn8fj/vvfceMzMzTE5OYrVaicVibG5usrS0xOrqak+s5yJGQ4Q7XLhwgZGREYxGI9VqlaWlJTY2NkgkElKR7fZnfhZE81eTyURfXx/z8/M4HA6Wl5fZ29sjEomQTqdfWsuRY4mmEVYXr9eLz+ej2WyysbFBJBKRDwlnK8XsIGIxPhj93263aTab1Ov1U5mW+TIQyp3ZbMbpdMqOru12m0qlImt6FAoFaVHo1XGkKAo2m41AIIDT6cTj8UjF1+/3Y7VaqVarso7L6uqqXECEdeq4fc8vC7FRlMtlMpkMuVyOfD6PTqfD6/XKIGadTkc8Ht/37AdRu0pEELTdbqevr4+RkRHZhVlkT6hrx5xWOaprRNVqNaLRKOvr6wwPD1OtVmV9nOHhYfnve/fuYTKZSKfTD7l8RGn8oaEhZmdnmZ2dxev1YjAYKJfLRKNRotGodD2Jz+e0yucw1BY8k8mEw+FgYGCAQCAgLQvxeJxIJCIbMopA6LOAWj5i7AwMDKAoCltbW+zt7clY1pcVCnIsbiOv18vly5eZmJhAr9eTyWT45JNP2NjYoFAo7OupcZZQuz2EgiKaowmZOJ1O6vU6wWCQTCbD6upqT5ycnwe1a+hg0bVMJkM6ne7JiH918KjBYODixYu88847spqpWBhcLhd+v59SqSTNt7/85S/JZDIsLCyQy+VIJpNUq9Wu6V90GOJzTqfTVCoVVlZWZNfn0dFRLl68iKIo3Lhxg0QiQblcplAo7FPe1BusXq+nr68Pu93O66+/Tjgc5vXXX6e/v1/GCy0tLcm4odMuQzFHxLq6tbVFuVzGZrNhs9kYGhpicnJSukQmJyf55S9/ST6fl72gRCyHCO6dnZ3F7/czMTGB2+1Gp9NJxWhhYYFoNNoz1uFOpyPlNDw8TH9/Pw6Hg1QqRSQS4euvv2ZtbY1KpXLqx8JxIApEhkIh+vr6cDqd5HI5rl+/zu7urtzHXpZsjsXy4nQ6mZycZHBwUDayunv3rpxMveR/f1bEAiP8p6J2glh0bDYbtVpNVg8VFoezZoFRP6+QTafTkZaXQqFAoVCQzdJ6DRH4bjKZmJyclMqLz+d7qKqnoiiy6+1rr71GoVAgl8uhKArZbPapUmK7AVHDplAoUCwW2dnZYXNzk76+PhwOBxMTE3i9XprNJr/5zW9ot9vkcrmHOo6rFRm/308gEODNN9/k8uXLDAwM4PV62dnZYX19nd3dXeLxuAycP+2o3T7xeJxUKkUoFCIUCmEymRgfH5eZIsJFUqvVZF0OsRaJqt+jo6M4nU6sVqtsVtloNEilUmxubh57IbKXiXjuvr4+AoEAXq8Xo9FILpcjHo+ztLTE5ubmmS7vYTabZSyQaNYpUqRFjCa8nLFwpMqLOM2I4lk2m41CoUA2m5Vphr2ipb8IYuFstVpyEy6Xy7IYksFgwG63Y7PZZLfks4Z6jDxqvKhP0b2IoiiyKFY2myUajaLT6WSgJUCxWCQej7O3t8cHH3wgC2dVq1V2dnZkg8JesnKqrZfLy8vU63WsViuDg4MYDAbcbjfT09P84he/IB6Ps7CwINOGReCpqFFhs9mYnp7G5/Nx6dIlWSU0mUyytLTE559/ztramiyk2U0yVBRFWi43NjbQ6XQ0Gg1ZFTcUCmGxWPB4PLRaLex2u3RZi6BkUWnYZDJJS/Hi4iI7OzvSCpHJZHpmfOl0OkKhEO+88w5TU1PY7Xby+TzffPMNa2trJJPJM7+HiUNCf3+/bCJcr9dPpNjskSkv6gJPouOr3W6Xp8BsNitdRmcdtfIiMgJqtRr1el2eeESNAZFydtAkfBZQZ2Id/Bl0/0nvSYhgUlEkTVjixPM3m022t7dZXV3lgw8+oFgsygUln8/LzIhe2VwEYi6srq6yubnJyMgIly5dkq02pqam+PnPf8729jZ6vV7WlhKWB6fTydTUlFR0REC0xWJhZ2eHZDLJ8vIyX375pexhIw5m3YD4rEXc0+bmJvF4XJavGBkZYWhoCIPB8NieRMKyIL6LjKK7d+/y5ZdfsrCwwPr6ek+4bdVuRaG8iMO3KHcvynuUSqUze6AEsNvthMNhQqGQVJDF3vWy9/YjUV7UpiJhNRAnHK1S7MMI5aXZbJJIJHA6ndy+fZtQKMTc3JysJSAKP6nldlYUGHEKFBMDkOOoF6rEPg0iJbpWq1EoFPZ1OBbB3na7XSq5IiZIuCR72d0oLFNCifnwww+ZnZ3FbDbTarUYGBiQcqnX69LU7/V65eHKarVK90k8HqdWq3Hr1i3W1ta4e/cu2Wy2q7No1IekarXK9vY2X331FYlEQrqohWvEZrPJNbrdblMsFqnX6yQSCUqlErFYjHw+L/vWiIrN3T6+DpauMBgMcgxFo1F2d3fZ2NhgZ2dHZmOdhfX3cbTbbVkEsVQqUS6XpSvxZXPklhfRVyMYDMpTz0F/81lHnGjq9Tp7e3u0220+++wzRkZGGBwcRKfTSVMc0HMn5ychNu1GoyHLnKvjFHpdFgdruVQqFbmRqjEajbLjrahDkcvlaDQa+06HvSYvtWWh0WjImjbvvvuujFkZHx9HURSuXbu271p1nSD4rmbM9vY2e3t7/P3f/z1fffWV7L3WzYqy2kJXr9dZW1ujXC4zMTFBrVYjGAxy4cIFWehQHS8jLOVffvklkUiEzc1NUqkUS0tL7OzsyPf1ymYuFBfRy6jZbLK1tcXq6ir37t0jkUjIoqpnGXGYEtZM0TJBHKxepnyOTHkRC26z2WR3d5df//rXchPe3t4mm83KYLBuXQyOEnHCqdfr5HI57t+/TyqVwmazodfruXv3LtFodF8XYHFdL6MOaM7n8zKuYWpqilarxe7urqwg2isL56NQF/zKZDJsbGwQCATY2dnBYDBgNBpJJpPcuXOHjY0NeVKGs6XsKopCtVqVG+uHH35If38/586dk2X+ReCz2p1WrVZlYb9qtcqdO3fY29tjY2ODXC5HrVbrqfGlKIrseWUymbhx4wZer5dkMonVasXr9Uq3WKvVkm6ihYUFUqkUiUSCYrEo2yP0kmzguwPT3t4eH330kQwM39vbo1AonOrKyi8LRVEoFousrKyQTCYpFArEYrGH1p6Xdj+Hmf0sFssz2QTFgHY6nQSDQQBZeC2ZTNJsNmVA2FEO/Gq1eiKzyOl0vpDNVJ0FIhbY/v5+6WMWlXZF0Z/nlVmhUOgq+Qi5iMae4XCY/+g/+o8A+P3vf08sFuPDDz8klUo91KPleTit8hFmebfbjdvt5o033uBnP/sZFosFu93O5uYmv/rVr0ilUty/f59arSZdKUc5v05KPg6H46nGjxgvNpsNp9MpU577+vqYm5vDYrHgdrup1+usrq5SLpdl9WFRt2NhYYFYLEalUqFer8uKvU9DsVg81fIRiABesdaYTCZcLpcslqnuWSQKHKZSKdmnRhw8n9WidxLysVqtz7X2OBwOAoGA3LdEwPyjXPhHQaVSOZGx86zyEXqC6KkmxlCz2ZRVmsX7jlJGh8nnyCvswoMiSdlsdp81RgSPQe9bD54VYWkQVhhRS+E4FL1uotlsSu3+5s2bAGxubpLNZnve8gLfzZNGo0G5XGZvb4+bN29iMpkwm83E43EZiyDGyllFtNYolUokk0lWV1dlgKWoSNxsNmVKZy6Xo16vk0qlKJfLMmj+LKxRQladzoNeajqdjmq1uu89IubuJAIxT5JGoyGr5woZnIUx8STUruxarSaz1w62aXmZMjpSy4tAXYdC/qFj3Gi61fIiUFf7VHNUcUKn1bLwtIhAcPjOGnGUC2o3yEfUfVHXlxAHg+NWWk675UVw0Dqgtp6o42SAfTEvB9erZ51v3WJ5gcevNY9bew7++1H/fxLdZHl52XtXt1heBI8aPycln2MpUgf7XSJwtrXWJ6GOb1AvwGcdIQtxCoLvJkqvW13UqGUgfMsCIYNeDc59HlqtlqwU+zScpTGlLrmg3qgfpwCr16Fel43gRRTZs4B6zJykfI5FeVEXENM+/KdDXctF/bOzzKNkIn5+llBvqo/aZM6aPB7HWVA+jgK1AvM08jpLMlXvXeqfaTzgUWvRScnn2Cwv2gf+fGhyexhNJt+hyULjKNDG0ePRZPN0nLScDo150dDQ0NDQ0NA4bWiBFRoaGhoaGhpdhaa8aGhoaGhoaHQVmvKioaGhoaGh0VVoyouGhoaGhoZGV6EpLxoaGhoaGhpdhaa8aGhoaGhoaHQVR6a8KIryXyuK8pWiKDVFUf7tM177I0VRPlAUJacoysZR3dNpQlEUn6Io/05RlJKiKJuKovyzZ7jWrCjKv1EUJa8oSlRRlH9xnPf6stHGzuFoY+dwNPkcjja/DkeTz+GcVvkcpeVlD/hXwL95jmtL31733xzh/Zw2/iegDvQD/wnwrxVFufCU1/45MA2MAT8C/ltFUX5+HDd5Qmhj53C0sXM4mnwOR5tfh6PJ53BOpXyOTHnpdDp/1el0/hpIPce1v+90On8BrB3V/ZwmFEWxA38K/H86nU6x0+l8DPw/wH/6lL/iPwP+h06nk+l0OveA/wX4z4/lZk8Abew8Hm3sHI4mnyejza/D0eRzOKdVPlrMy8vhHNDsdDpLqp/dBJ54OlQUxQsMfPv+Z7pWoyfQxs7haPLR0DiDaMrLy8EB5A/8LAc4n/Ja8f5nvVaj+9HGzuFo8tHQOINoysvLoQi4DvzMBRSe8lrx/me9VqP70cbO4Wjy0dA4g2jKy8thCTAoijKt+tll4O6TLux0Ohkg8u37n+lajZ5AGzuHo8lHQ+MMcpSp0gZFUSyAHtArimJRFMWger2jKMq7j7lW9+21xgf/VSyKopiO6t5Omk6nUwL+CvjvFUWxK4ryFvDHwF8AKIoS/lY+4cf8iv8V+JeKongVRZkF/kvg3x7/nb8ctLHzeLSxcziafJ6MNr8OR5PP4Zxa+XQ6nSP54kHKYefA159/+9oID/zS/sdc++4jrv3tUd3bafgCfMBf8yB1bAv4Z6rX3gE2AONjrjXzIN0sD8SAf3HSz3PEstHGjjZ2NPkcn3y0+aXJp+fko3z7B44VRVH+OXCh0+n8d8f+x7oQRVH+JZDodDr/80nfy2lDGzuHo42dw9Hkczja/DocTT6Hc5LyeSnKi4aGhoaGhobGUaEF7GpoaGhoaGh0FZryoqGhoaGhodFVaMqLhoaGhoaGRlehKS8aGhoaGhoaXYXhsBftdntXRPOWSiXlJP6u2+3uCvnkcrkTkY82fg7HZrN1hXzK5fKJyMfhcHSFfIrFoja/DuEk5pc2tw6nF+RzqPJyHIjsJkU5kc+sa3hcFpgmNw2NZ+fgfNLm0bOhye/xPGqt1uRz/Lw05aXdbu/7rtPpUBRF+5BViElwoMgP8N1kUMtMk52GxuE8VNhKm0fPhFiD2u22lJ8mt+943NjSOH5emvKiKAqdTgedTif/r/EdjzrZaDLS0Hg+njSftLn1bGjr0ePR5PJk1MrdUfFSlBedTofZbMZgMGC1WgEol8s0m03q9TqtVutMD4BOpyMtUkajEb1ej91ux2AwyEWj0WjQarWoVCrUarV97jehEJ4VOp0OzWZTKsNCBjqd7rHutrOG2oqn5uDJudfkJU7BYk0xGAyYTCYcDgc6nY52u0273aZardJut2k2m7Tb7TO9/jyKg1Zfl8uF2WymXq/Ldbter59ZpUa9/losFoxGI61WS46pRqMhXz+rtFqtfZYpvV6PXq8/st9/7MqL2GCsVismkwmX67vu87VajUajocXBfItOp8NkMmE0GnG5XJhMJqmYVKtVudCK70LhOYscNP+f9bHzKJ6kmPSi4qJGp9NhNBqxWCy43W4URZHzRhwIzvo8ehJ6vR6dTofD4cDhcFAqlajX63Q6Her1+knf3olwcJxZLBbMZjOtVkseMIXycpZRH46OY30+NuVFfdM+n4+f/exn9PX1cenSJTqdDn/7t3/L9vY2t2/fplwuYzAYjlQr6waExcVkMuH3+3G5XFy9ehWfz8e5c+dwOp2YTCYURSGbzVIsFllaWmJnZ4ft7W329vao1+vUajXgbCh/YkMaHR3FYrGQzWap1Wrkcjmq1eqZkMGTUBQFs9mMXq/HYDBIi1Sn06FarcoDw3GYck8K9bM4nU4CgQBer5fp6Wn8fj8XLlzAYDDQbDapVCrcvn2bdDrNF198QSKR2CePXpHJ8yLWboPBwMzMDMFgkHfffZeJiQm2traIxWJ88803fPPNN2fOyqAeJ8Kq98Mf/pCZmRny+TyVSoXr169z48aNY9+8TyvCYBEMBrFYLNhsNkwmE7FYjGw2S7vdPhJvy0txGzkcDi5cuMDo6CjvvvsurVaLxcVFGo0G9+/fp9VqnTnFBdhnTnO73VK5C4VCXL16Fa/Xi9lsRlEUkskkhUIBu92O3W6n2WySzWYBpPLSy4gFQ1inBgYGcDqdGAwGCoUC5XKZSqUCnK2F4lEoiiIteGazWVrvhLIsToi9srCqT8LCytvX18fQ0BBXrlwhFArx5ptvYjabqdVqFItF9Ho9Ozs73Lt3j2Qy2XPK3POilqVer2dgYICxsTG+//3vc+nSJRYWFtjY2CAWi3Hnzh1pgTlrchPrtslkYnp6mtdff51UKkUul2NnZ0e+56zJRY3D4cDlcuHz+bBarVQqFQqFwj6X9ovI51iVF3GKMZlMeDweHA4HuVyOcrnM7u4uu7u71Ot19Hr9mfqQxSZiNBpxOByEQiF+8pOfMDAwwOuvv47b7cblckmLS6PRQKfT4XK5uHz5MuFwmImJCWZnZ1lZWeHmzZtUKhWKxeK+oOheQS2vYDCI3+/nxz/+MYFAgN/85jdsb2+TSqV6erFQn+IOZqIJ+RgMBrxeL06nk+9973v4fD5cLhcGg0G6R/b29kgmk6yvr7O5udn1J2e1T31wcJDx8XEmJyd588038Xg8jI6OYrfbpaJrtVqx2WxcvXqV0dFRIpEIXq+XtbU1UqkUgHQrdaM8XgQRK6TX6wkEArjdbr7//e8zOzuLx+OhVCqxvb3NvXv3iMViZzZeqN1uS8XO7/czNTXF5OQkjUZDHijPmkzUiLVobm6O0dFRhoeHcblcdDodcrmcdD2+6Bw7FuXl4EnIYDDgdDqxWq2USiWy2SyJRIJkMkmz2ZRBl2cFsdno9XpcLhcDAwO89tprDAwMMDc3h9VqlfFAhUKBSqWC3+/HZrPhdrsxGAwEAgFCoRAGg4HNzU0URaFQKMjf32vyFIuk2+2mv7+fq1evEgqFuHfvHplMBr1ef6ZOz2oFRmw6BoMBu91OMBjk9ddfZ3R0FI/HI/3xzWaT1dVVdnd3abVaxGIxqtXqvpNzNwXxqp+/3W7j9XqZm5vj4sWL/MEf/AE2mw2HwyGfSVEU9Ho9NpuN6elpQqEQd+/eBSCZTJJOp8/UGHoUYl1yu92EQiEuXLjApUuXAKhUKsRiMTY2NshkMjJA9awhxp3P52NwcFB+bW1tdc3cOS7EXFQUhdHRUS5cuMDU1BRer/f/3957/MiVZWmeP9NaCzfXmlo6g4xkVkZkRmZEVnYlGlmoTQGN3s5uVr2bQS8aM72Yf2AWs2k0ZhpdAzSQlQ30dKWIzKrO0AySQdIpXCszN621tlmw7o3nHk4Z7qSb+fsAByOcbk57x+4999xzvvMdFhcXefjwIfV6/VD22JFlXrrdLiaTCbfbLW+DVquVdrtNo9GgXq9Lxv9JueUIxygOmfHxcd577z3Gx8eZnp7G6XTKctCjR4/IZrM8evSIXC7H/Pw8gUCA06dPMzY2hsvlYnZ2lng8zsTEBJFIhHg8LtOZgwiDwcDw8DBjY2N4PB5sNhvVapVMJjPw5MFer4fNZsPpdGK1WnG73bTbbUnkrlarOBwOfvjDHzI8PMyFCxdkzVl0rfV6PSwWC6OjoxQKBXZ3d8lkMtTr9T3/Tr9AOMqhoSGCwSDvvvsuP//5z/H5fADEYjFWV1dlsG82mwkEArKkZrFYuHHjBjMzMzgcDtbX1yWnTBkcDrpvEs8qsrtOp5O/+Iu/YGJigpGREUwmE/fu3SMcDvPVV1/x4MEDcrmczLycFCi5QGazmdnZWU6dOoXf78doNJLP59na2iKbzcpL+aBlwV8EwQUymUx4PB4CgQAWiwWtViu7jZQcvO+DI8u8KNPYXq93T+ZFBC+NRuNEpR2VdnE4HExOTvKLX/yCQCDA9PQ0Op2OWq1GPp/nzp07bG9v88knn5BKpXjnnXeYnJzEarUSDAZxOp0EAgF2d3cZHx+nVqt95zY+aHYVwcvIyAhut1sGL9lsduBJyyJ4GR4eluul2WySzWblnz6fj48++oiRkRHm5uaw2WzSeQrirt/vp9lssrOzw+PHj+l0OqRSKSlCBv1jQ1EKCwQCXLx4kR/84Ad89NFHNBoNisUi8Xic3/72txiNRs6ePYvb7abb7WK322Uwc+PGDTqdDiaTicnJSWq1GrFYbE9JpF/s8X0g/IXT6SQYDHLz5k3m5+dl8PLkyRNu3brF7du3WVtbQ6/X75FyGHTsJ+qK4OXChQv4/X4MBsOe4KXT6QCcqKqCsI/g2nm9XrnPRCB3mBSRI+02Eg8iFrryjZ+URQ/fRuyihDY0NMSlS5c4ffo0oVAIu91Oq9WiWCxy//59UqkUd+7cIR6Py26a7e1tyuUy8/Pz8gCz2WyYTCZsNptcIIN4ExLdM3a7nenpaSYmJmg2m+RyOWq1mmzdHMT1pAwoRLZtfHycK1eu0G63KRQKNJtNCoUCdrudsbExeUjXajUqlYr8uXq9LoO+UCjED3/4Q1ZWVgDI5/MkEom+yLyI9yikBCYmJrhy5QojIyN0u12SyST37t1jfX2dpaUltFothUIBm83G1tYWNpuNiYkJHA4H09PTkndmNpuJRCI0m00ikQiRSGQg19RB6PV6mEwm5ufnGRsbY3JyklAoRKvVolarkUwmicVi1Ot1eXs+SbYR0Ov1zMzMEAgEmJ2dZXJyEoBCoUChUKBYLJ5Y/RtxMff5fPj9fslPLJfLFItF2VjRbrcPxT5HTtg1Go0YjcZnBi8n4QMWUbvQnJiYmODDDz+UTkKj0VAul0mlUnz88cdsb2/z1VdfkcvlpJNYWVlBq9XK4MVsNjM2NobFYsHpdMrUXD8cPq8CUQYzm8243W7OnTvH+Pg49XqdcrksyV+DGLTtb9/1+XxcuHCBs2fP8rOf/YxOp0OlUqHVaskOGr/fLzN45XKZWCxGuVxmaWmJdDrN1atXmZ2dlUS64eFhADY3N0kmk32xfoRdrFYrFouFU6dO8eMf/xiXy0W32yUcDvMP//AP7OzscPv2bXq9Hg8fPpQ6U3a7nXPnzhEIBPjoo4+YmJhgcnKSs2fPUiqVsFgsfPrpp3vKR4Pqp5TZWovFwpUrV5iZmZEt0ltbW6RSKcLhMNvb21QqlROXcRF/9no9jEYj58+fZ2pqisuXLzM3N0c+nyeTycivWq124kYFKLkuo6Oj8mtoaIhMJkMymSSfz0t/dayDF41GQ6fTIZ/PS40SvV4/0I5gP/arVHq9XsbGxpifn2dychKv10u326VSqbC8vMzu7i4bGxvEYrE90bsQ1+p0OjQajT0HtlAvFrfQQTvERS3ebrfjdrulHk44HCaVSlEsFgem/PisjiJxk5mbm5O3PvhW80ZZV282m7RaLVZWVigUCmxtbZHP59nd3aVUKsmym2i5Hx0dZX5+nkajgdlsptVqHfvuI7EnXC4XHo8Hv9+P2+2m2WyyubnJ5uamPHSFHYW2Tblcptlsyu4it9tNNBplYWGB0dFRqQkTDodZWlqi0WhQrVYHUsl6PwcvEAgwPj7O6OgoOp2OZrNJNBpld3eXbDZLtVqV+hzHdW0cBYSftdlsuN1upqammJ2dlWTwdDpNIpEglUpRKpVOXOZF+CmRHRcZKdFcUiqViMfjMvt7WIr6RxK8CFZ/s9kkFovhdrtJp9MAsrxxkj5Y8azj4+N88MEHnDt3jmvXrgHQarVIJBL8/ve/JxwOc+vWLYrFohQYEwea+KpUKuRyOelIxCgBQcwUEfAgQJmxGhoaYnh4WHbQfPbZZ2xsbJBKpSiXywNDjhOBqjLwnZqaYmFhgXfeeYcbN25gNptlTd1gMGA0GrFYLHQ6HXn7+/3vf8/W1haLi4uk02lZWhsfH2dmZgaPx8Po6CgajQaLxYJGo+Hzzz+XN6PjeMlQltA0Gg2hUIiZmRmmp6cZGRnh8ePHkpfxzTff7AnChJx9uVwGYHd3F71ez9bWFn6/X+oHTU1Nce7cORKJBEtLSySTSUql0sCsr/0QEgRjY2MymzA2NoZer6dcLvPo0SOWl5cJh8Pk8/mBtcOzoGwfF91F169fl+KHGo2G9fV1Hj58yOrqKvF4XHZsHbf9cxRQ2sdutxMIBLh58yanTp0iFAphMBiIx+MsLy8Ti8UolUqSSvJ9caRlI/FgoqVuUA7Vl4U4fF0uF263W2pQiBbner1OIpEgGo0SDoeJxWJ7xiUIiE2gLKEIAma325W37f2H3iBBefPtdrtUq1V5ix6U26Cym0Gn00ltpPn5eekMzGYz7XabVCqFTqeTIoY6nY5qtSoP3O3tbWKxGMViUd524OlMMaG1pOwIdDgc0qkc9zUkOHTBYJCZmRmcTieNRoN8Pi8zcmIWmFgXysBH7MtOp0OpVEKj0RAOh6Vmh8PhIBgMcubMGfR6fd8Sml8EYQeTycTExIS8GFgsFin6GI/H2d3dpVqtvu23+0ah/KwNBgM2m42ZmRmZnbPb7eRyOVqtFhsbG6yvr0v12OMY+B81RHNOIBDA7/fj9XppNpsUi0VSqZTkSx1m4HukZSP4dpT6fod4GK1Sxxni+brdLtPT0ywsLPDuu+/yl3/5l3L4Yj6f5/bt26ysrPD5559TLBZptVrfqSkr6+52ux2v14vNZkOn00lhJHFjHkS1YmXgIlrts9ksqVSKWq1Gu90eiGcWwb2QFbhx4wanT59mYWGBhYUFzGYzFouFaDTK4uIiFouFkZERKVMej8f5u7/7O8LhMGtra5TLZVlSE0z/TCbD1tYWPp+PWq2GxWJhamqKoaEhSRyH49kyLexjsViw2WxcvXqVjz76CJfLRT6fZ21tjU8//ZREIiGf4yB+htL3pFIpstksn3zyCdFolH/5L/8lQ0NDnD9/HrfbzZ/+9CcikQjVapVisQgwEGsNvuUpuFwufvrTnzI5OSnHbiwvLxOPx7lz5w4PHjygUCi87bf7xiEuixaLhVAoxF/91V8xOTnJqVOnsNlsfPrpp+zs7PDf//t/5/79+3vK1ycteDGZTJw6dYqJiQlOnz7N+Pi4bDhZXFzkzp07FAoFeek+DLyR8QD7MehkXeUNWqvV4vP5mJiYkHoAgueSz+eJxWKk0+nviIXt/11Go1EKbFksFnQ6nZzuWq1W5cYZNIigTTy3GEy5XydoUCDaVb1eL6Ojo0xNTREMBrHZbMBTTotoybRYLLRaLZmBSSQSJBIJ0um0HA63n0NTq9Vk7Vmkt5WlgH6wpV6vlzotwi4iEyeySvDsjkbl90RGKpvNYjabSafT0skODQ0xNDREKBQim81KafN+v1krdV1EQOxyuaQKMTwdBFupVKhWqydOj0tA8IFERiEUChEMBmm325TLZXZ3d9nc3JQk3UH0vy8DUXINBoMMDQ1htVrR6XQUCgUSiYTck4fFdRE48uBlP+v6JLRKC+fgdrvlsMWf//znOBwOut2uJFAuLy9LHRehOqjkAyn5K36/H4fDIdUcjUYjpVKJTCZDNBolk8ns2TyDYFuR2jcajYyPj8tW2Gq1Sj6flyqfg8KhEq30Z86cYX5+ng8//JCFhQVMJhMGg4FyuUwul2NxcZHf/OY3kgskdBWKxSKPHj2iWCzu0ZmAp4d0q9UimUyyvr7O6dOn5c/A3q6K4whltkR0nonOoXw+L0X3RKvzi7IjYr0I+6ytrbGzs8PMzAwul4u5uTnOnz9PpVKRdhX6L+L99OuaE/tKOQMqFArh9/vlKIlUKkU8Hpe6XIN2SXgehN9tt9tYrVauX7/O7Ows169fx+PxEIlESKVS/P73v2dxcZFkMkmz2TyRfCCheyNGSczMzOD1etFoNDx+/JilpSU2Nzdl52xflI32QxysIsVtMplkvX4QbjNKiOex2Wz4fD48Hg8ulwuDwUC73aZUKhGJRCSLv1QqPTNq38/ktlqtkvQsJN8F32UQITaHOKwEh0rU5AfBqYrPWJBvRdeHz+fDZrPJ56vVamQyGbLZLNlsVnKexJ4SNnleK6LI1ikDl36B0lkqv7rdrhS9bDQar3TDE/6n2WzSbrfJZDLE43FZjnO5XIyNjZFIJDCbzTQaDRnA9DsMBoOco2axWDCbzZJDVywWpfijcojnoEP4bjF0UXz+w8PDMuMt1kg6nSaXyw28uvdBUHaqOZ1O2Qnq8XikxpTowhKZu8MO7I48eBFRfrVapdVq4fF4MJvNUs0yl8tJBnK/Q2xwEUjMzc1x9epVTp8+jcfjoV6vUyqVePz4Mf/pP/0nkskka2tr0hkexFAXh4zoDgkGg1L+vN1uy+ClHw+jF0EZuCwsLBAMBqnX6xQKBeLxOPF4XJZN+jmAERtbELvff/99PvjgA6xWq2zz7XQ6bG1t8cUXX/Dw4UMSiQSdTofd3V2APeTtQb79CYKp3W7HbDbLuU2lUkmKYIl18zJrQsnN63a7PH78mGq1itVq5cyZMwwPD/Pzn/8cg8HA7du3yeVyxGKx72RJ+wFKEqooh1y6dIn5+XlGR0dxOByym/Gbb75hdXWVVCpFo9GQ0u6DDCWR2+FwMDMzw/z8PL/85S8JBAIYDAZyuRz/9E//xMrKCuvr6/ISMei22Q9hJ5fLxcLCArOzs1I7SVAhvv76a+7fv08mkzmSvXJkrdLKNLTQJ2k2mxgMBrrdLlarFavVOpDCakJzxePxEAwGcTgcklxbLBZlqSefz8ty0f4DWNhEpNrsdjsejwer1YrRaJT2FH8K1cJBsaW4ZQs+h5jpIxQ/xdcgZF7g2/q6uO37fL49zrTRaFAoFIjFYvK21263ZdC6vwzyLIgsTT86W6VgnyBxizUv5PxFIPgqa0JZpi2VSiQSiT2XKjHixOPxyKxWP2c6hf3MZjPBYBCv1ys7GIUSqmi5Fy32g479mlxGoxGfz0cwGCQYDOJyuWg2m5RKJZLJJPF4XKrFGgyGgaZBHATxvCJTLMYA6HQ6SqUS2WxWlnPFnjlsvJFW6VKpxMrKCs1mk4sXL2I0GqVDEKWjfoZy4Wu1WqanpwkGg9y4cYMf/OAHeDweGo0GGxsb3Lp1i4cPHxKPx2k0Gt8hLyu7lADZWfTOO+9w4cIFpqamMJlMMgDa2tpiY2NDtnz2uy3h25qz2WzG4/Hg8/mkHSKRCLFYjGQySS6XGzjxMHHwikyKRqORDnNxcZGPP/6Ycrksu2ledFAruSJarZahoSGpnioI5crf0W/r56Auotc5bMUlKplMks1muXv3LkNDQ8zNzXHx4kVGR0flOIXNzc2+zXR2u12MRiNWq5XR0VFu3rxJKBTC4XDIqeORSITl5WU2Njao1+uH2iFynCGyaWKG2s9+9jM5JqHb7XL79m0ikQjffPMNW1tb1Go1KRLZb/vmdaEsq5nNZoaGhrhx4wajo6N0u10ymYy0TzQalST3vsm8wF49BcHzKJfLUhTJbDZjs9n2bIxB4L2I9L+IRn0+n5Rrz+VyhMNh2V0ksiXwXaKkOFQcDgdut5uhoSFGRkakGFm5XCadTkv1YhEIQf8dQAdBrAWj0YjZbJbt5WJej8g4GY3GgXheAWUAqmwPF11CQnBOBDYvOlTEuhKihw6HA4/Hs2echNBhUv58P0J5EXgdX6LRaGQmVPAahoaGpIq1x+ORqqr9CGWXkQhgfD4fbrcbvV5Po9Egl8vJjivhr09C4ALfBi9msxmn08nIyIgsFynnO+XzeUql0qF3z/QLhJ2sVisOh4NAILBnyn0ymSSZTMq5ake1fo488yKCF9F2J9ozxUyfL7/8klQqtWe8er8uCHGYjI2Nce7cOcbGxvD5fKysrLCyssKtW7f43e9+J/k/wsHuvzHq9XpGR0dxu9188MEHTE5Ocu3aNUZHR8lkMjx69IjPPvuML774gnA4TKFQGJjyyX4ogzsRtJXL5RPpOJQH8/5uomf9vOAqiLV45coVrl27xvDwMHq9nlwuRzKZlOMD6vW6fO1xh8jQCSExs9mM3W6XpG54+edQ2rXT6RCNRnnw4AGBQED+O0LOoN9hMpnk5UopSpdMJvn0009l+2+r1eqrFvrvAzF+ZXh4mMuXL3P+/HnZ6ReLxYjFYnJeljivBl3y4yCIc8rj8XDjxg1mZ2eZnZ3FbDZLgchbt26xubm5Rxuo78pG8K2DETNTRNQmMgriVj0oCrwajQaHw4HX68XhcGA2m6lUKkQiEXZ2dtjZ2ZE/t3/hK+uIXq+XQCDAqVOnmJubY2RkBI/HQzweJ5lMsrOzw/LyspwqPGjlk4MgCKmNRqOvMwTPg9gvYi8oy0g6nU52rL3IGYjXGQwGDAYDfr+fUCgktSpEF1O9XieTychpuMeZO7VfQkBkjESLucjSiedQvuZVIPRw8vm81IwRa28QOm8MBgN2u11Opdfr9TSbTSqVimw3V2q7DLpfEej1elgsFkZHRxkbGyMQCNDtdmXAsrm5SSQSkVy7fm8UeFUo172w08jICC6XC41GQ6FQkGq6ghZxlHgjrdIHabyYTCYsFossB7xuqvdtQ5kxEc/k8Xj2SLkXi0WpNigCDSEGJRRxxa3x7Nmz+Hw+rl+/TiAQ4MKFC3KGRi6X49GjR3zzzTc8fvyYeDwudU4GEaL+LDrRWq0WkUiESCQib4X9tl4OgghaxdDNRCLBzs4OPp8Ph8OBy+VCp9Nx7do1SdpdWlp6Ju9CELyNRiPz8/N4vV4uXbrEyMgIk5OTuN1uer0exWKR9fV1PvnkEx4/fryHS3Pc7Ko8RIW+TzKZJJ1OY7fbuXDhAul0mtXVVaLRKEtLS5IE/aJnUQaMdrsdi8XC6dOnZbZTCANubm4SjUb7+pLV6/Xw+/28++67nD59Gq/XKzvZtra25FgJke4/buvgsKGUKTCbzUxNTfGTn/yE4eFh6XM///xztre35XDBk1RKExB7RMxRm5iY4L333iMUCslRCbdu3WJra4tEIkG1Wj3yjrw3pvOy/yGESqYIXgYBIkUvaoFCg6LRaFAqlWTErlz4IoIX7Z/z8/OEQiGuXr1KIBAgGAxisVikSmEsFmN9fZ14PE6pVJL/5iA5mf0lNLF2xCgEcXgN0jPD0+BMdBVls1lsNpvM3hkMBsbHx7l48SJ2u51MJnOg3ogg03m9Xux2O5cvX2Z4eJhLly4xOjoq5yaJMm4ymWRlZYVoNHrsxcjE+xKOsVgsUi6XpRLu6Ogo4+PjsvSl7Hh81jPtVyC2Wq243W5CoZCcjCsUVdPptCQg9jNsNhsTExOMjIxgtVple7TQLTlpB7SSgOrxeJibm8PpdAJP19rGxgbhcJhisUitVpP+9rjuk6OAkqhrtVrxer3Mzc3h8XgwGo202222trZYX1+Xk7VfVq7gdfFGxwMoa9SBQEBK54s2NFEO6NdFIXRXhBLqyMgIAKOjo1y/fl0OwNtfIrPZbExPT+P1erlx4wZut5vR0VFZcsrlcjx8+JBoNMrt27dZXl4mn89Lafd+tdeLIDJUyixVOp0mmUy+VOmkXyCeQ5BFnzx5Ij9bp9MpS0ahUIiFhQWmpqaYmZk5MPMi9o/b7ZYD94QSbavVolKp0Ol0WF1dZW1tjfv377O4uEixWDz2h7Kwk2jfXVtb48svv+Sdd95hZGSEmZkZfvGLXzAxMUG73SafzxONRveUevbz6sT6OnXqFMFgUGqezM3NMTs7Sz6f589//jN3795lc3NTzjfq531nsVgYHh7G6/Wi0+mo1+usr6+zubkp238Hifz/PAhf7HK5mJycZGpqCr/fL8tFa2trPHr0iHg8LrPmJy1wgb2T3G/cuMHFixcJhUK0223u37/P9vY2a2tr7O7uSu2to8Ybn22kdK4ajQaXy4Xdbiebzb7pt3IoUPIDhKR0tVqlVCrJDzEQCHDmzBlMJhM6nY5msylvh1qtFrfbzeXLl/F6vVy8eFHOhuh0OhQKBfL5PE+ePGFlZYWlpSXC4bB87SBvIkE4Fc+ptMegTJKGvYdyu91mZ2cHrVbL/Pw8c3NzUpdF8KgajQZzc3PPnCKu1Wqx2WwYDAY5LbpYLNJoNKhWq1SrVdbW1rh165ZsiVUKmB13CH2bSCQip/3qdDqGh4ex2WwYjUbC4TC7u7uSeLrfVmLtCKXvs2fPcvr0aS5fvszs7CxutxuPx8Pt27dZXFxkaWlJTn0Xr+9XCE6dw+FAq9XSbDaJRqNy8q8oZffzM74sBEnbZrMxPj7O8PAwLpeLYrFIIpFgd3eXjY0Nstms/OxPKjQaDR6Ph8uXL8usSy6XY319nY2NDXZ3d0kmk29sTMIbC172R6y1Wk0O/RKtn/0K8UwijR+NRrHb7UxNTTE5OYnNZmNsbEy2JoogRxzOZrOZUCgk1UKLxaLs/rh37x6JRIInT56QSCT2BHmD7lyUbbz79UoGEeLzTCaT9Ho97t27R7fbldN+BWFXZGKelSnp9XpSKl90Rgi11GQyST6fl+TxTCbTN4GLkrDb6/Uk52t6eprJyUnsdjtOp5OZmRl++ctfksvleOedd2QnY7vdliVHp9OJwWDAarViMpmYnZ3F7/fjdDqpVqtEo1GKxSL379/nk08+kZOq+9lPCXQ6HSqViiTl2mw2Ll68iMfjIRwOy5bger0+8Bek/eUQg8EgL0lLS0t7prPD4PqeZ0HYR/A5h4eHOXXqFB6Ph1gsRjgc5osvvpCT198k3mjwotwI4hY4KMGLaAkXku1arZaFhQWazSZ2ux2fzyc/eFE+E4RUYRvB6RAjBOLxOH/4wx/Y3t6WnQ/9LCj2OhBdJf1ywH5f9Ho9UqkU5XIZt9st9SQCgYCcPyNIc0ooD3aR/atWq4TDYfL5PJ9++ikbGxuSUNdqtfZ05CgvFse9fARP36NQwp2dnWVycpLp6WkCgQBTU1PMz89L3RKxhhqNBjs7O3Q6HYaGhrBYLDgcDkwmk9xXYtbY8vIy9+/f58mTJ9y6dUu2UA9C940oHwrukNVq5fz58/h8PlZXV3E4HGSzWTlqYdD3HDwtH4oGEnGBXFpaYmdnRwYvJ5XrIoi6ohFlfn6ebrdLNBplbW2NL774Qk6yf5N4I1Olu90u5XKZSqUib0CpVIpEIiFF1gahDCCcWj6fR6vV8vDhQ0wmkxwVvv/QqdVqspZaKBSo1+vs7u5SLBZ5+PAh2WxWylDvn57cz3Z6EcQB0W63ZZkol8vRarVwOp14PB4SiYS8FQyKLfZzOra2tqhUKgBUKhU5AXj/OlKO30ilUtRqNSKRCOVymVgsRqlUkpkXMXFaabd+CloERBmx2WyytraGyWRie3ubVCqFw+HA7/fv2S8i+BDDB4X2VKFQoNPpkMvlqFarMiDa3NyUHSZKnswgrLVCocDDhw+p1WrMzMxIeyi/+mktfB8oO/1EUwQ87RwNhULU63UpDPoyBPBBhKB5TE9P43a75ZkteFJCH0o5vuNN4EiDF/EgIpIVjrPdbhONRqXarHAg/bwgxIErbs75fJ4vv/ySZDLJuXPnOHv2LH6/n5GREVkTrFQqrK6uks/nWV9fl6nKUqkkDy6RqhaOuJ9t9LJQBi/pdBqPxyNrqR6Ph2azyfr6+sA5WBH81ut1arWazABEo1GWl5eZn5/nypUr8nAWGb9ms0kul5Nljnw+L0dGiGCm0WjsaS/v57WkDEja7bbkpExPT7OyskIoFOLs2bM4HA6pkCsghl0Ke0QiEQqFAk+ePCEej7Ozs0MymZRiiMqhe/2ecYGntkulUnz55ZeUy2U5rqVer9NqtWg2mzJ47uc18irQaDQ0m8093WRWq5WpqSlarRY2m41Wq7XngD4JUD6r3+/n0qVLBAIB0uk0sViMzz//nFgsRjabpV6vS37im8IbybyIQ0iv1/OnP/0Jm83Go0ePSKfTFIvFgbrZiExTq9UikUjI/85ms7jd7j03wmKxKBn+0WhUtkLX63XZtgqcqMBFQFmKK5fLrK2tyZa8QThEnoX9zrHX61EoFIhEInQ6HVqtFlarVQpD9XpPp06Licrr6+tUKhVSqRT1el2OoVAeRoO2jsQeE0FbLpejXC5jsVjwer171st+cnQqlaJSqcjyWiaToVar7VFQHRQoswypVIr19XX+x//4H+j1eur1uuRCiTb8QXr2Z0GsDSH/v7S0xO9//3vK5TIbGxtEo9G+pzW8DkRW22AwoNfr94gZxmIxdnd32dnZIZvNvjV5Bc3zbq82m+1Qrrai5dVoNBIMBtHpdBSLRTnzSLSgvS4qlcpb2WUul+tA+yjTi4CcoC1IT2LDiHJRq9WSoxNE+6uS7f99F0ahUHgr9vm+60cQdp1OJxcuXMBms8nMyyeffEImkzmUroi3tX6sVutL2UeQlpXCjkajUf69UsFacGT2Z1i+j42q1epbsY/dbn8p+yjJ3MAeZ2s2m5/5mm63K1uDRTCjHFT5sjYrl8t9s79EECvmNbndbgAZGIssOBxeafpt7K+X3VvK2V86nQ673Y7X6wWQGl3pdFqSvg+be/e29taL7COeVSgxX7hwgZs3b9JsNimXy2xvb/OnP/1pT5buKPA8+7wRwq44hMRhrdVqZUtev05nfRkIpyrmoQgRMuXAPaUdDgokT8Lt53kQZUdBIGw0GrIuf9JsI/ZQu92WY+bFGhNraP8N8STZSGkLsU6edWMWzllI/p+Um7VYDyLgLZfL8v+VfugkrRv4Vv+nXq/vmcmzP2iBk7Gn9q+TbDbL+vo6zWaTWq1GKpXaM57jrbzHN5F5ge9mI+DwykTHLfMioHzWF/Ez9tvhMDdIv2ZelBBrRWyUw5wxc9wzL0q86jo6DBz3zIvAq+w3gYPs9ao27KfMi8BB/hi+O8rlMHCcMy8C+5WWlc9/lOXD45p5ERDPrdRuUY7TOOpA961nXpRQpt0Gvf1V+Yzw3fT2QZtiUG3xfbE/lX3S7fSsQ+ckY/9+g2cHMYcRtAwCDsoonEQ7KLE/gBHr6iTaRayPgzrQ3jYn7I3qvABSXOukLIT9EfyLfkbFwRhkku6rQD1gno+D7KOWY78L5Y36oO+fNCife7+0/Um1iRJvO1A5CG888wInczGcxGdWoeI4QN17z4Zqm71Q7dE/eC7nRYUKFSpUqFCh4rhBzcWrUKFChQoVKvoKavCiQoUKFSpUqOgrqMGLChUqVKhQoaKvoAYvKlSoUKFChYq+ghq8qFChQoUKFSr6CmrwokKFChUqVKjoKxxa8KLRaLwajebvNRpNRaPRbGs0mn/1Cq81aTSa/6DRaIoajSau0Wj+zWG9r+MC1T7Ph2qfZ0Oj0fzPGo3mtkajaWg0mv/4iq/9QKPR/KNGoyloNJqto3mHbxeqfZ4PdW89H6p9no/jur8OU6Tu/wSawBBwBfj/NBrN/V6v9+glXvvvgHlgEggB/6jRaB73er3fHuL7e9tQ7fN8qPZ5NqLAvwf+ErC84msrwH8A/g74Xw/5fR0XqPZ5PtS99Xyo9nk+juf+EnMcvs8XYOPph39K8b3/B/g/XvL1UeDniv//34H/9zDe23H4Uu2j2ueQ7PTvgf/4mq/9ENh628+g2ueN20TdW6p9DstWx2p/HVbZ6BTQ7vV6K4rv3QfOv+iFGo3GAwz/88+/0mv7CKp9ng/VPipUHA3UvfV8qPbpUxxW8GIHivu+VwAcL/la8fOv+tp+gWqf50O1jwoVRwN1bz0fqn36FIcVvJQB577vOYHSS75W/PyrvrZfoNrn+VDto0LF0UDdW8+Hap8+xWEFLyuAXqPRzCu+dxl4IeGp1+vlgNg///wrvbaPoNrn+VDto0LF0UDdW8+Hap8+xaEEL71erwL8GvjfNBqNTaPR/AXwK54Sn9BoNFMajaan0WimnvEr/m/g32o0Go9GozkD/E/AfzyM93YcoNrn+VDt83xoNBq9RqMxAzpAp9FozBqNRq/4+55Go/nJM16r/efXGp7+r8as0WiMb+J9vymo9nk21L31fKj2eTGO7f46RCayF/gNT1ujdoB/pfi794AtwPCM15p42k5VBBLAv3nbzOojYGqr9lHt87q2+XdAb9/Xv/vnvxv/5+f2PeO1Pzngtf/0tp9Jtc8btY+6t1T7fB/7HMv9pfnnf+BIodFo/i2Q6vV6/9eR/2N9CNU+z4dqn2dDo9H8a+B8r9f7X972ezmOUO3zfKh76/lQ7fN8vM399UaCFxUqVKhQoUKFisOCOttIhQoVKlSoUNFXUIMXFSpUqFChQkVfQQ1eVKhQoUKFChV9BTV4UaFChQoVKlT0FZ47Vdput/cFm7dcLmvexr9rtVr7wj7VavWt2MfpdPaFfYrF4luxj81m6wv7VCqVt2Ifh8PRF/YplUpvxT4+n68v7JPJZN64fdSz6/kYhL313ODlTeJ5XU8azVv5fFUcQ+xfJ+raUKHiaKD6ZBXfB0ftq9968CIesNvtHviwygcexA2jfGaFsI/8/2c9s7DNINrkWdgnfgSAVqs9UTZQoeKoodxj+/eb0ucM+r5TL0rfD8q1cxRr5q0FLy8KVE4ihA32f+DP+9mTgv0OdP/fnSRbqHh9vMzeOsl4Fb980vadGsy8Oo4y0H0rwYvyBi1uzna7HaPRiF6vR6vV0m636XQ6NBoN6vX6gVFcv6LX69HtdtFoNGi1WrRaLQaDAa1Wi8lkQqvVotfrD3xOYYdms0mj0aDT6dBqtd7CU7w5KNeKzWZDq9XK79dqNdrtNtD/6+JVIZ5XuZ86nQ7AnvWj/LmTCmEfsVcMBsOJuwA8D8pMi0ajwWw2o9frMZvN6HQ6+XONRoNms0mz2aTVakkfNihQ+ma9Xi//X6wd5Rkkzi7xp7qWnkLYz263YzAY5N6r1+s0m03gcHz1W8+86HQ6dDodVqsVm82GwWBAr9fLDaLVamm1WnS7XemYBwUiaNHpdNJJ2Gw2dDodRqMRnU73nYxDp9Oh1+tRLpdlEDPowYuAVqvFYrGg1z9dtsKhiHVx0m6CB0GZuVPxXaj2eT7EYWw2mzEajTgcDgwGg/z7crksL5OD7HeEbwbodDp0u13a7bbqY14SGo0Gq9WK2WyWdmu32zQajUOz3xsNXpRRrdVqxWQyce7cOQKBABcvXmR4eBi73Y7FYqFSqVCpVHj48CG3bt0inU6zs7OzJ1vTbxDPbzKZsNls+Hw+zp07h91uZ2hoCKvVytDQEGazGafTKQ9pQN5u6vU6jUaDL774gi+++IJ0Ok21Wt3zM4MCYS+j0Yjb7cbv9/Phhx/icrnodrs0Gg0+++wzdnd3yWQylMtlmck6CVBeAMRhEwgEAAiHw9RqtT0/dxIh/IXJZMJisTAzM4NWq2VtbY1yuSwPpn70J4cBZcbOaDQyNjaGy+Xi3Xffxe/3MzIygsPhkD8bjUbJZDI8ePCAxcVFqtUqpVKp7zMPwg5msxmXy4Xf7+fixYuyCtBsNsnn87TbbXmRTqfT1Ot18vk8tVrt24GBfW6L14V4doPBgM1m41e/+hUzMzPE43FKpRKffPIJDx8+lAmK74s3nnkRDygO8OnpaSYmJrh58yYzMzO4XC5sNhvFYpFisYjBYCASidBut9ne3v5OerPf0Ov10Ol02O12gsEg586dw+PxMDk5id1uZ2JiAqvVis/nw2g0yucVB3K5XKZWq5HP51leXqZarfatLV4Gwl5ut5uhoSGuXr1KIBCg0+lQrVbZ3t6mXq9TLBZP5CG9fz8NDw8DkEwm5Q35pEPpUGdmZtDpdMTjcer1+sBlc18XYp95vV5CoRBXrlxhbGyMubk5nE6n/JnNzU1isRjlcpmdnR06nQ7FYhHo77KtuCgZDAbcbjcjIyNcvHgRg8FAq9Wi2WySSCRkprfdbmMwGCgUCvJCKbLi/WyHw4AoN168eJGrV6+yvr5OKpXiwYMHdDqdPWXI7/XvHMpveQ6Ukb1er8fr9eJ0OvnRj35EKBTi8uXLBINBgsEgWq2WSCRCpVJBp9Oh1WpxuVz84Ac/wGazsbGxIcsk/cR/ERvDarXicDiYnZ3l5s2bDA8Pc+XKFcxmMxaLBYBcLkcmkyGZTAJQKBRoNBqSA5TP5ymVSty6dYudnR2KxeLARfrKA9dgMBAIBPjwww/x+Xx0Oh3i8TgrKyuUy2W8Xi9Xr16VNlLW4gfJJs9Cr9dDr9fj9/sJhUL81V/9FVqtllKpRCQSIZvN0mg0+jZbeRjQaDTodDo8Hg+/+MUvsFqtVCoVtra2WF9fp1AoSK7dScD+UrTdbmd6ehqfz8d7770nM+Fut1uWsYUP8/v9WCwWtra22NraAiCRSPQ196XX68lswMzMDB988AEjIyPcuHFD8l46nQ6VSkUGKJ1Oh2g0SrlcZmtri0wmw/Lyssx4VqvVE5cF7nQ6GAwGQqEQwWCQ4eFhAoEA4XCYbrd76P/mG8u8iODF6XQSCAR49913mZ6eZm5uDpfLJQ+fZDLJ7u4uLpcLt9uN3W7n/PnzFItFrFYrgCT99AM0Gg3dbleWi7xeL7Ozs/zkJz8hEAgwPz8veT2VSoVYLEa1WpU11mg0SqVSoVar0Wq1SKfTFItFNjY2SKVStFqtgdwgIt2v1+txu90sLCzgcDjI5/Pk83kePnxIsVjk3Xffxefzsb6+Tjqdptvt0mw2T8RBLS4G4mAeHh7m3XffRa/X8+c//5lSqUSxWKRWqw3kGnkRlCR/nU6Hw+Hg+vXruN1uvv76a9rtNuFw+FBvg/0CZYbAarVy6tQpRkdHuXnzpgyETSaTtGG320Wr1eJ2u3E6nYRCIUKhEJlMRv5dP0K5PiwWC8PDw9y4cYPh4WHOnTuH0WiUPClllk6UjcrlMuvr6ySTSZrNJoVCQZaWToIPUkIEKD6fj1AohNfrxe12YzAY5Bl4mDjS4EXJWRgaGsLr9fLjH/+Y4eFhLl26hN/vp9frkc1mefDgAbu7u6ytrbG7u8vU1BSTk5OMjY0xOzuL3+/HbrfT7XYlWbUfFke320Wn06HX6xkbG+PatWucP3+eqakp9Ho9qVSKfD7PvXv3yOVyLC0t7eEqiKCuXC7TbDapVCrU63UKhQLNZvNIItq3DeEorFYro6OjTE5OEgwG6Xa73L17V2ZeqtUqgUCAcrnMqVOnmJub48GDB2xsbMjbTz/fCF8WgjzZ7Xax2+2YzWZCoRD5fJ54PC67JfphvxwmxPN2u10qlQqZTIb79+/j8/kwm80MDQ3t6YY4CVBmrD0eD3Nzc4yOjvLBBx/g8/kIBAKYTCYymQydTkeW1kwmEwaDQa4vo9GI0+nEarVKYmu/ZcNF1lKv1zM/P8/ly5c5e/Ysc3Nz2Gw2ut0uuVyO7e1t2fVqMBg4e/YsNpsNm82G0WgEIBQKYTAYOHfuHA8ePJCXq2w2O/A+SHzuBoMBq9XK7Ows09PTuN1u2XxTqVRot9v9ofOijNgNBgOjo6NMTEzwL/7Fv2B0dJSpqSlMJhPb29vkcjm++uor7t27x+rqKtFolCtXrlAqlXA6nQwPDxMMBrHb7TQaDfn7jzuUN2Nhgxs3bjA7O8vU1BSlUon19XU2Njb4+7//e5LJJKurq9Trdbng7XY7er1e1lZF2lJ0afWDo3hViODFZDJJTpTf7yefz/PgwQM2NzeJRqN0Oh38fj/1ep2f/vSnzMzM0Gq1yOfz8lY0yE5DQNl1ZbPZcDgcMngxGAwy63kSO21E5lMEL4uLiwQCASwWC8FgEKPROJAXgIOwX9zR4/Fw7do1pqenef/993E4HDILvLW1Rblcplgs0mq1cLlcWK1WNBoNRqPxO8GL8Ev9AnE2CbL77OysPJtmZ2fpdDo0m01yuRz37t2TtrDZbIyOjmKz2bBYLNhsNtxuN91ul/HxcSqVCi6Xi0qlQjgcJpVKDXT5SElS1uv1WCwW5ubmOHXqFC6XS3KGRPAChxfcHknwouyqcbvdhEIh3n//fYaGhnA4HHS7XZaXl+l0OtRqNXkoiwhemXZrNpsUi0VKpRK1Wu1Q+8SPGsIOFosFj8fD6OgoMzMz+Hw+arUasVhMclfC4TD5fF7yeUSAMj8/j8/nI5vNUqlUiEajZLPZPancfrDFy0B5cxMdRmfOnCEQCLCzs0M6nZZdRYLHIGwRiUSwWCzodDqGhoao1+vy1jOoELyeTqdDoVCgWCzSbDZlsGI0Gvc4zX46XA4TyouU4I4ZDAYsFsvAHioHQZkJt1gs+Hw+5ufnGRkZwWKx0Ov1SCQSFAoFPvnkE5LJJPl8nmazSTAYxOFwcOPGDU6fPo3L5eLMmTMyW1wqlchms0B/+GYBr9fLyMgIU1NTDA0N4XK5AKhWq2xtbRGJRPjyyy+p1Wro9XocDgc7OzuS/2MymeTvEhpdU1NTXLt2DZ1OJ39WBMj9ZJuXhSjxW61WnE4nPp8Pn88HQK1WI5vNkkgkqNfrh3peHXrwoiToWiwWpqenOXXqFL/61a9wuVxoNBrq9Tp37tyhWCzi8/lk5C4WgjJ4qdfrZDIZ8vk8xWKRer1+2G/5yCAWrd1uZ2RkhNnZWS5evEiv16NarRIOh/nDH/5ANBplZWWFZrMpsyki+Lty5QqnT58mHo9TLBb56quvqFare4jLg7QhRNCm1+sZGhrixo0bMtiNRqOkUimKxaK8zcRiMdLptCzD6XQ6RkdHyefze8TZBslGSmg0GtrtNtlslmw2S71ep91uYzQaMZvNAxXcfh+IvdhqtWi32zLLcBgtm/0ApV/W6XSSt3L58mW8Xi8Wi4V6vc7Ozg67u7v81//6X9nc3CSVStFoNBgfH8fn80k5B6/Xy+joKIVCgSdPnhCLxchkMn3TKiwCeWGD06dPMzExgdlsBp42Sty/f5+1tTX+8Ic/UK/XGRkZwefzsbKyQqPRwGw27wleRGbq7Nmzskpw+/ZtWXKCwQxeut2uDOwE9050PZbLZeLxOOFwmHK5fKjVgkPducoykUivXb9+nbGxMaxWK+12m42NDXK5HHfv3qVUKuH1ejEYDGxtbe3pjBBlEaUyn/j9/QKxid1uNxMTE3i9Xpkt2NzcZGVlhXg8Tj6fP3DTd7tdSqUS+XyeYDDIxMQE7XYbj8fD7u4usVhMivkNApQZJ6H3o9PpqNfrbG1tEY/H92TeRNYBnjqbdDoNIFPZ+7MOg+g4BPbf7qxWK3a7HZPJNLDlxdfFSbOFUsNElMvm5+eZn5+XZelarUahUGBtbY1IJEI+n5fla71eL1P/1WqVer2O1WrFYrHIzLoghYs1eFz3m7CFxWLBaDQyPj7O+fPnGR0dxWw2SyLu7u4uT548IRKJUK/XabVaFAoFut0ui4uLZDIZdDqdpDOYTCbJBzKbzfh8PkZGRpibm9ujUTZoUBKe/X4/Q0NDsktNBG3ZbJZ0Oi1FZ49l8CLEe0Sm4eLFi/zt3/4tDocDs9lMKpXi448/Zmdnh6+//ppCoYDL5ZJ6JkI5Vty8BUtZdNuIjAz0hwMSH9To6ChXr15lfHwcvV5PLBbjd7/7Hevr6ywtLcnumP23wG63Szwex2Qy8dd//decP3+e+fl5YrEYn376KZ999hm5XI5EIgH0h01ehF6vh8lkwu/343K50Ol0lMtl7ty5QyqV+s4NptPpyPbpXq/H2NgYfr9fKhb3W8D7OhA3akHMFXod5XIZi8Wyh1A5CGvk+2D/5WDQIdZ/t9vF4XAwNTXFmTNn+OCDDwgGg/h8Pnq9Hrlcjt3dXT799FPC4TCJRIJqtSr9cKPRoNvtUigUKBQKUvJiZGSEM2fO0Gg0JL/hOEPsEZfLhc/nY2FhgZ///OfY7XbsdjvpdJqNjQ0WFxf5x3/8R/L5PNVqVfoYIQrqdDrJ5/NMTEwwOTmJ1+tlbGwMg8GA0+mUXbKxWIylpSV2d3f38IIGbR8KfuLU1JTMUAl9l+3tbTY3N2m324fa1XcowYtSSE20083OzjI+Po7NZgNgZ2eHeDxOJBKRAlHtdlv+qfxd4uYtMhXFYpFqtbpHCv44Q9hDMPT9fr/MPhUKBTKZDJFIhFQq9UxhI0GuTKVS6HQ6Njc35c3A7/czMzMj2/TK5bIUUhKv7Vf0ej2MRiMej0cqLZfLZUqlkhTkO4inoMzaiNEKJxW9Xk9m5E5qp9GzMIi332dB7Amj0SgbBs6dO8f09DShUAin0ykvB5ubm+zu7pJMJslms7Iz5KASkPIAFhlysd+OOylc+I6hoSGmp6cZGhrCZrOh1+tpt9vkcjlWV1fZ3t6W5w7snQ8mvre+vi65mIFAQCqji8qBy+ViamqKXC6H2WweuFEuwq8I/tjw8LBssQfkWSfO7sMO3A4teBGbxGQyMT8/z1//9V8zPDyMx+MhkUjwxz/+ke3tbW7dukUul5OHkGgLFhCHTygU4sKFC+TzeRn4VKtV2u22XIDH1SGLD8nlcuH1ejl37hw3b96kWCyys7PDkydP+PLLL/cwsPdDpGCfPHnC2toa7XabBw8e8OGHH3L16lWCwSA3b97k448/JpvNUiwWpbBdvx/cTqdT6v9Eo1EZ6BWLRSleKJykcvigkIEXLYzKNPagQ+lc2+22bNMUl4OTJML2LAg/JdZNv++TF0EErg6Hg0AgwA9+8AP+5m/+RvISxP7I5/P87ne/IxwO8/DhQ0qlkjyAlfZS7jXxJXhDx700qSxvGAwGFhYW+PGPf8yZM2dwu900Gg0qlQqrq6v8+te/JplMkkgkJEdIuVZKpRKlUonf/e53aLVazpw5QygUkqUTUUaamJjAbrfT6XSk7pI4+/p97Slbza1WK8FgkHfeeYfp6Wn5zOFwmNXVVTKZjORzHqYP+l7By36lRpvNRiAQIBQKMTQ0hMViIZlMygNImXFRfnj7W/hEv7jT6ZSDwISORb9ABHN2u1221YmOEKHV8jJcFVEqS6fTGI1GIpEIgUAAq9WKx+PB4/Hg9/vpdrsyeOlnKIMQQBK1xXCv/VC26Sl5LsqD6iRBlG4bjcYeyfKTZof9EMGKUFJttVqyFDLocDgcUm7C6/VKsc9arUY6nSYSiZBIJOQhs7+LUaydfl5D4r0Lro7X6yUQCMjKQL1eJ5VKkclkyOVylEolGfwd9LuEPAE8zTCYTCZKpRL1eh2z2Sw7Jh0OB1arVQZ4gwRlidrn8+HxeHA6nbLNPJ1OyzP/KPC9My/KqHxycpL33nuP8+fPc+nSJaLRKL/97W/Z3t7mT3/6k+wWElG68vYsFoTJZJKR3OTkpIxylYOvjjvE+3S73YyNjUmWvpJ4KhRzX3RbEbZZXV1la2uLUqnEgwcP+Oijj/joo4+Ym5vj2rVrLC0tyVkj/V4mEITver3O48ePSaVSezQClM+mdEpCFEmUF/uhxHiYEA5VDJETBHjROn0SoQxujUYjLpcLh8NBMpkkl8sNpBrz/vT8zMwMP/3pTzl//jwjIyO0Wi0pa//xxx8TiUT45ptv5KDKg7IoSr/7rAP9OEP4grGxMUZHRzlz5gynTp3CYrHQ7XbZ3d3l7t27fPPNN2xtbUmy8kEBh/BBgksmBEOTyaS8ZLpcrj1ifoI4D8ffVi8DkeG1Wq0sLCwwPT0tZT1EAHj37l3u3btHOp0+kj12KJkXwWIfGhpifHwct9st9VlE650yAFFCqYIJSAa7aPMUv2d/eek4Q3mgejweGYk3m01KpZKckfEq5GNRL83lcpK4KiStPR4Pdru978skyjS0aN0UmSrx9896ndlsxmazSZGtkyTRLZysaLEXo+dFZkGZ7h8Ex/k6EGUQwf9ot9t76vCDCJGmdzgc+Hw+7HY7Op2ORqMhSxiCnFupVGg0Gs+8+ChLRUq5/EajQbValWvtONpTPJNWq5U6JC6XC4vFgkajodFoUCgUpIaWUDV/Wey3jRLKMvegQNhT6AUNDQ0RDAYxmUxoNBry+bzkTolJ3EfRPv9awYsyjdjr9ZiampK8jl/+8pdkMhlu377N4uIi/+2//Tc5CVlJ7N0PsfDF7woEAhQKBcLhsIze9pepjitEADE+Ps61a9cYGhqi2WySzWZZX18nHo/Lw/VFqURlcNfr9SgWi8TjcSqVihxcOTU1RTKZxGw202g0+k7/RXyuQqrb4/EwPj6+R+lzf51YWcPW6/WMjIxw6tQpEokEiURCKoMOshIxfJu6FR0OzWaTcrlMJpMhnU5LjthJDlyUQbGwk2j9HcTsnPicHQ4HLpeLiYkJZmdn8Xq9dDodMpkMDx8+5NGjR/z5z3+W/lkEugdlNkXJTajrttttSqUSsViMJ0+esLu7K/3OceJWifcv+JinT5/mypUrTExM4HA4yGQyUnn5D3/4A+l0Wk5jf5FvFvZyOp2ydOJ2uzGZTPIM0Gq1fVMxeBmIzjWlyOFPfvIThoeHMZvNlMtlPvnkEzY2NlhZWSGVSgEHn/nfF9/rN4qDw+VyMTo6SiAQkPOHotEo8Xhckkmf1fWg7BIR48iHhoYwGo2yyySfz8vMS78cQkJXweFwoNPpaDabVKtV+Syvu5jF5GSxccT4cYPB0BfiUM+DVquVN2NR5hDj5uHgkpEoBwipbjH7SugC9VMQ97rQarVSX0IEKa1Wq++C2KOECGDE4Su4QYNyqOxHr9eT0vUul0uSSAGpvZHNZikUCrJctN8WSt6YwWCQfkbYr1qtUi6XZTux8pA+LmtOeTESbcyijK/Vamk0GuTzeXK5HOl0mlKp9EprQgRr+78E+jkT/iwoO4LdbjeBQECOSBDcIdFgo+SqHqvMi8vlwmazsbCwwC9+8QtMJhM7OzvcvXuXX//61/Lm96xDVdTNAILBIC6Xi6tXr/Lee++RSCS4ffu2VG/sdDrHvstoP0RAVq1WiUajbGxs8PDhQwqFAvB6z/G81/SLXfZDOD2z2SwltwVRt1ar7QlexM+L4M3j8ci0uNPpJJvN8ujRI9Lp9KGz248bhB1EG+zo6CgOh0MGsoPoOF8HyiDf6/Xi8Xhot9tUKpUDsw39DFHOAZidneXy5cucP3+eUCgkLwTZbJY7d+4QDoflRWh/lkH4eGEf0QY7NTXF8PCwbCleXl7myZMnVCqVZ5ZO3ibEc1gsFpxOp9S5cTqdtFotIpEI9+7dY3l5md3d3VeekC3oAKIBQxzWooSv/FK+pl8h1pfVauXs2bOcPXuWyclJzGazbMpZXFxkdXVVzpY7qjXx2p5dkGuF2M/w8LA8dASDXZR6lDV35evF9zQaDXa7Xc5E8Hq9dLtdEomEVHp8lRrkcYF4NjGdtVKpUCgUvhd/Rzhi+PbwEhumn2+R4gYjWpyFTomSG7T/5+Epr8jhcMjXCZ5Mo9E4do70qKDMvOyvsffzmjhMCM6LkHTvdDpy3wziGhGZl0AgIEVCxYHaaDT2dNQIHNRdtJ8r4nQ6MZvNUhNFeck4zhBVApvNhsvlQq/XS7+cz+dlp5DoIHqVNSH8sFIoEpC/X/CB+v0yofQlRqNRlslEN5WYbyXG+RwV10XglTMv4oMCmJub48KFC5w5cwav10s4HObTTz/d0yFyUDQuojeNRiOlhH/2s59x7tw5JiYmaDabrK2t8U//9E+kUqljGdG/LETw0mw2pVzyy3QZCeznF4kOJjE7IxqNcu/ePTY2NqjVavLW1Y+2EocLIEXp9pcblUGv0WjkypUrTE5OYjQaZalyUPRuXhbiQDqINNmP6+CwIPaMGBrncDjw+/04nU5KpZL0UYNIqARkqcdoNKLRaCTPR8i1iwyw8nXiS/hnp9OJzWbj5s2bLCwsMD8/j8ViIZ/Py2yF4KQdZxsqeU9KzaODSl3i2Z+V6Vf6ZFEq0ev1MvsifPDW1hbffPPNHnXwfj3HxLPqdDpMJhOhUIgrV64wPj6OVqulVCrx+eefs7m5SSwWo1gsHjn/6ZV+836nKA5Sj8eD0WikVqsRiURIJpN7JkA/D6IuOzExwfz8vJyJIOSqC4VCX37YSogPXny9zm1YdJTYbDY8Ho9sCS6XyyQSCXK5XN922Ci5KSKr9CI1ZaEHNDQ0xNjYGICs3wsxw351FK8D0RquZlq+C9HWKpRAjUaj7JIRB9WgQRwc4qAWZURR4hD++VnrRRzeotwyOjrK9PS0HK5bq9XIZDKUSiW5V/thvwn/qwxYRFbmWcrcz2oTP+h5lb4+n8+zsbFBLBaTw3T7FcqLgMViweFwyAGd8JRHtbu7SyQSoVKp7OHbvfXMi/IDt9lsmEwmJicnOX36NGazmXg8zvb2tuQbHJRtUR5SVqsVm83Gj3/8Y6anp6XS4ddff83y8jL37t2Tg7CO+4Z4EUwmE263WwrKFYtFMpmM3Dj7sZ+drtPp5GDHDz/8kIWFBex2Ozs7O6ytrfHo0SOp3NivEJwXj8eDy+XCZDKh1+v3rBt4ekgbDAZOnTpFIBDg3LlzjI+P89vf/pYnT56QSCS+M5Rx0CGCPuVBpeJbKG/R4rAVCqpCMHOQbSZ8sZCdKBaLMtAXAYxQYRbDBUU3zuzsLD6fT/JmxKGcSCTY3Nwkk8kc6zUnnr1UKtFut3n06BFWq5ULFy4wNTXF9PS0JPJaLBY526her8vzZ78/FutJlEyuXbvG5OQk169fZ3JyUmbEl5aW+PLLL0kkEseyE+tloSQlDw8Pc/HiRS5evMj58+cxGAxEIhHC4TBPnjxha2tLDlc+6jXxSmUjZduZ1WrF6/UyPDxMr9eT9a5kMil1OQ56rQhezGYzdrudU6dOcfbsWdknHo/HuX//vpzmKWrV/QglU99ischJv8obz/50q/L7gGz19fv9jI+Pc/r0aS5evEgqlSKdTpNMJqXoXb9C2EnpPA9SpFR2pg0PDzM6Oiqj/3w+L+c8DfphdBAGvSX8+0IcGqK0ViqVZJfNSbCZknsn5BSEarXyYBYdkpOTk/j9fs6ePYvf72dkZASHwyE7i/L5PJlMhlqtdqztJ96b+NxjsRhbW1tMTU2h1Wrx+XwYjUZZUtve3t6jhK4cBiygbLt3uVycOnWKM2fOMDExgcfjIR6Pk8vliMfjbG5u9vU6U64NIc0xNzfH1NQUoVCIZrMpFZrF4EqR9T5qvFbwIsTXnE4nDoeDSqUiJe+V6UjRtilS/GJIocvl4saNGwQCAU6fPo3D4eD27dskEgm+/PJL1tfXqVarfeuMxXsuFotEo1G8Xq9sJz979izb29skk8kDp4yKQ0iIKAnVwqtXrzI2NsbQ0JBU2f36669ZWVmRehX9aCv49nYkWjjNZjNWq1VOGxdkODHC3u/386Mf/YhgMMjW1haPHj1iY2ODQqGwZ/bVSYAIjoPBIIFAQJIylYMZ+3VdHCYEjyOXy6HT6aS/OurU9tuAeJZqtUo2m5WHsMlkIhgMcunSJf72b/+WWq22h68ipCqsViunTp3C5XLJgYOtVovt7W0ePnzI5uYmt2/fZmdn59jzOMQZJBoblpeXqVQqkshst9vlgexwOEilUpw+fZpKpSI1X3K5nPSvQg7fbDbL8QLnzp0jGAxit9splUo8efKEO3fusLi4SLFYpNlsHuvs1IsgsuJ2u53JyUlu3rzJyMgIer2eXC7Ho0eP2NzcJJ/P7xHGPJaZFxGdi5k9QupeOMz9c2hEWttisTA6OkowGOT9999nZGQEg8FAp9NhdXWVu3fvsrGxQTwel335/QqNRkOlUiGTydBut7Hb7fj9fiYmJuSoebGplA5UBHoi4FlYWGBiYoKFhQXGxsZIp9MUi0XW19f57LPPKBQKcnZEv24OePrehfBVq9WSXSHw7bwes9ksMy4XLlzA7/fzm9/8htXVVWKxGJVKZeDbo5VQZqKEnodWq5VaQMoMXz+vjcOACF7E0EHBQRjUgZXiMiC6aETw73a7mZmZwWAw0Gq19sydEdkEs9nM5OSk5AcBrK2tkUwmWVxc5N69e2xubpJIJGQW4jhDBDCdTodIJEKxWOTSpUtMTU0xPj5OMBiUU5GLxSJTU1MyaKlUKoTDYdntajAY5MBFr9eLxWLB7/djs9lktmZra4vbt2+zvb1NpVLp23IR7K0e2O12OTBZ6JeJkTfb29tyT70pH/xKq044QNGOms1mSaVSaLVa/H4/8/Pz/OhHP6JWq1GpVGTAIoIWm83GxMQENpsNq9VKqVRiY2ODTCbD48ePiUQi1Gq1vncoQlUxlUqxtrbG1NQU2WwWl8vFD3/4Q8bGxvB6vfJmBE95MaL2KpyHw+Fgenoah8NBu91mZ2dH3nzu379PKpUaiNksImir1+tkMhkmJiYYGhqi0Whw48YN6XydTifvv/8+drudjY0Nnjx5wr179wiHw/JQ6ndbvA40Go2cnSJk3kULq1If6SRDkCgFx0XZNTlIUPJ7xKwzUa72+/2EQiFJuu10OvJQFlluITkATwc3hsNhyuWy7CRZWVkhHA5TKBT6KjMunkmQ+W/fvi2DmIsXL0qRS3GWtdttvF4vzWZTcn0E70O0BsPTdbWzs0Or1WJjY4NoNMrdu3dZXV2lWCwC/XlxUJbyBefy6tWrXLhwQQ5fXFlZYW1tTWqxiS7aN/W8rx28lEolMpkMqVSKoaEhKRVcKBSoVqsUCgWMRiMejwe3282FCxew2+0Eg0G63S5ra2tkMhnu3LnDxsYGy8vLpFIpKezWzxAfYDqdRqvVcu7cObLZLG63m/Hxcebn55mZmSGXy7G5uYlGo8HhcGAymRgeHsZmszE9PS03Sa/XY3l5mUQiwa1bt7h79y67u7tSiO2433xeBGVdOpPJ0Ol0CAaD6HQ6rl+/LrN5Xq+Xn/3sZ2g0Gv7zf/7PbG5usri4SDKZ7PuA93WhLMuKjEKlUpGkzEFsBX5dKFV1B3nattgHu7u7xONxnE4nbrebs2fPMj4+LvW5YO/gSvi2a03MoltfXyeRSPDHP/6RR48eye/328weZfBSqVS4c+cOKysrlEolNBoNfr+f4eFh3G43o6OjMlBRiv6JoFfIXYiSXCQSIZVK8eWXX/LkyRMikQjRaFT+jn6xkRLKrK7JZGJiYoK/+Iu/YHp6GqfTSTqdZnV1lZWVFZaXl8lkMq8kAXIYeOlTTxlR1et1ut0uKysr6PV6zp07J0m4ly9fluPmxYOLg6VcLhONRqlUKiwuLpJOp1leXiadTr8xhvKbgHgGUU++f/8+ZrOZkZERZmZm0Gq1jIyMSFE+cXMWbWgA4XCYVqslRe1WV1dJJBKyo0bpQAYB4ibYaDRIpVI8evQIi8XC9evXpTPt9XosLS1RLpdZXV0lEonIdSZsPgjr53UgMgnlcplyuSw1JwbxcH5ViNuyuAwosxODDFGSFlpQooXV4XAQCARk44WyXFssFqnX68TjccrlsvTPsVhMzj8S+60f95qSXwfw6NEjarUaPp+PkZERvF4vExMTmEwmbDbbHs5MrVaj3W7LLq1UKiXLSrlcTlIearXakSrLvg2I8qDgjYXDYb7++mvC4bAUBYU3639f6cqujF6r1Sq3b99mfX2dWq2G0+lkbGyMS5cuSdKpIEkJFnc+n+fPf/4ziUSCL774QrLVxe2wn9KQz4N4hlwuRzabpdPpsL29zaVLl2g0GpLDouT1iMNbtDLev3+faDTKysoK2WyW1dVVSSBrtVp9UWt+WQh7iWnIkUiEr776iitXrvA3f/M3AMRiMWKxGP/lv/wXwuEw33zzDblcbk+7+SCsne8DoXoqJpfvD+xOIkRmym63Y7fb5VyeQYZSbK3X68nS/M7ODtFoVPppodUhMizlcplIJEKpVGJpaYl8Ps/Ozo5U0BV+p1+7P+G7GZh0Os2tW7ekSnwgEGBmZga73c7Q0JC8eLdaLWKxmOTCNBoNybNLJpOUSiWZtep3Gx0EMZFdyAysrq7yxz/+kUwmQzableXYN3kpeK3TT2wO0Sa3uroq2derq6syXSZqzCKSLxQKLC0tkcvlKJfL3xGyGTQnq2T9J5NJ1tfXMRgMbG1tkUgkZD1R6WzEprp79y7ZbFYqWIqb0SB2RwiIG3GtVmN7exudTsc//MM/AMhNsrq6SiaTkYJPJz3jIsTHarUapVKJRqOxh8St4mkAI0TpstmsLB8N6j4SEM+mFLPUarVkMhkqlQoWiwWv10uj0WB3d5darUY6nZZio2LworJ7dFCw/1kajYY8hJvNJlarlXg8Ls+ydrtNOp2m1WpJYqrYb6ISMUi+WTyDOMNjsRh37tzBarXicrnY3Nwkm81SLpeBo5ka/SK8VvCiFHwSmYI7d+7IdirxM+KGI1Jt7XZ7z8wDYM/NedAgFnKxWJTCTvfu3cNqteJ2u6UNhD3FQmm1WlJOWsxfUdaYB9leOp2OXC7HnTt3ePLkCbdu3ZITbNvttnQw/VxPPiyI9SUydmKiqxitoPy5k1o+EvYpl8vkcjm2t7dlMDPoa0dZIqnVanKYougWNRqNck6R8M+CHK/0O8pOyEHB/iBDNJlEo9E9Mvji58QYAOWEZGVpaBDtA8h5TY8fPyYcDu9prhAXgf2TtN8UDqXu0Gq15Fyd/QMURb2w0Wh8R8J80J2HgHheURZRKg3vt4GwkZiRcZIOHXHICpKcGJrW6/WkPU6aTV4EMcQyEomg1+slYV4l6u4dNFir1aTEACBLICfJPmJvCX8t5q0Jrpm4PPX7kNfDgFAdVq4hpd8+CVA+u6B/iDNLVAHe6vt73huw2+3PfXcHzXw46PcpP+xn/ff3QblcfiuryWq1vvKn9zw7KXGY5ZBqtfpW7ON0Ol9rdStttN9ZHEWZqFgsvhX72Gy2Q9n9ymycuBkeZidNpVJ5K/ZxOByH5h3FehF2OswguFQqvRX7+Hy+V3qAZ83oUWL/vjqMfZbJZN64fV50dj0PL+ubDwNv6+x61b11kD95EyWy5+2t75V5UUZm4s+DPviDsgwnJXo9CC9zsAxK7fT74qAslWqXp9h/M9ov5b1/f55kKEvYcHImjiuxv3y4X0h0/8+eVDxr35xkm8CLh1K+aRxK2ehVDpW3/cBvG8fhQz/uUNpn/yGj2u5bvMiZqEHLt9ifeTmpOOos+CBBtcm32M/pOQ62ObRe2+PwMCoGC+qaUqHiaKDuLRWviuO2Zp7LeVGhQoUKFSpUqDhuGJzeLhUqVKhQoULFiYAavKhQoUKFChUq+gpq8KJChQoVKlSo6CuowYsKFSpUqFChoq+gBi8qVKhQoUKFir6CGryoUKFChQoVKvoK/z9yCPfJTZMEJAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 64 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8, 8, figsize=(8, 8))\\n\",\n    \"fig.tight_layout(pad=0.1, rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i, ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20, 20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"   \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]}, {Yhat[random_index, 0]}\\\")\\n\",\n    \"    ax.set_axis_off() \\n\",\n    \"fig.suptitle(\\\"Label, Yhat\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can see how one of the misclassified images looks.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAEQAAABUCAYAAAA7xZEpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAJUElEQVR4nO2bv28jxxXHP7M/uD+4FH/p9MsGJJx99gExlBgH2ICDuEjhNl0KVynyZ6RKlTJNinQB0idp0ruK6ytytg82cKezIFInUeRql9zlLmdTCDNZ7f2AbWjJuwO/wIDUiEsOP3zz3ps3s6IoCtb6v4xVD+BV0xpIRWsgFa2BVLQGUtEaSEVrIBXVDkQI0RNC/FMIEQshHgshPv8R1/5WCPEfIcRUCPFFjcPUspbwGX8B5sA28Avg30KI+0VR/PcHXDsC/gzcBX5d1wCvqSiK2hrQ5ArGe6W+vwN/+pHv83vgizrHqlrdU+Y9IC+K4mGp7z7ws5o/9yerbiABEFb6JkCr5s/9yaobSARsVPo2gMuaP/cnq24gDwFLCHGn1Pdz4Ic41JWoViBFUcTAP4A/CiGaQohfAr/hyrEihDgQQhRCiIPnXS+EMIUQLlfR0BBCuEIIu84x1+61gR7wLyAGjoDPS//7FfAIsF9w7e+AotL+Vud4xSoLREKIPwBPi6L468oGUdFKgbyKWq9lKloDqWgNpKKXLu48z3tjHcxsNhPP619bSEXLWP6/VOUoV414Qohrj8vQSoEoAJVkTEsIgWEYVwnTkqCs3ELgxRZQ7l8WlJUBKYoCwzAwDAMhBLZtY9tXyxQpJVJKFosF8/mcxWKBEGIpUFYCRE0NwzCwbRvDMGg2m/i+D0CWZeR5rh8Xi4WGUTeUpQMp+wnDMGg0GliWRbfbpd/vA1dAsiwjjmOklMxmMxaLhQZTJ5SVWIiUEgDf99nd3SUIAj7++GM++ugjTNMkiiLSNOXk5IQvv/ySwWDAZDLh9PSULMsQQrxZQJSVNBoNut0unU6Hw8NDPvvsMyzLYjwekyQJ3333HScnJ0gpKYqCs7OzN2/KvExlUADNZpNer0ccx8xmMwyj/jzylQGyWCzIsgzTNPE8D9/32dvb4/DwkO3tbYQQfP3118Rx/Ey+cpNaORDlJFWoLYoC27axLAvf9+n1ekgpCYJgKRay0rXMizLUcp9hGJimiWmaSxnTyhd3VSjKWtTfpmli2zamaS4lU105EBVCqwu5ar7xxq5lyhZhWRZBENBqtXAcR6fxZV/xgmp8bVoqEPVl1JQoA3FdVwOpXrMMEEpLA1J2kq7rIoQgCALa7TbtdvuFQJTFlNtrnZiVnSWA53lsb2/jeR6Hh4d88skn9Ho99vf3sSzr2nVqFey6rm6e5+l1jdJNAlr6lLFtm83NTTqdDgcHB7z//vv0ej1ardYzFlAuCzQaDVzXpdFo6JJAHWl8rUDK815FErV+uXXrFt1uF9/3cV1X10LUder1CoZt2ziOg+M416zjprWUKaP8gBCCdrvN3bt32d/f591332VnZ4cgCIBnQ65pmjSbTQzDoNPp0Ol0yLIMKSXT6VQDv0krWVoeoqCULaTT6WgLsSzrWjSRUiKEwLIsLMvS1tFoNHTWWkfkqX3KSCmxLIt2u02z2WR7e5utrS22trawbZuLiwvCMNTNsix2d3fpdDoURcFsNiOOY8IwZDKZEIYhaZoC1FIXWQoQwzDo9/vs7u5y+/ZtDg4O2N/fJ0kSTk5OmM/nPHnyhKOjI5rNJp9++in9fh8pJWEYMh6POT8/Zzgccn5+rmusdYTfpUQZwzDwfZ8gCAiCQE+T+XxOmqYkSUIYhlxcXOjCsvqyqtic57l+XnXWN6kbB1IerGVZNBoN2u0277zzDh988AF7e3tsbm4SBAFRFDGdTomiiNFoxHA4ZD6fkyQJQghM08R1XXzfv9YUyDqcai0WoiKLcoatVos7d+5w7949Op0Om5ubOnoo/6CA5HlOkiTAlWU5jqMLRkEQMJvNKIpCv+amVWuUUUDKv7LrujpKLBYLkiRhNpuRpulzp4Tqk1LqLQlVpK5lzDf9hmVH2mq12NraYmdnh52dHZ2ym6apHeajR48YjUaMRiPyPNfvI4RgsVgQxzGXl5dMJhPG4zGTyUQ71TpUGxAhBK7r0u/36fV6urpuWRamaVIUBdPplKdPn3J2dsbl5aUuIwJ6UypNU6bTqW5xHNdaQbtxIOVwOJ/PiaII3/eJoog4jq8lV6oaZts2GxsbOI5Dr9fDcRzthzzPo9ls6p29ZrP5ei3uVHQQQhCGofYTx8fHHB8fs7Gxge/7OI6DbdsEQUCe59q/dDod2u02Ukps26bf7+N5Hnt7e7z99ts4jsPFxQXj8VjvD9+kaokyZQspigLf9/XeSqPR0L+u2tt1HEfXRlT1TFmI67rA1R5NEAQ6TMOzZcabUK2JmYoYaZoyn8/JsuyaqWdZph1ms9nUEUlZWJZlnJ+fE4ah9jNxHGvn+1qk7uUB5nmugSRJQpqmZFmmw2qSJJyfnzOZTNja2tKh2bZthBCkacr333/P6ekpT5480XDSNK0tytS+2i1Hjmp9tFxxVw7WsiwdYfI812F3Op3qIxKvVR7yPL2ocLy5ucm9e/eYTqe89dZb7O3taf8RhiHD4ZAHDx7w+PFjjo6OCMOQJEn0tHstF3flQZcLQFJK+v0+H374IVmW0Wq1aLVaCCGQUurjD1999RXffPONXvpnWaZ38+rQUmuqVTi2beP7Pnme43kejUZDJ2zz+ZzZbEaSJCRJoiNW3VralFGJWLl5nsetW7eQUuqELU1TBoMBw+GQ4+Nj7XRVUei134ZQKu/KKUdaLi6bpollWbpeenZ2xmg00iWCst947YFIKbm8vGQ8HgPoR8uydIhVeUoURTx9+pTT01MuLi6Yz+fXNr/rVq1A1K+Z5zmDwYAHDx7Q7/cxDINut0u322V7exvTNBkOhwyHQ8bjMffv3+fhw4eMRiOd/tdRDHqelhJlpJTEccx4PMYwDEajkXaq6uRhHMeMRiPG4zFnZ2fadygLeV60qkO1WwhcpfDD4RCAwWBAFEUEQUCn02FnZ+eahcRxzLfffstgMGA6nepTh8vSS28xu4nbQ9SKVO2+qaNStm3jeZ4+KhVFEVEUkec5l5eXJEnyTAXtJsG86PaQ2oFUVT4R5DiOPr2cJAnT6RQppXaw1YiyDCBL3+yWUl47sq22HFRfueL2Rt8eUt6iVDmFSraUygBWAQNegTNmLzsdtGwYsEQLUUt69bzaV+6vPl+mVra4e1nfKrXyKfOqaX2re0VrC6loDaSiNZCK1kAqWgOpaA2kov8B6S2cICBQS5IAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 72x72 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(1, 1))\\n\",\n    \"errors = np.where(y != Yhat)\\n\",\n    \"random_index = errors[0][0]\\n\",\n    \"X_random_reshaped = X[random_index].reshape((20, 20)).T\\n\",\n    \"plt.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"plt.title(f\\\"{y[random_index,0]}, {Yhat[random_index, 0]}\\\")\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.7\\\"></a>\\n\",\n    \"### 2.7 Congratulations!\\n\",\n    \"You have successfully built and utilized a neural network.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.8\\\"></a>\\n\",\n    \"### 2.8 NumPy Broadcasting Tutorial (Optional)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"In the last example,  $\\\\mathbf{Z}=\\\\mathbf{XW} + \\\\mathbf{b}$ utilized NumPy broadcasting to expand the vector $\\\\mathbf{b}$. If you are not familiar with NumPy Broadcasting, this short tutorial is provided.\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{XW}$  is a matrix-matrix operation with dimensions $(m,j_1)(j_1,j_2)$ which results in a matrix with dimension  $(m,j_2)$. To that, we add a vector $\\\\mathbf{b}$ with dimension $(1,j_2)$.  $\\\\mathbf{b}$ must be expanded to be a $(m,j_2)$ matrix for this element-wise operation to make sense. This expansion is accomplished for you by NumPy broadcasting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Broadcasting applies to element-wise operations.  \\n\",\n    \"Its basic operation is to 'stretch' a smaller dimension by replicating elements to match a larger dimension.\\n\",\n    \"\\n\",\n    \"More [specifically](https://NumPy.org/doc/stable/user/basics.broadcasting.html): \\n\",\n    \"When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimensions and works its way left. Two dimensions are compatible when\\n\",\n    \"- they are equal, or\\n\",\n    \"- one of them is 1   \\n\",\n    \"\\n\",\n    \"If these conditions are not met, a ValueError: operands could not be broadcast together exception is thrown, indicating that the arrays have incompatible shapes. The size of the resulting array is the size that is not 1 along each axis of the inputs.\\n\",\n    \"\\n\",\n    \"Here are some examples:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C2_W1_Assign1_BroadcastIndexes.PNG\\\"  alt='missing' width=\\\"400\\\"  ><center/>\\n\",\n    \"    <figcaption>Calculating Broadcast Result shape</figcaption>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The graphic below describes expanding dimensions. Note the red text below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C2_W1_Assign1_Broadcasting.gif\\\"  alt='missing' width=\\\"600\\\"  ><center/>\\n\",\n    \"    <figcaption>Broadcast notionally expands arguments to match for element wise operations</figcaption>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The graphic above shows NumPy expanding the arguments to match before the final operation. Note that this is a notional description. The actual mechanics of NumPy operation choose the most efficient implementation.\\n\",\n    \"\\n\",\n    \"For each of the following examples, try to guess the size of the result before running the example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(a + b).shape: (3, 1), \\n\",\n      \"a + b = \\n\",\n      \"[[6]\\n\",\n      \" [7]\\n\",\n      \" [8]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\\n\",\n    \"b = 5\\n\",\n    \"print(f\\\"(a + b).shape: {(a + b).shape}, \\\\na + b = \\\\n{a + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that this applies to all element-wise operations:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(a * b).shape: (3, 1), \\n\",\n      \"a * b = \\n\",\n      \"[[ 5]\\n\",\n      \" [10]\\n\",\n      \" [15]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\\n\",\n    \"b = 5\\n\",\n    \"print(f\\\"(a * b).shape: {(a * b).shape}, \\\\na * b = \\\\n{a * b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C2_W1_Assign1_VectorAdd.PNG\\\"  alt='missing' width=\\\"740\\\" >\\n\",\n    \"    <center><figcaption><b>Row-Column Element-Wise Operations</b></figcaption></center>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[1]\\n\",\n      \" [2]\\n\",\n      \" [3]\\n\",\n      \" [4]]\\n\",\n      \"[[1 2 3]]\\n\",\n      \"(a + b).shape: (4, 3), \\n\",\n      \"a + b = \\n\",\n      \"[[2 3 4]\\n\",\n      \" [3 4 5]\\n\",\n      \" [4 5 6]\\n\",\n      \" [5 6 7]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a = np.array([1,2,3,4]).reshape(-1,1)\\n\",\n    \"b = np.array([1,2,3]).reshape(1,-1)\\n\",\n    \"print(a)\\n\",\n    \"print(b)\\n\",\n    \"print(f\\\"(a + b).shape: {(a + b).shape}, \\\\na + b = \\\\n{a + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is the scenario in the dense layer you built above. Adding a 1-D vector $b$ to a (m,j) matrix.\\n\",\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C2_W1_Assign1_BroadcastMatrix.PNG\\\"  alt='missing' width=\\\"740\\\" >\\n\",\n    \"    <center><figcaption><b>Matrix + 1-D Vector</b></figcaption></center>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"89367\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/C2W1A1/archive/.ipynb_checkpoints/C2_W1_Assignment-Copy1-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Neural Networks for Handwritten Digit Recognition, Binary\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize the hand-written digits zero and one.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Neural Networks](#2)\\n\",\n    \"  - [ 2.1 Problem Statement](#2.1)\\n\",\n    \"  - [ 2.2 Dataset](#2.2)\\n\",\n    \"  - [ 2.3 Model representation](#2.3)\\n\",\n    \"  - [ 2.4 Tensorflow Model Implementation](#2.4)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 2.5 NumPy Model Implementation (Forward Prop in NumPy)](#2.5)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"  - [ 2.6 Vectorized NumPy Model Implementation (Optional)](#2.6)\\n\",\n    \"    - [ Exercise 3](#ex03)\\n\",\n    \"  - [ 2.7 Congratulations!](#2.7)\\n\",\n    \"  - [ 2.8 NumPy Broadcasting Tutorial (Optional)](#2.8)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\\n\",\n    \"- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from autils import *\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tensorflow and Keras**  \\n\",\n    \"Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Neural Networks\\n\",\n    \"\\n\",\n    \"In Course 1, you implemented logistic regression. This was extended to handle non-linear boundaries using polynomial regression. For even more complex scenarios such as image recognition, neural networks are preferred.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Problem Statement\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize two handwritten digits, zero and one. This is a binary classification task. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. You will extend this network to recognize all 10 digits (0-9) in a future assignment. \\n\",\n    \"\\n\",\n    \"This exercise will show you how the methods you have learned can be used for this classification task.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_data()` function shown below loads the data into variables `X` and `y`\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- The data set contains 1000 training examples of handwritten digits $^1$, here limited to zero and one.  \\n\",\n    \"\\n\",\n    \"    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \\n\",\n    \"        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \\n\",\n    \"        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \\n\",\n    \"        - Each training example becomes a single row in our data matrix `X`. \\n\",\n    \"        - This gives us a 1000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\\n\",\n    \"\\n\",\n    \"$$X = \\n\",\n    \"\\\\left(\\\\begin{array}{cc} \\n\",\n    \"--- (x^{(1)}) --- \\\\\\\\\\n\",\n    \"--- (x^{(2)}) --- \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\ \\n\",\n    \"--- (x^{(m)}) --- \\n\",\n    \"\\\\end{array}\\\\right)$$ \\n\",\n    \"\\n\",\n    \"- The second part of the training set is a 1000 x 1 dimensional vector `y` that contains labels for the training set\\n\",\n    \"    - `y = 0` if the image is of the digit `0`, `y = 1` if the image is of the digit `1`.\\n\",\n    \"\\n\",\n    \"$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X, y = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.1\\\"></a>\\n\",\n    \"#### 2.2.1 View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints elements of the variables `X` and `y`.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The first element of X is: ', X[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The first element of y is: ', y[0,0])\\n\",\n    \"print ('The last element of y is: ', y[-1,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.2\\\"></a>\\n\",\n    \"#### 2.2.2 Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The shape of X is: ' + str(X.shape))\\n\",\n    \"print ('The shape of y is: ' + str(y.shape))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.3\\\"></a>\\n\",\n    \"#### 2.2.3 Visualizing the Data\\n\",\n    \"\\n\",\n    \"You will begin by visualizing a subset of the training set. \\n\",\n    \"- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \\n\",\n    \"- The label for each image is displayed above the image \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1)\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(y[random_index,0])\\n\",\n    \"    ax.set_axis_off()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3 Model representation\\n\",\n    \"\\n\",\n    \"The neural network you will use in this assignment is shown in the figure below. \\n\",\n    \"- This has three dense layers with sigmoid activations.\\n\",\n    \"    - Recall that our inputs are pixel values of digit images.\\n\",\n    \"    - Since the images are of size $20\\\\times20$, this gives us $400$ inputs  \\n\",\n    \"    \\n\",\n    \"<img src=\\\"images/C2_W1_Assign1.PNG\\\" width=\\\"500\\\" height=\\\"400\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $1$ output unit in layer 3. \\n\",\n    \"\\n\",\n    \"    - Recall that the dimensions of these parameters are determined as follows:\\n\",\n    \"        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \\n\",\n    \"            - $W$ will be of dimension $s_{in} \\\\times s_{out}$.\\n\",\n    \"            - $b$ will a vector with $s_{out}$ elements\\n\",\n    \"  \\n\",\n    \"    - Therefore, the shapes of `W`, and `b`,  are \\n\",\n    \"        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\\n\",\n    \"        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\\n\",\n    \"        - layer3: The shape of `W3` is (15, 1) and the shape of `b3` is: (1,)\\n\",\n    \">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (n,1) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention. \\n\",\n    \"               \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.4\\\"></a>\\n\",\n    \"### 2.4 Tensorflow Model Implementation\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statment below. \\n\",\n    \">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \\n\",\n    \"`tf.keras.Input(shape=(400,)),    #specify input shape`  \\n\",\n    \"We will include that here to illuminate some model sizing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a sigmoid activation to construct the network described above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED CELL: Sequential model\\n\",\n    \"\\n\",\n    \"model = Sequential(\\n\",\n    \"    [               \\n\",\n    \"        tf.keras.Input(shape=(400,)),    #specify input size\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"my_model\\\" \\n\",\n    \")                            \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Expected Output (Click to Expand) </b></font></summary>\\n\",\n    \"The `model.summary()` function displays a useful summary of the model. Because we have specified an input layer size, the shape of the weight and bias arrays are determined and the total number of parameters per layer can be shown. Note, the names of the layers may vary as they are auto-generated.  \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"```\\n\",\n    \"Model: \\\"my_model\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"dense (Dense)                (None, 25)                10025     \\n\",\n    \"_________________________________________________________________\\n\",\n    \"dense_1 (Dense)              (None, 15)                390       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"dense_2 (Dense)              (None, 1)                 16        \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 10,431\\n\",\n    \"Trainable params: 10,431\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"As described in the lecture:\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"model = Sequential(                      \\n\",\n    \"    [                                   \\n\",\n    \"        tf.keras.Input(shape=(400,)),    # specify input size (optional)\\n\",\n    \"        Dense(25, activation='sigmoid'), \\n\",\n    \"        Dense(15, activation='sigmoid'), \\n\",\n    \"        Dense(1,  activation='sigmoid')  \\n\",\n    \"    ], name = \\\"my_model\\\"                                    \\n\",\n    \")                                       \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"from public_tests import * \\n\",\n    \"\\n\",\n    \"test_c1(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"L1_num_params = 400 * 25 + 25  # W1 parameters  + b1 parameters\\n\",\n    \"L2_num_params = 25 * 15 + 15   # W2 parameters  + b2 parameters\\n\",\n    \"L3_num_params = 15 * 1 + 1     # W3 parameters  + b3 parameters\\n\",\n    \"print(\\\"L1 params = \\\", L1_num_params, \\\", L2 params = \\\", L2_num_params, \\\",  L3 params = \\\", L3_num_params )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's further examine the weights to verify that tensorflow produced the same dimensions as we calculated above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"[layer1, layer2, layer3] = model.layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#### Examine Weights shapes\\n\",\n    \"W1,b1 = layer1.get_weights()\\n\",\n    \"W2,b2 = layer2.get_weights()\\n\",\n    \"W3,b3 = layer3.get_weights()\\n\",\n    \"print(f\\\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\\\")\\n\",\n    \"print(f\\\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\\\")\\n\",\n    \"print(f\\\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"W1 shape = (400, 25), b1 shape = (25,)  \\n\",\n    \"W2 shape = (25, 15), b2 shape = (15,)  \\n\",\n    \"W3 shape = (15, 1), b3 shape = (1,)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`xx.get_weights` returns a NumPy array. One can also access the weights directly in their tensor form. Note the shape of the tensors in the final layer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(model.layers[2].weights)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following code will define a loss function and run gradient descent to fit the weights of the model to the training data. This will be explained in more detail in the following week.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.BinaryCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X,y,\\n\",\n    \"    epochs=20\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To run the model on an example to make a prediction, use [Keras `predict`](https://www.tensorflow.org/api_docs/python/tf/keras/Model). The input to `predict` is an array so the single example is reshaped to be two dimensional.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"prediction = model.predict(X[0].reshape(1,400))  # a zero\\n\",\n    \"print(f\\\" predicting a zero: {prediction}\\\")\\n\",\n    \"prediction = model.predict(X[500].reshape(1,400))  # a one\\n\",\n    \"print(f\\\" predicting a one:  {prediction}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output of the model is interpreted as a probability. In the first example above, the input is a zero. The model predicts the probability that the input is a one is nearly zero. \\n\",\n    \"In the second example, the input is a one. The model predicts the probability that the input is a one is nearly one.\\n\",\n    \"As in the case of logistic regression, the probability is compared to a threshold to make a final prediction.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print(f\\\"prediction after threshold: {yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Predict using the Neural Network\\n\",\n    \"    prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    if prediction >= 0.5:\\n\",\n    \"        yhat = 1\\n\",\n    \"    else:\\n\",\n    \"        yhat = 0\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{yhat}\\\")\\n\",\n    \"    ax.set_axis_off()\\n\",\n    \"fig.suptitle(\\\"Label, yhat\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.5\\\"></a>\\n\",\n    \"### 2.5 NumPy Model Implementation (Forward Prop in NumPy)\\n\",\n    \"As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_dense2.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Below, build a dense layer subroutine. The example in lecture utilized a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` is then applied to that result. This section will not utilize some of the matrix operations described in the optional lectures. These will be explored in a later section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: my_dense\\n\",\n    \"\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"### START CODE HERE ### \\n\",\n    \"        \\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(a_out)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Quick Check\\n\",\n    \"x_tst = 0.1*np.arange(1,3,1).reshape(2,)  # (1 examples, 2 features)\\n\",\n    \"W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\\n\",\n    \"b_tst = 0.1*np.arange(1,4,1).reshape(3,)  # (3 features)\\n\",\n    \"A_tst = my_dense(x_tst, W_tst, b_tst, sigmoid)\\n\",\n    \"print(A_tst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"[0.54735762 0.57932425 0.61063923]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"As described in the lecture:\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):             \\n\",\n    \"        w =                            # Select weights for unit j. These are in column j of W\\n\",\n    \"        z =                            # dot product of w and a_in + b\\n\",\n    \"        a_out[j] =                     # apply activation to z\\n\",\n    \"    return(a_out)\\n\",\n    \"```\\n\",\n    \"   \\n\",\n    \"    \\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):             \\n\",\n    \"        w = W[:,j]                     \\n\",\n    \"        z = np.dot(w, a_in) + b[j]     \\n\",\n    \"        a_out[j] = g(z)                \\n\",\n    \"    return(a_out)\\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"test_c2(my_dense)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a three-layer neural network utilizing the `my_dense` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential(x, W1, b1, W2, b2, W3, b3):\\n\",\n    \"    a1 = my_dense(x,  W1, b1, sigmoid)\\n\",\n    \"    a2 = my_dense(a1, W2, b2, sigmoid)\\n\",\n    \"    a3 = my_dense(a2, W3, b3, sigmoid)\\n\",\n    \"    return(a3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can copy trained weights and biases from Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp,b1_tmp = layer1.get_weights()\\n\",\n    \"W2_tmp,b2_tmp = layer2.get_weights()\\n\",\n    \"W3_tmp,b3_tmp = layer3.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# make predictions\\n\",\n    \"prediction = my_sequential(X[0], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print( \\\"yhat = \\\", yhat, \\\" label= \\\", y[0,0])\\n\",\n    \"prediction = my_sequential(X[500], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print( \\\"yhat = \\\", yhat, \\\" label= \\\", y[500,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following cell to see predictions from both the Numpy model and the Tensorflow model. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"\\n\",\n    \"    # Predict using the Neural Network implemented in Numpy\\n\",\n    \"    my_prediction = my_sequential(X[random_index], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"    my_yhat = int(my_prediction >= 0.5)\\n\",\n    \"\\n\",\n    \"    # Predict using the Neural Network implemented in Tensorflow\\n\",\n    \"    tf_prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    tf_yhat = int(tf_prediction >= 0.5)\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{tf_yhat},{my_yhat}\\\")\\n\",\n    \"    ax.set_axis_off() \\n\",\n    \"fig.suptitle(\\\"Label, yhat Tensorflow, yhat Numpy\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.6\\\"></a>\\n\",\n    \"### 2.6 Vectorized NumPy Model Implementation (Optional)\\n\",\n    \"The optional lectures described vector and matrix operations that can be used to speed the calculations.\\n\",\n    \"Below describes a layer operation that computes the output for all units in a layer on a given input example:\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_VectorMatrix.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"\\n\",\n    \"We can demonstrate this using the examples `X` and the `W1`,`b1` parameters above. We use `np.matmul` to perform the matrix multiply. Note, the dimensions of x and W must be compatible as shown in the diagram above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x = X[0].reshape(-1,1)         # column vector (400,1)\\n\",\n    \"z1 = np.matmul(x.T,W1) + b1    # (1,400)(400,25) = (1,25)\\n\",\n    \"a1 = sigmoid(z1)\\n\",\n    \"print(a1.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can take this a step further and compute all the units for all examples in one Matrix-Matrix operation.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_MatrixMatrix.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"The full operation is $\\\\mathbf{Z}=\\\\mathbf{XW}+\\\\mathbf{b}$. This will utilize NumPy broadcasting to expand $\\\\mathbf{b}$ to $m$ rows. If this is unfamiliar, a short tutorial is provided at the end of the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex03\\\"></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"\\n\",\n    \"Below, compose a new `my_dense_v` subroutine that performs the layer calculations for a matrix of examples. This will utilize `np.matmul()`. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED FUNCTION: my_dense_v\\n\",\n    \"\\n\",\n    \"def my_dense_v(A_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      A_in (ndarray (m,n)) : Data, m examples, n features each\\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j,1)) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      A_out (ndarray (m,j)) : m examples, j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"### START CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(A_out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_tst = 0.1*np.arange(1,9,1).reshape(4,2) # (4 examples, 2 features)\\n\",\n    \"W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\\n\",\n    \"b_tst = 0.1*np.arange(1,4,1).reshape(1,3) # (3 features, 1)\\n\",\n    \"A_tst = my_dense_v(X_tst, W_tst, b_tst, sigmoid)\\n\",\n    \"print(A_tst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"[[0.54735762 0.57932425 0.61063923]\\n\",\n    \" [0.57199613 0.61301418 0.65248946]\\n\",\n    \" [0.5962827  0.64565631 0.6921095 ]\\n\",\n    \" [0.62010643 0.67699586 0.72908792]]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    In matrix form, this can be written in one or two lines. \\n\",\n    \"    \\n\",\n    \"       Z = np.matmul of A_in and W plus b    \\n\",\n    \"       A_out is g(Z)  \\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for code</b></font></summary>\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"def my_dense_v(A_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      A_in (ndarray (m,n)) : Data, m examples, n features each\\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j,1)) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      A_out (ndarray (m,j)) : m examples, j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Z = np.matmul(A_in,W) + b    \\n\",\n    \"    A_out = g(Z)                 \\n\",\n    \"    return(A_out)\\n\",\n    \"```\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"test_c3(my_dense_v)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a three-layer neural network utilizing the `my_dense_v` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential_v(X, W1, b1, W2, b2, W3, b3):\\n\",\n    \"    A1 = my_dense_v(X,  W1, b1, sigmoid)\\n\",\n    \"    A2 = my_dense_v(A1, W2, b2, sigmoid)\\n\",\n    \"    A3 = my_dense_v(A2, W3, b3, sigmoid)\\n\",\n    \"    return(A3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can again copy trained weights and biases from Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp,b1_tmp = layer1.get_weights()\\n\",\n    \"W2_tmp,b2_tmp = layer2.get_weights()\\n\",\n    \"W3_tmp,b3_tmp = layer3.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's make a prediction with the new model. This will make a prediction on *all of the examples at once*. Note the shape of the output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"Prediction = my_sequential_v(X, W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"Prediction.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll apply a threshold of 0.5 as before, but to all predictions at once.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"Yhat = (Prediction >= 0.5).numpy().astype(int)\\n\",\n    \"print(\\\"predict a zero: \\\",Yhat[0], \\\"predict a one: \\\", Yhat[500])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following cell to see predictions. This will use the predictions we just calculated above. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8, 8, figsize=(8, 8))\\n\",\n    \"fig.tight_layout(pad=0.1, rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i, ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20, 20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"   \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]}, {Yhat[random_index, 0]}\\\")\\n\",\n    \"    ax.set_axis_off() \\n\",\n    \"fig.suptitle(\\\"Label, Yhat\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can see how one of the misclassified images looks.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fig = plt.figure(figsize=(1, 1))\\n\",\n    \"errors = np.where(y != Yhat)\\n\",\n    \"random_index = errors[0][0]\\n\",\n    \"X_random_reshaped = X[random_index].reshape((20, 20)).T\\n\",\n    \"plt.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"plt.title(f\\\"{y[random_index,0]}, {Yhat[random_index, 0]}\\\")\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.7\\\"></a>\\n\",\n    \"### 2.7 Congratulations!\\n\",\n    \"You have successfully built and utilized a neural network.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.8\\\"></a>\\n\",\n    \"### 2.8 NumPy Broadcasting Tutorial (Optional)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"In the last example,  $\\\\mathbf{Z}=\\\\mathbf{XW} + \\\\mathbf{b}$ utilized NumPy broadcasting to expand the vector $\\\\mathbf{b}$. If you are not familiar with NumPy Broadcasting, this short tutorial is provided.\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{XW}$  is a matrix-matrix operation with dimensions $(m,j_1)(j_1,j_2)$ which results in a matrix with dimension  $(m,j_2)$. To that, we add a vector $\\\\mathbf{b}$ with dimension $(j_2,)$.  $\\\\mathbf{b}$ must be expanded to be a $(m,j_2)$ matrix for this element-wise operation to make sense. This expansion is accomplished for you by NumPy broadcasting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Broadcasting applies to element-wise operations.  \\n\",\n    \"Its basic operation is to 'stretch' a smaller dimension by replicating elements to match a larger dimension.\\n\",\n    \"\\n\",\n    \"More [specifically](https://NumPy.org/doc/stable/user/basics.broadcasting.html): \\n\",\n    \"When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimensions and works its way left. Two dimensions are compatible when\\n\",\n    \"- they are equal, or\\n\",\n    \"- one of them is 1   \\n\",\n    \"\\n\",\n    \"If these conditions are not met, a ValueError: operands could not be broadcast together exception is thrown, indicating that the arrays have incompatible shapes. The size of the resulting array is the size that is not 1 along each axis of the inputs.\\n\",\n    \"\\n\",\n    \"Here are some examples:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C2_W1_Assign1_BroadcastIndexes.PNG\\\"  alt='missing' width=\\\"400\\\"  ><center/>\\n\",\n    \"    <figcaption>Calculating Broadcast Result shape</figcaption>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The graphic below describes expanding dimensions. Note the red text below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C2_W1_Assign1_Broadcasting.gif\\\"  alt='missing' width=\\\"600\\\"  ><center/>\\n\",\n    \"    <figcaption>Broadcast notionally expands arguments to match for element wise operations</figcaption>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The graphic above shows NumPy expanding the arguments to match before the final operation. Note that this is a notional description. The actual mechanics of NumPy operation choose the most efficient implementation.\\n\",\n    \"\\n\",\n    \"For each of the following examples, try to guess the size of the result before running the example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\\n\",\n    \"b = 5\\n\",\n    \"print(f\\\"(a + b).shape: {(a + b).shape}, \\\\na + b = \\\\n{a + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that this applies to all element-wise operations:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\\n\",\n    \"b = 5\\n\",\n    \"print(f\\\"(a * b).shape: {(a * b).shape}, \\\\na * b = \\\\n{a * b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C2_W1_Assign1_VectorAdd.PNG\\\"  alt='missing' width=\\\"740\\\" >\\n\",\n    \"    <center><figcaption><b>Row-Column Element-Wise Operations</b></figcaption></center>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = np.array([1,2,3,4]).reshape(-1,1)\\n\",\n    \"b = np.array([1,2,3]).reshape(1,-1)\\n\",\n    \"print(a)\\n\",\n    \"print(b)\\n\",\n    \"print(f\\\"(a + b).shape: {(a + b).shape}, \\\\na + b = \\\\n{a + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is the scenario in the dense layer you built above. Adding a 1-D vector $b$ to a (m,j) matrix.\\n\",\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C2_W1_Assign1_BroadcastMatrix.PNG\\\"  alt='missing' width=\\\"740\\\" >\\n\",\n    \"    <center><figcaption><b>Matrix + 1-D Vector</b></figcaption></center>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"89367\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/C2W1A1/archive/C2_W1_Assignment-Copy1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Neural Networks for Handwritten Digit Recognition, Binary\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize the hand-written digits zero and one.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Neural Networks](#2)\\n\",\n    \"  - [ 2.1 Problem Statement](#2.1)\\n\",\n    \"  - [ 2.2 Dataset](#2.2)\\n\",\n    \"  - [ 2.3 Model representation](#2.3)\\n\",\n    \"  - [ 2.4 Tensorflow Model Implementation](#2.4)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 2.5 NumPy Model Implementation (Forward Prop in NumPy)](#2.5)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"  - [ 2.6 Vectorized NumPy Model Implementation (Optional)](#2.6)\\n\",\n    \"    - [ Exercise 3](#ex03)\\n\",\n    \"  - [ 2.7 Congratulations!](#2.7)\\n\",\n    \"  - [ 2.8 NumPy Broadcasting Tutorial (Optional)](#2.8)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\\n\",\n    \"- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from autils import *\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tensorflow and Keras**  \\n\",\n    \"Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Neural Networks\\n\",\n    \"\\n\",\n    \"In Course 1, you implemented logistic regression. This was extended to handle non-linear boundaries using polynomial regression. For even more complex scenarios such as image recognition, neural networks are preferred.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Problem Statement\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize two handwritten digits, zero and one. This is a binary classification task. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. You will extend this network to recognize all 10 digits (0-9) in a future assignment. \\n\",\n    \"\\n\",\n    \"This exercise will show you how the methods you have learned can be used for this classification task.\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_data()` function shown below loads the data into variables `X` and `y`\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- The data set contains 1000 training examples of handwritten digits $^1$, here limited to zero and one.  \\n\",\n    \"\\n\",\n    \"    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \\n\",\n    \"        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \\n\",\n    \"        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \\n\",\n    \"        - Each training example becomes a single row in our data matrix `X`. \\n\",\n    \"        - This gives us a 1000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\\n\",\n    \"\\n\",\n    \"$$X = \\n\",\n    \"\\\\left(\\\\begin{array}{cc} \\n\",\n    \"--- (x^{(1)}) --- \\\\\\\\\\n\",\n    \"--- (x^{(2)}) --- \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\ \\n\",\n    \"--- (x^{(m)}) --- \\n\",\n    \"\\\\end{array}\\\\right)$$ \\n\",\n    \"\\n\",\n    \"- The second part of the training set is a 1000 x 1 dimensional vector `y` that contains labels for the training set\\n\",\n    \"    - `y = 0` if the image is of the digit `0`, `y = 1` if the image is of the digit `1`.\\n\",\n    \"\\n\",\n    \"$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X, y = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.1\\\"></a>\\n\",\n    \"#### 2.2.1 View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints elements of the variables `X` and `y`.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The first element of X is: ', X[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The first element of y is: ', y[0,0])\\n\",\n    \"print ('The last element of y is: ', y[-1,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.2\\\"></a>\\n\",\n    \"#### 2.2.2 Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The shape of X is: ' + str(X.shape))\\n\",\n    \"print ('The shape of y is: ' + str(y.shape))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"toc_89367_2.2.3\\\"></a>\\n\",\n    \"#### 2.2.3 Visualizing the Data\\n\",\n    \"\\n\",\n    \"You will begin by visualizing a subset of the training set. \\n\",\n    \"- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \\n\",\n    \"- The label for each image is displayed above the image \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1)\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(y[random_index,0])\\n\",\n    \"    ax.set_axis_off()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3 Model representation\\n\",\n    \"\\n\",\n    \"The neural network you will use in this assignment is shown in the figure below. \\n\",\n    \"- This has three dense layers with sigmoid activations.\\n\",\n    \"    - Recall that our inputs are pixel values of digit images.\\n\",\n    \"    - Since the images are of size $20\\\\times20$, this gives us $400$ inputs  \\n\",\n    \"    \\n\",\n    \"<img src=\\\"images/C2_W1_Assign1.PNG\\\" width=\\\"500\\\" height=\\\"400\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $1$ output unit in layer 3. \\n\",\n    \"\\n\",\n    \"    - Recall that the dimensions of these parameters are determined as follows:\\n\",\n    \"        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \\n\",\n    \"            - $W$ will be of dimension $s_{in} \\\\times s_{out}$.\\n\",\n    \"            - $b$ will a vector with $s_{out}$ elements\\n\",\n    \"  \\n\",\n    \"    - Therefore, the shapes of `W`, and `b`,  are \\n\",\n    \"        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\\n\",\n    \"        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\\n\",\n    \"        - layer3: The shape of `W3` is (15, 1) and the shape of `b3` is: (1,)\\n\",\n    \">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (n,1) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention. \\n\",\n    \"               \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.4\\\"></a>\\n\",\n    \"### 2.4 Tensorflow Model Implementation\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statment below. \\n\",\n    \">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \\n\",\n    \"`tf.keras.Input(shape=(400,)),    #specify input shape`  \\n\",\n    \"We will include that here to illuminate some model sizing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a sigmoid activation to construct the network described above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED CELL: Sequential model\\n\",\n    \"\\n\",\n    \"model = Sequential(\\n\",\n    \"    [               \\n\",\n    \"        tf.keras.Input(shape=(400,)),    #specify input size\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"my_model\\\" \\n\",\n    \")                            \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Expected Output (Click to Expand) </b></font></summary>\\n\",\n    \"The `model.summary()` function displays a useful summary of the model. Because we have specified an input layer size, the shape of the weight and bias arrays are determined and the total number of parameters per layer can be shown. Note, the names of the layers may vary as they are auto-generated.  \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"```\\n\",\n    \"Model: \\\"my_model\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"dense (Dense)                (None, 25)                10025     \\n\",\n    \"_________________________________________________________________\\n\",\n    \"dense_1 (Dense)              (None, 15)                390       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"dense_2 (Dense)              (None, 1)                 16        \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 10,431\\n\",\n    \"Trainable params: 10,431\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"As described in the lecture:\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"model = Sequential(                      \\n\",\n    \"    [                                   \\n\",\n    \"        tf.keras.Input(shape=(400,)),    # specify input size (optional)\\n\",\n    \"        Dense(25, activation='sigmoid'), \\n\",\n    \"        Dense(15, activation='sigmoid'), \\n\",\n    \"        Dense(1,  activation='sigmoid')  \\n\",\n    \"    ], name = \\\"my_model\\\"                                    \\n\",\n    \")                                       \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"from public_tests import * \\n\",\n    \"\\n\",\n    \"test_c1(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"L1_num_params = 400 * 25 + 25  # W1 parameters  + b1 parameters\\n\",\n    \"L2_num_params = 25 * 15 + 15   # W2 parameters  + b2 parameters\\n\",\n    \"L3_num_params = 15 * 1 + 1     # W3 parameters  + b3 parameters\\n\",\n    \"print(\\\"L1 params = \\\", L1_num_params, \\\", L2 params = \\\", L2_num_params, \\\",  L3 params = \\\", L3_num_params )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's further examine the weights to verify that tensorflow produced the same dimensions as we calculated above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"[layer1, layer2, layer3] = model.layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#### Examine Weights shapes\\n\",\n    \"W1,b1 = layer1.get_weights()\\n\",\n    \"W2,b2 = layer2.get_weights()\\n\",\n    \"W3,b3 = layer3.get_weights()\\n\",\n    \"print(f\\\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\\\")\\n\",\n    \"print(f\\\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\\\")\\n\",\n    \"print(f\\\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"W1 shape = (400, 25), b1 shape = (25,)  \\n\",\n    \"W2 shape = (25, 15), b2 shape = (15,)  \\n\",\n    \"W3 shape = (15, 1), b3 shape = (1,)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`xx.get_weights` returns a NumPy array. One can also access the weights directly in their tensor form. Note the shape of the tensors in the final layer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(model.layers[2].weights)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following code will define a loss function and run gradient descent to fit the weights of the model to the training data. This will be explained in more detail in the following week.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.BinaryCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X,y,\\n\",\n    \"    epochs=20\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To run the model on an example to make a prediction, use [Keras `predict`](https://www.tensorflow.org/api_docs/python/tf/keras/Model). The input to `predict` is an array so the single example is reshaped to be two dimensional.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"prediction = model.predict(X[0].reshape(1,400))  # a zero\\n\",\n    \"print(f\\\" predicting a zero: {prediction}\\\")\\n\",\n    \"prediction = model.predict(X[500].reshape(1,400))  # a one\\n\",\n    \"print(f\\\" predicting a one:  {prediction}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output of the model is interpreted as a probability. In the first example above, the input is a zero. The model predicts the probability that the input is a one is nearly zero. \\n\",\n    \"In the second example, the input is a one. The model predicts the probability that the input is a one is nearly one.\\n\",\n    \"As in the case of logistic regression, the probability is compared to a threshold to make a final prediction.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print(f\\\"prediction after threshold: {yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Predict using the Neural Network\\n\",\n    \"    prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    if prediction >= 0.5:\\n\",\n    \"        yhat = 1\\n\",\n    \"    else:\\n\",\n    \"        yhat = 0\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{yhat}\\\")\\n\",\n    \"    ax.set_axis_off()\\n\",\n    \"fig.suptitle(\\\"Label, yhat\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.5\\\"></a>\\n\",\n    \"### 2.5 NumPy Model Implementation (Forward Prop in NumPy)\\n\",\n    \"As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_dense2.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Below, build a dense layer subroutine. The example in lecture utilized a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` is then applied to that result. This section will not utilize some of the matrix operations described in the optional lectures. These will be explored in a later section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: my_dense\\n\",\n    \"\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"### START CODE HERE ### \\n\",\n    \"        \\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(a_out)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Quick Check\\n\",\n    \"x_tst = 0.1*np.arange(1,3,1).reshape(2,)  # (1 examples, 2 features)\\n\",\n    \"W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\\n\",\n    \"b_tst = 0.1*np.arange(1,4,1).reshape(3,)  # (3 features)\\n\",\n    \"A_tst = my_dense(x_tst, W_tst, b_tst, sigmoid)\\n\",\n    \"print(A_tst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"[0.54735762 0.57932425 0.61063923]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"As described in the lecture:\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):             \\n\",\n    \"        w =                            # Select weights for unit j. These are in column j of W\\n\",\n    \"        z =                            # dot product of w and a_in + b\\n\",\n    \"        a_out[j] =                     # apply activation to z\\n\",\n    \"    return(a_out)\\n\",\n    \"```\\n\",\n    \"   \\n\",\n    \"    \\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):             \\n\",\n    \"        w = W[:,j]                     \\n\",\n    \"        z = np.dot(w, a_in) + b[j]     \\n\",\n    \"        a_out[j] = g(z)                \\n\",\n    \"    return(a_out)\\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"test_c2(my_dense)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a three-layer neural network utilizing the `my_dense` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential(x, W1, b1, W2, b2, W3, b3):\\n\",\n    \"    a1 = my_dense(x,  W1, b1, sigmoid)\\n\",\n    \"    a2 = my_dense(a1, W2, b2, sigmoid)\\n\",\n    \"    a3 = my_dense(a2, W3, b3, sigmoid)\\n\",\n    \"    return(a3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can copy trained weights and biases from Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp,b1_tmp = layer1.get_weights()\\n\",\n    \"W2_tmp,b2_tmp = layer2.get_weights()\\n\",\n    \"W3_tmp,b3_tmp = layer3.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# make predictions\\n\",\n    \"prediction = my_sequential(X[0], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print( \\\"yhat = \\\", yhat, \\\" label= \\\", y[0,0])\\n\",\n    \"prediction = my_sequential(X[500], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"if prediction >= 0.5:\\n\",\n    \"    yhat = 1\\n\",\n    \"else:\\n\",\n    \"    yhat = 0\\n\",\n    \"print( \\\"yhat = \\\", yhat, \\\" label= \\\", y[500,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following cell to see predictions from both the Numpy model and the Tensorflow model. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(8,8))\\n\",\n    \"fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"\\n\",\n    \"    # Predict using the Neural Network implemented in Numpy\\n\",\n    \"    my_prediction = my_sequential(X[random_index], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"    my_yhat = int(my_prediction >= 0.5)\\n\",\n    \"\\n\",\n    \"    # Predict using the Neural Network implemented in Tensorflow\\n\",\n    \"    tf_prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    tf_yhat = int(tf_prediction >= 0.5)\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{tf_yhat},{my_yhat}\\\")\\n\",\n    \"    ax.set_axis_off() \\n\",\n    \"fig.suptitle(\\\"Label, yhat Tensorflow, yhat Numpy\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.6\\\"></a>\\n\",\n    \"### 2.6 Vectorized NumPy Model Implementation (Optional)\\n\",\n    \"The optional lectures described vector and matrix operations that can be used to speed the calculations.\\n\",\n    \"Below describes a layer operation that computes the output for all units in a layer on a given input example:\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_VectorMatrix.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"\\n\",\n    \"We can demonstrate this using the examples `X` and the `W1`,`b1` parameters above. We use `np.matmul` to perform the matrix multiply. Note, the dimensions of x and W must be compatible as shown in the diagram above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x = X[0].reshape(-1,1)         # column vector (400,1)\\n\",\n    \"z1 = np.matmul(x.T,W1) + b1    # (1,400)(400,25) = (1,25)\\n\",\n    \"a1 = sigmoid(z1)\\n\",\n    \"print(a1.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can take this a step further and compute all the units for all examples in one Matrix-Matrix operation.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_MatrixMatrix.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"The full operation is $\\\\mathbf{Z}=\\\\mathbf{XW}+\\\\mathbf{b}$. This will utilize NumPy broadcasting to expand $\\\\mathbf{b}$ to $m$ rows. If this is unfamiliar, a short tutorial is provided at the end of the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex03\\\"></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"\\n\",\n    \"Below, compose a new `my_dense_v` subroutine that performs the layer calculations for a matrix of examples. This will utilize `np.matmul()`. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED FUNCTION: my_dense_v\\n\",\n    \"\\n\",\n    \"def my_dense_v(A_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      A_in (ndarray (m,n)) : Data, m examples, n features each\\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j,1)) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      A_out (ndarray (m,j)) : m examples, j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"### START CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(A_out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_tst = 0.1*np.arange(1,9,1).reshape(4,2) # (4 examples, 2 features)\\n\",\n    \"W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\\n\",\n    \"b_tst = 0.1*np.arange(1,4,1).reshape(1,3) # (3 features, 1)\\n\",\n    \"A_tst = my_dense_v(X_tst, W_tst, b_tst, sigmoid)\\n\",\n    \"print(A_tst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"[[0.54735762 0.57932425 0.61063923]\\n\",\n    \" [0.57199613 0.61301418 0.65248946]\\n\",\n    \" [0.5962827  0.64565631 0.6921095 ]\\n\",\n    \" [0.62010643 0.67699586 0.72908792]]\\n\",\n    \" ```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    In matrix form, this can be written in one or two lines. \\n\",\n    \"    \\n\",\n    \"       Z = np.matmul of A_in and W plus b    \\n\",\n    \"       A_out is g(Z)  \\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for code</b></font></summary>\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"def my_dense_v(A_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      A_in (ndarray (m,n)) : Data, m examples, n features each\\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j,1)) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      A_out (ndarray (m,j)) : m examples, j units\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Z = np.matmul(A_in,W) + b    \\n\",\n    \"    A_out = g(Z)                 \\n\",\n    \"    return(A_out)\\n\",\n    \"```\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNIT TESTS\\n\",\n    \"test_c3(my_dense_v)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a three-layer neural network utilizing the `my_dense_v` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential_v(X, W1, b1, W2, b2, W3, b3):\\n\",\n    \"    A1 = my_dense_v(X,  W1, b1, sigmoid)\\n\",\n    \"    A2 = my_dense_v(A1, W2, b2, sigmoid)\\n\",\n    \"    A3 = my_dense_v(A2, W3, b3, sigmoid)\\n\",\n    \"    return(A3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can again copy trained weights and biases from Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp,b1_tmp = layer1.get_weights()\\n\",\n    \"W2_tmp,b2_tmp = layer2.get_weights()\\n\",\n    \"W3_tmp,b3_tmp = layer3.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's make a prediction with the new model. This will make a prediction on *all of the examples at once*. Note the shape of the output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"Prediction = my_sequential_v(X, W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\\n\",\n    \"Prediction.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll apply a threshold of 0.5 as before, but to all predictions at once.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"Yhat = (Prediction >= 0.5).numpy().astype(int)\\n\",\n    \"print(\\\"predict a zero: \\\",Yhat[0], \\\"predict a one: \\\", Yhat[500])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Run the following cell to see predictions. This will use the predictions we just calculated above. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8, 8, figsize=(8, 8))\\n\",\n    \"fig.tight_layout(pad=0.1, rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"for i, ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20, 20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"   \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]}, {Yhat[random_index, 0]}\\\")\\n\",\n    \"    ax.set_axis_off() \\n\",\n    \"fig.suptitle(\\\"Label, Yhat\\\", fontsize=16)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can see how one of the misclassified images looks.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fig = plt.figure(figsize=(1, 1))\\n\",\n    \"errors = np.where(y != Yhat)\\n\",\n    \"random_index = errors[0][0]\\n\",\n    \"X_random_reshaped = X[random_index].reshape((20, 20)).T\\n\",\n    \"plt.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"plt.title(f\\\"{y[random_index,0]}, {Yhat[random_index, 0]}\\\")\\n\",\n    \"plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.7\\\"></a>\\n\",\n    \"### 2.7 Congratulations!\\n\",\n    \"You have successfully built and utilized a neural network.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2.8\\\"></a>\\n\",\n    \"### 2.8 NumPy Broadcasting Tutorial (Optional)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"In the last example,  $\\\\mathbf{Z}=\\\\mathbf{XW} + \\\\mathbf{b}$ utilized NumPy broadcasting to expand the vector $\\\\mathbf{b}$. If you are not familiar with NumPy Broadcasting, this short tutorial is provided.\\n\",\n    \"\\n\",\n    \"$\\\\mathbf{XW}$  is a matrix-matrix operation with dimensions $(m,j_1)(j_1,j_2)$ which results in a matrix with dimension  $(m,j_2)$. To that, we add a vector $\\\\mathbf{b}$ with dimension $(j_2,)$.  $\\\\mathbf{b}$ must be expanded to be a $(m,j_2)$ matrix for this element-wise operation to make sense. This expansion is accomplished for you by NumPy broadcasting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Broadcasting applies to element-wise operations.  \\n\",\n    \"Its basic operation is to 'stretch' a smaller dimension by replicating elements to match a larger dimension.\\n\",\n    \"\\n\",\n    \"More [specifically](https://NumPy.org/doc/stable/user/basics.broadcasting.html): \\n\",\n    \"When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimensions and works its way left. Two dimensions are compatible when\\n\",\n    \"- they are equal, or\\n\",\n    \"- one of them is 1   \\n\",\n    \"\\n\",\n    \"If these conditions are not met, a ValueError: operands could not be broadcast together exception is thrown, indicating that the arrays have incompatible shapes. The size of the resulting array is the size that is not 1 along each axis of the inputs.\\n\",\n    \"\\n\",\n    \"Here are some examples:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C2_W1_Assign1_BroadcastIndexes.PNG\\\"  alt='missing' width=\\\"400\\\"  ><center/>\\n\",\n    \"    <figcaption>Calculating Broadcast Result shape</figcaption>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The graphic below describes expanding dimensions. Note the red text below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/C2_W1_Assign1_Broadcasting.gif\\\"  alt='missing' width=\\\"600\\\"  ><center/>\\n\",\n    \"    <figcaption>Broadcast notionally expands arguments to match for element wise operations</figcaption>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The graphic above shows NumPy expanding the arguments to match before the final operation. Note that this is a notional description. The actual mechanics of NumPy operation choose the most efficient implementation.\\n\",\n    \"\\n\",\n    \"For each of the following examples, try to guess the size of the result before running the example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\\n\",\n    \"b = 5\\n\",\n    \"print(f\\\"(a + b).shape: {(a + b).shape}, \\\\na + b = \\\\n{a + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that this applies to all element-wise operations:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\\n\",\n    \"b = 5\\n\",\n    \"print(f\\\"(a * b).shape: {(a * b).shape}, \\\\na * b = \\\\n{a * b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C2_W1_Assign1_VectorAdd.PNG\\\"  alt='missing' width=\\\"740\\\" >\\n\",\n    \"    <center><figcaption><b>Row-Column Element-Wise Operations</b></figcaption></center>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"a = np.array([1,2,3,4]).reshape(-1,1)\\n\",\n    \"b = np.array([1,2,3]).reshape(1,-1)\\n\",\n    \"print(a)\\n\",\n    \"print(b)\\n\",\n    \"print(f\\\"(a + b).shape: {(a + b).shape}, \\\\na + b = \\\\n{a + b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is the scenario in the dense layer you built above. Adding a 1-D vector $b$ to a (m,j) matrix.\\n\",\n    \"<figure>\\n\",\n    \"    <img src=\\\"./images/C2_W1_Assign1_BroadcastMatrix.PNG\\\"  alt='missing' width=\\\"740\\\" >\\n\",\n    \"    <center><figcaption><b>Matrix + 1-D Vector</b></figcaption></center>\\n\",\n    \"<figure/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"89367\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/C2W1A1/autils.py",
    "content": "import numpy as np\n\ndef load_data():\n    X = np.load(\"data/X.npy\")\n    y = np.load(\"data/y.npy\")\n    X = X[0:1000]\n    y = y[0:1000]\n    return X, y\n\ndef load_weights():\n    w1 = np.load(\"data/w1.npy\")\n    b1 = np.load(\"data/b1.npy\")\n    w2 = np.load(\"data/w2.npy\")\n    b2 = np.load(\"data/b2.npy\")\n    return w1, b1, w2, b2\n\ndef sigmoid(x):\n    return 1. / (1. + np.exp(-x))\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/C2W1A1/public_tests.py",
    "content": "# UNIT TESTS\nfrom tensorflow.keras.activations import sigmoid\nfrom tensorflow.keras.layers import Dense\n\nimport numpy as np\n\ndef test_c1(target):\n    assert len(target.layers) == 3, \\\n        f\"Wrong number of layers. Expected 3 but got {len(target.layers)}\"\n    assert target.input.shape.as_list() == [None, 400], \\\n        f\"Wrong input shape. Expected [None,  400] but got {target.input.shape.as_list()}\"\n    i = 0\n    expected = [[Dense, [None, 25], sigmoid],\n                [Dense, [None, 15], sigmoid],\n                [Dense, [None, 1], sigmoid]]\n\n    for layer in target.layers:\n        assert type(layer) == expected[i][0], \\\n            f\"Wrong type in layer {i}. Expected {expected[i][0]} but got {type(layer)}\"\n        assert layer.output.shape.as_list() == expected[i][1], \\\n            f\"Wrong number of units in layer {i}. Expected {expected[i][1]} but got {layer.output.shape.as_list()}\"\n        assert layer.activation == expected[i][2], \\\n            f\"Wrong activation in layer {i}. Expected {expected[i][2]} but got {layer.activation}\"\n        i = i + 1\n\n    print(\"\\033[92mAll tests passed!\")\n    \ndef test_c2(target):\n    \n    def linear(a):\n        return a\n    \n    def linear_times3(a):\n        return a * 3\n    \n    x_tst = np.array([1., 2., 3., 4.])  # (1 examples, 3 features)\n    W_tst = np.array([[1., 2.], [1., 2.], [1., 2.], [1., 2.]]) # (3 input features, 2 output features)\n    b_tst = np.array([0., 0.])  # (2 features)\n    \n    A_tst = target(x_tst, W_tst, b_tst, linear)\n    assert A_tst.shape[0] == len(b_tst)\n    assert np.allclose(A_tst, [10., 20.]), \\\n        \"Wrong output. Check the dot product\"\n    \n    b_tst = np.array([3., 5.])  # (2 features)\n    \n    A_tst = target(x_tst, W_tst, b_tst, linear)\n    assert np.allclose(A_tst, [13., 25.]), \\\n        \"Wrong output. Check the bias term in the formula\"\n    \n    A_tst = target(x_tst, W_tst, b_tst, linear_times3)\n    assert np.allclose(A_tst, [39., 75.]), \\\n        \"Wrong output. Did you apply the activation function at the end?\"\n    \n    print(\"\\033[92mAll tests passed!\")  \n    \ndef test_c3(target):\n    \n    def linear(a):\n        return a\n    \n    def linear_times3(a):\n        return a * 3\n    \n    x_tst = np.array([1., 2., 3., 4.])  # (1 examples, 3 features)\n    W_tst = np.array([[1., 2.], [1., 2.], [1., 2.], [1., 2.]]) # (3 input features, 2 output features)\n    b_tst = np.array([0., 0.])  # (2 features)\n    \n    A_tst = target(x_tst, W_tst, b_tst, linear)\n    assert A_tst.shape[0] == len(b_tst)\n    assert np.allclose(A_tst, [10., 20.]), \\\n        \"Wrong output. Check the dot product\"\n    \n    b_tst = np.array([3., 5.])  # (2 features)\n    \n    A_tst = target(x_tst, W_tst, b_tst, linear)\n    assert np.allclose(A_tst, [13., 25.]), \\\n        \"Wrong output. Check the bias term in the formula\"\n    \n    A_tst = target(x_tst, W_tst, b_tst, linear_times3)\n    assert np.allclose(A_tst, [39., 75.]), \\\n        \"Wrong output. Did you apply the activation function at the end?\"\n    \n    x_tst = np.array([[1., 2., 3., 4.], [5., 6., 7., 8.]])  # (2 examples, 4 features)\n    W_tst = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.], [10., 11., 12]]) # (3 input features, 2 output features)\n    b_tst = np.array([0., 0., 0.])  # (2 features)\n    \n    A_tst = target(x_tst, W_tst, b_tst, linear)\n    assert A_tst.shape == (2, 3)\n    assert np.allclose(A_tst, [[ 70.,  80.,  90.], [158., 184., 210.]]), \\\n        \"Wrong output. Check the dot product\"\n    \n    b_tst = np.array([3., 5., 6])  # (3 features)\n    \n    A_tst = target(x_tst, W_tst, b_tst, linear)\n    assert np.allclose(A_tst, [[ 73.,  85.,  96.], [161., 189., 216.]]), \\\n        \"Wrong output. Check the bias term in the formula\"\n    \n    A_tst = target(x_tst, W_tst, b_tst, linear_times3)\n    assert np.allclose(A_tst, [[ 219.,  255.,  288.], [483., 567., 648.]]), \\\n        \"Wrong output. Did you apply the activation function at the end?\"\n    \n    print(\"\\033[92mAll tests passed!\")  \n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/C2W1A1/utils.py",
    "content": "# C2_W1 Utilities\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_blobs\n\ndef sigmoid(x):\n    return 1 / (1 + np.exp(-x))\n\n# Plot  multi-class training points\ndef plot_mc_data(X, y, class_labels=None, legend=False,size=40):\n    classes = np.unique(y)\n    for i in classes:\n        label = class_labels[i] if class_labels else \"class {}\".format(i)\n        idx = np.where(y == i)\n        plt.scatter(X[idx, 0], X[idx, 1],  cmap=plt.cm.Paired,\n                    edgecolor='black', s=size, label=label)\n    if legend: plt.legend()\n        \n\n#Plot a multi-class categorical decision boundary\n# This version handles a non-vector prediction (adds a for-loop over points)\ndef plot_cat_decision_boundary(X,predict , class_labels=None, legend=False, vector=True):\n\n    # create a mesh to points to plot\n    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    h = max(x_max-x_min, y_max-y_min)/200\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                         np.arange(y_min, y_max, h))\n    points = np.c_[xx.ravel(), yy.ravel()]\n\n    #make predictions for each point in mesh\n    if vector:\n        Z = predict(points)\n    else:\n        Z = np.zeros((len(points),))\n        for i in range(len(points)):\n            Z[i] = predict(points[i].reshape(1,2))\n    Z = Z.reshape(xx.shape)\n\n    #contour plot highlights boundaries between values - classes in this case\n    plt.figure()\n    plt.contour(xx, yy, Z, colors='g') \n    plt.axis('tight')"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/Practice quiz - Neural network model/Readme.md",
    "content": "![](/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz:%20Neural%20network%20model/ss1.png)\n![](/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz:%20Neural%20network%20model/ss2.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/Practice quiz - Neural networks intuition/Readme.md",
    "content": "![](/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz:%20Neural%20networks%20intuition/ss1.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/Practice quiz - TensorFlow implementation/Readme.md",
    "content": "![](/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz:%20TensorFlow%20implementation/ss1.png)\n![](/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz:%20TensorFlow%20implementation/ss2.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/Practice-Quiz-Neural-Networks-Implementation-in-python/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/e5d6103f4bdf732390bd85aeb453002f276d8bf3/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice-Quiz-Neural-Networks-Implementation-in-python/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/e5d6103f4bdf732390bd85aeb453002f276d8bf3/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice-Quiz-Neural-Networks-Implementation-in-python/ss2.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/e5d6103f4bdf732390bd85aeb453002f276d8bf3/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice-Quiz-Neural-Networks-Implementation-in-python/ss3.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/Readme.md",
    "content": "### C2 - Week 1 Solutions \n\n<br/>\n\n- [Practice quiz: Neural networks intuition](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz%20-%20Neural%20networks%20intuition)\n- [Practice quiz: Neural network model](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz%20-%20Neural%20network%20model)\n- [Practice quiz: TensorFlow implementation](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz%20-%20TensorFlow%20implementation)\n- [Practice quiz : Neural Networks Implementation in Numpy](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/e5d6103f4bdf732390bd85aeb453002f276d8bf3/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice-Quiz-Neural-Networks-Implementation-in-python)\n- [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs)\n  - [Neurons and Layers](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs/C2_W1_Lab01_Neurons_and_Layers.ipynb)\n  - [Coffee Roasting](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs/C2_W1_Lab02_CoffeeRoasting_TF.ipynb)\n  - [Coffee Roasting Using Numpy](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs/C2_W1_Lab03_CoffeeRoasting_Numpy.ipynb)\n- [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/C2W1A1)\n  - [Neural Networks for Binary Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/794f84af434b89b90af8d21b25727661f71148d6/C2%20-%20Advanced%20Learning%20Algorithms/week1/C2W1A1/C2_W1_Assignment.ipynb)\n  "
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/.ipynb_checkpoints/C2_W1_Lab01_Neurons_and_Layers-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Neurons and Layers\\n\",\n    \"In this lab we will explore the inner workings of neurons/units and layers. In particular, the lab will draw parallels to the models you have mastered in Course 1, the regression/linear model and the logistic model. The lab will introduce Tensorflow and demonstrate how these models are implemented in that framework.\\n\",\n    \"<figure>\\n\",\n    \"   <img src=\\\"./images/C2_W1_NeuronsAndLayers.png\\\"  style=\\\"width:540px;height:200px;\\\" >\\n\",\n    \"</figure>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Packages\\n\",\n    \"**Tensorflow and Keras**  \\n\",\n    \"Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.layers import Dense, Input\\n\",\n    \"from tensorflow.keras import Sequential\\n\",\n    \"from tensorflow.keras.losses import MeanSquaredError, BinaryCrossentropy\\n\",\n    \"from tensorflow.keras.activations import sigmoid\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_neurons_utils import plt_prob_1d, sigmoidnp, plt_linear, plt_logistic\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Neuron without activation - Regression/Linear Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"### DataSet\\n\",\n    \"We'll use an example from Course 1, linear regression on house prices.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X_train = np.array([[1.0], [2.0]], dtype=np.float32)           #(size in 1000 square feet)\\n\",\n    \"Y_train = np.array([[300.0], [500.0]], dtype=np.float32)       #(price in 1000s of dollars)\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(1,1)\\n\",\n    \"ax.scatter(X_train, Y_train, marker='x', c='r', label=\\\"Data Points\\\")\\n\",\n    \"ax.legend( fontsize='xx-large')\\n\",\n    \"ax.set_ylabel('Price (in 1000s of dollars)', fontsize='xx-large')\\n\",\n    \"ax.set_xlabel('Size (1000 sqft)', fontsize='xx-large')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Regression/Linear Model \\n\",\n    \"The function implemented by a neuron with no activation is the same as in Course 1, linear regression:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(x^{(i)}) = \\\\mathbf{w}\\\\cdot x^{(i)} + b \\\\tag{1}$$\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can define a layer with one neuron or unit and compare it to the familiar linear regression function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"linear_layer = tf.keras.layers.Dense(units=1, activation = 'linear', )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's examine the weights.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[]\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"linear_layer.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There are no weights as the weights are not yet instantiated. Let's try the model on one example in `X_train`. This will trigger the instantiation of the weights. Note, the input to the layer must be 2-D, so we'll reshape it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tf.Tensor([[1.37]], shape=(1, 1), dtype=float32)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a1 = linear_layer(X_train[0].reshape(1,1))\\n\",\n    \"print(a1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The result is a tensor (another name for an array) with a shape of (1,1) or one entry.   \\n\",\n    \"Now let's look at the weights and bias. These weights are randomly initialized to small numbers and the bias defaults to being initialized to zero.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"w = [[1.37]], b=[0.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w, b= linear_layer.get_weights()\\n\",\n    \"print(f\\\"w = {w}, b={b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A linear regression model (1) with a single input feature will have a single weight and bias. This matches the dimensions of our `linear_layer` above.   \\n\",\n    \"\\n\",\n    \"The weights are initialized to random values so let's set them to some known values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[200.]], dtype=float32), array([100.], dtype=float32)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"set_w = np.array([[200]])\\n\",\n    \"set_b = np.array([100])\\n\",\n    \"\\n\",\n    \"# set_weights takes a list of numpy arrays\\n\",\n    \"linear_layer.set_weights([set_w, set_b])\\n\",\n    \"print(linear_layer.get_weights())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare equation (1) to the layer output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tf.Tensor([[300.]], shape=(1, 1), dtype=float32)\\n\",\n      \"[[300.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a1 = linear_layer(X_train[0].reshape(1,1))\\n\",\n    \"print(a1)\\n\",\n    \"alin = np.dot(set_w,X_train[0].reshape(1,1)) + set_b\\n\",\n    \"print(alin)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"They produce the same values!\\n\",\n    \"Now, we can use our linear layer to make predictions on our training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"prediction_tf = linear_layer(X_train)\\n\",\n    \"prediction_np = np.dot( X_train, set_w) + set_b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_linear(X_train, Y_train, prediction_tf, prediction_np)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Neuron with Sigmoid activation\\n\",\n    \"The function implemented by a neuron/unit with a sigmoid activation is the same as in Course 1, logistic  regression:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(x^{(i)}) = g(\\\\mathbf{w}x^{(i)} + b) \\\\tag{2}$$\\n\",\n    \"where $$g(x) = sigmoid(x)$$ \\n\",\n    \"\\n\",\n    \"Let's set $w$ and $b$ to some known values and check the model.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"### DataSet\\n\",\n    \"We'll use an example from Course 1, logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([0., 1, 2, 3, 4, 5], dtype=np.float32).reshape(-1,1)  # 2-D Matrix\\n\",\n    \"Y_train = np.array([0,  0, 0, 1, 1, 1], dtype=np.float32).reshape(-1,1)  # 2-D Matrix\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([3., 4., 5.], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pos = Y_train == 1\\n\",\n    \"neg = Y_train == 0\\n\",\n    \"X_train[pos]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 288x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pos = Y_train == 1\\n\",\n    \"neg = Y_train == 0\\n\",\n    \"\\n\",\n    \"fig,ax = plt.subplots(1,1,figsize=(4,3))\\n\",\n    \"ax.scatter(X_train[pos], Y_train[pos], marker='x', s=80, c = 'red', label=\\\"y=1\\\")\\n\",\n    \"ax.scatter(X_train[neg], Y_train[neg], marker='o', s=100, label=\\\"y=0\\\", facecolors='none', \\n\",\n    \"              edgecolors=dlc[\\\"dlblue\\\"],lw=3)\\n\",\n    \"\\n\",\n    \"ax.set_ylim(-0.08,1.1)\\n\",\n    \"ax.set_ylabel('y', fontsize=12)\\n\",\n    \"ax.set_xlabel('x', fontsize=12)\\n\",\n    \"ax.set_title('one variable plot')\\n\",\n    \"ax.legend(fontsize=12)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Logistic Neuron\\n\",\n    \"We can implement a 'logistic neuron' by adding a sigmoid activation. The function of the neuron is then described by (2) above.   \\n\",\n    \"This section will create a Tensorflow Model that contains our logistic layer to demonstrate an alternate method of creating models. Tensorflow is most often used to create multi-layer models. The [Sequential](https://keras.io/guides/sequential_model/) model is a convenient means of constructing these models.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        tf.keras.layers.Dense(1, input_dim=1,  activation = 'sigmoid', name='L1')\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`model.summary()` shows the layers and number of parameters in the model. There is only one layer in this model and that layer has only one unit. The unit has two parameters, $w$ and $b$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"sequential\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" L1 (Dense)                  (None, 1)                 2         \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 2\\n\",\n      \"Trainable params: 2\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[-0.11]] [0.]\\n\",\n      \"(1, 1) (1,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"logistic_layer = model.get_layer('L1')\\n\",\n    \"w,b = logistic_layer.get_weights()\\n\",\n    \"print(w,b)\\n\",\n    \"print(w.shape,b.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's set the weight and bias to some known values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[2.]], dtype=float32), array([-4.5], dtype=float32)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"set_w = np.array([[2]])\\n\",\n    \"set_b = np.array([-4.5])\\n\",\n    \"# set_weights takes a list of numpy arrays\\n\",\n    \"logistic_layer.set_weights([set_w, set_b])\\n\",\n    \"print(logistic_layer.get_weights())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare equation (2) to the layer output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0.01]]\\n\",\n      \"[[0.01]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a1 = model.predict(X_train[0].reshape(1,1))\\n\",\n    \"print(a1)\\n\",\n    \"alog = sigmoidnp(np.dot(set_w,X_train[0].reshape(1,1)) + set_b)\\n\",\n    \"print(alog)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"They produce the same values!\\n\",\n    \"Now, we can use our logistic layer and NumPy model to make predictions on our training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_logistic(X_train, Y_train, model, set_w, set_b, pos, neg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The shading above reflects the output of the sigmoid which varies from 0 to 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Congratulations!\\n\",\n    \"You built a very simple neural network and have explored the similarities of a neuron to the linear and logistic regression from Course 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/.ipynb_checkpoints/C2_W1_Lab02_CoffeeRoasting_TF-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Simple Neural Network\\n\",\n    \"In this lab we will build a small neural network using Tensorflow.\\n\",\n    \"   <center> <img  src=\\\"./images/C2_W1_CoffeeRoasting.png\\\" width=\\\"400\\\" />   <center/>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_coffee_utils import load_coffee_data, plt_roast, plt_prob, plt_layer, plt_network, plt_output_unit\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## DataSet\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(200, 2) (200, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X,Y = load_coffee_data();\\n\",\n    \"print(X.shape, Y.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the coffee roasting data below. The two features are Temperature in Celsius and Duration in minutes. [Coffee Roasting at Home](https://www.merchantsofgreencoffee.com/how-to-roast-green-coffee-in-your-oven/) suggests that the duration is best kept between 12 and 15 minutes while the temp should be between 175 and 260 degrees Celsius. Of course, as temperature rises, the duration should shrink. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_roast(X,Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Normalize Data\\n\",\n    \"Fitting the weights to the data (back-propagation, covered in next week's lectures) will proceed more quickly if the data is normalized. This is the same procedure you used in Course 1 where features in the data are each normalized to have a similar range. \\n\",\n    \"The procedure below uses a Keras [normalization layer](https://keras.io/api/layers/preprocessing_layers/numerical/normalization/). It has the following steps:\\n\",\n    \"- create a \\\"Normalization Layer\\\". Note, as applied here, this is not a layer in your model.\\n\",\n    \"- 'adapt' the data. This learns the mean and variance of the data set and saves the values internally.\\n\",\n    \"- normalize the data.  \\n\",\n    \"It is important to apply normalization to any future data that utilizes the learned model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Temperature Max, Min pre normalization: 284.99, 151.32\\n\",\n      \"Duration    Max, Min pre normalization: 15.45, 11.51\\n\",\n      \"Temperature Max, Min post normalization: 1.66, -1.69\\n\",\n      \"Duration    Max, Min post normalization: 1.79, -1.70\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Temperature Max, Min pre normalization: {np.max(X[:,0]):0.2f}, {np.min(X[:,0]):0.2f}\\\")\\n\",\n    \"print(f\\\"Duration    Max, Min pre normalization: {np.max(X[:,1]):0.2f}, {np.min(X[:,1]):0.2f}\\\")\\n\",\n    \"norm_l = tf.keras.layers.Normalization(axis=-1)\\n\",\n    \"norm_l.adapt(X)  # learns mean, variance\\n\",\n    \"Xn = norm_l(X)\\n\",\n    \"print(f\\\"Temperature Max, Min post normalization: {np.max(Xn[:,0]):0.2f}, {np.min(Xn[:,0]):0.2f}\\\")\\n\",\n    \"print(f\\\"Duration    Max, Min post normalization: {np.max(Xn[:,1]):0.2f}, {np.min(Xn[:,1]):0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Tile/copy our data to increase the training set size and reduce the number of training epochs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(200000, 2) (200000, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Xt = np.tile(Xn,(1000,1))\\n\",\n    \"Yt= np.tile(Y,(1000,1))   \\n\",\n    \"print(Xt.shape, Yt.shape)   \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Tensorflow Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Model\\n\",\n    \"   <center> <img  src=\\\"./images/C2_W1_RoastingNetwork.PNG\\\" width=\\\"200\\\" />   <center/>  \\n\",\n    \"Let's build the \\\"Coffee Roasting Network\\\" described in lecture. There are two layers with sigmoid activations as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tf.random.set_seed(1234)  # applied to achieve consistent results\\n\",\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        tf.keras.Input(shape=(2,)),\\n\",\n    \"        Dense(3, activation='sigmoid', name = 'layer1'),\\n\",\n    \"        Dense(1, activation='sigmoid', name = 'layer2')\\n\",\n    \"     ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \">**Note 1:** The `tf.keras.Input(shape=(2,)),` specifies the expected shape of the input. This allows Tensorflow to size the weights and bias parameters at this point.  This is useful when exploring Tensorflow models. This statement can be omitted in practice and Tensorflow will size the network parameters when the input data is specified in the `model.fit` statement.  \\n\",\n    \">**Note 2:** Including the sigmoid activation in the final layer is not considered best practice. It would instead be accounted for in the loss which improves numerical stability. This will be described in more detail in a later lab.\\n\",\n    \"\\n\",\n    \"The `model.summary()` provides a description of the network:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"sequential\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" layer1 (Dense)              (None, 3)                 9         \\n\",\n      \"                                                                 \\n\",\n      \" layer2 (Dense)              (None, 1)                 4         \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 13\\n\",\n      \"Trainable params: 13\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"L1 params =  9 , L2 params =  4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"L1_num_params = 2 * 3 + 3   # W1 parameters  + b1 parameters\\n\",\n    \"L2_num_params = 3 * 1 + 1   # W2 parameters  + b2 parameters\\n\",\n    \"print(\\\"L1 params = \\\", L1_num_params, \\\", L2 params = \\\", L2_num_params  )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's examine the weights and biases Tensorflow has instantiated.  The weights $W$ should be of size (number of features in input, number of units in the layer) while the bias $b$ size should match the number of units in the layer:\\n\",\n    \"- In the first layer with 3 units, we expect W to have a size of (2,3) and $b$ should have 3 elements.\\n\",\n    \"- In the second layer with 1 unit, we expect W to have a size of (3,1) and $b$ should have 1 element.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"W1(2, 3):\\n\",\n      \" [[ 0.08 -0.3   0.18]\\n\",\n      \" [-0.56 -0.15  0.89]] \\n\",\n      \"b1(3,): [0. 0. 0.]\\n\",\n      \"W2(3, 1):\\n\",\n      \" [[-0.43]\\n\",\n      \" [-0.88]\\n\",\n      \" [ 0.36]] \\n\",\n      \"b2(1,): [0.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"W1, b1 = model.get_layer(\\\"layer1\\\").get_weights()\\n\",\n    \"W2, b2 = model.get_layer(\\\"layer2\\\").get_weights()\\n\",\n    \"print(f\\\"W1{W1.shape}:\\\\n\\\", W1, f\\\"\\\\nb1{b1.shape}:\\\", b1)\\n\",\n    \"print(f\\\"W2{W2.shape}:\\\\n\\\", W2, f\\\"\\\\nb2{b2.shape}:\\\", b2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following statements will be described in detail in Week2. For now:\\n\",\n    \"- The `model.compile` statement defines a loss function and specifies a compile optimization.\\n\",\n    \"- The `model.fit` statement runs gradient descent and fits the weights to the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/10\\n\",\n      \"6250/6250 [==============================] - 5s 768us/step - loss: 0.1782\\n\",\n      \"Epoch 2/10\\n\",\n      \"6250/6250 [==============================] - 5s 781us/step - loss: 0.1165\\n\",\n      \"Epoch 3/10\\n\",\n      \"6250/6250 [==============================] - 5s 792us/step - loss: 0.0426\\n\",\n      \"Epoch 4/10\\n\",\n      \"6250/6250 [==============================] - 5s 791us/step - loss: 0.0160\\n\",\n      \"Epoch 5/10\\n\",\n      \"6250/6250 [==============================] - 5s 787us/step - loss: 0.0104\\n\",\n      \"Epoch 6/10\\n\",\n      \"6250/6250 [==============================] - 5s 795us/step - loss: 0.0073\\n\",\n      \"Epoch 7/10\\n\",\n      \"6250/6250 [==============================] - 5s 787us/step - loss: 0.0052\\n\",\n      \"Epoch 8/10\\n\",\n      \"6250/6250 [==============================] - 5s 789us/step - loss: 0.0037\\n\",\n      \"Epoch 9/10\\n\",\n      \"6250/6250 [==============================] - 5s 780us/step - loss: 0.0027\\n\",\n      \"Epoch 10/10\\n\",\n      \"6250/6250 [==============================] - 5s 773us/step - loss: 0.0020\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f5edc665990>\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss = tf.keras.losses.BinaryCrossentropy(),\\n\",\n    \"    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    Xt,Yt,            \\n\",\n    \"    epochs=10,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Updated Weights\\n\",\n    \"After fitting, the weights have been updated: \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"W1:\\n\",\n      \" [[ -0.13  14.3  -11.1 ]\\n\",\n      \" [ -8.92  11.85  -0.25]] \\n\",\n      \"b1: [-11.16   1.76 -12.1 ]\\n\",\n      \"W2:\\n\",\n      \" [[-45.71]\\n\",\n      \" [-42.95]\\n\",\n      \" [-50.19]] \\n\",\n      \"b2: [26.14]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"W1, b1 = model.get_layer(\\\"layer1\\\").get_weights()\\n\",\n    \"W2, b2 = model.get_layer(\\\"layer2\\\").get_weights()\\n\",\n    \"print(\\\"W1:\\\\n\\\", W1, \\\"\\\\nb1:\\\", b1)\\n\",\n    \"print(\\\"W2:\\\\n\\\", W2, \\\"\\\\nb2:\\\", b2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we will load some saved weights from a previous training run. This is so that this notebook remains robust to changes in Tensorflow over time. Different training runs can produce somewhat different results and the discussion below applies to a particular solution. Feel free to re-run the notebook with this cell commented out to see the difference.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1 = np.array([\\n\",\n    \"    [-8.94,  0.29, 12.89],\\n\",\n    \"    [-0.17, -7.34, 10.79]] )\\n\",\n    \"b1 = np.array([-9.87, -9.28,  1.01])\\n\",\n    \"W2 = np.array([\\n\",\n    \"    [-31.38],\\n\",\n    \"    [-27.86],\\n\",\n    \"    [-32.79]])\\n\",\n    \"b2 = np.array([15.54])\\n\",\n    \"model.get_layer(\\\"layer1\\\").set_weights([W1,b1])\\n\",\n    \"model.get_layer(\\\"layer2\\\").set_weights([W2,b2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Predictions\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W1_RoastingDecision.PNG\\\"     style=\\\" width:380px; padding: 10px 20px; \\\" >\\n\",\n    \"\\n\",\n    \"Once you have a trained model, you can then use it to make predictions. Recall that the output of our model is a probability. In this case, the probability of a good roast. To make a decision, one must apply the probability to a threshold. In this case, we will use 0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start by creating input data. The model is expecting one or more examples where examples are in the rows of matrix. In this case, we have two features so the matrix will be (m,2) where m is the number of examples.\\n\",\n    \"Recall, we have normalized the input features so we must normalize our test data as well.   \\n\",\n    \"To make a prediction, you apply the `predict` method.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"predictions = \\n\",\n      \" [[9.63e-01]\\n\",\n      \" [3.03e-08]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_test = np.array([\\n\",\n    \"    [200,13.9],  # postive example\\n\",\n    \"    [200,17]])   # negative example\\n\",\n    \"X_testn = norm_l(X_test)\\n\",\n    \"predictions = model.predict(X_testn)\\n\",\n    \"print(\\\"predictions = \\\\n\\\", predictions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Epochs and batches\\n\",\n    \"In the `compile` statement above, the number of `epochs` was set to 10. This specifies that the entire data set should be applied during training 10 times.  During training, you see output describing the progress of training that looks like this:\\n\",\n    \"```\\n\",\n    \"Epoch 1/10\\n\",\n    \"6250/6250 [==============================] - 6s 910us/step - loss: 0.1782\\n\",\n    \"```\\n\",\n    \"The first line, `Epoch 1/10`, describes which epoch the model is currently running. For efficiency, the training data set is broken into 'batches'. The default size of a batch in Tensorflow is 32. There are 200000 examples in our expanded data set or 6250 batches. The notation on the 2nd line `6250/6250 [====` is describing which batch has been executed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To convert the probabilities to a decision, we apply a threshold:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"decisions = \\n\",\n      \"[[1.]\\n\",\n      \" [0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = np.zeros_like(predictions)\\n\",\n    \"for i in range(len(predictions)):\\n\",\n    \"    if predictions[i] >= 0.5:\\n\",\n    \"        yhat[i] = 1\\n\",\n    \"    else:\\n\",\n    \"        yhat[i] = 0\\n\",\n    \"print(f\\\"decisions = \\\\n{yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This can be accomplished more succinctly:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"decisions = \\n\",\n      \"[[1]\\n\",\n      \" [0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = (predictions >= 0.5).astype(int)\\n\",\n    \"print(f\\\"decisions = \\\\n{yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Layer Functions\\n\",\n    \"Let's examine the functions of the units to determine their role in the coffee roasting decision. We will plot the output of each node for all values of the inputs (duration,temp). Each unit is a logistic function whose output can range from zero to one. The shading in the graph represents the output value.\\n\",\n    \"> Note: In labs we typically number things starting at zero while the lectures may start with 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 6 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_layer(X,Y.reshape(-1,),W1,b1,norm_l)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The shading shows that each unit is responsible for a different \\\"bad roast\\\" region. unit 0 has larger values when the temperature is too low. unit 1 has larger values when the duration is too short and unit 2 has larger values for bad combinations of time/temp. It is worth noting that the network learned these functions on its own through the process of gradient descent. They are very much the same sort of functions a person might choose to make the same decisions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The function plot of the final layer is a bit more difficult to visualize. It's inputs are the output of the first layer. We know that the first layer uses sigmoids so their output range is between zero and one. We can create a 3-D plot that calculates the output for all possible combinations of the three inputs. This is shown below. Above, high output values correspond to 'bad roast' area's. Below, the maximum output is in area's where the three inputs are small values corresponding to 'good roast' area's.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_output_unit(W2,b2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The final graph shows the whole network in action.  \\n\",\n    \"The left graph is the raw output of the final layer represented by the blue shading. This is overlaid on the training data represented by the X's and O's.   \\n\",\n    \"The right graph is the output of the network after a decision threshold. The X's and O's here correspond to decisions made by the network.  \\n\",\n    \"The following takes a moment to run\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"netf= lambda x : model.predict(norm_l(x))\\n\",\n    \"plt_network(X,Y,netf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have built a small neural network in Tensorflow. \\n\",\n    \"The network demonstrated the ability of neural networks to handle complex decisions by dividing the decisions between multiple units.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/.ipynb_checkpoints/C2_W1_Lab03_CoffeeRoasting_Numpy-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Simple Neural Network\\n\",\n    \"In this lab, we will build a small neural network using Numpy. It will be the same \\\"coffee roasting\\\" network you implemented in Tensorflow.\\n\",\n    \"   <center> <img  src=\\\"./images/C2_W1_CoffeeRoasting.png\\\" width=\\\"400\\\" />   <center/>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from lab_utils_common import dlc, sigmoid\\n\",\n    \"from lab_coffee_utils import load_coffee_data, plt_roast, plt_prob, plt_layer, plt_network, plt_output_unit\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## DataSet\\n\",\n    \"This is the same data set as the previous lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(200, 2) (200, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X,Y = load_coffee_data();\\n\",\n    \"print(X.shape, Y.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the coffee roasting data below. The two features are Temperature in Celsius and Duration in minutes. [Coffee Roasting at Home](https://www.merchantsofgreencoffee.com/how-to-roast-green-coffee-in-your-oven/) suggests that the duration is best kept between 12 and 15 minutes while the temp should be between 175 and 260 degrees Celsius. Of course, as the temperature rises, the duration should shrink. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_roast(X,Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Normalize Data\\n\",\n    \"To match the previous lab, we'll normalize the data. Refer to that lab for more details\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Temperature Max, Min pre normalization: 284.99, 151.32\\n\",\n      \"Duration    Max, Min pre normalization: 15.45, 11.51\\n\",\n      \"Temperature Max, Min post normalization: 1.66, -1.69\\n\",\n      \"Duration    Max, Min post normalization: 1.79, -1.70\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Temperature Max, Min pre normalization: {np.max(X[:,0]):0.2f}, {np.min(X[:,0]):0.2f}\\\")\\n\",\n    \"print(f\\\"Duration    Max, Min pre normalization: {np.max(X[:,1]):0.2f}, {np.min(X[:,1]):0.2f}\\\")\\n\",\n    \"norm_l = tf.keras.layers.Normalization(axis=-1)\\n\",\n    \"norm_l.adapt(X)  # learns mean, variance\\n\",\n    \"Xn = norm_l(X)\\n\",\n    \"print(f\\\"Temperature Max, Min post normalization: {np.max(Xn[:,0]):0.2f}, {np.min(Xn[:,0]):0.2f}\\\")\\n\",\n    \"print(f\\\"Duration    Max, Min post normalization: {np.max(Xn[:,1]):0.2f}, {np.min(Xn[:,1]):0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Numpy Model (Forward Prop in NumPy)\\n\",\n    \"<center> <img  src=\\\"./images/C2_W1_RoastingNetwork.PNG\\\" width=\\\"200\\\" />   <center/>  \\n\",\n    \"Let's build the \\\"Coffee Roasting Network\\\" described in lecture. There are two layers with sigmoid activations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_dense2.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"\\n\",\n    \"In the first optional lab, you constructed a neuron in NumPy and in Tensorflow and noted their similarity. A layer simply contains multiple neurons/units. As described in lecture, one can utilize a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` can then be applied to that result. Let's try that below to build a \\\"dense layer\\\" subroutine.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units|\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):               \\n\",\n    \"        w = W[:,j]                                    \\n\",\n    \"        z = np.dot(w, a_in) + b[j]         \\n\",\n    \"        a_out[j] = g(z)               \\n\",\n    \"    return(a_out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a two-layer neural network utilizing the `my_dense` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential(x, W1, b1, W2, b2):\\n\",\n    \"    a1 = my_dense(x,  W1, b1, sigmoid)\\n\",\n    \"    a2 = my_dense(a1, W2, b2, sigmoid)\\n\",\n    \"    return(a2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can copy trained weights and biases from the previous lab in Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp = np.array( [[-8.93,  0.29, 12.9 ], [-0.1,  -7.32, 10.81]] )\\n\",\n    \"b1_tmp = np.array( [-9.82, -9.28,  0.96] )\\n\",\n    \"W2_tmp = np.array( [[-31.18], [-27.59], [-32.56]] )\\n\",\n    \"b2_tmp = np.array( [15.41] )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Predictions\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W1_RoastingDecision.PNG\\\"     style=\\\" width:380px; padding: 10px 20px; \\\" >\\n\",\n    \"\\n\",\n    \"Once you have a trained model, you can then use it to make predictions. Recall that the output of our model is a probability. In this case, the probability of a good roast. To make a decision, one must apply the probability to a threshold. In this case, we will use 0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start by writing a routine similar to Tensorflow's `model.predict()`. This will take a matrix $X$ with all $m$ examples in the rows and make a prediction by running the model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_predict(X, W1, b1, W2, b2):\\n\",\n    \"    m = X.shape[0]\\n\",\n    \"    p = np.zeros((m,1))\\n\",\n    \"    for i in range(m):\\n\",\n    \"        p[i,0] = my_sequential(X[i], W1, b1, W2, b2)\\n\",\n    \"    return(p)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can try this routine on two examples:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_tst = np.array([\\n\",\n    \"    [200,13.9],  # postive example\\n\",\n    \"    [200,17]])   # negative example\\n\",\n    \"X_tstn = norm_l(X_tst)  # remember to normalize\\n\",\n    \"predictions = my_predict(X_tstn, W1_tmp, b1_tmp, W2_tmp, b2_tmp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To convert the probabilities to a decision, we apply a threshold:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"decisions = \\n\",\n      \"[[1.]\\n\",\n      \" [0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = np.zeros_like(predictions)\\n\",\n    \"for i in range(len(predictions)):\\n\",\n    \"    if predictions[i] >= 0.5:\\n\",\n    \"        yhat[i] = 1\\n\",\n    \"    else:\\n\",\n    \"        yhat[i] = 0\\n\",\n    \"print(f\\\"decisions = \\\\n{yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This can be accomplished more succinctly:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"decisions = \\n\",\n      \"[[1]\\n\",\n      \" [0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = (predictions >= 0.5).astype(int)\\n\",\n    \"print(f\\\"decisions = \\\\n{yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Network function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This graph shows the operation of the whole network and is identical to the Tensorflow result from the previous lab.\\n\",\n    \"The left graph is the raw output of the final layer represented by the blue shading. This is overlaid on the training data represented by the X's and O's.   \\n\",\n    \"The right graph is the output of the network after a decision threshold. The X's and O's here correspond to decisions made by the network.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"netf= lambda x : my_predict(norm_l(x),W1_tmp, b1_tmp, W2_tmp, b2_tmp)\\n\",\n    \"plt_network(X,Y,netf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have built a small neural network in NumPy. \\n\",\n    \"Hopefully this lab revealed the fairly simple and familiar functions which make up a layer in a neural network. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/C2_W1_Lab01_Neurons_and_Layers.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Neurons and Layers\\n\",\n    \"In this lab we will explore the inner workings of neurons/units and layers. In particular, the lab will draw parallels to the models you have mastered in Course 1, the regression/linear model and the logistic model. The lab will introduce Tensorflow and demonstrate how these models are implemented in that framework.\\n\",\n    \"<figure>\\n\",\n    \"   <img src=\\\"./images/C2_W1_NeuronsAndLayers.png\\\"  style=\\\"width:540px;height:200px;\\\" >\\n\",\n    \"</figure>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Packages\\n\",\n    \"**Tensorflow and Keras**  \\n\",\n    \"Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.layers import Dense, Input\\n\",\n    \"from tensorflow.keras import Sequential\\n\",\n    \"from tensorflow.keras.losses import MeanSquaredError, BinaryCrossentropy\\n\",\n    \"from tensorflow.keras.activations import sigmoid\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_neurons_utils import plt_prob_1d, sigmoidnp, plt_linear, plt_logistic\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Neuron without activation - Regression/Linear Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"### DataSet\\n\",\n    \"We'll use an example from Course 1, linear regression on house prices.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X_train = np.array([[1.0], [2.0]], dtype=np.float32)           #(size in 1000 square feet)\\n\",\n    \"Y_train = np.array([[300.0], [500.0]], dtype=np.float32)       #(price in 1000s of dollars)\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(1,1)\\n\",\n    \"ax.scatter(X_train, Y_train, marker='x', c='r', label=\\\"Data Points\\\")\\n\",\n    \"ax.legend( fontsize='xx-large')\\n\",\n    \"ax.set_ylabel('Price (in 1000s of dollars)', fontsize='xx-large')\\n\",\n    \"ax.set_xlabel('Size (1000 sqft)', fontsize='xx-large')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Regression/Linear Model \\n\",\n    \"The function implemented by a neuron with no activation is the same as in Course 1, linear regression:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(x^{(i)}) = \\\\mathbf{w}\\\\cdot x^{(i)} + b \\\\tag{1}$$\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can define a layer with one neuron or unit and compare it to the familiar linear regression function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"linear_layer = tf.keras.layers.Dense(units=1, activation = 'linear', )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's examine the weights.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[]\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"linear_layer.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There are no weights as the weights are not yet instantiated. Let's try the model on one example in `X_train`. This will trigger the instantiation of the weights. Note, the input to the layer must be 2-D, so we'll reshape it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tf.Tensor([[1.37]], shape=(1, 1), dtype=float32)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a1 = linear_layer(X_train[0].reshape(1,1))\\n\",\n    \"print(a1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The result is a tensor (another name for an array) with a shape of (1,1) or one entry.   \\n\",\n    \"Now let's look at the weights and bias. These weights are randomly initialized to small numbers and the bias defaults to being initialized to zero.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"w = [[1.37]], b=[0.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w, b= linear_layer.get_weights()\\n\",\n    \"print(f\\\"w = {w}, b={b}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A linear regression model (1) with a single input feature will have a single weight and bias. This matches the dimensions of our `linear_layer` above.   \\n\",\n    \"\\n\",\n    \"The weights are initialized to random values so let's set them to some known values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[200.]], dtype=float32), array([100.], dtype=float32)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"set_w = np.array([[200]])\\n\",\n    \"set_b = np.array([100])\\n\",\n    \"\\n\",\n    \"# set_weights takes a list of numpy arrays\\n\",\n    \"linear_layer.set_weights([set_w, set_b])\\n\",\n    \"print(linear_layer.get_weights())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare equation (1) to the layer output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tf.Tensor([[300.]], shape=(1, 1), dtype=float32)\\n\",\n      \"[[300.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a1 = linear_layer(X_train[0].reshape(1,1))\\n\",\n    \"print(a1)\\n\",\n    \"alin = np.dot(set_w,X_train[0].reshape(1,1)) + set_b\\n\",\n    \"print(alin)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"They produce the same values!\\n\",\n    \"Now, we can use our linear layer to make predictions on our training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"prediction_tf = linear_layer(X_train)\\n\",\n    \"prediction_np = np.dot( X_train, set_w) + set_b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_linear(X_train, Y_train, prediction_tf, prediction_np)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Neuron with Sigmoid activation\\n\",\n    \"The function implemented by a neuron/unit with a sigmoid activation is the same as in Course 1, logistic  regression:\\n\",\n    \"$$ f_{\\\\mathbf{w},b}(x^{(i)}) = g(\\\\mathbf{w}x^{(i)} + b) \\\\tag{2}$$\\n\",\n    \"where $$g(x) = sigmoid(x)$$ \\n\",\n    \"\\n\",\n    \"Let's set $w$ and $b$ to some known values and check the model.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"### DataSet\\n\",\n    \"We'll use an example from Course 1, logistic regression.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([0., 1, 2, 3, 4, 5], dtype=np.float32).reshape(-1,1)  # 2-D Matrix\\n\",\n    \"Y_train = np.array([0,  0, 0, 1, 1, 1], dtype=np.float32).reshape(-1,1)  # 2-D Matrix\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([3., 4., 5.], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pos = Y_train == 1\\n\",\n    \"neg = Y_train == 0\\n\",\n    \"X_train[pos]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 288x216 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pos = Y_train == 1\\n\",\n    \"neg = Y_train == 0\\n\",\n    \"\\n\",\n    \"fig,ax = plt.subplots(1,1,figsize=(4,3))\\n\",\n    \"ax.scatter(X_train[pos], Y_train[pos], marker='x', s=80, c = 'red', label=\\\"y=1\\\")\\n\",\n    \"ax.scatter(X_train[neg], Y_train[neg], marker='o', s=100, label=\\\"y=0\\\", facecolors='none', \\n\",\n    \"              edgecolors=dlc[\\\"dlblue\\\"],lw=3)\\n\",\n    \"\\n\",\n    \"ax.set_ylim(-0.08,1.1)\\n\",\n    \"ax.set_ylabel('y', fontsize=12)\\n\",\n    \"ax.set_xlabel('x', fontsize=12)\\n\",\n    \"ax.set_title('one variable plot')\\n\",\n    \"ax.legend(fontsize=12)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Logistic Neuron\\n\",\n    \"We can implement a 'logistic neuron' by adding a sigmoid activation. The function of the neuron is then described by (2) above.   \\n\",\n    \"This section will create a Tensorflow Model that contains our logistic layer to demonstrate an alternate method of creating models. Tensorflow is most often used to create multi-layer models. The [Sequential](https://keras.io/guides/sequential_model/) model is a convenient means of constructing these models.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        tf.keras.layers.Dense(1, input_dim=1,  activation = 'sigmoid', name='L1')\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`model.summary()` shows the layers and number of parameters in the model. There is only one layer in this model and that layer has only one unit. The unit has two parameters, $w$ and $b$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"sequential\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" L1 (Dense)                  (None, 1)                 2         \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 2\\n\",\n      \"Trainable params: 2\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[-0.11]] [0.]\\n\",\n      \"(1, 1) (1,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"logistic_layer = model.get_layer('L1')\\n\",\n    \"w,b = logistic_layer.get_weights()\\n\",\n    \"print(w,b)\\n\",\n    \"print(w.shape,b.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's set the weight and bias to some known values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[array([[2.]], dtype=float32), array([-4.5], dtype=float32)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"set_w = np.array([[2]])\\n\",\n    \"set_b = np.array([-4.5])\\n\",\n    \"# set_weights takes a list of numpy arrays\\n\",\n    \"logistic_layer.set_weights([set_w, set_b])\\n\",\n    \"print(logistic_layer.get_weights())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare equation (2) to the layer output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[0.01]]\\n\",\n      \"[[0.01]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a1 = model.predict(X_train[0].reshape(1,1))\\n\",\n    \"print(a1)\\n\",\n    \"alog = sigmoidnp(np.dot(set_w,X_train[0].reshape(1,1)) + set_b)\\n\",\n    \"print(alog)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"They produce the same values!\\n\",\n    \"Now, we can use our logistic layer and NumPy model to make predictions on our training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_logistic(X_train, Y_train, model, set_w, set_b, pos, neg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The shading above reflects the output of the sigmoid which varies from 0 to 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Congratulations!\\n\",\n    \"You built a very simple neural network and have explored the similarities of a neuron to the linear and logistic regression from Course 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/C2_W1_Lab02_CoffeeRoasting_TF.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Simple Neural Network\\n\",\n    \"In this lab we will build a small neural network using Tensorflow.\\n\",\n    \"   <center> <img  src=\\\"./images/C2_W1_CoffeeRoasting.png\\\" width=\\\"400\\\" />   <center/>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_coffee_utils import load_coffee_data, plt_roast, plt_prob, plt_layer, plt_network, plt_output_unit\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## DataSet\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(200, 2) (200, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X,Y = load_coffee_data();\\n\",\n    \"print(X.shape, Y.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the coffee roasting data below. The two features are Temperature in Celsius and Duration in minutes. [Coffee Roasting at Home](https://www.merchantsofgreencoffee.com/how-to-roast-green-coffee-in-your-oven/) suggests that the duration is best kept between 12 and 15 minutes while the temp should be between 175 and 260 degrees Celsius. Of course, as temperature rises, the duration should shrink. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_roast(X,Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Normalize Data\\n\",\n    \"Fitting the weights to the data (back-propagation, covered in next week's lectures) will proceed more quickly if the data is normalized. This is the same procedure you used in Course 1 where features in the data are each normalized to have a similar range. \\n\",\n    \"The procedure below uses a Keras [normalization layer](https://keras.io/api/layers/preprocessing_layers/numerical/normalization/). It has the following steps:\\n\",\n    \"- create a \\\"Normalization Layer\\\". Note, as applied here, this is not a layer in your model.\\n\",\n    \"- 'adapt' the data. This learns the mean and variance of the data set and saves the values internally.\\n\",\n    \"- normalize the data.  \\n\",\n    \"It is important to apply normalization to any future data that utilizes the learned model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Temperature Max, Min pre normalization: 284.99, 151.32\\n\",\n      \"Duration    Max, Min pre normalization: 15.45, 11.51\\n\",\n      \"Temperature Max, Min post normalization: 1.66, -1.69\\n\",\n      \"Duration    Max, Min post normalization: 1.79, -1.70\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Temperature Max, Min pre normalization: {np.max(X[:,0]):0.2f}, {np.min(X[:,0]):0.2f}\\\")\\n\",\n    \"print(f\\\"Duration    Max, Min pre normalization: {np.max(X[:,1]):0.2f}, {np.min(X[:,1]):0.2f}\\\")\\n\",\n    \"norm_l = tf.keras.layers.Normalization(axis=-1)\\n\",\n    \"norm_l.adapt(X)  # learns mean, variance\\n\",\n    \"Xn = norm_l(X)\\n\",\n    \"print(f\\\"Temperature Max, Min post normalization: {np.max(Xn[:,0]):0.2f}, {np.min(Xn[:,0]):0.2f}\\\")\\n\",\n    \"print(f\\\"Duration    Max, Min post normalization: {np.max(Xn[:,1]):0.2f}, {np.min(Xn[:,1]):0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Tile/copy our data to increase the training set size and reduce the number of training epochs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(200000, 2) (200000, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"Xt = np.tile(Xn,(1000,1))\\n\",\n    \"Yt= np.tile(Y,(1000,1))   \\n\",\n    \"print(Xt.shape, Yt.shape)   \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Tensorflow Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Model\\n\",\n    \"   <center> <img  src=\\\"./images/C2_W1_RoastingNetwork.PNG\\\" width=\\\"200\\\" />   <center/>  \\n\",\n    \"Let's build the \\\"Coffee Roasting Network\\\" described in lecture. There are two layers with sigmoid activations as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tf.random.set_seed(1234)  # applied to achieve consistent results\\n\",\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        tf.keras.Input(shape=(2,)),\\n\",\n    \"        Dense(3, activation='sigmoid', name = 'layer1'),\\n\",\n    \"        Dense(1, activation='sigmoid', name = 'layer2')\\n\",\n    \"     ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \">**Note 1:** The `tf.keras.Input(shape=(2,)),` specifies the expected shape of the input. This allows Tensorflow to size the weights and bias parameters at this point.  This is useful when exploring Tensorflow models. This statement can be omitted in practice and Tensorflow will size the network parameters when the input data is specified in the `model.fit` statement.  \\n\",\n    \">**Note 2:** Including the sigmoid activation in the final layer is not considered best practice. It would instead be accounted for in the loss which improves numerical stability. This will be described in more detail in a later lab.\\n\",\n    \"\\n\",\n    \"The `model.summary()` provides a description of the network:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"sequential\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" layer1 (Dense)              (None, 3)                 9         \\n\",\n      \"                                                                 \\n\",\n      \" layer2 (Dense)              (None, 1)                 4         \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 13\\n\",\n      \"Trainable params: 13\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"L1 params =  9 , L2 params =  4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"L1_num_params = 2 * 3 + 3   # W1 parameters  + b1 parameters\\n\",\n    \"L2_num_params = 3 * 1 + 1   # W2 parameters  + b2 parameters\\n\",\n    \"print(\\\"L1 params = \\\", L1_num_params, \\\", L2 params = \\\", L2_num_params  )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's examine the weights and biases Tensorflow has instantiated.  The weights $W$ should be of size (number of features in input, number of units in the layer) while the bias $b$ size should match the number of units in the layer:\\n\",\n    \"- In the first layer with 3 units, we expect W to have a size of (2,3) and $b$ should have 3 elements.\\n\",\n    \"- In the second layer with 1 unit, we expect W to have a size of (3,1) and $b$ should have 1 element.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"W1(2, 3):\\n\",\n      \" [[ 0.08 -0.3   0.18]\\n\",\n      \" [-0.56 -0.15  0.89]] \\n\",\n      \"b1(3,): [0. 0. 0.]\\n\",\n      \"W2(3, 1):\\n\",\n      \" [[-0.43]\\n\",\n      \" [-0.88]\\n\",\n      \" [ 0.36]] \\n\",\n      \"b2(1,): [0.]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"W1, b1 = model.get_layer(\\\"layer1\\\").get_weights()\\n\",\n    \"W2, b2 = model.get_layer(\\\"layer2\\\").get_weights()\\n\",\n    \"print(f\\\"W1{W1.shape}:\\\\n\\\", W1, f\\\"\\\\nb1{b1.shape}:\\\", b1)\\n\",\n    \"print(f\\\"W2{W2.shape}:\\\\n\\\", W2, f\\\"\\\\nb2{b2.shape}:\\\", b2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following statements will be described in detail in Week2. For now:\\n\",\n    \"- The `model.compile` statement defines a loss function and specifies a compile optimization.\\n\",\n    \"- The `model.fit` statement runs gradient descent and fits the weights to the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/10\\n\",\n      \"6250/6250 [==============================] - 5s 768us/step - loss: 0.1782\\n\",\n      \"Epoch 2/10\\n\",\n      \"6250/6250 [==============================] - 5s 781us/step - loss: 0.1165\\n\",\n      \"Epoch 3/10\\n\",\n      \"6250/6250 [==============================] - 5s 792us/step - loss: 0.0426\\n\",\n      \"Epoch 4/10\\n\",\n      \"6250/6250 [==============================] - 5s 791us/step - loss: 0.0160\\n\",\n      \"Epoch 5/10\\n\",\n      \"6250/6250 [==============================] - 5s 787us/step - loss: 0.0104\\n\",\n      \"Epoch 6/10\\n\",\n      \"6250/6250 [==============================] - 5s 795us/step - loss: 0.0073\\n\",\n      \"Epoch 7/10\\n\",\n      \"6250/6250 [==============================] - 5s 787us/step - loss: 0.0052\\n\",\n      \"Epoch 8/10\\n\",\n      \"6250/6250 [==============================] - 5s 789us/step - loss: 0.0037\\n\",\n      \"Epoch 9/10\\n\",\n      \"6250/6250 [==============================] - 5s 780us/step - loss: 0.0027\\n\",\n      \"Epoch 10/10\\n\",\n      \"6250/6250 [==============================] - 5s 773us/step - loss: 0.0020\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f5edc665990>\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss = tf.keras.losses.BinaryCrossentropy(),\\n\",\n    \"    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    Xt,Yt,            \\n\",\n    \"    epochs=10,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Updated Weights\\n\",\n    \"After fitting, the weights have been updated: \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"W1:\\n\",\n      \" [[ -0.13  14.3  -11.1 ]\\n\",\n      \" [ -8.92  11.85  -0.25]] \\n\",\n      \"b1: [-11.16   1.76 -12.1 ]\\n\",\n      \"W2:\\n\",\n      \" [[-45.71]\\n\",\n      \" [-42.95]\\n\",\n      \" [-50.19]] \\n\",\n      \"b2: [26.14]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"W1, b1 = model.get_layer(\\\"layer1\\\").get_weights()\\n\",\n    \"W2, b2 = model.get_layer(\\\"layer2\\\").get_weights()\\n\",\n    \"print(\\\"W1:\\\\n\\\", W1, \\\"\\\\nb1:\\\", b1)\\n\",\n    \"print(\\\"W2:\\\\n\\\", W2, \\\"\\\\nb2:\\\", b2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we will load some saved weights from a previous training run. This is so that this notebook remains robust to changes in Tensorflow over time. Different training runs can produce somewhat different results and the discussion below applies to a particular solution. Feel free to re-run the notebook with this cell commented out to see the difference.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1 = np.array([\\n\",\n    \"    [-8.94,  0.29, 12.89],\\n\",\n    \"    [-0.17, -7.34, 10.79]] )\\n\",\n    \"b1 = np.array([-9.87, -9.28,  1.01])\\n\",\n    \"W2 = np.array([\\n\",\n    \"    [-31.38],\\n\",\n    \"    [-27.86],\\n\",\n    \"    [-32.79]])\\n\",\n    \"b2 = np.array([15.54])\\n\",\n    \"model.get_layer(\\\"layer1\\\").set_weights([W1,b1])\\n\",\n    \"model.get_layer(\\\"layer2\\\").set_weights([W2,b2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Predictions\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W1_RoastingDecision.PNG\\\"     style=\\\" width:380px; padding: 10px 20px; \\\" >\\n\",\n    \"\\n\",\n    \"Once you have a trained model, you can then use it to make predictions. Recall that the output of our model is a probability. In this case, the probability of a good roast. To make a decision, one must apply the probability to a threshold. In this case, we will use 0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start by creating input data. The model is expecting one or more examples where examples are in the rows of matrix. In this case, we have two features so the matrix will be (m,2) where m is the number of examples.\\n\",\n    \"Recall, we have normalized the input features so we must normalize our test data as well.   \\n\",\n    \"To make a prediction, you apply the `predict` method.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"predictions = \\n\",\n      \" [[9.63e-01]\\n\",\n      \" [3.03e-08]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_test = np.array([\\n\",\n    \"    [200,13.9],  # postive example\\n\",\n    \"    [200,17]])   # negative example\\n\",\n    \"X_testn = norm_l(X_test)\\n\",\n    \"predictions = model.predict(X_testn)\\n\",\n    \"print(\\\"predictions = \\\\n\\\", predictions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Epochs and batches\\n\",\n    \"In the `compile` statement above, the number of `epochs` was set to 10. This specifies that the entire data set should be applied during training 10 times.  During training, you see output describing the progress of training that looks like this:\\n\",\n    \"```\\n\",\n    \"Epoch 1/10\\n\",\n    \"6250/6250 [==============================] - 6s 910us/step - loss: 0.1782\\n\",\n    \"```\\n\",\n    \"The first line, `Epoch 1/10`, describes which epoch the model is currently running. For efficiency, the training data set is broken into 'batches'. The default size of a batch in Tensorflow is 32. There are 200000 examples in our expanded data set or 6250 batches. The notation on the 2nd line `6250/6250 [====` is describing which batch has been executed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To convert the probabilities to a decision, we apply a threshold:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"decisions = \\n\",\n      \"[[1.]\\n\",\n      \" [0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = np.zeros_like(predictions)\\n\",\n    \"for i in range(len(predictions)):\\n\",\n    \"    if predictions[i] >= 0.5:\\n\",\n    \"        yhat[i] = 1\\n\",\n    \"    else:\\n\",\n    \"        yhat[i] = 0\\n\",\n    \"print(f\\\"decisions = \\\\n{yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This can be accomplished more succinctly:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"decisions = \\n\",\n      \"[[1]\\n\",\n      \" [0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = (predictions >= 0.5).astype(int)\\n\",\n    \"print(f\\\"decisions = \\\\n{yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Layer Functions\\n\",\n    \"Let's examine the functions of the units to determine their role in the coffee roasting decision. We will plot the output of each node for all values of the inputs (duration,temp). Each unit is a logistic function whose output can range from zero to one. The shading in the graph represents the output value.\\n\",\n    \"> Note: In labs we typically number things starting at zero while the lectures may start with 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 6 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_layer(X,Y.reshape(-1,),W1,b1,norm_l)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The shading shows that each unit is responsible for a different \\\"bad roast\\\" region. unit 0 has larger values when the temperature is too low. unit 1 has larger values when the duration is too short and unit 2 has larger values for bad combinations of time/temp. It is worth noting that the network learned these functions on its own through the process of gradient descent. They are very much the same sort of functions a person might choose to make the same decisions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The function plot of the final layer is a bit more difficult to visualize. It's inputs are the output of the first layer. We know that the first layer uses sigmoids so their output range is between zero and one. We can create a 3-D plot that calculates the output for all possible combinations of the three inputs. This is shown below. Above, high output values correspond to 'bad roast' area's. Below, the maximum output is in area's where the three inputs are small values corresponding to 'good roast' area's.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_output_unit(W2,b2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The final graph shows the whole network in action.  \\n\",\n    \"The left graph is the raw output of the final layer represented by the blue shading. This is overlaid on the training data represented by the X's and O's.   \\n\",\n    \"The right graph is the output of the network after a decision threshold. The X's and O's here correspond to decisions made by the network.  \\n\",\n    \"The following takes a moment to run\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"netf= lambda x : model.predict(norm_l(x))\\n\",\n    \"plt_network(X,Y,netf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have built a small neural network in Tensorflow. \\n\",\n    \"The network demonstrated the ability of neural networks to handle complex decisions by dividing the decisions between multiple units.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/C2_W1_Lab03_CoffeeRoasting_Numpy.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Simple Neural Network\\n\",\n    \"In this lab, we will build a small neural network using Numpy. It will be the same \\\"coffee roasting\\\" network you implemented in Tensorflow.\\n\",\n    \"   <center> <img  src=\\\"./images/C2_W1_CoffeeRoasting.png\\\" width=\\\"400\\\" />   <center/>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from lab_utils_common import dlc, sigmoid\\n\",\n    \"from lab_coffee_utils import load_coffee_data, plt_roast, plt_prob, plt_layer, plt_network, plt_output_unit\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## DataSet\\n\",\n    \"This is the same data set as the previous lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(200, 2) (200, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X,Y = load_coffee_data();\\n\",\n    \"print(X.shape, Y.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the coffee roasting data below. The two features are Temperature in Celsius and Duration in minutes. [Coffee Roasting at Home](https://www.merchantsofgreencoffee.com/how-to-roast-green-coffee-in-your-oven/) suggests that the duration is best kept between 12 and 15 minutes while the temp should be between 175 and 260 degrees Celsius. Of course, as the temperature rises, the duration should shrink. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_roast(X,Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Normalize Data\\n\",\n    \"To match the previous lab, we'll normalize the data. Refer to that lab for more details\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Temperature Max, Min pre normalization: 284.99, 151.32\\n\",\n      \"Duration    Max, Min pre normalization: 15.45, 11.51\\n\",\n      \"Temperature Max, Min post normalization: 1.66, -1.69\\n\",\n      \"Duration    Max, Min post normalization: 1.79, -1.70\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Temperature Max, Min pre normalization: {np.max(X[:,0]):0.2f}, {np.min(X[:,0]):0.2f}\\\")\\n\",\n    \"print(f\\\"Duration    Max, Min pre normalization: {np.max(X[:,1]):0.2f}, {np.min(X[:,1]):0.2f}\\\")\\n\",\n    \"norm_l = tf.keras.layers.Normalization(axis=-1)\\n\",\n    \"norm_l.adapt(X)  # learns mean, variance\\n\",\n    \"Xn = norm_l(X)\\n\",\n    \"print(f\\\"Temperature Max, Min post normalization: {np.max(Xn[:,0]):0.2f}, {np.min(Xn[:,0]):0.2f}\\\")\\n\",\n    \"print(f\\\"Duration    Max, Min post normalization: {np.max(Xn[:,1]):0.2f}, {np.min(Xn[:,1]):0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Numpy Model (Forward Prop in NumPy)\\n\",\n    \"<center> <img  src=\\\"./images/C2_W1_RoastingNetwork.PNG\\\" width=\\\"200\\\" />   <center/>  \\n\",\n    \"Let's build the \\\"Coffee Roasting Network\\\" described in lecture. There are two layers with sigmoid activations.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \\n\",\n    \"\\n\",\n    \"<img src=\\\"images/C2_W1_dense2.PNG\\\" width=\\\"600\\\" height=\\\"450\\\">\\n\",\n    \"\\n\",\n    \"In the first optional lab, you constructed a neuron in NumPy and in Tensorflow and noted their similarity. A layer simply contains multiple neurons/units. As described in lecture, one can utilize a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` can then be applied to that result. Let's try that below to build a \\\"dense layer\\\" subroutine.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def my_dense(a_in, W, b, g):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes dense layer\\n\",\n    \"    Args:\\n\",\n    \"      a_in (ndarray (n, )) : Data, 1 example \\n\",\n    \"      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\\n\",\n    \"      b    (ndarray (j, )) : bias vector, j units  \\n\",\n    \"      g    activation function (e.g. sigmoid, relu..)\\n\",\n    \"    Returns\\n\",\n    \"      a_out (ndarray (j,))  : j units|\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    units = W.shape[1]\\n\",\n    \"    a_out = np.zeros(units)\\n\",\n    \"    for j in range(units):               \\n\",\n    \"        w = W[:,j]                                    \\n\",\n    \"        z = np.dot(w, a_in) + b[j]         \\n\",\n    \"        a_out[j] = g(z)               \\n\",\n    \"    return(a_out)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following cell builds a two-layer neural network utilizing the `my_dense` subroutine above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_sequential(x, W1, b1, W2, b2):\\n\",\n    \"    a1 = my_dense(x,  W1, b1, sigmoid)\\n\",\n    \"    a2 = my_dense(a1, W2, b2, sigmoid)\\n\",\n    \"    return(a2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can copy trained weights and biases from the previous lab in Tensorflow.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"W1_tmp = np.array( [[-8.93,  0.29, 12.9 ], [-0.1,  -7.32, 10.81]] )\\n\",\n    \"b1_tmp = np.array( [-9.82, -9.28,  0.96] )\\n\",\n    \"W2_tmp = np.array( [[-31.18], [-27.59], [-32.56]] )\\n\",\n    \"b2_tmp = np.array( [15.41] )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Predictions\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W1_RoastingDecision.PNG\\\"     style=\\\" width:380px; padding: 10px 20px; \\\" >\\n\",\n    \"\\n\",\n    \"Once you have a trained model, you can then use it to make predictions. Recall that the output of our model is a probability. In this case, the probability of a good roast. To make a decision, one must apply the probability to a threshold. In this case, we will use 0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's start by writing a routine similar to Tensorflow's `model.predict()`. This will take a matrix $X$ with all $m$ examples in the rows and make a prediction by running the model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_predict(X, W1, b1, W2, b2):\\n\",\n    \"    m = X.shape[0]\\n\",\n    \"    p = np.zeros((m,1))\\n\",\n    \"    for i in range(m):\\n\",\n    \"        p[i,0] = my_sequential(X[i], W1, b1, W2, b2)\\n\",\n    \"    return(p)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can try this routine on two examples:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_tst = np.array([\\n\",\n    \"    [200,13.9],  # postive example\\n\",\n    \"    [200,17]])   # negative example\\n\",\n    \"X_tstn = norm_l(X_tst)  # remember to normalize\\n\",\n    \"predictions = my_predict(X_tstn, W1_tmp, b1_tmp, W2_tmp, b2_tmp)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To convert the probabilities to a decision, we apply a threshold:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"decisions = \\n\",\n      \"[[1.]\\n\",\n      \" [0.]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = np.zeros_like(predictions)\\n\",\n    \"for i in range(len(predictions)):\\n\",\n    \"    if predictions[i] >= 0.5:\\n\",\n    \"        yhat[i] = 1\\n\",\n    \"    else:\\n\",\n    \"        yhat[i] = 0\\n\",\n    \"print(f\\\"decisions = \\\\n{yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This can be accomplished more succinctly:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"decisions = \\n\",\n      \"[[1]\\n\",\n      \" [0]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = (predictions >= 0.5).astype(int)\\n\",\n    \"print(f\\\"decisions = \\\\n{yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Network function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This graph shows the operation of the whole network and is identical to the Tensorflow result from the previous lab.\\n\",\n    \"The left graph is the raw output of the final layer represented by the blue shading. This is overlaid on the training data represented by the X's and O's.   \\n\",\n    \"The right graph is the output of the network after a decision threshold. The X's and O's here correspond to decisions made by the network.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1152x288 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"netf= lambda x : my_predict(norm_l(x),W1_tmp, b1_tmp, W2_tmp, b2_tmp)\\n\",\n    \"plt_network(X,Y,netf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have built a small neural network in NumPy. \\n\",\n    \"Hopefully this lab revealed the fairly simple and familiar functions which make up a layer in a neural network. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/deeplearning.mplstyle",
    "content": "# see https://matplotlib.org/stable/tutorials/introductory/customizing.html\nlines.linewidth: 4\nlines.solid_capstyle: butt\n\nlegend.fancybox: true\n\n# Verdana\" for non-math text,\n# Cambria Math\n\n#Blue (Crayon-Aqua) 0096FF\n#Dark Red C00000\n#Orange (Apple Orange) FF9300\n#Black 000000\n#Magenta FF40FF\n#Purple 7030A0\n\naxes.prop_cycle: cycler('color', ['0096FF', 'FF9300', 'FF40FF', '7030A0', 'C00000'])\n#axes.facecolor: f0f0f0 # grey\naxes.facecolor: ffffff  # white\naxes.labelsize: large\naxes.axisbelow: true\naxes.grid: False\naxes.edgecolor: f0f0f0\naxes.linewidth: 3.0\naxes.titlesize: x-large\n\npatch.edgecolor: f0f0f0\npatch.linewidth: 0.5\n\nsvg.fonttype: path\n\ngrid.linestyle: -\ngrid.linewidth: 1.0\ngrid.color: cbcbcb\n\nxtick.major.size: 0\nxtick.minor.size: 0\nytick.major.size: 0\nytick.minor.size: 0\n\nsavefig.edgecolor: f0f0f0\nsavefig.facecolor: f0f0f0\n\n#figure.subplot.left: 0.08\n#figure.subplot.right: 0.95\n#figure.subplot.bottom: 0.07\n\n#figure.facecolor: f0f0f0  # grey\nfigure.facecolor: ffffff  # white\n\n## ***************************************************************************\n## * FONT                                                                    *\n## ***************************************************************************\n## The font properties used by `text.Text`.\n## See https://matplotlib.org/api/font_manager_api.html for more information\n## on font properties.  The 6 font properties used for font matching are\n## given below with their default values.\n##\n## The font.family property can take either a concrete font name (not supported\n## when rendering text with usetex), or one of the following five generic\n## values:\n##     - 'serif' (e.g., Times),\n##     - 'sans-serif' (e.g., Helvetica),\n##     - 'cursive' (e.g., Zapf-Chancery),\n##     - 'fantasy' (e.g., Western), and\n##     - 'monospace' (e.g., Courier).\n## Each of these values has a corresponding default list of font names\n## (font.serif, etc.); the first available font in the list is used.  Note that\n## for font.serif, font.sans-serif, and font.monospace, the first element of\n## the list (a DejaVu font) will always be used because DejaVu is shipped with\n## Matplotlib and is thus guaranteed to be available; the other entries are\n## left as examples of other possible values.\n##\n## The font.style property has three values: normal (or roman), italic\n## or oblique.  The oblique style will be used for italic, if it is not\n## present.\n##\n## The font.variant property has two values: normal or small-caps.  For\n## TrueType fonts, which are scalable fonts, small-caps is equivalent\n## to using a font size of 'smaller', or about 83%% of the current font\n## size.\n##\n## The font.weight property has effectively 13 values: normal, bold,\n## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as\n## 400, and bold is 700.  bolder and lighter are relative values with\n## respect to the current weight.\n##\n## The font.stretch property has 11 values: ultra-condensed,\n## extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This\n## property is not currently implemented.\n##\n## The font.size property is the default font size for text, given in points.\n## 10 pt is the standard value.\n##\n## Note that font.size controls default text sizes.  To configure\n## special text sizes tick labels, axes, labels, title, etc., see the rc\n## settings for axes and ticks.  Special text sizes can be defined\n## relative to font.size, using the following values: xx-small, x-small,\n## small, medium, large, x-large, xx-large, larger, or smaller\n\n\nfont.family:  sans-serif\nfont.style:   normal\nfont.variant: normal\nfont.weight:  normal\nfont.stretch: normal\nfont.size:    8.0\n\nfont.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\nfont.sans-serif: Verdana, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\nfont.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive\nfont.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy\nfont.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n\n\n## ***************************************************************************\n## * TEXT                                                                    *\n## ***************************************************************************\n## The text properties used by `text.Text`.\n## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\n## for more information on text properties\n#text.color: black\n\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/lab_coffee_utils.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('./deeplearning.mplstyle')\nimport tensorflow as tf\nfrom tensorflow.keras.activations import sigmoid\nfrom matplotlib import cm\nimport matplotlib.colors as colors\nfrom lab_utils_common import dlc\n\ndef load_coffee_data():\n    \"\"\" Creates a coffee roasting data set.\n        roasting duration: 12-15 minutes is best\n        temperature range: 175-260C is best\n    \"\"\"\n    rng = np.random.default_rng(2)\n    X = rng.random(400).reshape(-1,2)\n    X[:,1] = X[:,1] * 4 + 11.5          # 12-15 min is best\n    X[:,0] = X[:,0] * (285-150) + 150  # 350-500 F (175-260 C) is best\n    Y = np.zeros(len(X))\n    \n    i=0\n    for t,d in X:\n        y = -3/(260-175)*t + 21\n        if (t > 175 and t < 260 and d > 12 and d < 15 and d<=y ):\n            Y[i] = 1\n        else:\n            Y[i] = 0\n        i += 1\n\n    return (X, Y.reshape(-1,1))\n\ndef plt_roast(X,Y):\n    Y = Y.reshape(-1,)\n    colormap = np.array(['r', 'b'])\n    fig, ax = plt.subplots(1,1,)\n    ax.scatter(X[Y==1,0],X[Y==1,1], s=70, marker='x', c='red', label=\"Good Roast\" )\n    ax.scatter(X[Y==0,0],X[Y==0,1], s=100, marker='o', facecolors='none', \n               edgecolors=dlc[\"dldarkblue\"],linewidth=1,  label=\"Bad Roast\")\n    tr = np.linspace(175,260,50)\n    ax.plot(tr, (-3/85) * tr + 21, color=dlc[\"dlpurple\"],linewidth=1)\n    ax.axhline(y=12,color=dlc[\"dlpurple\"],linewidth=1)\n    ax.axvline(x=175,color=dlc[\"dlpurple\"],linewidth=1)\n    ax.set_title(f\"Coffee Roasting\", size=16)\n    ax.set_xlabel(\"Temperature \\n(Celsius)\",size=12)\n    ax.set_ylabel(\"Duration \\n(minutes)\",size=12)\n    ax.legend(loc='upper right')\n    plt.show()\n\ndef plt_prob(ax,fwb):\n    \"\"\" plots a decision boundary but include shading to indicate the probability \"\"\"\n    #setup useful ranges and common linspaces\n    x0_space  = np.linspace(150, 285 , 40)\n    x1_space  = np.linspace(11.5, 15.5 , 40)\n\n    # get probability for x0,x1 ranges\n    tmp_x0,tmp_x1 = np.meshgrid(x0_space,x1_space)\n    z = np.zeros_like(tmp_x0)\n    for i in range(tmp_x0.shape[0]):\n        for j in range(tmp_x1.shape[1]):\n            x = np.array([[tmp_x0[i,j],tmp_x1[i,j]]])\n            z[i,j] = fwb(x)\n\n\n    cmap = plt.get_cmap('Blues')\n    new_cmap = truncate_colormap(cmap, 0.0, 0.5)\n    pcm = ax.pcolormesh(tmp_x0, tmp_x1, z,\n                   norm=cm.colors.Normalize(vmin=0, vmax=1),\n                   cmap=new_cmap, shading='nearest', alpha = 0.9)\n    ax.figure.colorbar(pcm, ax=ax)\n\ndef truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n    \"\"\" truncates color map \"\"\"\n    new_cmap = colors.LinearSegmentedColormap.from_list(\n        'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n        cmap(np.linspace(minval, maxval, n)))\n    return new_cmap\n\ndef plt_layer(X,Y,W1,b1,norm_l):\n    Y = Y.reshape(-1,)\n    fig,ax = plt.subplots(1,W1.shape[1], figsize=(16,4))\n    for i in range(W1.shape[1]):\n        layerf= lambda x : sigmoid(np.dot(norm_l(x),W1[:,i]) + b1[i])\n        plt_prob(ax[i], layerf)\n        ax[i].scatter(X[Y==1,0],X[Y==1,1], s=70, marker='x', c='red', label=\"Good Roast\" )\n        ax[i].scatter(X[Y==0,0],X[Y==0,1], s=100, marker='o', facecolors='none', \n                   edgecolors=dlc[\"dldarkblue\"],linewidth=1,  label=\"Bad Roast\")\n        tr = np.linspace(175,260,50)\n        ax[i].plot(tr, (-3/85) * tr + 21, color=dlc[\"dlpurple\"],linewidth=2)\n        ax[i].axhline(y= 12, color=dlc[\"dlpurple\"], linewidth=2)\n        ax[i].axvline(x=175, color=dlc[\"dlpurple\"], linewidth=2)\n        ax[i].set_title(f\"Layer 1, unit {i}\")\n        ax[i].set_xlabel(\"Temperature \\n(Celsius)\",size=12)\n    ax[0].set_ylabel(\"Duration \\n(minutes)\",size=12)\n    plt.show()\n        \ndef plt_network(X,Y,netf):\n    fig, ax = plt.subplots(1,2,figsize=(16,4))\n    Y = Y.reshape(-1,)\n    plt_prob(ax[0], netf)\n    ax[0].scatter(X[Y==1,0],X[Y==1,1], s=70, marker='x', c='red', label=\"Good Roast\" )\n    ax[0].scatter(X[Y==0,0],X[Y==0,1], s=100, marker='o', facecolors='none', \n                   edgecolors=dlc[\"dldarkblue\"],linewidth=1,  label=\"Bad Roast\")\n    ax[0].plot(X[:,0], (-3/85) * X[:,0] + 21, color=dlc[\"dlpurple\"],linewidth=1)\n    ax[0].axhline(y= 12, color=dlc[\"dlpurple\"], linewidth=1)\n    ax[0].axvline(x=175, color=dlc[\"dlpurple\"], linewidth=1)\n    ax[0].set_xlabel(\"Temperature \\n(Celsius)\",size=12)\n    ax[0].set_ylabel(\"Duration \\n(minutes)\",size=12)\n    ax[0].legend(loc='upper right')\n    ax[0].set_title(f\"network probability\")\n\n    ax[1].plot(X[:,0], (-3/85) * X[:,0] + 21, color=dlc[\"dlpurple\"],linewidth=1)\n    ax[1].axhline(y= 12, color=dlc[\"dlpurple\"], linewidth=1)\n    ax[1].axvline(x=175, color=dlc[\"dlpurple\"], linewidth=1)\n    fwb = netf(X)\n    yhat = (fwb > 0.5).astype(int)\n    ax[1].scatter(X[yhat[:,0]==1,0],X[yhat[:,0]==1,1], s=70, marker='x', c='orange', label=\"Predicted Good Roast\" )\n    ax[1].scatter(X[yhat[:,0]==0,0],X[yhat[:,0]==0,1], s=100, marker='o', facecolors='none', \n                   edgecolors=dlc[\"dldarkblue\"],linewidth=1,  label=\"Bad Roast\")\n    ax[1].set_title(f\"network decision\")\n    ax[1].set_xlabel(\"Temperature \\n(Celsius)\",size=12)\n    ax[1].set_ylabel(\"Duration \\n(minutes)\",size=12)\n    ax[1].legend(loc='upper right')\n\n\ndef plt_output_unit(W,b):\n    \"\"\" plots a single unit function with 3 inputs \"\"\"\n    steps = 10\n    fig = plt.figure()\n    ax = fig.add_subplot(projection='3d')\n    x_ = np.linspace(0., 1., steps)\n    y_ = np.linspace(0., 1., steps)\n    z_ = np.linspace(0., 1., steps)\n    x, y, z = np.meshgrid(x_, y_, z_, indexing='ij')\n    d = np.zeros((steps,steps,steps))\n    cmap = plt.get_cmap('Blues')\n    for i in range(steps):\n        for j in range(steps):\n            for k in range(steps):\n                v = np.array([x[i,j,k],y[i,j,k],z[i,j,k]])\n                d[i,j,k] = tf.keras.activations.sigmoid(np.dot(v,W[:,0])+b).numpy()\n    pcm = ax.scatter(x, y, z, c=d, cmap=cmap, alpha = 1 )\n    ax.set_xlabel(\"unit 0\"); \n    ax.set_ylabel(\"unit 1\"); \n    ax.set_zlabel(\"unit 2\"); \n    ax.view_init(30, -120)\n    ax.figure.colorbar(pcm, ax=ax)\n    ax.set_title(f\"Layer 2, output unit\")\n\n    plt.show()"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/lab_neurons_utils.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('./deeplearning.mplstyle')\nfrom matplotlib import cm\nimport matplotlib.colors as colors\nfrom lab_utils_common import dlc\n\ndef plt_prob_1d(ax,fwb):\n    \"\"\" plots a decision boundary but include shading to indicate the probability \"\"\"\n    #setup useful ranges and common linspaces\n    x_space  = np.linspace(0, 5 , 50)\n    y_space  = np.linspace(0, 1 , 50)\n\n    # get probability for x range, extend to y\n    z = np.zeros((len(x_space),len(y_space)))\n    for i in range(len(x_space)):\n        x = np.array([[x_space[i]]])\n        z[:,i] = fwb(x)\n\n    cmap = plt.get_cmap('Blues')\n    new_cmap = truncate_colormap(cmap, 0.0, 0.5)\n    pcm = ax.pcolormesh(x_space, y_space, z,\n                   norm=cm.colors.Normalize(vmin=0, vmax=1),\n                   cmap=new_cmap, shading='nearest', alpha = 0.9)\n    ax.figure.colorbar(pcm, ax=ax)\n    \ndef truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n    \"\"\" truncates color map \"\"\"\n    new_cmap = colors.LinearSegmentedColormap.from_list(\n        'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n        cmap(np.linspace(minval, maxval, n)))\n    return new_cmap\n\n\ndef sigmoidnp(z):\n    \"\"\"\n    Compute the sigmoid of z\n\n    Parameters\n    ----------\n    z : array_like\n        A scalar or numpy array of any size.\n\n    Returns\n    -------\n     g : array_like\n         sigmoid(z)\n    \"\"\"\n    z = np.clip( z, -500, 500 )           # protect against overflow\n    g = 1.0/(1.0+np.exp(-z))\n\n    return g\n\ndef plt_linear(X_train, Y_train, prediction_tf, prediction_np):\n    fig, ax = plt.subplots(1,2, figsize=(16,4))\n    ax[0].scatter(X_train, Y_train, marker='x', c='r', label=\"Data Points\")\n    ax[0].plot(X_train, prediction_tf,  c=dlc['dlblue'], label=\"model output\")\n    ax[0].text(1.6,350,r\"y=$200 x + 100$\", fontsize='xx-large', color=dlc['dlmagenta'])\n    ax[0].legend(fontsize='xx-large')\n    ax[0].set_ylabel('Price (in 1000s of dollars)', fontsize='xx-large')\n    ax[0].set_xlabel('Size (1000 sqft)', fontsize='xx-large')\n    ax[0].set_title(\"Tensorflow prediction\",fontsize='xx-large')\n\n    ax[1].scatter(X_train, Y_train, marker='x', c='r', label=\"Data Points\")\n    ax[1].plot(X_train, prediction_np,  c=dlc['dlblue'], label=\"model output\")\n    ax[1].text(1.6,350,r\"y=$200 x + 100$\", fontsize='xx-large', color=dlc['dlmagenta'])\n    ax[1].legend(fontsize='xx-large')\n    ax[1].set_ylabel('Price (in 1000s of dollars)', fontsize='xx-large')\n    ax[1].set_xlabel('Size (1000 sqft)', fontsize='xx-large')\n    ax[1].set_title(\"Numpy prediction\",fontsize='xx-large')\n    plt.show()\n    \n    \ndef plt_logistic(X_train, Y_train, model, set_w, set_b, pos, neg):\n    fig,ax = plt.subplots(1,2,figsize=(16,4))\n\n    layerf= lambda x : model.predict(x)\n    plt_prob_1d(ax[0], layerf)\n\n    ax[0].scatter(X_train[pos], Y_train[pos], marker='x', s=80, c = 'red', label=\"y=1\")\n    ax[0].scatter(X_train[neg], Y_train[neg], marker='o', s=100, label=\"y=0\", facecolors='none', \n                  edgecolors=dlc[\"dlblue\"],lw=3)\n\n    ax[0].set_ylim(-0.08,1.1)\n    ax[0].set_xlim(-0.5,5.5)\n    ax[0].set_ylabel('y', fontsize=16)\n    ax[0].set_xlabel('x', fontsize=16)\n    ax[0].set_title('Tensorflow Model', fontsize=20)\n    ax[0].legend(fontsize=16)\n\n    layerf= lambda x : sigmoidnp(np.dot(set_w,x.reshape(1,1)) + set_b)\n    plt_prob_1d(ax[1], layerf)\n\n    ax[1].scatter(X_train[pos], Y_train[pos], marker='x', s=80, c = 'red', label=\"y=1\")\n    ax[1].scatter(X_train[neg], Y_train[neg], marker='o', s=100, label=\"y=0\", facecolors='none', \n                  edgecolors=dlc[\"dlblue\"],lw=3)\n\n    ax[1].set_ylim(-0.08,1.1)\n    ax[1].set_xlim(-0.5,5.5)\n    ax[1].set_ylabel('y', fontsize=16)\n    ax[1].set_xlabel('x', fontsize=16)\n    ax[1].set_title('Numpy Model', fontsize=20)\n    ax[1].legend(fontsize=16)\n    plt.show()\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week1/optional-labs/lab_utils_common.py",
    "content": "\"\"\"\nlab_utils_common\n   contains common routines and variable definitions\n   used by all the labs in this week.\n   by contrast, specific, large plotting routines will be in separate files\n   and are generally imported into the week where they are used.\n   those files will import this file\n\"\"\"\nimport copy\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import FancyArrowPatch\nfrom ipywidgets import Output\nfrom matplotlib.widgets import Button, CheckButtons\n\nnp.set_printoptions(precision=2)\n\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0', dldarkblue =  '#0D5BDC')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0'; dldarkblue =  '#0D5BDC'\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\nplt.style.use('./deeplearning.mplstyle')\n\ndef sigmoid(z):\n    \"\"\"\n    Compute the sigmoid of z\n\n    Parameters\n    ----------\n    z : array_like\n        A scalar or numpy array of any size.\n\n    Returns\n    -------\n     g : array_like\n         sigmoid(z)\n    \"\"\"\n    z = np.clip( z, -500, 500 )           # protect against overflow\n    g = 1.0/(1.0+np.exp(-z))\n\n    return g\n\n##########################################################\n# Regression Routines\n##########################################################\n\ndef predict_logistic(X, w, b):\n    \"\"\" performs prediction \"\"\"\n    return sigmoid(X @ w + b)\n\ndef predict_linear(X, w, b):\n    \"\"\" performs prediction \"\"\"\n    return X @ w + b\n\ndef compute_cost_logistic(X, y, w, b, lambda_=0, safe=False):\n    \"\"\"\n    Computes cost using logistic loss, non-matrix version\n\n    Args:\n      X (ndarray): Shape (m,n)  matrix of examples with n features\n      y (ndarray): Shape (m,)   target values\n      w (ndarray): Shape (n,)   parameters for prediction\n      b (scalar):               parameter  for prediction\n      lambda_ : (scalar, float) Controls amount of regularization, 0 = no regularization\n      safe : (boolean)          True-selects under/overflow safe algorithm\n    Returns:\n      cost (scalar): cost\n    \"\"\"\n\n    m,n = X.shape\n    cost = 0.0\n    for i in range(m):\n        z_i    = np.dot(X[i],w) + b                                             #(n,)(n,) or (n,) ()\n        if safe:  #avoids overflows\n            cost += -(y[i] * z_i ) + log_1pexp(z_i)\n        else:\n            f_wb_i = sigmoid(z_i)                                                   #(n,)\n            cost  += -y[i] * np.log(f_wb_i) - (1 - y[i]) * np.log(1 - f_wb_i)       # scalar\n    cost = cost/m\n\n    reg_cost = 0\n    if lambda_ != 0:\n        for j in range(n):\n            reg_cost += (w[j]**2)                                               # scalar\n        reg_cost = (lambda_/(2*m))*reg_cost\n\n    return cost + reg_cost\n\n\ndef log_1pexp(x, maximum=20):\n    ''' approximate log(1+exp^x)\n        https://stats.stackexchange.com/questions/475589/numerical-computation-of-cross-entropy-in-practice\n    Args:\n    x   : (ndarray Shape (n,1) or (n,)  input\n    out : (ndarray Shape matches x      output ~= np.log(1+exp(x))\n    '''\n\n    out  = np.zeros_like(x,dtype=float)\n    i    = x <= maximum\n    ni   = np.logical_not(i)\n\n    out[i]  = np.log(1 + np.exp(x[i]))\n    out[ni] = x[ni]\n    return out\n\n\ndef compute_cost_matrix(X, y, w, b, logistic=False, lambda_=0, safe=True):\n    \"\"\"\n    Computes the cost using  using matrices\n    Args:\n      X : (ndarray, Shape (m,n))          matrix of examples\n      y : (ndarray  Shape (m,) or (m,1))  target value of each example\n      w : (ndarray  Shape (n,) or (n,1))  Values of parameter(s) of the model\n      b : (scalar )                       Values of parameter of the model\n      verbose : (Boolean) If true, print out intermediate value f_wb\n    Returns:\n      total_cost: (scalar)                cost\n    \"\"\"\n    m = X.shape[0]\n    y = y.reshape(-1,1)             # ensure 2D\n    w = w.reshape(-1,1)             # ensure 2D\n    if logistic:\n        if safe:  #safe from overflow\n            z = X @ w + b                                                           #(m,n)(n,1)=(m,1)\n            cost = -(y * z) + log_1pexp(z)\n            cost = np.sum(cost)/m                                                   # (scalar)\n        else:\n            f    = sigmoid(X @ w + b)                                               # (m,n)(n,1) = (m,1)\n            cost = (1/m)*(np.dot(-y.T, np.log(f)) - np.dot((1-y).T, np.log(1-f)))   # (1,m)(m,1) = (1,1)\n            cost = cost[0,0]                                                        # scalar\n    else:\n        f    = X @ w + b                                                        # (m,n)(n,1) = (m,1)\n        cost = (1/(2*m)) * np.sum((f - y)**2)                                   # scalar\n\n    reg_cost = (lambda_/(2*m)) * np.sum(w**2)                                   # scalar\n\n    total_cost = cost + reg_cost                                                # scalar\n\n    return total_cost                                                           # scalar\n\ndef compute_gradient_matrix(X, y, w, b, logistic=False, lambda_=0):\n    \"\"\"\n    Computes the gradient using matrices\n\n    Args:\n      X : (ndarray, Shape (m,n))          matrix of examples\n      y : (ndarray  Shape (m,) or (m,1))  target value of each example\n      w : (ndarray  Shape (n,) or (n,1))  Values of parameters of the model\n      b : (scalar )                       Values of parameter of the model\n      logistic: (boolean)                 linear if false, logistic if true\n      lambda_:  (float)                   applies regularization if non-zero\n    Returns\n      dj_dw: (array_like Shape (n,1))     The gradient of the cost w.r.t. the parameters w\n      dj_db: (scalar)                     The gradient of the cost w.r.t. the parameter b\n    \"\"\"\n    m = X.shape[0]\n    y = y.reshape(-1,1)             # ensure 2D\n    w = w.reshape(-1,1)             # ensure 2D\n\n    f_wb  = sigmoid( X @ w + b ) if logistic else  X @ w + b      # (m,n)(n,1) = (m,1)\n    err   = f_wb - y                                              # (m,1)\n    dj_dw = (1/m) * (X.T @ err)                                   # (n,m)(m,1) = (n,1)\n    dj_db = (1/m) * np.sum(err)                                   # scalar\n\n    dj_dw += (lambda_/m) * w        # regularize                  # (n,1)\n\n    return dj_db, dj_dw                                           # scalar, (n,1)\n\ndef gradient_descent(X, y, w_in, b_in, alpha, num_iters, logistic=False, lambda_=0, verbose=True, Trace=True):\n    \"\"\"\n    Performs batch gradient descent to learn theta. Updates theta by taking\n    num_iters gradient steps with learning rate alpha\n\n    Args:\n      X (ndarray):    Shape (m,n)         matrix of examples\n      y (ndarray):    Shape (m,) or (m,1) target value of each example\n      w_in (ndarray): Shape (n,) or (n,1) Initial values of parameters of the model\n      b_in (scalar):                      Initial value of parameter of the model\n      logistic: (boolean)                 linear if false, logistic if true\n      lambda_:  (float)                   applies regularization if non-zero\n      alpha (float):                      Learning rate\n      num_iters (int):                    number of iterations to run gradient descent\n\n    Returns:\n      w (ndarray): Shape (n,) or (n,1)    Updated values of parameters; matches incoming shape\n      b (scalar):                         Updated value of parameter\n    \"\"\"\n    # An array to store cost J and w's at each iteration primarily for graphing later\n    J_history = []\n    w = copy.deepcopy(w_in)  #avoid modifying global w within function\n    b = b_in\n    w = w.reshape(-1,1)      #prep for matrix operations\n    y = y.reshape(-1,1)\n    last_cost = np.Inf\n\n    for i in range(num_iters):\n\n        # Calculate the gradient and update the parameters\n        dj_db,dj_dw = compute_gradient_matrix(X, y, w, b, logistic, lambda_)\n\n        # Update Parameters using w, b, alpha and gradient\n        w = w - alpha * dj_dw\n        b = b - alpha * dj_db\n\n        # Save cost J at each iteration\n        ccost = compute_cost_matrix(X, y, w, b, logistic, lambda_)\n        if Trace and i<100000:      # prevent resource exhaustion\n            J_history.append( ccost )\n\n        # Print cost every at intervals 10 times or as many iterations if < 10\n        if i% math.ceil(num_iters / 10) == 0:\n            if verbose: print(f\"Iteration {i:4d}: Cost {ccost}   \")\n            if verbose ==2: print(f\"dj_db, dj_dw = {dj_db: 0.3f}, {dj_dw.reshape(-1)}\")\n\n            if ccost == last_cost:\n                alpha = alpha/10\n                print(f\" alpha now {alpha}\")\n            last_cost = ccost\n\n    return w.reshape(w_in.shape), b, J_history  #return final w,b and J history for graphing\n\ndef zscore_normalize_features(X):\n    \"\"\"\n    computes  X, zcore normalized by column\n\n    Args:\n      X (ndarray): Shape (m,n) input data, m examples, n features\n\n    Returns:\n      X_norm (ndarray): Shape (m,n)  input normalized by column\n      mu (ndarray):     Shape (n,)   mean of each feature\n      sigma (ndarray):  Shape (n,)   standard deviation of each feature\n    \"\"\"\n    # find the mean of each column/feature\n    mu     = np.mean(X, axis=0)                 # mu will have shape (n,)\n    # find the standard deviation of each column/feature\n    sigma  = np.std(X, axis=0)                  # sigma will have shape (n,)\n    # element-wise, subtract mu for that column from each example, divide by std for that column\n    X_norm = (X - mu) / sigma\n\n    return X_norm, mu, sigma\n\n#check our work\n#from sklearn.preprocessing import scale\n#scale(X_orig, axis=0, with_mean=True, with_std=True, copy=True)\n\n######################################################\n# Common Plotting Routines\n######################################################\n\n\ndef plot_data(X, y, ax, pos_label=\"y=1\", neg_label=\"y=0\", s=80, loc='best' ):\n    \"\"\" plots logistic data with two axis \"\"\"\n    # Find Indices of Positive and Negative Examples\n    pos = y == 1\n    neg = y == 0\n    pos = pos.reshape(-1,)  #work with 1D or 1D y vectors\n    neg = neg.reshape(-1,)\n\n    # Plot examples\n    ax.scatter(X[pos, 0], X[pos, 1], marker='x', s=s, c = 'red', label=pos_label)\n    ax.scatter(X[neg, 0], X[neg, 1], marker='o', s=s, label=neg_label, facecolors='none', edgecolors=dlblue, lw=3)\n    ax.legend(loc=loc)\n\n    ax.figure.canvas.toolbar_visible = False\n    ax.figure.canvas.header_visible = False\n    ax.figure.canvas.footer_visible = False\n\ndef plt_tumor_data(x, y, ax):\n    \"\"\" plots tumor data on one axis \"\"\"\n    pos = y == 1\n    neg = y == 0\n\n    ax.scatter(x[pos], y[pos], marker='x', s=80, c = 'red', label=\"malignant\")\n    ax.scatter(x[neg], y[neg], marker='o', s=100, label=\"benign\", facecolors='none', edgecolors=dlblue,lw=3)\n    ax.set_ylim(-0.175,1.1)\n    ax.set_ylabel('y')\n    ax.set_xlabel('Tumor Size')\n    ax.set_title(\"Logistic Regression on Categorical Data\")\n\n    ax.figure.canvas.toolbar_visible = False\n    ax.figure.canvas.header_visible = False\n    ax.figure.canvas.footer_visible = False\n\n# Draws a threshold at 0.5\ndef draw_vthresh(ax,x):\n    \"\"\" draws a threshold \"\"\"\n    ylim = ax.get_ylim()\n    xlim = ax.get_xlim()\n    ax.fill_between([xlim[0], x], [ylim[1], ylim[1]], alpha=0.2, color=dlblue)\n    ax.fill_between([x, xlim[1]], [ylim[1], ylim[1]], alpha=0.2, color=dldarkred)\n    ax.annotate(\"z >= 0\", xy= [x,0.5], xycoords='data',\n                xytext=[30,5],textcoords='offset points')\n    d = FancyArrowPatch(\n        posA=(x, 0.5), posB=(x+3, 0.5), color=dldarkred,\n        arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n    )\n    ax.add_artist(d)\n    ax.annotate(\"z < 0\", xy= [x,0.5], xycoords='data',\n                 xytext=[-50,5],textcoords='offset points', ha='left')\n    f = FancyArrowPatch(\n        posA=(x, 0.5), posB=(x-3, 0.5), color=dlblue,\n        arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n    )\n    ax.add_artist(f)\n\n\n#-----------------------------------------------------\n# common interactive plotting routines\n#-----------------------------------------------------\n\nclass button_manager:\n    ''' Handles some missing features of matplotlib check buttons\n    on init:\n        creates button, links to button_click routine,\n        calls call_on_click with active index and firsttime=True\n    on click:\n        maintains single button on state, calls call_on_click\n    '''\n\n    #@output.capture()  # debug\n    def __init__(self,fig, dim, labels, init, call_on_click):\n        '''\n        dim: (list)     [leftbottom_x,bottom_y,width,height]\n        labels: (list)  for example ['1','2','3','4','5','6']\n        init: (list)    for example [True, False, False, False, False, False]\n        '''\n        self.fig = fig\n        self.ax = plt.axes(dim)  #lx,by,w,h\n        self.init_state = init\n        self.call_on_click = call_on_click\n        self.button  = CheckButtons(self.ax,labels,init)\n        self.button.on_clicked(self.button_click)\n        self.status = self.button.get_status()\n        self.call_on_click(self.status.index(True),firsttime=True)\n\n    #@output.capture()  # debug\n    def reinit(self):\n        self.status = self.init_state\n        self.button.set_active(self.status.index(True))      #turn off old, will trigger update and set to status\n\n    #@output.capture()  # debug\n    def button_click(self, event):\n        ''' maintains one-on state. If on-button is clicked, will process correctly '''\n        #new_status = self.button.get_status()\n        #new = [self.status[i] ^ new_status[i] for i in range(len(self.status))]\n        #newidx = new.index(True)\n        self.button.eventson = False\n        self.button.set_active(self.status.index(True))  #turn off old or reenable if same\n        self.button.eventson = True\n        self.status = self.button.get_status()\n        self.call_on_click(self.status.index(True))\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/C2W2A1/.ipynb_checkpoints/C2_W2_Assignment-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Practice Lab: Neural Networks for Handwritten Digit Recognition, Multiclass \\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize the hand-written digits 0-9.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - ReLU Activation](#2)\\n\",\n    \"- [ 3 - Softmax Function](#3)\\n\",\n    \"  - [ Exercise 1](#ex01)\\n\",\n    \"- [ 4 - Neural Networks](#4)\\n\",\n    \"  - [ 4.1 Problem Statement](#4.1)\\n\",\n    \"  - [ 4.2 Dataset](#4.2)\\n\",\n    \"  - [ 4.3 Model representation](#4.3)\\n\",\n    \"  - [ 4.4 Tensorflow Model Implementation](#4.4)\\n\",\n    \"  - [ 4.5 Softmax placement](#4.5)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\\n\",\n    \"- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from tensorflow.keras.activations import linear, relu, sigmoid\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\\n\",\n    \"\\n\",\n    \"from public_tests import * \\n\",\n    \"\\n\",\n    \"from autils import *\\n\",\n    \"from lab_utils_softmax import plt_softmax\\n\",\n    \"np.set_printoptions(precision=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - ReLU Activation\\n\",\n    \"This week, a new activation was introduced, the Rectified Linear Unit (ReLU). \\n\",\n    \"$$ a = max(0,z) \\\\quad\\\\quad\\\\text {# ReLU function} $$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"0aef69aa4f6146dfb4e92e7786bf102b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_act_trio()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img align=\\\"right\\\" src=\\\"./images/C2_W2_ReLu.png\\\"     style=\\\" width:380px; padding: 10px 20px; \\\" >\\n\",\n    \"The example from the lecture on the right shows an application of the ReLU. In this example, the derived \\\"awareness\\\" feature is not binary but has a continuous range of values. The sigmoid is best for on/off or binary situations. The ReLU provides a continuous linear relationship. Additionally it has an 'off' range where the output is zero.     \\n\",\n    \"The \\\"off\\\" feature makes the ReLU a Non-Linear activation. Why is this needed? This enables multiple units to contribute to to the resulting function without interfering. This is examined more in the supporting optional lab. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Softmax Function\\n\",\n    \"A multiclass neural network generates N outputs. One output is selected as the predicted answer. In the output layer, a vector $\\\\mathbf{z}$ is generated by a linear function which is fed into a softmax function. The softmax function converts $\\\\mathbf{z}$  into a probability distribution as described below. After applying softmax, each output will be between 0 and 1 and the outputs will sum to 1. They can be interpreted as probabilities. The larger inputs to the softmax will correspond to larger output probabilities.\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_NNSoftmax.PNG\\\" width=\\\"600\\\" />  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The softmax function can be written:\\n\",\n    \"$$a_j = \\\\frac{e^{z_j}}{ \\\\sum_{k=0}^{N-1}{e^{z_k} }} \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"Where $z = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$ and N is the number of feature/categories in the output layer.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"Let's create a NumPy implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED CELL: my_softmax\\n\",\n    \"\\n\",\n    \"def my_softmax(z):  \\n\",\n    \"    \\\"\\\"\\\" Softmax converts a vector of values to a probability distribution.\\n\",\n    \"    Args:\\n\",\n    \"      z (ndarray (N,))  : input data, N features\\n\",\n    \"    Returns:\\n\",\n    \"      a (ndarray (N,))  : softmax of z\\n\",\n    \"    \\\"\\\"\\\"    \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    ez = np.exp(z)\\n\",\n    \"    a = ez/np.sum(ez)\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    return a\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"my_softmax(z):         [0.03 0.09 0.24 0.64]\\n\",\n      \"tensorflow softmax(z): [0.03 0.09 0.24 0.64]\\n\",\n      \"\\u001b[92m All tests passed.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"z = np.array([1., 2., 3., 4.])\\n\",\n    \"a = my_softmax(z)\\n\",\n    \"atf = tf.nn.softmax(z)\\n\",\n    \"print(f\\\"my_softmax(z):         {a}\\\")\\n\",\n    \"print(f\\\"tensorflow softmax(z): {atf}\\\")\\n\",\n    \"\\n\",\n    \"# BEGIN UNIT TEST  \\n\",\n    \"test_my_softmax(my_softmax)\\n\",\n    \"# END UNIT TEST  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    One implementation uses for loop to first build the denominator and then a second loop to calculate each output.\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_softmax(z):  \\n\",\n    \"    N = len(z)\\n\",\n    \"    a =                     # initialize a to zeros \\n\",\n    \"    ez_sum =                # initialize sum to zero\\n\",\n    \"    for k in range(N):      # loop over number of outputs             \\n\",\n    \"        ez_sum +=           # sum exp(z[k]) to build the shared denominator      \\n\",\n    \"    for j in range(N):      # loop over number of outputs again                \\n\",\n    \"        a[j] =              # divide each the exp of each output by the denominator   \\n\",\n    \"    return(a)\\n\",\n    \"```\\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for code</b></font></summary>\\n\",\n    \"   \\n\",\n    \"```python\\n\",\n    \"def my_softmax(z):  \\n\",\n    \"    N = len(z)\\n\",\n    \"    a = np.zeros(N)\\n\",\n    \"    ez_sum = 0\\n\",\n    \"    for k in range(N):                \\n\",\n    \"        ez_sum += np.exp(z[k])       \\n\",\n    \"    for j in range(N):                \\n\",\n    \"        a[j] = np.exp(z[j])/ez_sum   \\n\",\n    \"    return(a)\\n\",\n    \"\\n\",\n    \"Or, a vector implementation:\\n\",\n    \"\\n\",\n    \"def my_softmax(z):  \\n\",\n    \"    ez = np.exp(z)              \\n\",\n    \"    a = ez/np.sum(ez)           \\n\",\n    \"    return(a)\\n\",\n    \"\\n\",\n    \"```\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below, vary the values of the `z` inputs. Note in particular how the exponential in the numerator magnifies small differences in the values. Note as well that the output values sum to one.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2b5ea2c37bba41209c8e2157f63c2738\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_softmax(my_softmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Neural Networks\\n\",\n    \"\\n\",\n    \"In last weeks assignment, you implemented a neural network to do binary classification. This week you will extend that to multiclass classification. This will utilize the softmax activation.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"<a name=\\\"4.1\\\"></a>\\n\",\n    \"### 4.1 Problem Statement\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize ten handwritten digits, 0-9. This is a multiclass classification task where one of n choices is selected. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"<a name=\\\"4.2\\\"></a>\\n\",\n    \"### 4.2 Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_data()` function shown below loads the data into variables `X` and `y`\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- The data set contains 5000 training examples of handwritten digits $^1$.  \\n\",\n    \"\\n\",\n    \"    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \\n\",\n    \"        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \\n\",\n    \"        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \\n\",\n    \"        - Each training examples becomes a single row in our data matrix `X`. \\n\",\n    \"        - This gives us a 5000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\\n\",\n    \"\\n\",\n    \"$$X = \\n\",\n    \"\\\\left(\\\\begin{array}{cc} \\n\",\n    \"--- (x^{(1)}) --- \\\\\\\\\\n\",\n    \"--- (x^{(2)}) --- \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\ \\n\",\n    \"--- (x^{(m)}) --- \\n\",\n    \"\\\\end{array}\\\\right)$$ \\n\",\n    \"\\n\",\n    \"- The second part of the training set is a 5000 x 1 dimensional vector `y` that contains labels for the training set\\n\",\n    \"    - `y = 0` if the image is of the digit `0`, `y = 4` if the image is of the digit `4` and so on.\\n\",\n    \"\\n\",\n    \"$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X, y = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4.2.1 View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints the first element in the variables `X` and `y`.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The first element of X is: ', X[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first element of y is:  0\\n\",\n      \"The last element of y is:  9\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The first element of y is: ', y[0,0])\\n\",\n    \"print ('The last element of y is: ', y[-1,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4.2.2 Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of X is: (5000, 400)\\n\",\n      \"The shape of y is: (5000, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of X is: ' + str(X.shape))\\n\",\n    \"print ('The shape of y is: ' + str(y.shape))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 4.2.3 Visualizing the Data\\n\",\n    \"\\n\",\n    \"You will begin by visualizing a subset of the training set. \\n\",\n    \"- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \\n\",\n    \"- The label for each image is displayed above the image \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f66746b327ff4eb4a92df796eff793bb\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(5,5))\\n\",\n    \"fig.tight_layout(pad=0.13,rect=[0, 0.03, 1, 0.91]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"#fig.tight_layout(pad=0.5)\\n\",\n    \"widgvis(fig)\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(y[random_index,0])\\n\",\n    \"    ax.set_axis_off()\\n\",\n    \"    fig.suptitle(\\\"Label, image\\\", fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.3\\\"></a>\\n\",\n    \"### 4.3 Model representation\\n\",\n    \"\\n\",\n    \"The neural network you will use in this assignment is shown in the figure below. \\n\",\n    \"- This has two dense layers with ReLU activations followed by an output layer with a linear activation. \\n\",\n    \"    - Recall that our inputs are pixel values of digit images.\\n\",\n    \"    - Since the images are of size $20\\\\times20$, this gives us $400$ inputs  \\n\",\n    \"    \\n\",\n    \"<img src=\\\"images/C2_W2_Assigment_NN.png\\\" width=\\\"600\\\" height=\\\"450\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $10$ output units in layer 3, one for each digit.\\n\",\n    \"\\n\",\n    \"    - Recall that the dimensions of these parameters is determined as follows:\\n\",\n    \"        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \\n\",\n    \"            - $W$ will be of dimension $s_{in} \\\\times s_{out}$.\\n\",\n    \"            - $b$ will be a vector with $s_{out}$ elements\\n\",\n    \"  \\n\",\n    \"    - Therefore, the shapes of `W`, and `b`,  are \\n\",\n    \"        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\\n\",\n    \"        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\\n\",\n    \"        - layer3: The shape of `W3` is (15, 10) and the shape of `b3` is: (10,)\\n\",\n    \">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (n,1) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention: \\n\",\n    \"               \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.4\\\"></a>\\n\",\n    \"### 4.4 Tensorflow Model Implementation\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statement below. \\n\",\n    \">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \\n\",\n    \"`tf.keras.Input(shape=(400,)),    #specify input shape`  \\n\",\n    \"We will include that here to illuminate some model sizing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.5\\\"></a>\\n\",\n    \"### 4.5 Softmax placement\\n\",\n    \"As described in the lecture and the optional softmax lab, numerical stability is improved if the softmax is grouped with the loss function rather than the output layer during training. This has implications when *building* the model and *using* the model.  \\n\",\n    \"Building:  \\n\",\n    \"* The final Dense layer should use a 'linear' activation. This is effectively no activation. \\n\",\n    \"* The `model.compile` statement will indicate this by including `from_logits=True`.\\n\",\n    \"`loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) `  \\n\",\n    \"* This does not impact the form of the target. In the case of SparseCategorialCrossentropy, the target is the expected digit, 0-9.\\n\",\n    \"\\n\",\n    \"Using the model:\\n\",\n    \"* The outputs are not probabilities. If output probabilities are desired, apply a softmax function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a ReLU activation to construct the three layer network described above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED CELL: Sequential model\\n\",\n    \"tf.random.set_seed(1234) # for consistent results\\n\",\n    \"model = Sequential(\\n\",\n    \"    [               \\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        tf.keras.layers.InputLayer((400,)),\\n\",\n    \"        tf.keras.layers.Dense(25, activation=\\\"relu\\\", name=\\\"L1\\\"),\\n\",\n    \"        tf.keras.layers.Dense(15, activation=\\\"relu\\\", name=\\\"L2\\\"),\\n\",\n    \"        tf.keras.layers.Dense(10, activation=\\\"linear\\\", name=\\\"L3\\\")\\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"my_model\\\" \\n\",\n    \")\\n\",\n    \"model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"my_model\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" L1 (Dense)                  (None, 25)                10025     \\n\",\n      \"                                                                 \\n\",\n      \" L2 (Dense)                  (None, 15)                390       \\n\",\n      \"                                                                 \\n\",\n      \" L3 (Dense)                  (None, 10)                160       \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 10,575\\n\",\n      \"Trainable params: 10,575\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Expected Output (Click to expand)</b></font></summary>\\n\",\n    \"The `model.summary()` function displays a useful summary of the model. Note, the names of the layers may vary as they are auto-generated unless the name is specified.    \\n\",\n    \"    \\n\",\n    \"```\\n\",\n    \"Model: \\\"my_model\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"L1 (Dense)                   (None, 25)                10025     \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L2 (Dense)                   (None, 15)                390       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L3 (Dense)                   (None, 10)                160       \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 10,575\\n\",\n    \"Trainable params: 10,575\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model = Sequential(\\n\",\n    \"    [               \\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        tf.keras.Input(shape=(400,)),     # @REPLACE \\n\",\n    \"        Dense(25, activation='relu', name = \\\"L1\\\"), # @REPLACE \\n\",\n    \"        Dense(15, activation='relu',  name = \\\"L2\\\"), # @REPLACE  \\n\",\n    \"        Dense(10, activation='linear', name = \\\"L3\\\"),  # @REPLACE \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"my_model\\\" \\n\",\n    \")\\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# BEGIN UNIT TEST     \\n\",\n    \"test_model(model, 10, 400)\\n\",\n    \"# END UNIT TEST     \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's further examine the weights to verify that tensorflow produced the same dimensions as we calculated above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"[layer1, layer2, layer3] = model.layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"W1 shape = (400, 25), b1 shape = (25,)\\n\",\n      \"W2 shape = (25, 15), b2 shape = (15,)\\n\",\n      \"W3 shape = (15, 10), b3 shape = (10,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#### Examine Weights shapes\\n\",\n    \"W1,b1 = layer1.get_weights()\\n\",\n    \"W2,b2 = layer2.get_weights()\\n\",\n    \"W3,b3 = layer3.get_weights()\\n\",\n    \"print(f\\\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\\\")\\n\",\n    \"print(f\\\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\\\")\\n\",\n    \"print(f\\\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"W1 shape = (400, 25), b1 shape = (25,)  \\n\",\n    \"W2 shape = (25, 15), b2 shape = (15,)  \\n\",\n    \"W3 shape = (15, 10), b3 shape = (10,)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following code:\\n\",\n    \"* defines a loss function, `SparseCategoricalCrossentropy` and indicates the softmax should be included with the  loss calculation by adding `from_logits=True`)\\n\",\n    \"* defines an optimizer. A popular choice is Adaptive Moment (Adam) which was described in lecture.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/40\\n\",\n      \"157/157 [==============================] - 1s 2ms/step - loss: 1.7094\\n\",\n      \"Epoch 2/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.7480\\n\",\n      \"Epoch 3/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.4428\\n\",\n      \"Epoch 4/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.3463\\n\",\n      \"Epoch 5/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2977\\n\",\n      \"Epoch 6/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2630\\n\",\n      \"Epoch 7/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2361\\n\",\n      \"Epoch 8/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2131\\n\",\n      \"Epoch 9/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2004\\n\",\n      \"Epoch 10/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1805\\n\",\n      \"Epoch 11/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1692\\n\",\n      \"Epoch 12/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1580\\n\",\n      \"Epoch 13/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1507\\n\",\n      \"Epoch 14/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1396\\n\",\n      \"Epoch 15/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1289\\n\",\n      \"Epoch 16/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1255\\n\",\n      \"Epoch 17/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1154\\n\",\n      \"Epoch 18/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1102\\n\",\n      \"Epoch 19/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1016\\n\",\n      \"Epoch 20/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0970\\n\",\n      \"Epoch 21/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0926\\n\",\n      \"Epoch 22/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0891\\n\",\n      \"Epoch 23/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0828\\n\",\n      \"Epoch 24/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0785\\n\",\n      \"Epoch 25/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0755\\n\",\n      \"Epoch 26/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0713\\n\",\n      \"Epoch 27/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0701\\n\",\n      \"Epoch 28/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0617\\n\",\n      \"Epoch 29/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0578\\n\",\n      \"Epoch 30/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0550\\n\",\n      \"Epoch 31/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0511\\n\",\n      \"Epoch 32/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0499\\n\",\n      \"Epoch 33/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0462\\n\",\n      \"Epoch 34/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0437\\n\",\n      \"Epoch 35/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0422\\n\",\n      \"Epoch 36/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0396\\n\",\n      \"Epoch 37/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0366\\n\",\n      \"Epoch 38/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0344\\n\",\n      \"Epoch 39/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0312\\n\",\n      \"Epoch 40/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0294\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"history = model.fit(\\n\",\n    \"    X,y,\\n\",\n    \"    epochs=40\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Epochs and batches\\n\",\n    \"In the `compile` statement above, the number of `epochs` was set to 100. This specifies that the entire data set should be applied during training 100 times.  During training, you see output describing the progress of training that looks like this:\\n\",\n    \"```\\n\",\n    \"Epoch 1/100\\n\",\n    \"157/157 [==============================] - 0s 1ms/step - loss: 2.2770\\n\",\n    \"```\\n\",\n    \"The first line, `Epoch 1/100`, describes which epoch the model is currently running. For efficiency, the training data set is broken into 'batches'. The default size of a batch in Tensorflow is 32. There are 5000 examples in our data set or roughly 157 batches. The notation on the 2nd line `157/157 [====` is describing which batch has been executed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Loss  (cost)\\n\",\n    \"In course 1, we learned to track the progress of gradient descent by monitoring the cost. Ideally, the cost will decrease as the number of iterations of the algorithm increases. Tensorflow refers to the cost as `loss`. Above, you saw the loss displayed each epoch as `model.fit` was executing. The [.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model) method returns a variety of metrics including the loss. This is captured in the `history` variable above. This can be used to examine the loss in a plot as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"85765547adaa44e197f57464c49a0ab9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_loss_tf(history)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Prediction \\n\",\n    \"To make a prediction, use Keras `predict`. Below, X[1015] contains an image of a two.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6d0cfe835bd14b1fb3b8a9fa09b3167e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" predicting a Two: \\n\",\n      \"[[ -7.99  -2.23   0.77  -2.41 -11.66 -11.15  -9.53  -3.36  -4.42  -7.17]]\\n\",\n      \" Largest Prediction index: 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_of_two = X[1015]\\n\",\n    \"display_digit(image_of_two)\\n\",\n    \"\\n\",\n    \"prediction = model.predict(image_of_two.reshape(1,400))  # prediction\\n\",\n    \"\\n\",\n    \"print(f\\\" predicting a Two: \\\\n{prediction}\\\")\\n\",\n    \"print(f\\\" Largest Prediction index: {np.argmax(prediction)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The largest output is prediction[2], indicating the predicted digit is a '2'. If the problem only requires a selection, that is sufficient. Use NumPy [argmax](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html) to select it. If the problem requires a probability, a softmax is required:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" predicting a Two. Probability vector: \\n\",\n      \"[[1.42e-04 4.49e-02 8.98e-01 3.76e-02 3.61e-06 5.97e-06 3.03e-05 1.44e-02\\n\",\n      \"  5.03e-03 3.22e-04]]\\n\",\n      \"Total of predictions: 1.000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"prediction_p = tf.nn.softmax(prediction)\\n\",\n    \"\\n\",\n    \"print(f\\\" predicting a Two. Probability vector: \\\\n{prediction_p}\\\")\\n\",\n    \"print(f\\\"Total of predictions: {np.sum(prediction_p):0.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To return an integer representing the predicted target, you want the index of the largest probability. This is accomplished with the Numpy [argmax](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html) function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"np.argmax(prediction_p): 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = np.argmax(prediction_p)\\n\",\n    \"\\n\",\n    \"print(f\\\"np.argmax(prediction_p): {yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4190acc8b7cf4617856f2ce6f839880f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(5,5))\\n\",\n    \"fig.tight_layout(pad=0.13,rect=[0, 0.03, 1, 0.91]) #[left, bottom, right, top]\\n\",\n    \"widgvis(fig)\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Predict using the Neural Network\\n\",\n    \"    prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    prediction_p = tf.nn.softmax(prediction)\\n\",\n    \"    yhat = np.argmax(prediction_p)\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{yhat}\\\",fontsize=10)\\n\",\n    \"    ax.set_axis_off()\\n\",\n    \"fig.suptitle(\\\"Label, yhat\\\", fontsize=14)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's look at some of the errors. \\n\",\n    \">Note: increasing the number of training epochs can eliminate the errors on this data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"ec4df6ce60e9470a9c5b1fe8bef3df02\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"15 errors out of 5000 images\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print( f\\\"{display_errors(model,X,y)} errors out of {len(X)} images\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Congratulations!\\n\",\n    \"You have successfully built and utilized a neural network to do multiclass classification.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"89367\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/C2W2A1/C2_W2_Assignment.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Practice Lab: Neural Networks for Handwritten Digit Recognition, Multiclass \\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize the hand-written digits 0-9.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - ReLU Activation](#2)\\n\",\n    \"- [ 3 - Softmax Function](#3)\\n\",\n    \"  - [ Exercise 1](#ex01)\\n\",\n    \"- [ 4 - Neural Networks](#4)\\n\",\n    \"  - [ 4.1 Problem Statement](#4.1)\\n\",\n    \"  - [ 4.2 Dataset](#4.2)\\n\",\n    \"  - [ 4.3 Model representation](#4.3)\\n\",\n    \"  - [ 4.4 Tensorflow Model Implementation](#4.4)\\n\",\n    \"  - [ 4.5 Softmax placement](#4.5)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\\n\",\n    \"- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from tensorflow.keras.activations import linear, relu, sigmoid\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\\n\",\n    \"\\n\",\n    \"from public_tests import * \\n\",\n    \"\\n\",\n    \"from autils import *\\n\",\n    \"from lab_utils_softmax import plt_softmax\\n\",\n    \"np.set_printoptions(precision=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - ReLU Activation\\n\",\n    \"This week, a new activation was introduced, the Rectified Linear Unit (ReLU). \\n\",\n    \"$$ a = max(0,z) \\\\quad\\\\quad\\\\text {# ReLU function} $$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"0aef69aa4f6146dfb4e92e7786bf102b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_act_trio()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<img align=\\\"right\\\" src=\\\"./images/C2_W2_ReLu.png\\\"     style=\\\" width:380px; padding: 10px 20px; \\\" >\\n\",\n    \"The example from the lecture on the right shows an application of the ReLU. In this example, the derived \\\"awareness\\\" feature is not binary but has a continuous range of values. The sigmoid is best for on/off or binary situations. The ReLU provides a continuous linear relationship. Additionally it has an 'off' range where the output is zero.     \\n\",\n    \"The \\\"off\\\" feature makes the ReLU a Non-Linear activation. Why is this needed? This enables multiple units to contribute to to the resulting function without interfering. This is examined more in the supporting optional lab. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Softmax Function\\n\",\n    \"A multiclass neural network generates N outputs. One output is selected as the predicted answer. In the output layer, a vector $\\\\mathbf{z}$ is generated by a linear function which is fed into a softmax function. The softmax function converts $\\\\mathbf{z}$  into a probability distribution as described below. After applying softmax, each output will be between 0 and 1 and the outputs will sum to 1. They can be interpreted as probabilities. The larger inputs to the softmax will correspond to larger output probabilities.\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_NNSoftmax.PNG\\\" width=\\\"600\\\" />  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The softmax function can be written:\\n\",\n    \"$$a_j = \\\\frac{e^{z_j}}{ \\\\sum_{k=0}^{N-1}{e^{z_k} }} \\\\tag{1}$$\\n\",\n    \"\\n\",\n    \"Where $z = \\\\mathbf{w} \\\\cdot \\\\mathbf{x} + b$ and N is the number of feature/categories in the output layer.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"Let's create a NumPy implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED CELL: my_softmax\\n\",\n    \"\\n\",\n    \"def my_softmax(z):  \\n\",\n    \"    \\\"\\\"\\\" Softmax converts a vector of values to a probability distribution.\\n\",\n    \"    Args:\\n\",\n    \"      z (ndarray (N,))  : input data, N features\\n\",\n    \"    Returns:\\n\",\n    \"      a (ndarray (N,))  : softmax of z\\n\",\n    \"    \\\"\\\"\\\"    \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    ez = np.exp(z)\\n\",\n    \"    a = ez/np.sum(ez)\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    return a\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"my_softmax(z):         [0.03 0.09 0.24 0.64]\\n\",\n      \"tensorflow softmax(z): [0.03 0.09 0.24 0.64]\\n\",\n      \"\\u001B[92m All tests passed.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"z = np.array([1., 2., 3., 4.])\\n\",\n    \"a = my_softmax(z)\\n\",\n    \"atf = tf.nn.softmax(z)\\n\",\n    \"print(f\\\"my_softmax(z):         {a}\\\")\\n\",\n    \"print(f\\\"tensorflow softmax(z): {atf}\\\")\\n\",\n    \"\\n\",\n    \"# BEGIN UNIT TEST  \\n\",\n    \"test_my_softmax(my_softmax)\\n\",\n    \"# END UNIT TEST  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    One implementation uses for loop to first build the denominator and then a second loop to calculate each output.\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def my_softmax(z):  \\n\",\n    \"    N = len(z)\\n\",\n    \"    a =                     # initialize a to zeros \\n\",\n    \"    ez_sum =                # initialize sum to zero\\n\",\n    \"    for k in range(N):      # loop over number of outputs             \\n\",\n    \"        ez_sum +=           # sum exp(z[k]) to build the shared denominator      \\n\",\n    \"    for j in range(N):      # loop over number of outputs again                \\n\",\n    \"        a[j] =              # divide each the exp of each output by the denominator   \\n\",\n    \"    return(a)\\n\",\n    \"```\\n\",\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for code</b></font></summary>\\n\",\n    \"   \\n\",\n    \"```python\\n\",\n    \"def my_softmax(z):  \\n\",\n    \"    N = len(z)\\n\",\n    \"    a = np.zeros(N)\\n\",\n    \"    ez_sum = 0\\n\",\n    \"    for k in range(N):                \\n\",\n    \"        ez_sum += np.exp(z[k])       \\n\",\n    \"    for j in range(N):                \\n\",\n    \"        a[j] = np.exp(z[j])/ez_sum   \\n\",\n    \"    return(a)\\n\",\n    \"\\n\",\n    \"Or, a vector implementation:\\n\",\n    \"\\n\",\n    \"def my_softmax(z):  \\n\",\n    \"    ez = np.exp(z)              \\n\",\n    \"    a = ez/np.sum(ez)           \\n\",\n    \"    return(a)\\n\",\n    \"\\n\",\n    \"```\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Below, vary the values of the `z` inputs. Note in particular how the exponential in the numerator magnifies small differences in the values. Note as well that the output values sum to one.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2b5ea2c37bba41209c8e2157f63c2738\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_softmax(my_softmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Neural Networks\\n\",\n    \"\\n\",\n    \"In last weeks assignment, you implemented a neural network to do binary classification. This week you will extend that to multiclass classification. This will utilize the softmax activation.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"<a name=\\\"4.1\\\"></a>\\n\",\n    \"### 4.1 Problem Statement\\n\",\n    \"\\n\",\n    \"In this exercise, you will use a neural network to recognize ten handwritten digits, 0-9. This is a multiclass classification task where one of n choices is selected. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"<a name=\\\"4.2\\\"></a>\\n\",\n    \"### 4.2 Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_data()` function shown below loads the data into variables `X` and `y`\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"- The data set contains 5000 training examples of handwritten digits $^1$.  \\n\",\n    \"\\n\",\n    \"    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \\n\",\n    \"        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \\n\",\n    \"        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \\n\",\n    \"        - Each training examples becomes a single row in our data matrix `X`. \\n\",\n    \"        - This gives us a 5000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\\n\",\n    \"\\n\",\n    \"$$X = \\n\",\n    \"\\\\left(\\\\begin{array}{cc} \\n\",\n    \"--- (x^{(1)}) --- \\\\\\\\\\n\",\n    \"--- (x^{(2)}) --- \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\ \\n\",\n    \"--- (x^{(m)}) --- \\n\",\n    \"\\\\end{array}\\\\right)$$ \\n\",\n    \"\\n\",\n    \"- The second part of the training set is a 5000 x 1 dimensional vector `y` that contains labels for the training set\\n\",\n    \"    - `y = 0` if the image is of the digit `0`, `y = 4` if the image is of the digit `4` and so on.\\n\",\n    \"\\n\",\n    \"$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# load dataset\\n\",\n    \"X, y = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#### 4.2.1 View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints the first element in the variables `X` and `y`.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The first element of X is: ', X[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first element of y is:  0\\n\",\n      \"The last element of y is:  9\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The first element of y is: ', y[0,0])\\n\",\n    \"print ('The last element of y is: ', y[-1,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#### 4.2.2 Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of X is: (5000, 400)\\n\",\n      \"The shape of y is: (5000, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of X is: ' + str(X.shape))\\n\",\n    \"print ('The shape of y is: ' + str(y.shape))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#### 4.2.3 Visualizing the Data\\n\",\n    \"\\n\",\n    \"You will begin by visualizing a subset of the training set. \\n\",\n    \"- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \\n\",\n    \"- The label for each image is displayed above the image \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f66746b327ff4eb4a92df796eff793bb\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(5,5))\\n\",\n    \"fig.tight_layout(pad=0.13,rect=[0, 0.03, 1, 0.91]) #[left, bottom, right, top]\\n\",\n    \"\\n\",\n    \"#fig.tight_layout(pad=0.5)\\n\",\n    \"widgvis(fig)\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(y[random_index,0])\\n\",\n    \"    ax.set_axis_off()\\n\",\n    \"    fig.suptitle(\\\"Label, image\\\", fontsize=14)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"4.3\\\"></a>\\n\",\n    \"### 4.3 Model representation\\n\",\n    \"\\n\",\n    \"The neural network you will use in this assignment is shown in the figure below. \\n\",\n    \"- This has two dense layers with ReLU activations followed by an output layer with a linear activation. \\n\",\n    \"    - Recall that our inputs are pixel values of digit images.\\n\",\n    \"    - Since the images are of size $20\\\\times20$, this gives us $400$ inputs  \\n\",\n    \"    \\n\",\n    \"<img src=\\\"images/C2_W2_Assigment_NN.png\\\" width=\\\"600\\\" height=\\\"450\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $10$ output units in layer 3, one for each digit.\\n\",\n    \"\\n\",\n    \"    - Recall that the dimensions of these parameters is determined as follows:\\n\",\n    \"        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \\n\",\n    \"            - $W$ will be of dimension $s_{in} \\\\times s_{out}$.\\n\",\n    \"            - $b$ will be a vector with $s_{out}$ elements\\n\",\n    \"  \\n\",\n    \"    - Therefore, the shapes of `W`, and `b`,  are \\n\",\n    \"        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\\n\",\n    \"        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\\n\",\n    \"        - layer3: The shape of `W3` is (15, 10) and the shape of `b3` is: (10,)\\n\",\n    \">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (n,1) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention: \\n\",\n    \"               \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"4.4\\\"></a>\\n\",\n    \"### 4.4 Tensorflow Model Implementation\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statement below. \\n\",\n    \">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \\n\",\n    \"`tf.keras.Input(shape=(400,)),    #specify input shape`  \\n\",\n    \"We will include that here to illuminate some model sizing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"4.5\\\"></a>\\n\",\n    \"### 4.5 Softmax placement\\n\",\n    \"As described in the lecture and the optional softmax lab, numerical stability is improved if the softmax is grouped with the loss function rather than the output layer during training. This has implications when *building* the model and *using* the model.  \\n\",\n    \"Building:  \\n\",\n    \"* The final Dense layer should use a 'linear' activation. This is effectively no activation. \\n\",\n    \"* The `model.compile` statement will indicate this by including `from_logits=True`.\\n\",\n    \"`loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) `  \\n\",\n    \"* This does not impact the form of the target. In the case of SparseCategorialCrossentropy, the target is the expected digit, 0-9.\\n\",\n    \"\\n\",\n    \"Using the model:\\n\",\n    \"* The outputs are not probabilities. If output probabilities are desired, apply a softmax function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a ReLU activation to construct the three layer network described above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED CELL: Sequential model\\n\",\n    \"tf.random.set_seed(1234) # for consistent results\\n\",\n    \"model = Sequential(\\n\",\n    \"    [               \\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        tf.keras.layers.InputLayer((400,)),\\n\",\n    \"        tf.keras.layers.Dense(25, activation=\\\"relu\\\", name=\\\"L1\\\"),\\n\",\n    \"        tf.keras.layers.Dense(15, activation=\\\"relu\\\", name=\\\"L2\\\"),\\n\",\n    \"        tf.keras.layers.Dense(10, activation=\\\"linear\\\", name=\\\"L3\\\")\\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"my_model\\\" \\n\",\n    \")\\n\",\n    \"model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"my_model\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" L1 (Dense)                  (None, 25)                10025     \\n\",\n      \"                                                                 \\n\",\n      \" L2 (Dense)                  (None, 15)                390       \\n\",\n      \"                                                                 \\n\",\n      \" L3 (Dense)                  (None, 10)                160       \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 10,575\\n\",\n      \"Trainable params: 10,575\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Expected Output (Click to expand)</b></font></summary>\\n\",\n    \"The `model.summary()` function displays a useful summary of the model. Note, the names of the layers may vary as they are auto-generated unless the name is specified.    \\n\",\n    \"    \\n\",\n    \"```\\n\",\n    \"Model: \\\"my_model\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"L1 (Dense)                   (None, 25)                10025     \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L2 (Dense)                   (None, 15)                390       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L3 (Dense)                   (None, 10)                160       \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 10,575\\n\",\n    \"Trainable params: 10,575\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model = Sequential(\\n\",\n    \"    [               \\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        tf.keras.Input(shape=(400,)),     # @REPLACE \\n\",\n    \"        Dense(25, activation='relu', name = \\\"L1\\\"), # @REPLACE \\n\",\n    \"        Dense(15, activation='relu',  name = \\\"L2\\\"), # @REPLACE  \\n\",\n    \"        Dense(10, activation='linear', name = \\\"L3\\\"),  # @REPLACE \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"my_model\\\" \\n\",\n    \")\\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001B[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# BEGIN UNIT TEST     \\n\",\n    \"test_model(model, 10, 400)\\n\",\n    \"# END UNIT TEST     \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's further examine the weights to verify that tensorflow produced the same dimensions as we calculated above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"[layer1, layer2, layer3] = model.layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"W1 shape = (400, 25), b1 shape = (25,)\\n\",\n      \"W2 shape = (25, 15), b2 shape = (15,)\\n\",\n      \"W3 shape = (15, 10), b3 shape = (10,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#### Examine Weights shapes\\n\",\n    \"W1,b1 = layer1.get_weights()\\n\",\n    \"W2,b2 = layer2.get_weights()\\n\",\n    \"W3,b3 = layer3.get_weights()\\n\",\n    \"print(f\\\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\\\")\\n\",\n    \"print(f\\\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\\\")\\n\",\n    \"print(f\\\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"**Expected Output**\\n\",\n    \"```\\n\",\n    \"W1 shape = (400, 25), b1 shape = (25,)  \\n\",\n    \"W2 shape = (25, 15), b2 shape = (15,)  \\n\",\n    \"W3 shape = (15, 10), b3 shape = (10,)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The following code:\\n\",\n    \"* defines a loss function, `SparseCategoricalCrossentropy` and indicates the softmax should be included with the  loss calculation by adding `from_logits=True`)\\n\",\n    \"* defines an optimizer. A popular choice is Adaptive Moment (Adam) which was described in lecture.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/40\\n\",\n      \"157/157 [==============================] - 1s 2ms/step - loss: 1.7094\\n\",\n      \"Epoch 2/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.7480\\n\",\n      \"Epoch 3/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.4428\\n\",\n      \"Epoch 4/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.3463\\n\",\n      \"Epoch 5/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2977\\n\",\n      \"Epoch 6/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2630\\n\",\n      \"Epoch 7/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2361\\n\",\n      \"Epoch 8/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2131\\n\",\n      \"Epoch 9/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.2004\\n\",\n      \"Epoch 10/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1805\\n\",\n      \"Epoch 11/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1692\\n\",\n      \"Epoch 12/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1580\\n\",\n      \"Epoch 13/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1507\\n\",\n      \"Epoch 14/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1396\\n\",\n      \"Epoch 15/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1289\\n\",\n      \"Epoch 16/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1255\\n\",\n      \"Epoch 17/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1154\\n\",\n      \"Epoch 18/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1102\\n\",\n      \"Epoch 19/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.1016\\n\",\n      \"Epoch 20/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0970\\n\",\n      \"Epoch 21/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0926\\n\",\n      \"Epoch 22/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0891\\n\",\n      \"Epoch 23/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0828\\n\",\n      \"Epoch 24/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0785\\n\",\n      \"Epoch 25/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0755\\n\",\n      \"Epoch 26/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0713\\n\",\n      \"Epoch 27/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0701\\n\",\n      \"Epoch 28/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0617\\n\",\n      \"Epoch 29/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0578\\n\",\n      \"Epoch 30/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0550\\n\",\n      \"Epoch 31/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0511\\n\",\n      \"Epoch 32/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0499\\n\",\n      \"Epoch 33/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0462\\n\",\n      \"Epoch 34/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0437\\n\",\n      \"Epoch 35/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0422\\n\",\n      \"Epoch 36/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0396\\n\",\n      \"Epoch 37/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0366\\n\",\n      \"Epoch 38/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0344\\n\",\n      \"Epoch 39/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0312\\n\",\n      \"Epoch 40/40\\n\",\n      \"157/157 [==============================] - 0s 2ms/step - loss: 0.0294\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"history = model.fit(\\n\",\n    \"    X,y,\\n\",\n    \"    epochs=40\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#### Epochs and batches\\n\",\n    \"In the `compile` statement above, the number of `epochs` was set to 100. This specifies that the entire data set should be applied during training 100 times.  During training, you see output describing the progress of training that looks like this:\\n\",\n    \"```\\n\",\n    \"Epoch 1/100\\n\",\n    \"157/157 [==============================] - 0s 1ms/step - loss: 2.2770\\n\",\n    \"```\\n\",\n    \"The first line, `Epoch 1/100`, describes which epoch the model is currently running. For efficiency, the training data set is broken into 'batches'. The default size of a batch in Tensorflow is 32. There are 5000 examples in our data set or roughly 157 batches. The notation on the 2nd line `157/157 [====` is describing which batch has been executed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#### Loss  (cost)\\n\",\n    \"In course 1, we learned to track the progress of gradient descent by monitoring the cost. Ideally, the cost will decrease as the number of iterations of the algorithm increases. Tensorflow refers to the cost as `loss`. Above, you saw the loss displayed each epoch as `model.fit` was executing. The [.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model) method returns a variety of metrics including the loss. This is captured in the `history` variable above. This can be used to examine the loss in a plot as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"85765547adaa44e197f57464c49a0ab9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_loss_tf(history)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#### Prediction \\n\",\n    \"To make a prediction, use Keras `predict`. Below, X[1015] contains an image of a two.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6d0cfe835bd14b1fb3b8a9fa09b3167e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" predicting a Two: \\n\",\n      \"[[ -7.99  -2.23   0.77  -2.41 -11.66 -11.15  -9.53  -3.36  -4.42  -7.17]]\\n\",\n      \" Largest Prediction index: 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_of_two = X[1015]\\n\",\n    \"display_digit(image_of_two)\\n\",\n    \"\\n\",\n    \"prediction = model.predict(image_of_two.reshape(1,400))  # prediction\\n\",\n    \"\\n\",\n    \"print(f\\\" predicting a Two: \\\\n{prediction}\\\")\\n\",\n    \"print(f\\\" Largest Prediction index: {np.argmax(prediction)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The largest output is prediction[2], indicating the predicted digit is a '2'. If the problem only requires a selection, that is sufficient. Use NumPy [argmax](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html) to select it. If the problem requires a probability, a softmax is required:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" predicting a Two. Probability vector: \\n\",\n      \"[[1.42e-04 4.49e-02 8.98e-01 3.76e-02 3.61e-06 5.97e-06 3.03e-05 1.44e-02\\n\",\n      \"  5.03e-03 3.22e-04]]\\n\",\n      \"Total of predictions: 1.000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"prediction_p = tf.nn.softmax(prediction)\\n\",\n    \"\\n\",\n    \"print(f\\\" predicting a Two. Probability vector: \\\\n{prediction_p}\\\")\\n\",\n    \"print(f\\\"Total of predictions: {np.sum(prediction_p):0.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"To return an integer representing the predicted target, you want the index of the largest probability. This is accomplished with the Numpy [argmax](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html) function.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"np.argmax(prediction_p): 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"yhat = np.argmax(prediction_p)\\n\",\n    \"\\n\",\n    \"print(f\\\"np.argmax(prediction_p): {yhat}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4190acc8b7cf4617856f2ce6f839880f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=FutureWarning)\\n\",\n    \"# You do not need to modify anything in this cell\\n\",\n    \"\\n\",\n    \"m, n = X.shape\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(8,8, figsize=(5,5))\\n\",\n    \"fig.tight_layout(pad=0.13,rect=[0, 0.03, 1, 0.91]) #[left, bottom, right, top]\\n\",\n    \"widgvis(fig)\\n\",\n    \"for i,ax in enumerate(axes.flat):\\n\",\n    \"    # Select random indices\\n\",\n    \"    random_index = np.random.randint(m)\\n\",\n    \"    \\n\",\n    \"    # Select rows corresponding to the random indices and\\n\",\n    \"    # reshape the image\\n\",\n    \"    X_random_reshaped = X[random_index].reshape((20,20)).T\\n\",\n    \"    \\n\",\n    \"    # Display the image\\n\",\n    \"    ax.imshow(X_random_reshaped, cmap='gray')\\n\",\n    \"    \\n\",\n    \"    # Predict using the Neural Network\\n\",\n    \"    prediction = model.predict(X[random_index].reshape(1,400))\\n\",\n    \"    prediction_p = tf.nn.softmax(prediction)\\n\",\n    \"    yhat = np.argmax(prediction_p)\\n\",\n    \"    \\n\",\n    \"    # Display the label above the image\\n\",\n    \"    ax.set_title(f\\\"{y[random_index,0]},{yhat}\\\",fontsize=10)\\n\",\n    \"    ax.set_axis_off()\\n\",\n    \"fig.suptitle(\\\"Label, yhat\\\", fontsize=14)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's look at some of the errors. \\n\",\n    \">Note: increasing the number of training epochs can eliminate the errors on this data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"ec4df6ce60e9470a9c5b1fe8bef3df02\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"15 errors out of 5000 images\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print( f\\\"{display_errors(model,X,y)} errors out of {len(X)} images\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Congratulations!\\n\",\n    \"You have successfully built and utilized a neural network to do multiclass classification.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"89367\"\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/C2W2A1/autils.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.activations import linear, relu, sigmoid\n\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0', dldarkblue =  '#0D5BDC', dlmedblue='#4285F4')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0'; dldarkblue =  '#0D5BDC'; dlmedblue='#4285F4'\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\nplt.style.use('./deeplearning.mplstyle')\n\n\ndef load_data():\n    X = np.load(\"data/X.npy\")\n    y = np.load(\"data/y.npy\")\n    return X, y\n\ndef plt_act_trio():\n    X = np.linspace(-5,5,100)\n    fig,ax = plt.subplots(1,3, figsize=(6,2))\n    widgvis(fig)\n    ax[0].plot(X,tf.keras.activations.linear(X))\n    ax[0].axvline(0, lw=0.3, c=\"black\")\n    ax[0].axhline(0, lw=0.3, c=\"black\")\n    ax[0].set_title(\"Linear\")\n    ax[1].plot(X,tf.keras.activations.sigmoid(X))\n    ax[1].axvline(0, lw=0.3, c=\"black\")\n    ax[1].axhline(0, lw=0.3, c=\"black\")\n    ax[1].set_title(\"Sigmoid\")\n    ax[2].plot(X,tf.keras.activations.relu(X))\n    ax[2].axhline(0, lw=0.3, c=\"black\")\n    ax[2].axvline(0, lw=0.3, c=\"black\")\n    ax[2].set_title(\"ReLu\")\n    fig.suptitle(\"Common Activation Functions\", fontsize=14)\n    fig.tight_layout(pad=0.2)\n    plt.show()\n\ndef widgvis(fig):\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\ndef plt_ex1():\n    X = np.linspace(0,2*np.pi, 100)\n    y = np.cos(X)+1\n    y[50:100]=0\n    fig,ax = plt.subplots(1,1, figsize=(2,2))\n    widgvis(fig)\n    ax.set_title(\"Target\")\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"y\")\n    ax.plot(X,y)\n    fig.tight_layout(pad=0.1)\n    plt.show()\n    return(X,y)\n \ndef plt_ex2():\n    X = np.linspace(0,2*np.pi, 100)\n    y = np.cos(X)+1\n    y[0:49]=0\n    fig,ax = plt.subplots(1,1, figsize=(2,2))\n    widgvis(fig)\n    ax.set_title(\"Target\")\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"y\")\n    ax.plot(X,y)\n    fig.tight_layout(pad=0.1)\n    plt.show()\n    return(X,y)\n\ndef gen_data():\n    X = np.linspace(0,2*np.pi, 100)\n    y = np.cos(X)+1\n    X=X.reshape(-1,1)\n    return(X,y)\n\ndef plt_dual(X,y,yhat):\n    fig,ax = plt.subplots(1,2, figsize=(4,2))\n    widgvis(fig)\n    ax[0].set_title(\"Target\")\n    ax[0].set_xlabel(\"x\")\n    ax[0].set_ylabel(\"y\")\n    ax[0].plot(X,y)\n    ax[1].set_title(\"Prediction\")\n    ax[1].set_xlabel(\"x\")\n    ax[1].set_ylabel(\"y\")\n    ax[1].plot(X,y)\n    ax[1].plot(X,yhat)\n    fig.tight_layout(pad=0.1)\n    plt.show()\n\ndef plt_act1(X,y,z,a):\n    fig,ax = plt.subplots(1,3, figsize=(6,2.5))\n    widgvis(fig)\n    ax[0].plot(X,y,label=\"target\")\n    ax[0].axvline(0, lw=0.3, c=\"black\")\n    ax[0].axhline(0, lw=0.3, c=\"black\")\n    ax[0].set_title(\"y - target\")\n    ax[1].plot(X,y, label=\"target\")\n    ax[1].plot(X,z, c=dlc[\"dldarkred\"],label=\"z\")\n    ax[1].axvline(0, lw=0.3, c=\"black\")\n    ax[1].axhline(0, lw=0.3, c=\"black\")\n    ax[1].set_title(r\"$z = w \\cdot x+b$\")\n    ax[1].legend(loc=\"upper center\")\n    ax[2].plot(X,y, label=\"target\")\n    ax[2].plot(X,a, c=dlc[\"dldarkred\"],label=\"ReLu(z)\")\n    ax[2].axhline(0, lw=0.3, c=\"black\")\n    ax[2].axvline(0, lw=0.3, c=\"black\")\n    ax[2].set_title(\"max(0,z)\")\n    ax[2].legend()\n    fig.suptitle(\"Role of Non-Linear Activation\", fontsize=12)\n    fig.tight_layout(pad=0.22)\n    return(ax)\n\n\ndef plt_add_notation(ax):\n    ax[1].annotate(text = \"matches\\n here\", xy =(1.5,1.0), \n                   xytext = (0.1,-1.5), fontsize=9,\n                  arrowprops=dict(facecolor=dlc[\"dlpurple\"],width=2, headwidth=8))\n    ax[1].annotate(text = \"but not\\n here\", xy =(5,-2.5), \n                   xytext = (1,-3), fontsize=9,\n                  arrowprops=dict(facecolor=dlc[\"dlpurple\"],width=2, headwidth=8))\n    ax[2].annotate(text = \"ReLu\\n 'off'\", xy =(2.6,0), \n                   xytext = (0.1,0.1), fontsize=9,\n                  arrowprops=dict(facecolor=dlc[\"dlpurple\"],width=2, headwidth=8))\n\ndef compile_fit(model,X,y):\n    model.compile(\n        loss=tf.keras.losses.MeanSquaredError(),\n        optimizer=tf.keras.optimizers.Adam(0.01),\n    )\n\n    model.fit(\n        X,y,\n        epochs=100,\n        verbose = 0\n    )\n    l1=model.get_layer(\"l1\")\n    l2=model.get_layer(\"l2\")\n    w1,b1 = l1.get_weights()\n    w2,b2 = l2.get_weights()\n    return(w1,b1,w2,b2)\n\ndef plt_model(X,y,yhat_pre, yhat_post):\n    fig,ax = plt.subplots(1,3, figsize=(8,2))\n    widgvis(fig)\n    ax[0].set_title(\"Target\")\n    ax[0].set_xlabel(\"x\")\n    ax[0].set_ylabel(\"y\")\n    ax[0].plot(X,y)\n    ax[1].set_title(\"Prediction, pre-training\")\n    ax[1].set_xlabel(\"x\")\n    ax[1].set_ylabel(\"y\")\n    ax[1].plot(X,y)\n    ax[1].plot(X,yhat_pre)\n    ax[2].set_title(\"Prediction, post-training\")\n    ax[2].set_xlabel(\"x\")\n    ax[2].set_ylabel(\"y\")\n    ax[2].plot(X,y)\n    ax[2].plot(X,yhat_post)\n    fig.tight_layout(pad=0.1)\n    plt.show()\n\ndef display_errors(model,X,y):\n    f = model.predict(X)\n    yhat = np.argmax(f, axis=1)\n    doo = yhat != y[:,0]\n    idxs = np.where(yhat != y[:,0])[0]\n    if len(idxs) == 0:\n        print(\"no errors found\")\n    else:\n        cnt = min(8, len(idxs))\n        fig, ax = plt.subplots(1,cnt, figsize=(5,1.2))\n        fig.tight_layout(pad=0.13,rect=[0, 0.03, 1, 0.80]) #[left, bottom, right, top]\n        widgvis(fig)\n\n        for i in range(cnt):\n            j = idxs[i]\n            X_reshaped = X[j].reshape((20,20)).T\n\n            # Display the image\n            ax[i].imshow(X_reshaped, cmap='gray')\n\n            # Predict using the Neural Network\n            prediction = model.predict(X[j].reshape(1,400))\n            prediction_p = tf.nn.softmax(prediction)\n            yhat = np.argmax(prediction_p)\n\n            # Display the label above the image\n            ax[i].set_title(f\"{y[j,0]},{yhat}\",fontsize=10)\n            ax[i].set_axis_off()\n            fig.suptitle(\"Label, yhat\", fontsize=12)\n    return(len(idxs))\n\ndef display_digit(X):\n    \"\"\" display a single digit. The input is one digit (400,). \"\"\"\n    fig, ax = plt.subplots(1,1, figsize=(0.5,0.5))\n    widgvis(fig)\n    X_reshaped = X.reshape((20,20)).T\n    # Display the image\n    ax.imshow(X_reshaped, cmap='gray')\n    plt.show()\n    \n    \ndef plot_loss_tf(history):\n    fig,ax = plt.subplots(1,1, figsize = (4,3))\n    widgvis(fig)\n    ax.plot(history.history['loss'], label='loss')\n    ax.set_ylim([0, 2])\n    ax.set_xlabel('Epoch')\n    ax.set_ylabel('loss (cost)')\n    ax.legend()\n    ax.grid(True)\n    plt.show()\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/C2W2A1/deeplearning.mplstyle",
    "content": "# see https://matplotlib.org/stable/tutorials/introductory/customizing.html\nlines.linewidth: 4\nlines.solid_capstyle: butt\n\nlegend.fancybox: true\n\n# Verdana\" for non-math text,\n# Cambria Math\n\n#Blue (Crayon-Aqua) 0096FF\n#Dark Red C00000\n#Orange (Apple Orange) FF9300\n#Black 000000\n#Magenta FF40FF\n#Purple 7030A0\n\naxes.prop_cycle: cycler('color', ['0096FF', 'FF9300', 'FF40FF', '7030A0', 'C00000'])\n#axes.facecolor: f0f0f0 # grey\naxes.facecolor: ffffff  # white\naxes.labelsize: large\naxes.axisbelow: true\naxes.grid: False\naxes.edgecolor: f0f0f0\naxes.linewidth: 3.0\naxes.titlesize: x-large\n\npatch.edgecolor: f0f0f0\npatch.linewidth: 0.5\n\nsvg.fonttype: path\n\ngrid.linestyle: -\ngrid.linewidth: 1.0\ngrid.color: cbcbcb\n\nxtick.major.size: 0\nxtick.minor.size: 0\nytick.major.size: 0\nytick.minor.size: 0\n\nsavefig.edgecolor: f0f0f0\nsavefig.facecolor: f0f0f0\n\n#figure.subplot.left: 0.08\n#figure.subplot.right: 0.95\n#figure.subplot.bottom: 0.07\n\n#figure.facecolor: f0f0f0  # grey\nfigure.facecolor: ffffff  # white\n\n## ***************************************************************************\n## * FONT                                                                    *\n## ***************************************************************************\n## The font properties used by `text.Text`.\n## See https://matplotlib.org/api/font_manager_api.html for more information\n## on font properties.  The 6 font properties used for font matching are\n## given below with their default values.\n##\n## The font.family property can take either a concrete font name (not supported\n## when rendering text with usetex), or one of the following five generic\n## values:\n##     - 'serif' (e.g., Times),\n##     - 'sans-serif' (e.g., Helvetica),\n##     - 'cursive' (e.g., Zapf-Chancery),\n##     - 'fantasy' (e.g., Western), and\n##     - 'monospace' (e.g., Courier).\n## Each of these values has a corresponding default list of font names\n## (font.serif, etc.); the first available font in the list is used.  Note that\n## for font.serif, font.sans-serif, and font.monospace, the first element of\n## the list (a DejaVu font) will always be used because DejaVu is shipped with\n## Matplotlib and is thus guaranteed to be available; the other entries are\n## left as examples of other possible values.\n##\n## The font.style property has three values: normal (or roman), italic\n## or oblique.  The oblique style will be used for italic, if it is not\n## present.\n##\n## The font.variant property has two values: normal or small-caps.  For\n## TrueType fonts, which are scalable fonts, small-caps is equivalent\n## to using a font size of 'smaller', or about 83%% of the current font\n## size.\n##\n## The font.weight property has effectively 13 values: normal, bold,\n## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as\n## 400, and bold is 700.  bolder and lighter are relative values with\n## respect to the current weight.\n##\n## The font.stretch property has 11 values: ultra-condensed,\n## extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This\n## property is not currently implemented.\n##\n## The font.size property is the default font size for text, given in points.\n## 10 pt is the standard value.\n##\n## Note that font.size controls default text sizes.  To configure\n## special text sizes tick labels, axes, labels, title, etc., see the rc\n## settings for axes and ticks.  Special text sizes can be defined\n## relative to font.size, using the following values: xx-small, x-small,\n## small, medium, large, x-large, xx-large, larger, or smaller\n\n\nfont.family:  sans-serif\nfont.style:   normal\nfont.variant: normal\nfont.weight:  normal\nfont.stretch: normal\nfont.size:    8.0\n\nfont.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\nfont.sans-serif: Verdana, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\nfont.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive\nfont.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy\nfont.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n\n\n## ***************************************************************************\n## * TEXT                                                                    *\n## ***************************************************************************\n## The text properties used by `text.Text`.\n## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\n## for more information on text properties\n#text.color: black\n\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/C2W2A1/lab_utils_common.py",
    "content": "\"\"\"\nlab_utils_common\n   contains common routines and variable definitions\n   used by all the labs in this week.\n   by contrast, specific, large plotting routines will be in separate files\n   and are generally imported into the week where they are used.\n   those files will import this file\n\"\"\"\nimport copy\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import FancyArrowPatch\nfrom ipywidgets import Output\nfrom matplotlib.widgets import Button, CheckButtons\n\nnp.set_printoptions(precision=2)\n\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0', dldarkblue =  '#0D5BDC', dlmedblue='#4285F4')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0'; dldarkblue =  '#0D5BDC'; dlmedblue='#4285F4'\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\nplt.style.use('./deeplearning.mplstyle')\n\ndef sigmoid(z):\n    \"\"\"\n    Compute the sigmoid of z\n\n    Parameters\n    ----------\n    z : array_like\n        A scalar or numpy array of any size.\n\n    Returns\n    -------\n     g : array_like\n         sigmoid(z)\n    \"\"\"\n    z = np.clip( z, -500, 500 )           # protect against overflow\n    g = 1.0/(1.0+np.exp(-z))\n\n    return g\n\n##########################################################\n# Regression Routines\n##########################################################\n\ndef predict_logistic(X, w, b):\n    \"\"\" performs prediction \"\"\"\n    return sigmoid(X @ w + b)\n\ndef predict_linear(X, w, b):\n    \"\"\" performs prediction \"\"\"\n    return X @ w + b\n\ndef compute_cost_logistic(X, y, w, b, lambda_=0, safe=False):\n    \"\"\"\n    Computes cost using logistic loss, non-matrix version\n\n    Args:\n      X (ndarray): Shape (m,n)  matrix of examples with n features\n      y (ndarray): Shape (m,)   target values\n      w (ndarray): Shape (n,)   parameters for prediction\n      b (scalar):               parameter  for prediction\n      lambda_ : (scalar, float) Controls amount of regularization, 0 = no regularization\n      safe : (boolean)          True-selects under/overflow safe algorithm\n    Returns:\n      cost (scalar): cost\n    \"\"\"\n\n    m,n = X.shape\n    cost = 0.0\n    for i in range(m):\n        z_i    = np.dot(X[i],w) + b                                             #(n,)(n,) or (n,) ()\n        if safe:  #avoids overflows\n            cost += -(y[i] * z_i ) + log_1pexp(z_i)\n        else:\n            f_wb_i = sigmoid(z_i)                                                   #(n,)\n            cost  += -y[i] * np.log(f_wb_i) - (1 - y[i]) * np.log(1 - f_wb_i)       # scalar\n    cost = cost/m\n\n    reg_cost = 0\n    if lambda_ != 0:\n        for j in range(n):\n            reg_cost += (w[j]**2)                                               # scalar\n        reg_cost = (lambda_/(2*m))*reg_cost\n\n    return cost + reg_cost\n\n\ndef log_1pexp(x, maximum=20):\n    ''' approximate log(1+exp^x)\n        https://stats.stackexchange.com/questions/475589/numerical-computation-of-cross-entropy-in-practice\n    Args:\n    x   : (ndarray Shape (n,1) or (n,)  input\n    out : (ndarray Shape matches x      output ~= np.log(1+exp(x))\n    '''\n\n    out  = np.zeros_like(x,dtype=float)\n    i    = x <= maximum\n    ni   = np.logical_not(i)\n\n    out[i]  = np.log(1 + np.exp(x[i]))\n    out[ni] = x[ni]\n    return out\n\n\ndef compute_cost_matrix(X, y, w, b, logistic=False, lambda_=0, safe=True):\n    \"\"\"\n    Computes the cost using  using matrices\n    Args:\n      X : (ndarray, Shape (m,n))          matrix of examples\n      y : (ndarray  Shape (m,) or (m,1))  target value of each example\n      w : (ndarray  Shape (n,) or (n,1))  Values of parameter(s) of the model\n      b : (scalar )                       Values of parameter of the model\n      verbose : (Boolean) If true, print out intermediate value f_wb\n    Returns:\n      total_cost: (scalar)                cost\n    \"\"\"\n    m = X.shape[0]\n    y = y.reshape(-1,1)             # ensure 2D\n    w = w.reshape(-1,1)             # ensure 2D\n    if logistic:\n        if safe:  #safe from overflow\n            z = X @ w + b                                                           #(m,n)(n,1)=(m,1)\n            cost = -(y * z) + log_1pexp(z)\n            cost = np.sum(cost)/m                                                   # (scalar)\n        else:\n            f    = sigmoid(X @ w + b)                                               # (m,n)(n,1) = (m,1)\n            cost = (1/m)*(np.dot(-y.T, np.log(f)) - np.dot((1-y).T, np.log(1-f)))   # (1,m)(m,1) = (1,1)\n            cost = cost[0,0]                                                        # scalar\n    else:\n        f    = X @ w + b                                                        # (m,n)(n,1) = (m,1)\n        cost = (1/(2*m)) * np.sum((f - y)**2)                                   # scalar\n\n    reg_cost = (lambda_/(2*m)) * np.sum(w**2)                                   # scalar\n\n    total_cost = cost + reg_cost                                                # scalar\n\n    return total_cost                                                           # scalar\n\ndef compute_gradient_matrix(X, y, w, b, logistic=False, lambda_=0):\n    \"\"\"\n    Computes the gradient using matrices\n\n    Args:\n      X : (ndarray, Shape (m,n))          matrix of examples\n      y : (ndarray  Shape (m,) or (m,1))  target value of each example\n      w : (ndarray  Shape (n,) or (n,1))  Values of parameters of the model\n      b : (scalar )                       Values of parameter of the model\n      logistic: (boolean)                 linear if false, logistic if true\n      lambda_:  (float)                   applies regularization if non-zero\n    Returns\n      dj_dw: (array_like Shape (n,1))     The gradient of the cost w.r.t. the parameters w\n      dj_db: (scalar)                     The gradient of the cost w.r.t. the parameter b\n    \"\"\"\n    m = X.shape[0]\n    y = y.reshape(-1,1)             # ensure 2D\n    w = w.reshape(-1,1)             # ensure 2D\n\n    f_wb  = sigmoid( X @ w + b ) if logistic else  X @ w + b      # (m,n)(n,1) = (m,1)\n    err   = f_wb - y                                              # (m,1)\n    dj_dw = (1/m) * (X.T @ err)                                   # (n,m)(m,1) = (n,1)\n    dj_db = (1/m) * np.sum(err)                                   # scalar\n\n    dj_dw += (lambda_/m) * w        # regularize                  # (n,1)\n\n    return dj_db, dj_dw                                           # scalar, (n,1)\n\ndef gradient_descent(X, y, w_in, b_in, alpha, num_iters, logistic=False, lambda_=0, verbose=True, Trace=True):\n    \"\"\"\n    Performs batch gradient descent to learn theta. Updates theta by taking\n    num_iters gradient steps with learning rate alpha\n\n    Args:\n      X (ndarray):    Shape (m,n)         matrix of examples\n      y (ndarray):    Shape (m,) or (m,1) target value of each example\n      w_in (ndarray): Shape (n,) or (n,1) Initial values of parameters of the model\n      b_in (scalar):                      Initial value of parameter of the model\n      logistic: (boolean)                 linear if false, logistic if true\n      lambda_:  (float)                   applies regularization if non-zero\n      alpha (float):                      Learning rate\n      num_iters (int):                    number of iterations to run gradient descent\n\n    Returns:\n      w (ndarray): Shape (n,) or (n,1)    Updated values of parameters; matches incoming shape\n      b (scalar):                         Updated value of parameter\n    \"\"\"\n    # An array to store cost J and w's at each iteration primarily for graphing later\n    J_history = []\n    w = copy.deepcopy(w_in)  #avoid modifying global w within function\n    b = b_in\n    w = w.reshape(-1,1)      #prep for matrix operations\n    y = y.reshape(-1,1)\n    last_cost = np.Inf\n\n    for i in range(num_iters):\n\n        # Calculate the gradient and update the parameters\n        dj_db,dj_dw = compute_gradient_matrix(X, y, w, b, logistic, lambda_)\n\n        # Update Parameters using w, b, alpha and gradient\n        w = w - alpha * dj_dw\n        b = b - alpha * dj_db\n\n        # Save cost J at each iteration\n        ccost = compute_cost_matrix(X, y, w, b, logistic, lambda_)\n        if Trace and i<100000:      # prevent resource exhaustion\n            J_history.append( ccost )\n\n        # Print cost every at intervals 10 times or as many iterations if < 10\n        if i% math.ceil(num_iters / 10) == 0:\n            if verbose: print(f\"Iteration {i:4d}: Cost {ccost}   \")\n            if verbose ==2: print(f\"dj_db, dj_dw = {dj_db: 0.3f}, {dj_dw.reshape(-1)}\")\n\n            if ccost == last_cost:\n                alpha = alpha/10\n                print(f\" alpha now {alpha}\")\n            last_cost = ccost\n\n    return w.reshape(w_in.shape), b, J_history  #return final w,b and J history for graphing\n\ndef zscore_normalize_features(X):\n    \"\"\"\n    computes  X, zcore normalized by column\n\n    Args:\n      X (ndarray): Shape (m,n) input data, m examples, n features\n\n    Returns:\n      X_norm (ndarray): Shape (m,n)  input normalized by column\n      mu (ndarray):     Shape (n,)   mean of each feature\n      sigma (ndarray):  Shape (n,)   standard deviation of each feature\n    \"\"\"\n    # find the mean of each column/feature\n    mu     = np.mean(X, axis=0)                 # mu will have shape (n,)\n    # find the standard deviation of each column/feature\n    sigma  = np.std(X, axis=0)                  # sigma will have shape (n,)\n    # element-wise, subtract mu for that column from each example, divide by std for that column\n    X_norm = (X - mu) / sigma\n\n    return X_norm, mu, sigma\n\n#check our work\n#from sklearn.preprocessing import scale\n#scale(X_orig, axis=0, with_mean=True, with_std=True, copy=True)\n\n######################################################\n# Common Plotting Routines\n######################################################\n\n\ndef plot_data(X, y, ax, pos_label=\"y=1\", neg_label=\"y=0\", s=80, loc='best' ):\n    \"\"\" plots logistic data with two axis \"\"\"\n    # Find Indices of Positive and Negative Examples\n    pos = y == 1\n    neg = y == 0\n    pos = pos.reshape(-1,)  #work with 1D or 1D y vectors\n    neg = neg.reshape(-1,)\n\n    # Plot examples\n    ax.scatter(X[pos, 0], X[pos, 1], marker='x', s=s, c = 'red', label=pos_label)\n    ax.scatter(X[neg, 0], X[neg, 1], marker='o', s=s, label=neg_label, facecolors='none', edgecolors=dlblue, lw=3)\n    ax.legend(loc=loc)\n\n    ax.figure.canvas.toolbar_visible = False\n    ax.figure.canvas.header_visible = False\n    ax.figure.canvas.footer_visible = False\n\ndef plt_tumor_data(x, y, ax):\n    \"\"\" plots tumor data on one axis \"\"\"\n    pos = y == 1\n    neg = y == 0\n\n    ax.scatter(x[pos], y[pos], marker='x', s=80, c = 'red', label=\"malignant\")\n    ax.scatter(x[neg], y[neg], marker='o', s=100, label=\"benign\", facecolors='none', edgecolors=dlblue,lw=3)\n    ax.set_ylim(-0.175,1.1)\n    ax.set_ylabel('y')\n    ax.set_xlabel('Tumor Size')\n    ax.set_title(\"Logistic Regression on Categorical Data\")\n\n    ax.figure.canvas.toolbar_visible = False\n    ax.figure.canvas.header_visible = False\n    ax.figure.canvas.footer_visible = False\n\n# Draws a threshold at 0.5\ndef draw_vthresh(ax,x):\n    \"\"\" draws a threshold \"\"\"\n    ylim = ax.get_ylim()\n    xlim = ax.get_xlim()\n    ax.fill_between([xlim[0], x], [ylim[1], ylim[1]], alpha=0.2, color=dlblue)\n    ax.fill_between([x, xlim[1]], [ylim[1], ylim[1]], alpha=0.2, color=dldarkred)\n    ax.annotate(\"z >= 0\", xy= [x,0.5], xycoords='data',\n                xytext=[30,5],textcoords='offset points')\n    d = FancyArrowPatch(\n        posA=(x, 0.5), posB=(x+3, 0.5), color=dldarkred,\n        arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n    )\n    ax.add_artist(d)\n    ax.annotate(\"z < 0\", xy= [x,0.5], xycoords='data',\n                 xytext=[-50,5],textcoords='offset points', ha='left')\n    f = FancyArrowPatch(\n        posA=(x, 0.5), posB=(x-3, 0.5), color=dlblue,\n        arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n    )\n    ax.add_artist(f)\n\n\n#-----------------------------------------------------\n# common interactive plotting routines\n#-----------------------------------------------------\n\nclass button_manager:\n    ''' Handles some missing features of matplotlib check buttons\n    on init:\n        creates button, links to button_click routine,\n        calls call_on_click with active index and firsttime=True\n    on click:\n        maintains single button on state, calls call_on_click\n    '''\n\n    #@output.capture()  # debug\n    def __init__(self,fig, dim, labels, init, call_on_click):\n        '''\n        dim: (list)     [leftbottom_x,bottom_y,width,height]\n        labels: (list)  for example ['1','2','3','4','5','6']\n        init: (list)    for example [True, False, False, False, False, False]\n        '''\n        self.fig = fig\n        self.ax = plt.axes(dim)  #lx,by,w,h\n        self.init_state = init\n        self.call_on_click = call_on_click\n        self.button  = CheckButtons(self.ax,labels,init)\n        self.button.on_clicked(self.button_click)\n        self.status = self.button.get_status()\n        self.call_on_click(self.status.index(True),firsttime=True)\n\n    #@output.capture()  # debug\n    def reinit(self):\n        self.status = self.init_state\n        self.button.set_active(self.status.index(True))      #turn off old, will trigger update and set to status\n\n    #@output.capture()  # debug\n    def button_click(self, event):\n        ''' maintains one-on state. If on-button is clicked, will process correctly '''\n        #new_status = self.button.get_status()\n        #new = [self.status[i] ^ new_status[i] for i in range(len(self.status))]\n        #newidx = new.index(True)\n        self.button.eventson = False\n        self.button.set_active(self.status.index(True))  #turn off old or reenable if same\n        self.button.eventson = True\n        self.status = self.button.get_status()\n        self.call_on_click(self.status.index(True))\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/C2W2A1/lab_utils_softmax.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('./deeplearning.mplstyle')\nimport tensorflow as tf\nfrom IPython.display import display, Markdown, Latex\nfrom matplotlib.widgets import Slider\nfrom lab_utils_common import dlc\n\n\ndef plt_softmax(my_softmax):\n    fig, ax = plt.subplots(1,2,figsize=(8,4))\n    plt.subplots_adjust(bottom=0.35)\n\n    axz0 = fig.add_axes([0.15, 0.10, 0.30, 0.03]) # [left, bottom, width, height]\n    axz1 = fig.add_axes([0.15, 0.15, 0.30, 0.03])\n    axz2 = fig.add_axes([0.15, 0.20, 0.30, 0.03])\n    axz3 = fig.add_axes([0.15, 0.25, 0.30, 0.03])\n\n    z3 = Slider(axz3, 'z3', 0.1, 10.0, valinit=4, valstep=0.1)\n    z2 = Slider(axz2, 'z2', 0.1, 10.0, valinit=3, valstep=0.1)\n    z1 = Slider(axz1, 'z1', 0.1, 10.0, valinit=2, valstep=0.1)\n    z0 = Slider(axz0, 'z0', 0.1, 10.0, valinit=1, valstep=0.1)\n\n    z = np.array(['z0','z1','z2','z3'])\n    bar = ax[0].barh(z, height=0.6, width=[z0.val,z1.val,z2.val,z3.val], left=None, align='center')\n    bars = bar.get_children()\n    ax[0].set_xlim([0,10])\n    ax[0].set_title(\"z input to softmax\")\n\n    a = my_softmax(np.array([z0.val,z1.val,z2.val,z3.val]))\n    anames = np.array(['a0','a1','a2','a3'])\n    sbar = ax[1].barh(anames, height=0.6, width=a, left=None, align='center',color=dlc[\"dldarkred\"])\n    sbars = sbar.get_children()\n    ax[1].set_xlim([0,1])\n    ax[1].set_title(\"softmax(z)\")\n\n    def update(val):\n        bars[0].set_width(z0.val)\n        bars[1].set_width(z1.val)\n        bars[2].set_width(z2.val)\n        bars[3].set_width(z3.val)\n        a = my_softmax(np.array([z0.val,z1.val,z2.val,z3.val]))\n        sbars[0].set_width(a[0])\n        sbars[1].set_width(a[1])\n        sbars[2].set_width(a[2])\n        sbars[3].set_width(a[3])\n\n        fig.canvas.draw_idle()\n\n    z0.on_changed(update)\n    z1.on_changed(update)\n    z2.on_changed(update)\n    z3.on_changed(update)\n\n    plt.show()\n "
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/C2W2A1/public_tests.py",
    "content": "import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.activations import linear, sigmoid, relu\n\ndef test_my_softmax(target):\n    z = np.array([1., 2., 3., 4.])\n    a = target(z)\n    atf = tf.nn.softmax(z)\n    \n    assert np.allclose(a, atf, atol=1e-10), f\"Wrong values. Expected {atf}, got {a}\"\n    \n    z = np.array([np.log(0.1)] * 10)\n    a = target(z)\n    atf = tf.nn.softmax(z)\n    \n    assert np.allclose(a, atf, atol=1e-10), f\"Wrong values. Expected {atf}, got {a}\"\n    \n    print(\"\\033[92m All tests passed.\")\n    \ndef test_model(target, classes, input_size):\n    target.build(input_shape=(None,input_size))\n    \n    assert len(target.layers) == 3, \\\n        f\"Wrong number of layers. Expected 3 but got {len(target.layers)}\"\n    assert target.input.shape.as_list() == [None, input_size], \\\n        f\"Wrong input shape. Expected [None,  {input_size}] but got {target.input.shape.as_list()}\"\n    i = 0\n    expected = [[Dense, [None, 25], relu],\n                [Dense, [None, 15], relu],\n                [Dense, [None, classes], linear]]\n\n    for layer in target.layers:\n        assert type(layer) == expected[i][0], \\\n            f\"Wrong type in layer {i}. Expected {expected[i][0]} but got {type(layer)}\"\n        assert layer.output.shape.as_list() == expected[i][1], \\\n            f\"Wrong number of units in layer {i}. Expected {expected[i][1]} but got {layer.output.shape.as_list()}\"\n        assert layer.activation == expected[i][2], \\\n            f\"Wrong activation in layer {i}. Expected {expected[i][2]} but got {layer.activation}\"\n        i = i + 1\n\n    print(\"\\033[92mAll tests passed!\")\n    "
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/Practice-Quiz-Activation-Functions/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/505dfb62e1cd34b57fa905b047b07dfa2f2a14c2/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Activation-Functions/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/505dfb62e1cd34b57fa905b047b07dfa2f2a14c2/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Activation-Functions/ss2.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/Practice-Quiz-Additional-Neural-Network-Concepts/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/96a5eb1dd10b14343bbbc0cb749599698099760e/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Additional-Neural-Network-Concepts/ss1.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/Practice-Quiz-Neural-Network-Training/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/36877f8ee5f8437019dfb8f03e134dbbe3464514/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Neural-Network-Training/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/36877f8ee5f8437019dfb8f03e134dbbe3464514/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Neural-Network-Training/ss2.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/Practice-quiz-Multiclass-Classification/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/d98e05e9e0ef94b8fdad0c59b4a086620efe31b8/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-quiz-Multiclass-Classification/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/a722c112f6aaea3f2bc295b50d44a18932ca86d2/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-quiz-Multiclass-Classification/ss2.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/d98e05e9e0ef94b8fdad0c59b4a086620efe31b8/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-quiz-Multiclass-Classification/ss3.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/Readme.md",
    "content": "### C2 - Week 2 Solutions \n\n- [Practice quiz : Neural Networks Training](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7e5505d650d56554edde4abebc51a2c7c7fb81fb/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Neural-Network-Training)\n- [Practice quiz : Activation Functions](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/f2b84223545cc7c0062903cf4eac5c6fda53dc20/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Activation-Functions)\n- [Practice quiz : Multiclass Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/80c14a835b066568b075410bb2e5e1220b4c3653/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-quiz-Multiclass-Classification)\n- [Practice quiz : Additional Neural Networks Concepts](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/3bf176864d32d12eb2cb98ed4661e3ded627befa/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Additional-Neural-Network-Concepts)\n- [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs)\n    - [RElu](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs/C2_W2_Relu.ipynb)\n    - [Softmax](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs/C2_W2_SoftMax.ipynb)\n    - [Multiclass Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs/C2_W2_Multiclass_TF.ipynb)\n- [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/C2W2A1)\n  - [Neural Networks For Handwritten Digit Recogonition - Multiclass](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/fd18b6a34ba06c7743ad41917206227ec0d9ef12/C2%20-%20Advanced%20Learning%20Algorithms/week2/C2W2A1/C2_W2_Assignment.ipynb)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/.ipynb_checkpoints/C2_W2_Multiclass_TF-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab - Multi-class Classification\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.1 Goals\\n\",\n    \"In this lab, you will explore an example of multi-class classification using neural networks.\\n\",\n    \"<figure>\\n\",\n    \" <img src=\\\"./images/C2_W2_mclass_header.png\\\"   style=\\\"width500px;height:200px;\\\">\\n\",\n    \"</figure>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.2 Tools\\n\",\n    \"You will use some plotting routines. These are stored in `lab_utils_multiclass_TF.py` in this directory.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib widget\\n\",\n    \"from sklearn.datasets import make_blobs\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"np.set_printoptions(precision=2)\\n\",\n    \"from lab_utils_multiclass_TF import *\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# 2.0 Multi-class Classification\\n\",\n    \"Neural Networks are often used to classify data. Examples are neural networks:\\n\",\n    \"- take in photos and classify subjects in the photos as {dog,cat,horse,other}\\n\",\n    \"- take in a sentence and classify the 'parts of speech' of its elements: {noun, verb, adjective etc..}  \\n\",\n    \"\\n\",\n    \"A network of this type will have multiple units in its final layer. Each output is associated with a category. When an input example is applied to the network, the output with the highest value is the category predicted. If the output is applied to a softmax function, the output of the softmax will provide probabilities of the input being in each category. \\n\",\n    \"\\n\",\n    \"In this lab you will see an example of building a multiclass network in Tensorflow. We will then take a look at how the neural network makes its predictions.\\n\",\n    \"\\n\",\n    \"Let's start by creating a four-class data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2.1 Prepare and visualize our data\\n\",\n    \"We will use Scikit-Learn `make_blobs` function to make a training data set with 4 categories as shown in the plot below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# make 4-class dataset for classification\\n\",\n    \"classes = 4\\n\",\n    \"m = 100\\n\",\n    \"centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\\n\",\n    \"std = 1.0\\n\",\n    \"X_train, y_train = make_blobs(n_samples=m, centers=centers, cluster_std=std,random_state=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f6d15f0f6d8b45e49d488bb56a9211ec\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_mc(X_train,y_train,classes, centers, std=std)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Each dot represents a training example. The axis (x0,x1) are the inputs and the color represents the class the example is associated with. Once trained, the model will be presented with a new example, (x0,x1), and will predict the class.  \\n\",\n    \"\\n\",\n    \"While generated, this data set is representative of many real-world classification problems. There are several input features (x0,...,xn) and several output categories. The model is trained to use the input features to predict the correct output category.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"unique classes [0 1 2 3]\\n\",\n      \"class representation [3 3 3 0 3 3 3 3 2 0]\\n\",\n      \"shape of X_train: (100, 2), shape of y_train: (100,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# show classes in data set\\n\",\n    \"print(f\\\"unique classes {np.unique(y_train)}\\\")\\n\",\n    \"# show how classes are represented\\n\",\n    \"print(f\\\"class representation {y_train[:10]}\\\")\\n\",\n    \"# show shapes of our dataset\\n\",\n    \"print(f\\\"shape of X_train: {X_train.shape}, shape of y_train: {y_train.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## 2.2 Model\\n\",\n    \"<img align=\\\"Right\\\" src=\\\"./images/C2_W2_mclass_lab_network.PNG\\\"  style=\\\" width:350px; padding: 10px 20px ; \\\">\\n\",\n    \"This lab will use a 2-layer network as shown.\\n\",\n    \"Unlike the binary classification networks, this network has four outputs, one for each class. Given an input example, the output with the highest value is the predicted class of the input.   \\n\",\n    \"\\n\",\n    \"Below is an example of how to construct this network in Tensorflow. Notice the output layer uses a `linear` rather than a `softmax` activation. While it is possible to include the softmax in the output layer, it is more numerically stable if linear outputs are passed to the loss function during training. If the model is used to predict probabilities, the softmax can be applied at that point.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tf.random.set_seed(1234)  # applied to achieve consistent results\\n\",\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        Dense(2, activation = 'relu',   name = \\\"L1\\\"),\\n\",\n    \"        Dense(4, activation = 'linear', name = \\\"L2\\\")\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The statements below compile and train the network. Setting `from_logits=True` as an argument to the loss function specifies that the output activation was linear rather than a softmax.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.8158\\n\",\n      \"Epoch 2/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.6976\\n\",\n      \"Epoch 3/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.5989\\n\",\n      \"Epoch 4/200\\n\",\n      \"4/4 [==============================] - 0s 961us/step - loss: 1.5179\\n\",\n      \"Epoch 5/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.4369\\n\",\n      \"Epoch 6/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.3756\\n\",\n      \"Epoch 7/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.3154\\n\",\n      \"Epoch 8/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.2621\\n\",\n      \"Epoch 9/200\\n\",\n      \"4/4 [==============================] - 0s 991us/step - loss: 1.2188\\n\",\n      \"Epoch 10/200\\n\",\n      \"4/4 [==============================] - 0s 983us/step - loss: 1.1791\\n\",\n      \"Epoch 11/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 1.1446\\n\",\n      \"Epoch 12/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 1.1129\\n\",\n      \"Epoch 13/200\\n\",\n      \"4/4 [==============================] - 0s 993us/step - loss: 1.0827\\n\",\n      \"Epoch 14/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.0516\\n\",\n      \"Epoch 15/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.0225\\n\",\n      \"Epoch 16/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 0.9967\\n\",\n      \"Epoch 17/200\\n\",\n      \"4/4 [==============================] - 0s 970us/step - loss: 0.9681\\n\",\n      \"Epoch 18/200\\n\",\n      \"4/4 [==============================] - 0s 980us/step - loss: 0.9392\\n\",\n      \"Epoch 19/200\\n\",\n      \"4/4 [==============================] - 0s 989us/step - loss: 0.9092\\n\",\n      \"Epoch 20/200\\n\",\n      \"4/4 [==============================] - 0s 966us/step - loss: 0.8771\\n\",\n      \"Epoch 21/200\\n\",\n      \"4/4 [==============================] - 0s 995us/step - loss: 0.8461\\n\",\n      \"Epoch 22/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.8099\\n\",\n      \"Epoch 23/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.7771\\n\",\n      \"Epoch 24/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.7485\\n\",\n      \"Epoch 25/200\\n\",\n      \"4/4 [==============================] - 0s 2ms/step - loss: 0.7215\\n\",\n      \"Epoch 26/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6967\\n\",\n      \"Epoch 27/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6742\\n\",\n      \"Epoch 28/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6540\\n\",\n      \"Epoch 29/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6352\\n\",\n      \"Epoch 30/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6187\\n\",\n      \"Epoch 31/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6030\\n\",\n      \"Epoch 32/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5884\\n\",\n      \"Epoch 33/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5746\\n\",\n      \"Epoch 34/200\\n\",\n      \"4/4 [==============================] - 0s 997us/step - loss: 0.5621\\n\",\n      \"Epoch 35/200\\n\",\n      \"4/4 [==============================] - 0s 980us/step - loss: 0.5512\\n\",\n      \"Epoch 36/200\\n\",\n      \"4/4 [==============================] - 0s 999us/step - loss: 0.5414\\n\",\n      \"Epoch 37/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.5323\\n\",\n      \"Epoch 38/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5236\\n\",\n      \"Epoch 39/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5150\\n\",\n      \"Epoch 40/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5072\\n\",\n      \"Epoch 41/200\\n\",\n      \"4/4 [==============================] - 0s 991us/step - loss: 0.5006\\n\",\n      \"Epoch 42/200\\n\",\n      \"4/4 [==============================] - 0s 982us/step - loss: 0.4944\\n\",\n      \"Epoch 43/200\\n\",\n      \"4/4 [==============================] - 0s 992us/step - loss: 0.4888\\n\",\n      \"Epoch 44/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4830\\n\",\n      \"Epoch 45/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4775\\n\",\n      \"Epoch 46/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4725\\n\",\n      \"Epoch 47/200\\n\",\n      \"4/4 [==============================] - 0s 998us/step - loss: 0.4673\\n\",\n      \"Epoch 48/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.4624\\n\",\n      \"Epoch 49/200\\n\",\n      \"4/4 [==============================] - 0s 987us/step - loss: 0.4574\\n\",\n      \"Epoch 50/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4530\\n\",\n      \"Epoch 51/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4491\\n\",\n      \"Epoch 52/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.4451\\n\",\n      \"Epoch 53/200\\n\",\n      \"4/4 [==============================] - 0s 970us/step - loss: 0.4414\\n\",\n      \"Epoch 54/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.4374\\n\",\n      \"Epoch 55/200\\n\",\n      \"4/4 [==============================] - 0s 995us/step - loss: 0.4336\\n\",\n      \"Epoch 56/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.4295\\n\",\n      \"Epoch 57/200\\n\",\n      \"4/4 [==============================] - 0s 965us/step - loss: 0.4261\\n\",\n      \"Epoch 58/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4225\\n\",\n      \"Epoch 59/200\\n\",\n      \"4/4 [==============================] - 0s 997us/step - loss: 0.4193\\n\",\n      \"Epoch 60/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.4161\\n\",\n      \"Epoch 61/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4131\\n\",\n      \"Epoch 62/200\\n\",\n      \"4/4 [==============================] - 0s 982us/step - loss: 0.4098\\n\",\n      \"Epoch 63/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4067\\n\",\n      \"Epoch 64/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4029\\n\",\n      \"Epoch 65/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3994\\n\",\n      \"Epoch 66/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3957\\n\",\n      \"Epoch 67/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3920\\n\",\n      \"Epoch 68/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3878\\n\",\n      \"Epoch 69/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3837\\n\",\n      \"Epoch 70/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3792\\n\",\n      \"Epoch 71/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3755\\n\",\n      \"Epoch 72/200\\n\",\n      \"4/4 [==============================] - 0s 982us/step - loss: 0.3718\\n\",\n      \"Epoch 73/200\\n\",\n      \"4/4 [==============================] - 0s 971us/step - loss: 0.3683\\n\",\n      \"Epoch 74/200\\n\",\n      \"4/4 [==============================] - 0s 982us/step - loss: 0.3643\\n\",\n      \"Epoch 75/200\\n\",\n      \"4/4 [==============================] - 0s 993us/step - loss: 0.3600\\n\",\n      \"Epoch 76/200\\n\",\n      \"4/4 [==============================] - 0s 961us/step - loss: 0.3550\\n\",\n      \"Epoch 77/200\\n\",\n      \"4/4 [==============================] - 0s 966us/step - loss: 0.3491\\n\",\n      \"Epoch 78/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3425\\n\",\n      \"Epoch 79/200\\n\",\n      \"4/4 [==============================] - 0s 954us/step - loss: 0.3367\\n\",\n      \"Epoch 80/200\\n\",\n      \"4/4 [==============================] - 0s 930us/step - loss: 0.3293\\n\",\n      \"Epoch 81/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.3228\\n\",\n      \"Epoch 82/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3156\\n\",\n      \"Epoch 83/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3080\\n\",\n      \"Epoch 84/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3006\\n\",\n      \"Epoch 85/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2933\\n\",\n      \"Epoch 86/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2864\\n\",\n      \"Epoch 87/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2792\\n\",\n      \"Epoch 88/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 0.2720\\n\",\n      \"Epoch 89/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2645\\n\",\n      \"Epoch 90/200\\n\",\n      \"4/4 [==============================] - 0s 995us/step - loss: 0.2570\\n\",\n      \"Epoch 91/200\\n\",\n      \"4/4 [==============================] - 0s 958us/step - loss: 0.2498\\n\",\n      \"Epoch 92/200\\n\",\n      \"4/4 [==============================] - 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loss: 0.1850\\n\",\n      \"Epoch 102/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1804\\n\",\n      \"Epoch 103/200\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1758\\n\",\n      \"Epoch 104/200\\n\",\n      \"4/4 [==============================] - 0s 956us/step - loss: 0.1709\\n\",\n      \"Epoch 105/200\\n\",\n      \"4/4 [==============================] - 0s 977us/step - loss: 0.1662\\n\",\n      \"Epoch 106/200\\n\",\n      \"4/4 [==============================] - 0s 958us/step - loss: 0.1616\\n\",\n      \"Epoch 107/200\\n\",\n      \"4/4 [==============================] - 0s 952us/step - loss: 0.1575\\n\",\n      \"Epoch 108/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1527\\n\",\n      \"Epoch 109/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1480\\n\",\n      \"Epoch 110/200\\n\",\n      \"4/4 [==============================] - 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loss: 0.0601\\n\",\n      \"Epoch 148/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0591\\n\",\n      \"Epoch 149/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0582\\n\",\n      \"Epoch 150/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0575\\n\",\n      \"Epoch 151/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0570\\n\",\n      \"Epoch 152/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0563\\n\",\n      \"Epoch 153/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0553\\n\",\n      \"Epoch 154/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0541\\n\",\n      \"Epoch 155/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0530\\n\",\n      \"Epoch 156/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0519\\n\",\n      \"Epoch 157/200\\n\",\n      \"4/4 [==============================] - 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loss: 0.0445\\n\",\n      \"Epoch 167/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0440\\n\",\n      \"Epoch 168/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0434\\n\",\n      \"Epoch 169/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0429\\n\",\n      \"Epoch 170/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0423\\n\",\n      \"Epoch 171/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0418\\n\",\n      \"Epoch 172/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0414\\n\",\n      \"Epoch 173/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0413\\n\",\n      \"Epoch 174/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0408\\n\",\n      \"Epoch 175/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0401\\n\",\n      \"Epoch 176/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0393\\n\",\n      \"Epoch 177/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0388\\n\",\n      \"Epoch 178/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0384\\n\",\n      \"Epoch 179/200\\n\",\n      \"4/4 [==============================] - 0s 979us/step - loss: 0.0384\\n\",\n      \"Epoch 180/200\\n\",\n      \"4/4 [==============================] - 0s 999us/step - loss: 0.0377\\n\",\n      \"Epoch 181/200\\n\",\n      \"4/4 [==============================] - 0s 997us/step - loss: 0.0369\\n\",\n      \"Epoch 182/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0366\\n\",\n      \"Epoch 183/200\\n\",\n      \"4/4 [==============================] - 0s 962us/step - loss: 0.0363\\n\",\n      \"Epoch 184/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.0359\\n\",\n      \"Epoch 185/200\\n\",\n      \"4/4 [==============================] - 0s 975us/step - loss: 0.0353\\n\",\n      \"Epoch 186/200\\n\",\n      \"4/4 [==============================] - 0s 981us/step - loss: 0.0348\\n\",\n      \"Epoch 187/200\\n\",\n      \"4/4 [==============================] - 0s 969us/step - loss: 0.0345\\n\",\n      \"Epoch 188/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.0343\\n\",\n      \"Epoch 189/200\\n\",\n      \"4/4 [==============================] - 0s 965us/step - loss: 0.0339\\n\",\n      \"Epoch 190/200\\n\",\n      \"4/4 [==============================] - 0s 973us/step - loss: 0.0337\\n\",\n      \"Epoch 191/200\\n\",\n      \"4/4 [==============================] - 0s 990us/step - loss: 0.0333\\n\",\n      \"Epoch 192/200\\n\",\n      \"4/4 [==============================] - 0s 979us/step - loss: 0.0330\\n\",\n      \"Epoch 193/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0325\\n\",\n      \"Epoch 194/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0321\\n\",\n      \"Epoch 195/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0317\\n\",\n      \"Epoch 196/200\\n\",\n      \"4/4 [==============================] - 0s 993us/step - loss: 0.0314\\n\",\n      \"Epoch 197/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0310\\n\",\n      \"Epoch 198/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0306\\n\",\n      \"Epoch 199/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0303\\n\",\n      \"Epoch 200/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0300\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fd95c610950>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.01),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=200\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With the model trained, we can see how the model has classified the training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"34b34e13de7b47429128d1cf4695acb8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_cat_mc(X_train, y_train, model, classes)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Above, the decision boundaries show how the model has partitioned the input space.  This very simple model has had no trouble classifying the training data. How did it accomplish this? Let's look at the network in more detail. \\n\",\n    \"\\n\",\n    \"Below, we will pull the trained weights from the model and use that to plot the function of each of the network units. Further down, there is a more detailed explanation of the results. You don't need to know these details to successfully use neural networks, but it may be helpful to gain more intuition about how the layers combine to solve a classification problem.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# gather the trained parameters from the first layer\\n\",\n    \"l1 = model.get_layer(\\\"L1\\\")\\n\",\n    \"W1,b1 = l1.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3e67f9c7df94416a863b815e7f1d5219\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot the function of the first layer\\n\",\n    \"plt_layer_relu(X_train, y_train.reshape(-1,), W1, b1, classes)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c6e2d9d77482450b886880a8cbac2ebf\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# gather the trained parameters from the output layer\\n\",\n    \"l2 = model.get_layer(\\\"L2\\\")\\n\",\n    \"W2, b2 = l2.get_weights()\\n\",\n    \"# create the 'new features', the training examples after L1 transformation\\n\",\n    \"Xl2 = np.maximum(0, np.dot(X_train,W1) + b1)\\n\",\n    \"\\n\",\n    \"plt_output_layer_linear(Xl2, y_train.reshape(-1,), W2, b2, classes,\\n\",\n    \"                        x0_rng = (-0.25,np.amax(Xl2[:,0])), x1_rng = (-0.25,np.amax(Xl2[:,1])))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Explanation\\n\",\n    \"#### Layer 1 <img align=\\\"Right\\\" src=\\\"./images/C2_W2_mclass_layer1.png\\\"  style=\\\" width:600px; padding: 10px 20px ; \\\">\\n\",\n    \"These plots show the function of Units 0 and 1 in the first layer of the network. The inputs are ($x_0,x_1$) on the axis. The output of the unit is represented by the color of the background. This is indicated by the color bar on the right of each graph. Notice that since these units are using a ReLu, the outputs do not necessarily fall between 0 and 1 and in this case are greater than 20 at their peaks. \\n\",\n    \"The contour lines in this graph show the transition point between the output, $a^{[1]}_j$ being zero and non-zero. Recall the graph for a ReLu :<img align=\\\"right\\\" src=\\\"./images/C2_W2_mclass_relu.png\\\"  style=\\\" width:200px; padding: 10px 20px ; \\\"> The contour line in the graph is the inflection point in the ReLu.\\n\",\n    \"\\n\",\n    \"Unit 0 has separated classes 0 and 1 from classes 2 and 3. Points to the left of the line (classes 0 and 1) will output zero, while points to the right will output a value greater than zero.  \\n\",\n    \"Unit 1 has separated classes 0 and 2 from classes 1 and 3. Points above the line (classes 0 and 2 ) will output a zero, while points below will output a value greater than zero. Let's see how this works out in the next layer!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Layer 2, the output layer  <img align=\\\"Right\\\" src=\\\"./images/C2_W2_mclass_layer2.png\\\"  style=\\\" width:600px; padding: 10px 20px ; \\\">\\n\",\n    \"\\n\",\n    \"The dots in these graphs are the training examples translated by the first layer. One way to think of this is the first layer has created a new set of features for evaluation by the 2nd layer. The axes in these plots are the outputs of the previous layer $a^{[1]}_0$ and $a^{[1]}_1$. As predicted above, classes 0 and 1 (blue and green) have  $a^{[1]}_0 = 0$ while classes 0 and 2 (blue and orange) have $a^{[1]}_1 = 0$.  \\n\",\n    \"Once again, the intensity of the background color indicates the highest values.  \\n\",\n    \"Unit 0 will produce its maximum value for values near (0,0), where class 0 (blue) has been mapped.    \\n\",\n    \"Unit 1 produces its highest values in the upper left corner selecting class 1 (green).  \\n\",\n    \"Unit 2 targets the lower right corner where class 2 (orange) resides.  \\n\",\n    \"Unit 3 produces its highest values in the upper right selecting our final class (purple).  \\n\",\n    \"\\n\",\n    \"One other aspect that is not obvious from the graphs is that the values have been coordinated between the units. It is not sufficient for a unit to produce a maximum value for the class it is selecting for, it must also be the highest value of all the units for points in that class. This is done by the implied softmax function that is part of the loss function (`SparseCategoricalCrossEntropy`). Unlike other activation functions, the softmax works across all the outputs.\\n\",\n    \"\\n\",\n    \"You can successfully use neural networks without knowing the details of what each unit is up to. Hopefully, this example has provided some intuition about what is happening under the hood.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have learned to build and operate a neural network for multiclass classification.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/.ipynb_checkpoints/C2_W2_Relu-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab - ReLU activation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib.gridspec import GridSpec\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense, LeakyReLU\\n\",\n    \"from tensorflow.keras.activations import linear, relu, sigmoid\\n\",\n    \"%matplotlib widget\\n\",\n    \"from matplotlib.widgets import Slider\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from autils import plt_act_trio\\n\",\n    \"from lab_utils_relu import *\\n\",\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=UserWarning)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - ReLU Activation\\n\",\n    \"This week, a new activation was introduced, the Rectified Linear Unit (ReLU). \\n\",\n    \"$$ a = max(0,z) \\\\quad\\\\quad\\\\text {# ReLU function} $$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e8631645d39248c7bba85e5608139136\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_act_trio()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img align=\\\"right\\\" src=\\\"./images/C2_W2_ReLu.png\\\"     style=\\\" width:380px; padding: 10px 20px; \\\" >\\n\",\n    \"The example from the lecture on the right shows an application of the ReLU. In this example, the derived \\\"awareness\\\" feature is not binary but has a continuous range of values. The sigmoid is best for on/off or binary situations. The ReLU provides a continuous linear relationship. Additionally it has an 'off' range where the output is zero.     \\n\",\n    \"The \\\"off\\\" feature makes the ReLU a Non-Linear activation. Why is this needed? Let's examine this below. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Why Non-Linear Activations?  \\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W2_ReLU_Graph.png\\\"     style=\\\" width:250px; padding: 10px 20px; \\\" > The function shown is composed of linear pieces (piecewise linear). The slope is consistent during the linear portion and then changes abruptly at transition points. At transition points, a new linear function is added which, when added to the existing function, will produce the new slope. The new function is added at transition point but does not contribute to the output prior to that point. The non-linear activation function is responsible for disabling the input prior to and sometimes after the transition points. The following exercise provides a more tangible example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The exercise will use the network below in a regression problem where you must model a piecewise linear target :\\n\",\n    \"<img align=\\\"center\\\" src=\\\"./images/C2_W2_ReLU_Network.png\\\"     style=\\\" width:650px; padding: 10px 20px; \\\">  \\n\",\n    \"The network has 3 units in the first layer. Each is required to form the target. Unit 0 is pre-programmed and fixed to map the first segment. You will modify weights and biases in unit 1 and 2 to model the 2nd and 3rd segment. The output unit is also fixed and simply sums the outputs of the first layer.  \\n\",\n    \"\\n\",\n    \"Using the sliders below, modify weights and bias to match the target. \\n\",\n    \"Hints: Start with `w1` and `b1` and leave `w2` and `b2` zero until you match the 2nd segment. Clicking rather than sliding is quicker.  If you have trouble, don't worry, the text below will describe this in more detail.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d89e83fedc19457bb6a7267d3ba2f530\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = plt_relu_ex()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \" \\n\",\n    \"The goal of this exercise is to appreciate how the ReLU's non-linear behavior provides the needed ability to turn functions off until they are needed. Let's see how this worked in this example.\\n\",\n    \"<img align=\\\"right\\\" src=\\\"./images/C2_W2_ReLU_Plot.png\\\"     style=\\\" width:600px; padding: 10px 20px; \\\"> \\n\",\n    \"The plots on the right contain the output of the units in the first layer.   \\n\",\n    \"Starting at the top, unit 0 is responsible for the first segment marked with a 1. Both the linear function $z$ and the function following the ReLU $a$ are shown. You can see that the ReLU cuts off the function after the interval [0,1]. This is important as it prevents Unit 0 from interfering with the following segment. \\n\",\n    \"\\n\",\n    \"Unit 1 is responsible for the 2nd segment. Here the ReLU kept this unit quiet until after x is 1. Since the first unit is not contributing, the slope for unit 1, $w^{[1]}_1$, is just the slope of the target line. The bias must be adjusted to keep the output negative until x has reached 1. Note how the contribution of Unit 1 extends to the 3rd segment as well.\\n\",\n    \"\\n\",\n    \"Unit 2 is responsible for the 3rd segment. The ReLU again zeros the output until x reaches the right value.The slope of the unit, $w^{[1]}_2$, must be set so that the sum of unit 1 and 2 have the desired slope. The bias is again adjusted to keep the output negative until x has reached 2. \\n\",\n    \"\\n\",\n    \"The \\\"off\\\" or disable feature  of the ReLU activation enables models to stitch together linear segments to model complex non-linear functions.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You are now more familiar with the ReLU and the importance of its non-linear behavior.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/.ipynb_checkpoints/C2_W2_SoftMax-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Softmax Function\\n\",\n    \"In this lab, we will explore the softmax function. This function is used in both Softmax Regression and in Neural Networks when solving Multiclass Classification problems.  \\n\",\n    \"\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_Softmax_Header.PNG\\\" width=\\\"600\\\" />  <center/>\\n\",\n    \"\\n\",\n    \"  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from IPython.display import display, Markdown, Latex\\n\",\n    \"from sklearn.datasets import make_blobs\\n\",\n    \"%matplotlib widget\\n\",\n    \"from matplotlib.widgets import Slider\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_utils_softmax import plt_softmax\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> **Note**: Normally, in this course, the notebooks use the convention of starting counts with 0 and ending with N-1,  $\\\\sum_{i=0}^{N-1}$, while lectures start with 1 and end with N,  $\\\\sum_{i=1}^{N}$. This is because code will typically start iteration with 0 while in lecture, counting 1 to N leads to cleaner, more succinct equations. This notebook has more equations than is typical for a lab and thus  will break with the convention and will count 1 to N.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Softmax Function\\n\",\n    \"In both softmax regression and neural networks with Softmax outputs, N outputs are generated and one output is selected as the predicted category. In both cases a vector $\\\\mathbf{z}$ is generated by a linear function which is applied to a softmax function. The softmax function converts $\\\\mathbf{z}$  into a probability distribution as described below. After applying softmax, each output will be between 0 and 1 and the outputs will add to 1, so that they can be interpreted as probabilities. The larger inputs  will correspond to larger output probabilities.\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_SoftmaxReg_NN.png\\\" width=\\\"600\\\" />  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The softmax function can be written:\\n\",\n    \"$$a_j = \\\\frac{e^{z_j}}{ \\\\sum_{k=1}^{N}{e^{z_k} }} \\\\tag{1}$$\\n\",\n    \"The output $\\\\mathbf{a}$ is a vector of length N, so for softmax regression, you could also write:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\mathbf{a}(x) =\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"P(y = 1 | \\\\mathbf{x}; \\\\mathbf{w},b) \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"P(y = N | \\\\mathbf{x}; \\\\mathbf{w},b)\\n\",\n    \"\\\\end{bmatrix}\\n\",\n    \"=\\n\",\n    \"\\\\frac{1}{ \\\\sum_{k=1}^{N}{e^{z_k} }}\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"e^{z_1} \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"e^{z_{N}} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix} \\\\tag{2}\\n\",\n    \"\\\\end{align}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Which shows the output is a vector of probabilities. The first entry is the probability the input is the first category given the input $\\\\mathbf{x}$ and parameters $\\\\mathbf{w}$ and $\\\\mathbf{b}$.  \\n\",\n    \"Let's create a NumPy implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_softmax(z):\\n\",\n    \"    ez = np.exp(z)              #element-wise exponenial\\n\",\n    \"    sm = ez/np.sum(ez)\\n\",\n    \"    return(sm)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below, vary the values of the `z` inputs using the sliders.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"cce96a6a945f4b63988352bac2bf0a85\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_softmax(my_softmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you are varying the values of the z's above, there are a few things to note:\\n\",\n    \"* the exponential in the numerator of the softmax magnifies small differences in the values \\n\",\n    \"* the output values sum to one\\n\",\n    \"* the softmax spans all of the outputs. A change in `z0` for example will change the values of `a0`-`a3`. Compare this to other activations such as ReLU or Sigmoid which have a single input and single output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Cost\\n\",\n    \"<center> <img  src=\\\"./images/C2_W2_SoftMaxCost.png\\\" width=\\\"400\\\" />    <center/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The loss function associated with Softmax, the cross-entropy loss, is:\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"  L(\\\\mathbf{a},y)=\\\\begin{cases}\\n\",\n    \"    -log(a_1), & \\\\text{if $y=1$}.\\\\\\\\\\n\",\n    \"        &\\\\vdots\\\\\\\\\\n\",\n    \"     -log(a_N), & \\\\text{if $y=N$}\\n\",\n    \"  \\\\end{cases} \\\\tag{3}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\n\",\n    \"Where y is the target category for this example and $\\\\mathbf{a}$ is the output of a softmax function. In particular, the values in $\\\\mathbf{a}$ are probabilities that sum to one.\\n\",\n    \">**Recall:** In this course, Loss is for one example while Cost covers all examples. \\n\",\n    \" \\n\",\n    \" \\n\",\n    \"Note in (3) above, only the line that corresponds to the target contributes to the loss, other lines are zero. To write the cost equation we need an 'indicator function' that will be 1 when the index matches the target and zero otherwise. \\n\",\n    \"    $$\\\\mathbf{1}\\\\{y == n\\\\} = =\\\\begin{cases}\\n\",\n    \"    1, & \\\\text{if $y==n$}.\\\\\\\\\\n\",\n    \"    0, & \\\\text{otherwise}.\\n\",\n    \"  \\\\end{cases}$$\\n\",\n    \"Now the cost is:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"J(\\\\mathbf{w},b) = -\\\\frac{1}{m} \\\\left[ \\\\sum_{i=1}^{m} \\\\sum_{j=1}^{N}  1\\\\left\\\\{y^{(i)} == j\\\\right\\\\} \\\\log \\\\frac{e^{z^{(i)}_j}}{\\\\sum_{k=1}^N e^{z^{(i)}_k} }\\\\right] \\\\tag{4}\\n\",\n    \"\\\\end{align}\\n\",\n    \"\\n\",\n    \"Where $m$ is the number of examples, $N$ is the number of outputs. This is the average of all the losses.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Tensorflow\\n\",\n    \"This lab will discuss two ways of implementing the softmax, cross-entropy loss in Tensorflow, the 'obvious' method and the 'preferred' method. The former is the most straightforward while the latter is more numerically stable.\\n\",\n    \"\\n\",\n    \"Let's start by creating a dataset to train a multiclass classification model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# make  dataset for example\\n\",\n    \"centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\\n\",\n    \"X_train, y_train = make_blobs(n_samples=2000, centers=centers, cluster_std=1.0,random_state=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### The *Obvious* organization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model below is implemented with the softmax as an activation in the final Dense layer.\\n\",\n    \"The loss function is separately specified in the `compile` directive. \\n\",\n    \"\\n\",\n    \"The loss function is `SparseCategoricalCrossentropy`. This loss is described in (3) above. In this model, the softmax takes place in the last layer. The loss function takes in the softmax output which is a vector of probabilities. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/10\\n\",\n      \"63/63 [==============================] - 0s 966us/step - loss: 0.8312\\n\",\n      \"Epoch 2/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.3203\\n\",\n      \"Epoch 3/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.1408\\n\",\n      \"Epoch 4/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0847\\n\",\n      \"Epoch 5/10\\n\",\n      \"63/63 [==============================] - 0s 944us/step - loss: 0.0626\\n\",\n      \"Epoch 6/10\\n\",\n      \"63/63 [==============================] - 0s 974us/step - loss: 0.0515\\n\",\n      \"Epoch 7/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0447\\n\",\n      \"Epoch 8/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0402\\n\",\n      \"Epoch 9/10\\n\",\n      \"63/63 [==============================] - 0s 922us/step - loss: 0.0361\\n\",\n      \"Epoch 10/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0332\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fd399415490>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'softmax')    # < softmax activation here\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Because the softmax is integrated into the output layer, the output is a vector of probabilities.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[3.39e-03 1.02e-02 9.73e-01 1.30e-02]\\n\",\n      \" [9.97e-01 3.46e-03 9.35e-06 9.02e-06]]\\n\",\n      \"largest value 0.9999994 smallest value 4.1378247e-09\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"p_nonpreferred = model.predict(X_train)\\n\",\n    \"print(p_nonpreferred [:2])\\n\",\n    \"print(\\\"largest value\\\", np.max(p_nonpreferred), \\\"smallest value\\\", np.min(p_nonpreferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Preferred <img align=\\\"Right\\\" src=\\\"./images/C2_W2_softmax_accurate.png\\\"  style=\\\" width:400px; padding: 10px 20px ; \\\">\\n\",\n    \"Recall from lecture, more stable and accurate results can be obtained if the softmax and loss are combined during training.   This is enabled by the 'preferred' organization shown here.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the preferred organization the final layer has a linear activation. For historical reasons, the outputs in this form are referred to as *logits*. The loss function has an additional argument: `from_logits = True`. This informs the loss function that the softmax operation should be included in the loss calculation. This allows for an optimized implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 1.0936\\n\",\n      \"Epoch 2/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.4933\\n\",\n      \"Epoch 3/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.2809\\n\",\n      \"Epoch 4/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.1506\\n\",\n      \"Epoch 5/10\\n\",\n      \"63/63 [==============================] - 0s 966us/step - loss: 0.0916\\n\",\n      \"Epoch 6/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0694\\n\",\n      \"Epoch 7/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0592\\n\",\n      \"Epoch 8/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0527\\n\",\n      \"Epoch 9/10\\n\",\n      \"63/63 [==============================] - 0s 919us/step - loss: 0.0489\\n\",\n      \"Epoch 10/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0457\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fd23c552050>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"preferred_model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'linear')   #<-- Note\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"preferred_model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),  #<-- Note\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"preferred_model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Output Handling\\n\",\n    \"Notice that in the preferred model, the outputs are not probabilities, but can range from large negative numbers to large positive numbers. The output must be sent through a softmax when performing a prediction that expects a probability. \\n\",\n    \"Let's look at the preferred model outputs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"two example output vectors:\\n\",\n      \" [[-2.72 -4.45  2.9  -1.37]\\n\",\n      \" [ 6.42  1.32 -1.32 -6.74]]\\n\",\n      \"largest value 12.410254 smallest value -13.030992\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"p_preferred = preferred_model.predict(X_train)\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {p_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(p_preferred), \\\"smallest value\\\", np.min(p_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output predictions are not probabilities!\\n\",\n    \"If the desired output are probabilities, the output should be be processed by a [softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"two example output vectors:\\n\",\n      \" [[3.55e-03 6.34e-04 9.82e-01 1.38e-02]\\n\",\n      \" [9.94e-01 6.05e-03 4.35e-04 1.92e-06]]\\n\",\n      \"largest value 0.9999995 smallest value 1.5196424e-11\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sm_preferred = tf.nn.softmax(p_preferred).numpy()\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {sm_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(sm_preferred), \\\"smallest value\\\", np.min(sm_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To select the most likely category, the softmax is not required. One can find the index of the largest output using [np.argmax()](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-2.72 -4.45  2.9  -1.37], category: 2\\n\",\n      \"[ 6.42  1.32 -1.32 -6.74], category: 0\\n\",\n      \"[ 4.64  1.5  -1.31 -5.3 ], category: 0\\n\",\n      \"[-0.29  3.76 -3.11 -1.58], category: 1\\n\",\n      \"[-0.64 -6.05  5.23 -5.2 ], category: 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(5):\\n\",\n    \"    print( f\\\"{p_preferred[i]}, category: {np.argmax(p_preferred[i])}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## SparseCategorialCrossentropy or CategoricalCrossEntropy\\n\",\n    \"Tensorflow has two potential formats for target values and the selection of the loss defines which is expected.\\n\",\n    \"- SparseCategorialCrossentropy: expects the target to be an integer corresponding to the index. For example, if there are 10 potential target values, y would be between 0 and 9. \\n\",\n    \"- CategoricalCrossEntropy: Expects the target value of an example to be one-hot encoded where the value at the target index is 1 while the other N-1 entries are zero. An example with 10 potential target values, where the target is 2 would be [0,0,1,0,0,0,0,0,0,0].\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you \\n\",\n    \"- Became more familiar with the softmax function and its use in softmax regression and in softmax activations in neural networks. \\n\",\n    \"- Learned the preferred model construction in Tensorflow:\\n\",\n    \"    - No activation on the final layer (same as linear activation)\\n\",\n    \"    - SparseCategoricalCrossentropy loss function\\n\",\n    \"    - use from_logits=True\\n\",\n    \"- Recognized that unlike ReLU and Sigmoid, the softmax spans multiple outputs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/C2_W2_Multiclass_TF.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab - Multi-class Classification\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.1 Goals\\n\",\n    \"In this lab, you will explore an example of multi-class classification using neural networks.\\n\",\n    \"<figure>\\n\",\n    \" <img src=\\\"./images/C2_W2_mclass_header.png\\\"   style=\\\"width500px;height:200px;\\\">\\n\",\n    \"</figure>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.2 Tools\\n\",\n    \"You will use some plotting routines. These are stored in `lab_utils_multiclass_TF.py` in this directory.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib widget\\n\",\n    \"from sklearn.datasets import make_blobs\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"np.set_printoptions(precision=2)\\n\",\n    \"from lab_utils_multiclass_TF import *\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# 2.0 Multi-class Classification\\n\",\n    \"Neural Networks are often used to classify data. Examples are neural networks:\\n\",\n    \"- take in photos and classify subjects in the photos as {dog,cat,horse,other}\\n\",\n    \"- take in a sentence and classify the 'parts of speech' of its elements: {noun, verb, adjective etc..}  \\n\",\n    \"\\n\",\n    \"A network of this type will have multiple units in its final layer. Each output is associated with a category. When an input example is applied to the network, the output with the highest value is the category predicted. If the output is applied to a softmax function, the output of the softmax will provide probabilities of the input being in each category. \\n\",\n    \"\\n\",\n    \"In this lab you will see an example of building a multiclass network in Tensorflow. We will then take a look at how the neural network makes its predictions.\\n\",\n    \"\\n\",\n    \"Let's start by creating a four-class data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2.1 Prepare and visualize our data\\n\",\n    \"We will use Scikit-Learn `make_blobs` function to make a training data set with 4 categories as shown in the plot below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# make 4-class dataset for classification\\n\",\n    \"classes = 4\\n\",\n    \"m = 100\\n\",\n    \"centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\\n\",\n    \"std = 1.0\\n\",\n    \"X_train, y_train = make_blobs(n_samples=m, centers=centers, cluster_std=std,random_state=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f6d15f0f6d8b45e49d488bb56a9211ec\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_mc(X_train,y_train,classes, centers, std=std)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Each dot represents a training example. The axis (x0,x1) are the inputs and the color represents the class the example is associated with. Once trained, the model will be presented with a new example, (x0,x1), and will predict the class.  \\n\",\n    \"\\n\",\n    \"While generated, this data set is representative of many real-world classification problems. There are several input features (x0,...,xn) and several output categories. The model is trained to use the input features to predict the correct output category.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"unique classes [0 1 2 3]\\n\",\n      \"class representation [3 3 3 0 3 3 3 3 2 0]\\n\",\n      \"shape of X_train: (100, 2), shape of y_train: (100,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# show classes in data set\\n\",\n    \"print(f\\\"unique classes {np.unique(y_train)}\\\")\\n\",\n    \"# show how classes are represented\\n\",\n    \"print(f\\\"class representation {y_train[:10]}\\\")\\n\",\n    \"# show shapes of our dataset\\n\",\n    \"print(f\\\"shape of X_train: {X_train.shape}, shape of y_train: {y_train.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## 2.2 Model\\n\",\n    \"<img align=\\\"Right\\\" src=\\\"./images/C2_W2_mclass_lab_network.PNG\\\"  style=\\\" width:350px; padding: 10px 20px ; \\\">\\n\",\n    \"This lab will use a 2-layer network as shown.\\n\",\n    \"Unlike the binary classification networks, this network has four outputs, one for each class. Given an input example, the output with the highest value is the predicted class of the input.   \\n\",\n    \"\\n\",\n    \"Below is an example of how to construct this network in Tensorflow. Notice the output layer uses a `linear` rather than a `softmax` activation. While it is possible to include the softmax in the output layer, it is more numerically stable if linear outputs are passed to the loss function during training. If the model is used to predict probabilities, the softmax can be applied at that point.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"tf.random.set_seed(1234)  # applied to achieve consistent results\\n\",\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        Dense(2, activation = 'relu',   name = \\\"L1\\\"),\\n\",\n    \"        Dense(4, activation = 'linear', name = \\\"L2\\\")\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The statements below compile and train the network. Setting `from_logits=True` as an argument to the loss function specifies that the output activation was linear rather than a softmax.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.8158\\n\",\n      \"Epoch 2/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.6976\\n\",\n      \"Epoch 3/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.5989\\n\",\n      \"Epoch 4/200\\n\",\n      \"4/4 [==============================] - 0s 961us/step - loss: 1.5179\\n\",\n      \"Epoch 5/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.4369\\n\",\n      \"Epoch 6/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.3756\\n\",\n      \"Epoch 7/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.3154\\n\",\n      \"Epoch 8/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.2621\\n\",\n      \"Epoch 9/200\\n\",\n      \"4/4 [==============================] - 0s 991us/step - loss: 1.2188\\n\",\n      \"Epoch 10/200\\n\",\n      \"4/4 [==============================] - 0s 983us/step - loss: 1.1791\\n\",\n      \"Epoch 11/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 1.1446\\n\",\n      \"Epoch 12/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 1.1129\\n\",\n      \"Epoch 13/200\\n\",\n      \"4/4 [==============================] - 0s 993us/step - loss: 1.0827\\n\",\n      \"Epoch 14/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.0516\\n\",\n      \"Epoch 15/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.0225\\n\",\n      \"Epoch 16/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 0.9967\\n\",\n      \"Epoch 17/200\\n\",\n      \"4/4 [==============================] - 0s 970us/step - loss: 0.9681\\n\",\n      \"Epoch 18/200\\n\",\n      \"4/4 [==============================] - 0s 980us/step - loss: 0.9392\\n\",\n      \"Epoch 19/200\\n\",\n      \"4/4 [==============================] - 0s 989us/step - loss: 0.9092\\n\",\n      \"Epoch 20/200\\n\",\n      \"4/4 [==============================] - 0s 966us/step - loss: 0.8771\\n\",\n      \"Epoch 21/200\\n\",\n      \"4/4 [==============================] - 0s 995us/step - loss: 0.8461\\n\",\n      \"Epoch 22/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.8099\\n\",\n      \"Epoch 23/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.7771\\n\",\n      \"Epoch 24/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.7485\\n\",\n      \"Epoch 25/200\\n\",\n      \"4/4 [==============================] - 0s 2ms/step - loss: 0.7215\\n\",\n      \"Epoch 26/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6967\\n\",\n      \"Epoch 27/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6742\\n\",\n      \"Epoch 28/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6540\\n\",\n      \"Epoch 29/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6352\\n\",\n      \"Epoch 30/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6187\\n\",\n      \"Epoch 31/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.6030\\n\",\n      \"Epoch 32/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5884\\n\",\n      \"Epoch 33/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5746\\n\",\n      \"Epoch 34/200\\n\",\n      \"4/4 [==============================] - 0s 997us/step - loss: 0.5621\\n\",\n      \"Epoch 35/200\\n\",\n      \"4/4 [==============================] - 0s 980us/step - loss: 0.5512\\n\",\n      \"Epoch 36/200\\n\",\n      \"4/4 [==============================] - 0s 999us/step - loss: 0.5414\\n\",\n      \"Epoch 37/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.5323\\n\",\n      \"Epoch 38/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5236\\n\",\n      \"Epoch 39/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5150\\n\",\n      \"Epoch 40/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.5072\\n\",\n      \"Epoch 41/200\\n\",\n      \"4/4 [==============================] - 0s 991us/step - loss: 0.5006\\n\",\n      \"Epoch 42/200\\n\",\n      \"4/4 [==============================] - 0s 982us/step - loss: 0.4944\\n\",\n      \"Epoch 43/200\\n\",\n      \"4/4 [==============================] - 0s 992us/step - loss: 0.4888\\n\",\n      \"Epoch 44/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4830\\n\",\n      \"Epoch 45/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4775\\n\",\n      \"Epoch 46/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4725\\n\",\n      \"Epoch 47/200\\n\",\n      \"4/4 [==============================] - 0s 998us/step - loss: 0.4673\\n\",\n      \"Epoch 48/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.4624\\n\",\n      \"Epoch 49/200\\n\",\n      \"4/4 [==============================] - 0s 987us/step - loss: 0.4574\\n\",\n      \"Epoch 50/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4530\\n\",\n      \"Epoch 51/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4491\\n\",\n      \"Epoch 52/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.4451\\n\",\n      \"Epoch 53/200\\n\",\n      \"4/4 [==============================] - 0s 970us/step - loss: 0.4414\\n\",\n      \"Epoch 54/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.4374\\n\",\n      \"Epoch 55/200\\n\",\n      \"4/4 [==============================] - 0s 995us/step - loss: 0.4336\\n\",\n      \"Epoch 56/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.4295\\n\",\n      \"Epoch 57/200\\n\",\n      \"4/4 [==============================] - 0s 965us/step - loss: 0.4261\\n\",\n      \"Epoch 58/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4225\\n\",\n      \"Epoch 59/200\\n\",\n      \"4/4 [==============================] - 0s 997us/step - loss: 0.4193\\n\",\n      \"Epoch 60/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.4161\\n\",\n      \"Epoch 61/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4131\\n\",\n      \"Epoch 62/200\\n\",\n      \"4/4 [==============================] - 0s 982us/step - loss: 0.4098\\n\",\n      \"Epoch 63/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4067\\n\",\n      \"Epoch 64/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4029\\n\",\n      \"Epoch 65/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3994\\n\",\n      \"Epoch 66/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3957\\n\",\n      \"Epoch 67/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3920\\n\",\n      \"Epoch 68/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3878\\n\",\n      \"Epoch 69/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3837\\n\",\n      \"Epoch 70/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3792\\n\",\n      \"Epoch 71/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3755\\n\",\n      \"Epoch 72/200\\n\",\n      \"4/4 [==============================] - 0s 982us/step - loss: 0.3718\\n\",\n      \"Epoch 73/200\\n\",\n      \"4/4 [==============================] - 0s 971us/step - loss: 0.3683\\n\",\n      \"Epoch 74/200\\n\",\n      \"4/4 [==============================] - 0s 982us/step - loss: 0.3643\\n\",\n      \"Epoch 75/200\\n\",\n      \"4/4 [==============================] - 0s 993us/step - loss: 0.3600\\n\",\n      \"Epoch 76/200\\n\",\n      \"4/4 [==============================] - 0s 961us/step - loss: 0.3550\\n\",\n      \"Epoch 77/200\\n\",\n      \"4/4 [==============================] - 0s 966us/step - loss: 0.3491\\n\",\n      \"Epoch 78/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3425\\n\",\n      \"Epoch 79/200\\n\",\n      \"4/4 [==============================] - 0s 954us/step - loss: 0.3367\\n\",\n      \"Epoch 80/200\\n\",\n      \"4/4 [==============================] - 0s 930us/step - loss: 0.3293\\n\",\n      \"Epoch 81/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.3228\\n\",\n      \"Epoch 82/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3156\\n\",\n      \"Epoch 83/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3080\\n\",\n      \"Epoch 84/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.3006\\n\",\n      \"Epoch 85/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2933\\n\",\n      \"Epoch 86/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2864\\n\",\n      \"Epoch 87/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2792\\n\",\n      \"Epoch 88/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 0.2720\\n\",\n      \"Epoch 89/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2645\\n\",\n      \"Epoch 90/200\\n\",\n      \"4/4 [==============================] - 0s 995us/step - loss: 0.2570\\n\",\n      \"Epoch 91/200\\n\",\n      \"4/4 [==============================] - 0s 958us/step - loss: 0.2498\\n\",\n      \"Epoch 92/200\\n\",\n      \"4/4 [==============================] - 0s 996us/step - loss: 0.2432\\n\",\n      \"Epoch 93/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2354\\n\",\n      \"Epoch 94/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.2274\\n\",\n      \"Epoch 95/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.2194\\n\",\n      \"Epoch 96/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2127\\n\",\n      \"Epoch 97/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2060\\n\",\n      \"Epoch 98/200\\n\",\n      \"4/4 [==============================] - 0s 969us/step - loss: 0.1995\\n\",\n      \"Epoch 99/200\\n\",\n      \"4/4 [==============================] - 0s 997us/step - loss: 0.1950\\n\",\n      \"Epoch 100/200\\n\",\n      \"4/4 [==============================] - 0s 983us/step - loss: 0.1894\\n\",\n      \"Epoch 101/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1850\\n\",\n      \"Epoch 102/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1804\\n\",\n      \"Epoch 103/200\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1758\\n\",\n      \"Epoch 104/200\\n\",\n      \"4/4 [==============================] - 0s 956us/step - loss: 0.1709\\n\",\n      \"Epoch 105/200\\n\",\n      \"4/4 [==============================] - 0s 977us/step - loss: 0.1662\\n\",\n      \"Epoch 106/200\\n\",\n      \"4/4 [==============================] - 0s 958us/step - loss: 0.1616\\n\",\n      \"Epoch 107/200\\n\",\n      \"4/4 [==============================] - 0s 952us/step - loss: 0.1575\\n\",\n      \"Epoch 108/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1527\\n\",\n      \"Epoch 109/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1480\\n\",\n      \"Epoch 110/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1439\\n\",\n      \"Epoch 111/200\\n\",\n      \"4/4 [==============================] - 0s 996us/step - loss: 0.1396\\n\",\n      \"Epoch 112/200\\n\",\n      \"4/4 [==============================] - 0s 987us/step - loss: 0.1357\\n\",\n      \"Epoch 113/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1315\\n\",\n      \"Epoch 114/200\\n\",\n      \"4/4 [==============================] - 0s 998us/step - loss: 0.1277\\n\",\n      \"Epoch 115/200\\n\",\n      \"4/4 [==============================] - 0s 994us/step - loss: 0.1240\\n\",\n      \"Epoch 116/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1207\\n\",\n      \"Epoch 117/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1171\\n\",\n      \"Epoch 118/200\\n\",\n      \"4/4 [==============================] - 0s 996us/step - loss: 0.1139\\n\",\n      \"Epoch 119/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1110\\n\",\n      \"Epoch 120/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1084\\n\",\n      \"Epoch 121/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1058\\n\",\n      \"Epoch 122/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1029\\n\",\n      \"Epoch 123/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1001\\n\",\n      \"Epoch 124/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0975\\n\",\n      \"Epoch 125/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0951\\n\",\n      \"Epoch 126/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0925\\n\",\n      \"Epoch 127/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0902\\n\",\n      \"Epoch 128/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0882\\n\",\n      \"Epoch 129/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0862\\n\",\n      \"Epoch 130/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0840\\n\",\n      \"Epoch 131/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0826\\n\",\n      \"Epoch 132/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0807\\n\",\n      \"Epoch 133/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0790\\n\",\n      \"Epoch 134/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0772\\n\",\n      \"Epoch 135/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0757\\n\",\n      \"Epoch 136/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0741\\n\",\n      \"Epoch 137/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0728\\n\",\n      \"Epoch 138/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0714\\n\",\n      \"Epoch 139/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0700\\n\",\n      \"Epoch 140/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0685\\n\",\n      \"Epoch 141/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0670\\n\",\n      \"Epoch 142/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0657\\n\",\n      \"Epoch 143/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0645\\n\",\n      \"Epoch 144/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0634\\n\",\n      \"Epoch 145/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0622\\n\",\n      \"Epoch 146/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0611\\n\",\n      \"Epoch 147/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0601\\n\",\n      \"Epoch 148/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0591\\n\",\n      \"Epoch 149/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0582\\n\",\n      \"Epoch 150/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0575\\n\",\n      \"Epoch 151/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0570\\n\",\n      \"Epoch 152/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0563\\n\",\n      \"Epoch 153/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0553\\n\",\n      \"Epoch 154/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0541\\n\",\n      \"Epoch 155/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0530\\n\",\n      \"Epoch 156/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0519\\n\",\n      \"Epoch 157/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0510\\n\",\n      \"Epoch 158/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0502\\n\",\n      \"Epoch 159/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0496\\n\",\n      \"Epoch 160/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0490\\n\",\n      \"Epoch 161/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0481\\n\",\n      \"Epoch 162/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0473\\n\",\n      \"Epoch 163/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0466\\n\",\n      \"Epoch 164/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0458\\n\",\n      \"Epoch 165/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0452\\n\",\n      \"Epoch 166/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0445\\n\",\n      \"Epoch 167/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0440\\n\",\n      \"Epoch 168/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0434\\n\",\n      \"Epoch 169/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0429\\n\",\n      \"Epoch 170/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0423\\n\",\n      \"Epoch 171/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0418\\n\",\n      \"Epoch 172/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0414\\n\",\n      \"Epoch 173/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0413\\n\",\n      \"Epoch 174/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0408\\n\",\n      \"Epoch 175/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0401\\n\",\n      \"Epoch 176/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0393\\n\",\n      \"Epoch 177/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0388\\n\",\n      \"Epoch 178/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0384\\n\",\n      \"Epoch 179/200\\n\",\n      \"4/4 [==============================] - 0s 979us/step - loss: 0.0384\\n\",\n      \"Epoch 180/200\\n\",\n      \"4/4 [==============================] - 0s 999us/step - loss: 0.0377\\n\",\n      \"Epoch 181/200\\n\",\n      \"4/4 [==============================] - 0s 997us/step - loss: 0.0369\\n\",\n      \"Epoch 182/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0366\\n\",\n      \"Epoch 183/200\\n\",\n      \"4/4 [==============================] - 0s 962us/step - loss: 0.0363\\n\",\n      \"Epoch 184/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.0359\\n\",\n      \"Epoch 185/200\\n\",\n      \"4/4 [==============================] - 0s 975us/step - loss: 0.0353\\n\",\n      \"Epoch 186/200\\n\",\n      \"4/4 [==============================] - 0s 981us/step - loss: 0.0348\\n\",\n      \"Epoch 187/200\\n\",\n      \"4/4 [==============================] - 0s 969us/step - loss: 0.0345\\n\",\n      \"Epoch 188/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.0343\\n\",\n      \"Epoch 189/200\\n\",\n      \"4/4 [==============================] - 0s 965us/step - loss: 0.0339\\n\",\n      \"Epoch 190/200\\n\",\n      \"4/4 [==============================] - 0s 973us/step - loss: 0.0337\\n\",\n      \"Epoch 191/200\\n\",\n      \"4/4 [==============================] - 0s 990us/step - loss: 0.0333\\n\",\n      \"Epoch 192/200\\n\",\n      \"4/4 [==============================] - 0s 979us/step - loss: 0.0330\\n\",\n      \"Epoch 193/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0325\\n\",\n      \"Epoch 194/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0321\\n\",\n      \"Epoch 195/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0317\\n\",\n      \"Epoch 196/200\\n\",\n      \"4/4 [==============================] - 0s 993us/step - loss: 0.0314\\n\",\n      \"Epoch 197/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0310\\n\",\n      \"Epoch 198/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0306\\n\",\n      \"Epoch 199/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0303\\n\",\n      \"Epoch 200/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0300\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fd95c610950>\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.01),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=200\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With the model trained, we can see how the model has classified the training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"34b34e13de7b47429128d1cf4695acb8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_cat_mc(X_train, y_train, model, classes)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Above, the decision boundaries show how the model has partitioned the input space.  This very simple model has had no trouble classifying the training data. How did it accomplish this? Let's look at the network in more detail. \\n\",\n    \"\\n\",\n    \"Below, we will pull the trained weights from the model and use that to plot the function of each of the network units. Further down, there is a more detailed explanation of the results. You don't need to know these details to successfully use neural networks, but it may be helpful to gain more intuition about how the layers combine to solve a classification problem.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# gather the trained parameters from the first layer\\n\",\n    \"l1 = model.get_layer(\\\"L1\\\")\\n\",\n    \"W1,b1 = l1.get_weights()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3e67f9c7df94416a863b815e7f1d5219\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot the function of the first layer\\n\",\n    \"plt_layer_relu(X_train, y_train.reshape(-1,), W1, b1, classes)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c6e2d9d77482450b886880a8cbac2ebf\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# gather the trained parameters from the output layer\\n\",\n    \"l2 = model.get_layer(\\\"L2\\\")\\n\",\n    \"W2, b2 = l2.get_weights()\\n\",\n    \"# create the 'new features', the training examples after L1 transformation\\n\",\n    \"Xl2 = np.maximum(0, np.dot(X_train,W1) + b1)\\n\",\n    \"\\n\",\n    \"plt_output_layer_linear(Xl2, y_train.reshape(-1,), W2, b2, classes,\\n\",\n    \"                        x0_rng = (-0.25,np.amax(Xl2[:,0])), x1_rng = (-0.25,np.amax(Xl2[:,1])))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Explanation\\n\",\n    \"#### Layer 1 <img align=\\\"Right\\\" src=\\\"./images/C2_W2_mclass_layer1.png\\\"  style=\\\" width:600px; padding: 10px 20px ; \\\">\\n\",\n    \"These plots show the function of Units 0 and 1 in the first layer of the network. The inputs are ($x_0,x_1$) on the axis. The output of the unit is represented by the color of the background. This is indicated by the color bar on the right of each graph. Notice that since these units are using a ReLu, the outputs do not necessarily fall between 0 and 1 and in this case are greater than 20 at their peaks. \\n\",\n    \"The contour lines in this graph show the transition point between the output, $a^{[1]}_j$ being zero and non-zero. Recall the graph for a ReLu :<img align=\\\"right\\\" src=\\\"./images/C2_W2_mclass_relu.png\\\"  style=\\\" width:200px; padding: 10px 20px ; \\\"> The contour line in the graph is the inflection point in the ReLu.\\n\",\n    \"\\n\",\n    \"Unit 0 has separated classes 0 and 1 from classes 2 and 3. Points to the left of the line (classes 0 and 1) will output zero, while points to the right will output a value greater than zero.  \\n\",\n    \"Unit 1 has separated classes 0 and 2 from classes 1 and 3. Points above the line (classes 0 and 2 ) will output a zero, while points below will output a value greater than zero. Let's see how this works out in the next layer!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Layer 2, the output layer  <img align=\\\"Right\\\" src=\\\"./images/C2_W2_mclass_layer2.png\\\"  style=\\\" width:600px; padding: 10px 20px ; \\\">\\n\",\n    \"\\n\",\n    \"The dots in these graphs are the training examples translated by the first layer. One way to think of this is the first layer has created a new set of features for evaluation by the 2nd layer. The axes in these plots are the outputs of the previous layer $a^{[1]}_0$ and $a^{[1]}_1$. As predicted above, classes 0 and 1 (blue and green) have  $a^{[1]}_0 = 0$ while classes 0 and 2 (blue and orange) have $a^{[1]}_1 = 0$.  \\n\",\n    \"Once again, the intensity of the background color indicates the highest values.  \\n\",\n    \"Unit 0 will produce its maximum value for values near (0,0), where class 0 (blue) has been mapped.    \\n\",\n    \"Unit 1 produces its highest values in the upper left corner selecting class 1 (green).  \\n\",\n    \"Unit 2 targets the lower right corner where class 2 (orange) resides.  \\n\",\n    \"Unit 3 produces its highest values in the upper right selecting our final class (purple).  \\n\",\n    \"\\n\",\n    \"One other aspect that is not obvious from the graphs is that the values have been coordinated between the units. It is not sufficient for a unit to produce a maximum value for the class it is selecting for, it must also be the highest value of all the units for points in that class. This is done by the implied softmax function that is part of the loss function (`SparseCategoricalCrossEntropy`). Unlike other activation functions, the softmax works across all the outputs.\\n\",\n    \"\\n\",\n    \"You can successfully use neural networks without knowing the details of what each unit is up to. Hopefully, this example has provided some intuition about what is happening under the hood.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You have learned to build and operate a neural network for multiclass classification.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/C2_W2_Relu.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab - ReLU activation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib.gridspec import GridSpec\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense, LeakyReLU\\n\",\n    \"from tensorflow.keras.activations import linear, relu, sigmoid\\n\",\n    \"%matplotlib widget\\n\",\n    \"from matplotlib.widgets import Slider\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from autils import plt_act_trio\\n\",\n    \"from lab_utils_relu import *\\n\",\n    \"import warnings\\n\",\n    \"warnings.simplefilter(action='ignore', category=UserWarning)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - ReLU Activation\\n\",\n    \"This week, a new activation was introduced, the Rectified Linear Unit (ReLU). \\n\",\n    \"$$ a = max(0,z) \\\\quad\\\\quad\\\\text {# ReLU function} $$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e8631645d39248c7bba85e5608139136\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_act_trio()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img align=\\\"right\\\" src=\\\"./images/C2_W2_ReLu.png\\\"     style=\\\" width:380px; padding: 10px 20px; \\\" >\\n\",\n    \"The example from the lecture on the right shows an application of the ReLU. In this example, the derived \\\"awareness\\\" feature is not binary but has a continuous range of values. The sigmoid is best for on/off or binary situations. The ReLU provides a continuous linear relationship. Additionally it has an 'off' range where the output is zero.     \\n\",\n    \"The \\\"off\\\" feature makes the ReLU a Non-Linear activation. Why is this needed? Let's examine this below. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Why Non-Linear Activations?  \\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W2_ReLU_Graph.png\\\"     style=\\\" width:250px; padding: 10px 20px; \\\" > The function shown is composed of linear pieces (piecewise linear). The slope is consistent during the linear portion and then changes abruptly at transition points. At transition points, a new linear function is added which, when added to the existing function, will produce the new slope. The new function is added at transition point but does not contribute to the output prior to that point. The non-linear activation function is responsible for disabling the input prior to and sometimes after the transition points. The following exercise provides a more tangible example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The exercise will use the network below in a regression problem where you must model a piecewise linear target :\\n\",\n    \"<img align=\\\"center\\\" src=\\\"./images/C2_W2_ReLU_Network.png\\\"     style=\\\" width:650px; padding: 10px 20px; \\\">  \\n\",\n    \"The network has 3 units in the first layer. Each is required to form the target. Unit 0 is pre-programmed and fixed to map the first segment. You will modify weights and biases in unit 1 and 2 to model the 2nd and 3rd segment. The output unit is also fixed and simply sums the outputs of the first layer.  \\n\",\n    \"\\n\",\n    \"Using the sliders below, modify weights and bias to match the target. \\n\",\n    \"Hints: Start with `w1` and `b1` and leave `w2` and `b2` zero until you match the 2nd segment. Clicking rather than sliding is quicker.  If you have trouble, don't worry, the text below will describe this in more detail.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d89e83fedc19457bb6a7267d3ba2f530\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"_ = plt_relu_ex()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \" \\n\",\n    \"The goal of this exercise is to appreciate how the ReLU's non-linear behavior provides the needed ability to turn functions off until they are needed. Let's see how this worked in this example.\\n\",\n    \"<img align=\\\"right\\\" src=\\\"./images/C2_W2_ReLU_Plot.png\\\"     style=\\\" width:600px; padding: 10px 20px; \\\"> \\n\",\n    \"The plots on the right contain the output of the units in the first layer.   \\n\",\n    \"Starting at the top, unit 0 is responsible for the first segment marked with a 1. Both the linear function $z$ and the function following the ReLU $a$ are shown. You can see that the ReLU cuts off the function after the interval [0,1]. This is important as it prevents Unit 0 from interfering with the following segment. \\n\",\n    \"\\n\",\n    \"Unit 1 is responsible for the 2nd segment. Here the ReLU kept this unit quiet until after x is 1. Since the first unit is not contributing, the slope for unit 1, $w^{[1]}_1$, is just the slope of the target line. The bias must be adjusted to keep the output negative until x has reached 1. Note how the contribution of Unit 1 extends to the 3rd segment as well.\\n\",\n    \"\\n\",\n    \"Unit 2 is responsible for the 3rd segment. The ReLU again zeros the output until x reaches the right value.The slope of the unit, $w^{[1]}_2$, must be set so that the sum of unit 1 and 2 have the desired slope. The bias is again adjusted to keep the output negative until x has reached 2. \\n\",\n    \"\\n\",\n    \"The \\\"off\\\" or disable feature  of the ReLU activation enables models to stitch together linear segments to model complex non-linear functions.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You are now more familiar with the ReLU and the importance of its non-linear behavior.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/C2_W2_SoftMax.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Softmax Function\\n\",\n    \"In this lab, we will explore the softmax function. This function is used in both Softmax Regression and in Neural Networks when solving Multiclass Classification problems.  \\n\",\n    \"\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_Softmax_Header.PNG\\\" width=\\\"600\\\" />  <center/>\\n\",\n    \"\\n\",\n    \"  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from IPython.display import display, Markdown, Latex\\n\",\n    \"from sklearn.datasets import make_blobs\\n\",\n    \"%matplotlib widget\\n\",\n    \"from matplotlib.widgets import Slider\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_utils_softmax import plt_softmax\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> **Note**: Normally, in this course, the notebooks use the convention of starting counts with 0 and ending with N-1,  $\\\\sum_{i=0}^{N-1}$, while lectures start with 1 and end with N,  $\\\\sum_{i=1}^{N}$. This is because code will typically start iteration with 0 while in lecture, counting 1 to N leads to cleaner, more succinct equations. This notebook has more equations than is typical for a lab and thus  will break with the convention and will count 1 to N.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Softmax Function\\n\",\n    \"In both softmax regression and neural networks with Softmax outputs, N outputs are generated and one output is selected as the predicted category. In both cases a vector $\\\\mathbf{z}$ is generated by a linear function which is applied to a softmax function. The softmax function converts $\\\\mathbf{z}$  into a probability distribution as described below. After applying softmax, each output will be between 0 and 1 and the outputs will add to 1, so that they can be interpreted as probabilities. The larger inputs  will correspond to larger output probabilities.\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_SoftmaxReg_NN.png\\\" width=\\\"600\\\" />  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The softmax function can be written:\\n\",\n    \"$$a_j = \\\\frac{e^{z_j}}{ \\\\sum_{k=1}^{N}{e^{z_k} }} \\\\tag{1}$$\\n\",\n    \"The output $\\\\mathbf{a}$ is a vector of length N, so for softmax regression, you could also write:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\mathbf{a}(x) =\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"P(y = 1 | \\\\mathbf{x}; \\\\mathbf{w},b) \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"P(y = N | \\\\mathbf{x}; \\\\mathbf{w},b)\\n\",\n    \"\\\\end{bmatrix}\\n\",\n    \"=\\n\",\n    \"\\\\frac{1}{ \\\\sum_{k=1}^{N}{e^{z_k} }}\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"e^{z_1} \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"e^{z_{N}} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix} \\\\tag{2}\\n\",\n    \"\\\\end{align}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Which shows the output is a vector of probabilities. The first entry is the probability the input is the first category given the input $\\\\mathbf{x}$ and parameters $\\\\mathbf{w}$ and $\\\\mathbf{b}$.  \\n\",\n    \"Let's create a NumPy implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_softmax(z):\\n\",\n    \"    ez = np.exp(z)              #element-wise exponenial\\n\",\n    \"    sm = ez/np.sum(ez)\\n\",\n    \"    return(sm)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below, vary the values of the `z` inputs using the sliders.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"cce96a6a945f4b63988352bac2bf0a85\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_softmax(my_softmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you are varying the values of the z's above, there are a few things to note:\\n\",\n    \"* the exponential in the numerator of the softmax magnifies small differences in the values \\n\",\n    \"* the output values sum to one\\n\",\n    \"* the softmax spans all of the outputs. A change in `z0` for example will change the values of `a0`-`a3`. Compare this to other activations such as ReLU or Sigmoid which have a single input and single output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Cost\\n\",\n    \"<center> <img  src=\\\"./images/C2_W2_SoftMaxCost.png\\\" width=\\\"400\\\" />    <center/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The loss function associated with Softmax, the cross-entropy loss, is:\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"  L(\\\\mathbf{a},y)=\\\\begin{cases}\\n\",\n    \"    -log(a_1), & \\\\text{if $y=1$}.\\\\\\\\\\n\",\n    \"        &\\\\vdots\\\\\\\\\\n\",\n    \"     -log(a_N), & \\\\text{if $y=N$}\\n\",\n    \"  \\\\end{cases} \\\\tag{3}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\n\",\n    \"Where y is the target category for this example and $\\\\mathbf{a}$ is the output of a softmax function. In particular, the values in $\\\\mathbf{a}$ are probabilities that sum to one.\\n\",\n    \">**Recall:** In this course, Loss is for one example while Cost covers all examples. \\n\",\n    \" \\n\",\n    \" \\n\",\n    \"Note in (3) above, only the line that corresponds to the target contributes to the loss, other lines are zero. To write the cost equation we need an 'indicator function' that will be 1 when the index matches the target and zero otherwise. \\n\",\n    \"    $$\\\\mathbf{1}\\\\{y == n\\\\} = =\\\\begin{cases}\\n\",\n    \"    1, & \\\\text{if $y==n$}.\\\\\\\\\\n\",\n    \"    0, & \\\\text{otherwise}.\\n\",\n    \"  \\\\end{cases}$$\\n\",\n    \"Now the cost is:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"J(\\\\mathbf{w},b) = -\\\\frac{1}{m} \\\\left[ \\\\sum_{i=1}^{m} \\\\sum_{j=1}^{N}  1\\\\left\\\\{y^{(i)} == j\\\\right\\\\} \\\\log \\\\frac{e^{z^{(i)}_j}}{\\\\sum_{k=1}^N e^{z^{(i)}_k} }\\\\right] \\\\tag{4}\\n\",\n    \"\\\\end{align}\\n\",\n    \"\\n\",\n    \"Where $m$ is the number of examples, $N$ is the number of outputs. This is the average of all the losses.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Tensorflow\\n\",\n    \"This lab will discuss two ways of implementing the softmax, cross-entropy loss in Tensorflow, the 'obvious' method and the 'preferred' method. The former is the most straightforward while the latter is more numerically stable.\\n\",\n    \"\\n\",\n    \"Let's start by creating a dataset to train a multiclass classification model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# make  dataset for example\\n\",\n    \"centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\\n\",\n    \"X_train, y_train = make_blobs(n_samples=2000, centers=centers, cluster_std=1.0,random_state=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### The *Obvious* organization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model below is implemented with the softmax as an activation in the final Dense layer.\\n\",\n    \"The loss function is separately specified in the `compile` directive. \\n\",\n    \"\\n\",\n    \"The loss function is `SparseCategoricalCrossentropy`. This loss is described in (3) above. In this model, the softmax takes place in the last layer. The loss function takes in the softmax output which is a vector of probabilities. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/10\\n\",\n      \"63/63 [==============================] - 0s 966us/step - loss: 0.8312\\n\",\n      \"Epoch 2/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.3203\\n\",\n      \"Epoch 3/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.1408\\n\",\n      \"Epoch 4/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0847\\n\",\n      \"Epoch 5/10\\n\",\n      \"63/63 [==============================] - 0s 944us/step - loss: 0.0626\\n\",\n      \"Epoch 6/10\\n\",\n      \"63/63 [==============================] - 0s 974us/step - loss: 0.0515\\n\",\n      \"Epoch 7/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0447\\n\",\n      \"Epoch 8/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0402\\n\",\n      \"Epoch 9/10\\n\",\n      \"63/63 [==============================] - 0s 922us/step - loss: 0.0361\\n\",\n      \"Epoch 10/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0332\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fd399415490>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'softmax')    # < softmax activation here\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Because the softmax is integrated into the output layer, the output is a vector of probabilities.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[[3.39e-03 1.02e-02 9.73e-01 1.30e-02]\\n\",\n      \" [9.97e-01 3.46e-03 9.35e-06 9.02e-06]]\\n\",\n      \"largest value 0.9999994 smallest value 4.1378247e-09\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"p_nonpreferred = model.predict(X_train)\\n\",\n    \"print(p_nonpreferred [:2])\\n\",\n    \"print(\\\"largest value\\\", np.max(p_nonpreferred), \\\"smallest value\\\", np.min(p_nonpreferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Preferred <img align=\\\"Right\\\" src=\\\"./images/C2_W2_softmax_accurate.png\\\"  style=\\\" width:400px; padding: 10px 20px ; \\\">\\n\",\n    \"Recall from lecture, more stable and accurate results can be obtained if the softmax and loss are combined during training.   This is enabled by the 'preferred' organization shown here.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the preferred organization the final layer has a linear activation. For historical reasons, the outputs in this form are referred to as *logits*. The loss function has an additional argument: `from_logits = True`. This informs the loss function that the softmax operation should be included in the loss calculation. This allows for an optimized implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 1.0936\\n\",\n      \"Epoch 2/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.4933\\n\",\n      \"Epoch 3/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.2809\\n\",\n      \"Epoch 4/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.1506\\n\",\n      \"Epoch 5/10\\n\",\n      \"63/63 [==============================] - 0s 966us/step - loss: 0.0916\\n\",\n      \"Epoch 6/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0694\\n\",\n      \"Epoch 7/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0592\\n\",\n      \"Epoch 8/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0527\\n\",\n      \"Epoch 9/10\\n\",\n      \"63/63 [==============================] - 0s 919us/step - loss: 0.0489\\n\",\n      \"Epoch 10/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0457\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fd23c552050>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"preferred_model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'linear')   #<-- Note\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"preferred_model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),  #<-- Note\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"preferred_model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Output Handling\\n\",\n    \"Notice that in the preferred model, the outputs are not probabilities, but can range from large negative numbers to large positive numbers. The output must be sent through a softmax when performing a prediction that expects a probability. \\n\",\n    \"Let's look at the preferred model outputs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"two example output vectors:\\n\",\n      \" [[-2.72 -4.45  2.9  -1.37]\\n\",\n      \" [ 6.42  1.32 -1.32 -6.74]]\\n\",\n      \"largest value 12.410254 smallest value -13.030992\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"p_preferred = preferred_model.predict(X_train)\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {p_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(p_preferred), \\\"smallest value\\\", np.min(p_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output predictions are not probabilities!\\n\",\n    \"If the desired output are probabilities, the output should be be processed by a [softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"two example output vectors:\\n\",\n      \" [[3.55e-03 6.34e-04 9.82e-01 1.38e-02]\\n\",\n      \" [9.94e-01 6.05e-03 4.35e-04 1.92e-06]]\\n\",\n      \"largest value 0.9999995 smallest value 1.5196424e-11\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"sm_preferred = tf.nn.softmax(p_preferred).numpy()\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {sm_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(sm_preferred), \\\"smallest value\\\", np.min(sm_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To select the most likely category, the softmax is not required. One can find the index of the largest output using [np.argmax()](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-2.72 -4.45  2.9  -1.37], category: 2\\n\",\n      \"[ 6.42  1.32 -1.32 -6.74], category: 0\\n\",\n      \"[ 4.64  1.5  -1.31 -5.3 ], category: 0\\n\",\n      \"[-0.29  3.76 -3.11 -1.58], category: 1\\n\",\n      \"[-0.64 -6.05  5.23 -5.2 ], category: 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i in range(5):\\n\",\n    \"    print( f\\\"{p_preferred[i]}, category: {np.argmax(p_preferred[i])}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## SparseCategorialCrossentropy or CategoricalCrossEntropy\\n\",\n    \"Tensorflow has two potential formats for target values and the selection of the loss defines which is expected.\\n\",\n    \"- SparseCategorialCrossentropy: expects the target to be an integer corresponding to the index. For example, if there are 10 potential target values, y would be between 0 and 9. \\n\",\n    \"- CategoricalCrossEntropy: Expects the target value of an example to be one-hot encoded where the value at the target index is 1 while the other N-1 entries are zero. An example with 10 potential target values, where the target is 2 would be [0,0,1,0,0,0,0,0,0,0].\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you \\n\",\n    \"- Became more familiar with the softmax function and its use in softmax regression and in softmax activations in neural networks. \\n\",\n    \"- Learned the preferred model construction in Tensorflow:\\n\",\n    \"    - No activation on the final layer (same as linear activation)\\n\",\n    \"    - SparseCategoricalCrossentropy loss function\\n\",\n    \"    - use from_logits=True\\n\",\n    \"- Recognized that unlike ReLU and Sigmoid, the softmax spans multiple outputs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/archive/.ipynb_checkpoints/C2_W2_SoftMax-Copy1-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Softmax Function\\n\",\n    \"In this lab, we will explore the softmax function. This function is used in both Softmax Regression and in Neural Networks when solving Multiclass Classification problems.  \\n\",\n    \"\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_Softmax_Header.PNG\\\" width=\\\"600\\\" />  <center/>\\n\",\n    \"\\n\",\n    \"  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from IPython.display import display, Markdown, Latex\\n\",\n    \"from sklearn.datasets import make_blobs\\n\",\n    \"%matplotlib widget\\n\",\n    \"from matplotlib.widgets import Slider\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_utils_softmax import plt_softmax\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> **Note**: Normally, in this course, the notebooks use the convention of starting counts with 0 and ending with N-1,  $\\\\sum_{i=0}^{N-1}$, while lectures start with 1 and end with N,  $\\\\sum_{i=1}^{N}$. This is because code will typically start iteration with 0 while in lecture, counting 1 to N leads to cleaner, more succinct equations. This notebook has more equations than is typical for a lab and thus  will break with the convention and will count 1 to N.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Softmax Function\\n\",\n    \"In both softmax regression and neural networks with Softmax outputs, N outputs are generated and one output is selected as the predicted category. In both cases a vector $\\\\mathbf{z}$ is generated by a linear function which is applied to a softmax function. The softmax function converts $\\\\mathbf{z}$  into a probability distribution as described below. After applying softmax, each output will be between 0 and 1 and the outputs will add to 1, so that they can be interpreted as probabilities. The larger inputs  will correspond to larger output probabilities.\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_SoftmaxReg_NN.png\\\" width=\\\"600\\\" />  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The softmax function can be written:\\n\",\n    \"$$a_j = \\\\frac{e^{z_j}}{ \\\\sum_{k=1}^{N}{e^{z_k} }} \\\\tag{1}$$\\n\",\n    \"The output $\\\\mathbf{a}$ is a vector of length N, so for softmax regression, you could also write:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\mathbf{a}(x) =\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"P(y = 1 | \\\\mathbf{x}; \\\\mathbf{w},b) \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"P(y = N | \\\\mathbf{x}; \\\\mathbf{w},b)\\n\",\n    \"\\\\end{bmatrix}\\n\",\n    \"=\\n\",\n    \"\\\\frac{1}{ \\\\sum_{k=1}^{N}{e^{z_k} }}\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"e^{z_1} \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"e^{z_{N}} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix} \\\\tag{2}\\n\",\n    \"\\\\end{align}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Which shows the output is a vector of probabilities. The first entry is the probability the input is the first category given the input $\\\\mathbf{x}$ and parameters $\\\\mathbf{w}$ and $\\\\mathbf{b}$.  \\n\",\n    \"Let's create a NumPy implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_softmax(z):\\n\",\n    \"    ez = np.exp(z)              #element-wise exponenial\\n\",\n    \"    sm = ez/np.sum(ez)\\n\",\n    \"    return(sm)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below, vary the values of the `z` inputs using the sliders.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_softmax(my_softmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you are varying the values of the z's above, there are a few things to note:\\n\",\n    \"* the exponential in the numerator of the softmax magnifies small differences in the values \\n\",\n    \"* the output values sum to one\\n\",\n    \"* the softmax spans all of the outputs. A change in `z0` for example will change the values of `a0`-`a3`. Compare this to other activations such as ReLU or Sigmoid which have a single input and single output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Cost\\n\",\n    \"<center> <img  src=\\\"./images/C2_W2_SoftMaxCost.png\\\" width=\\\"400\\\" />    <center/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The loss function associated with Softmax, the cross-entropy loss, is:\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"  L(\\\\mathbf{a},y)=\\\\begin{cases}\\n\",\n    \"    -log(a_1), & \\\\text{if $y=1$}.\\\\\\\\\\n\",\n    \"        &\\\\vdots\\\\\\\\\\n\",\n    \"     -log(a_N), & \\\\text{if $y=N$}\\n\",\n    \"  \\\\end{cases} \\\\tag{3}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\n\",\n    \"Where y is the target category for this example and $\\\\mathbf{a}$ is the output of a softmax function. In particular, the values in $\\\\mathbf{a}$ are probabilities that sum to one.\\n\",\n    \">**Recall:** In this course, Loss is for one example while Cost covers all examples. \\n\",\n    \" \\n\",\n    \" \\n\",\n    \"Note in (3) above, only the line that corresponds to the target contributes to the loss, other lines are zero. To write the cost equation we need an 'indicator function' that will be 1 when the index matches the target and zero otherwise. \\n\",\n    \"    $$\\\\mathbf{1}\\\\{y == n\\\\} = =\\\\begin{cases}\\n\",\n    \"    1, & \\\\text{if $y==n$}.\\\\\\\\\\n\",\n    \"    0, & \\\\text{otherwise}.\\n\",\n    \"  \\\\end{cases}$$\\n\",\n    \"Now the cost is:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"J(\\\\mathbf{w},b) = - \\\\left[ \\\\sum_{i=1}^{m} \\\\sum_{j=1}^{N}  1\\\\left\\\\{y^{(i)} == j\\\\right\\\\} \\\\log \\\\frac{e^{z^{(i)}_j}}{\\\\sum_{k=1}^N e^{z^{(i)}_k} }\\\\right] \\\\tag{4}\\n\",\n    \"\\\\end{align}\\n\",\n    \"\\n\",\n    \"Where $m$ is the number of examples, $N$ is the number of outputs. This is the average of all the losses.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Tensorflow\\n\",\n    \"This lab will discuss two ways of implementing the softmax, cross-entropy loss in Tensorflow, the 'obvious' method and the 'preferred' method. The former is the most straightforward while the latter is more numerically stable.\\n\",\n    \"\\n\",\n    \"Let's start by creating a dataset to train a multiclass classification model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# make  dataset for example\\n\",\n    \"centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\\n\",\n    \"X_train, y_train = make_blobs(n_samples=2000, centers=centers, cluster_std=1.0,random_state=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### The *Obvious* organization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model below is implemented with the softmax as an activation in the final Dense layer.\\n\",\n    \"The loss function is separately specified in the `compile` directive. \\n\",\n    \"\\n\",\n    \"The loss function `SparseCategoricalCrossentropy`. The loss described in (3) above. In this model, the softmax takes place in the last layer. The loss function takes in the softmax output which is a vector of probabilities. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'softmax')    # < softmax activation here\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Because the softmax is integrated into the output layer, the output is a vector of probabilities.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"p_nonpreferred = model.predict(X_train)\\n\",\n    \"print(p_nonpreferred [:2])\\n\",\n    \"print(\\\"largest value\\\", np.max(p_nonpreferred), \\\"smallest value\\\", np.min(p_nonpreferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Preferred <img align=\\\"Right\\\" src=\\\"./images/C2_W2_softmax_accurate.png\\\"  style=\\\" width:400px; padding: 10px 20px ; \\\">\\n\",\n    \"Recall from lecture, more stable and accurate results can be obtained if the softmax and loss are combined during training.   This is enabled by the 'preferred' organization shown here.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the preferred organization the final layer has a linear activation. For historical reasons, the outputs in this form are referred to as *logits*. The loss function has an additional argument: `from_logits = True`. This informs the loss function that the softmax operation should be included in the loss calculation. This allows for an optimized implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"preferred_model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'linear')   #<-- Note\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"preferred_model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),  #<-- Note\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"preferred_model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Output Handling\\n\",\n    \"Notice that in the preferred model, the outputs are not probabilities, but can range from large negative numbers to large positive numbers. The output must be sent through a softmax when performing a prediction that expects a probability. \\n\",\n    \"Let's look at the preferred model outputs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"p_preferred = preferred_model.predict(X_train)\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {p_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(p_preferred), \\\"smallest value\\\", np.min(p_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output predictions are not probabilities!\\n\",\n    \"If the desired output are probabilities, the output should be be processed by a [softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sm_preferred = tf.nn.softmax(p_preferred).numpy()\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {sm_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(sm_preferred), \\\"smallest value\\\", np.min(sm_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To select the most likely category, the softmax is not required. One can find the index of the largest output using [np.argmax()](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for i in range(5):\\n\",\n    \"    print( f\\\"{p_preferred[i]}, category: {np.argmax(p_preferred[i])}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## SparseCategorialCrossentropy or CategoricalCrossEntropy\\n\",\n    \"Tensorflow has two potential formats for target values and the selection of the loss defines which is expected.\\n\",\n    \"- SparseCategorialCrossentropy: expects the target to be an integer corresponding to the index. For example, if there are 10 potential target values, y would be between 0 and 9. \\n\",\n    \"- CategoricalCrossEntropy: Expects the target value of an example to be one-hot encoded where the value at the target index is 1 while the other N-1 entries are zero. An example with 10 potential target values, where the target is 2 would be [0,0,1,0,0,0,0,0,0,0].\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you \\n\",\n    \"- Became more familiar with the softmax function and its use in softmax regression and in softmax activations in neural networks. \\n\",\n    \"- Learned the preferred model construction in Tensorflow:\\n\",\n    \"    - No activation on the final layer (same as linear activation)\\n\",\n    \"    - SparseCategoricalCrossentropy loss function\\n\",\n    \"    - use from_logits=True\\n\",\n    \"- Recognized that unlike ReLU and Sigmoid, the softmax spans multiple outputs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/archive/.ipynb_checkpoints/C2_W2_SoftMax-Copy2-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Softmax Function\\n\",\n    \"In this lab, we will explore the softmax function. This function is used in both Softmax Regression and in Neural Networks when solving Multiclass Classification problems.  \\n\",\n    \"\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_Softmax_Header.PNG\\\" width=\\\"600\\\" />  <center/>\\n\",\n    \"\\n\",\n    \"  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from IPython.display import display, Markdown, Latex\\n\",\n    \"from sklearn.datasets import make_blobs\\n\",\n    \"%matplotlib widget\\n\",\n    \"from matplotlib.widgets import Slider\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_utils_softmax import plt_softmax\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> **Note**: Normally, in this course, the notebooks use the convention of starting counts with 0 and ending with N-1,  $\\\\sum_{i=0}^{N-1}$, while lectures start with 1 and end with N,  $\\\\sum_{i=1}^{N}$. This is because code will typically start iteration with 0 while in lecture, counting 1 to N leads to cleaner, more succinct equations. This notebook has more equations than is typical for a lab and thus  will break with the convention and will count 1 to N.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Softmax Function\\n\",\n    \"In both softmax regression and neural networks with Softmax outputs, N outputs are generated and one output is selected as the predicted category. In both cases a vector $\\\\mathbf{z}$ is generated by a linear function which is applied to a softmax function. The softmax function converts $\\\\mathbf{z}$  into a probability distribution as described below. After applying softmax, each output will be between 0 and 1 and the outputs will add to 1, so that they can be interpreted as probabilities. The larger inputs  will correspond to larger output probabilities.\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_SoftmaxReg_NN.png\\\" width=\\\"600\\\" />  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The softmax function can be written:\\n\",\n    \"$$a_j = \\\\frac{e^{z_j}}{ \\\\sum_{k=1}^{N}{e^{z_k} }} \\\\tag{1}$$\\n\",\n    \"The output $\\\\mathbf{a}$ is a vector of length N, so for softmax regression, you could also write:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\mathbf{a}(x) =\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"P(y = 1 | \\\\mathbf{x}; \\\\mathbf{w},b) \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"P(y = N | \\\\mathbf{x}; \\\\mathbf{w},b)\\n\",\n    \"\\\\end{bmatrix}\\n\",\n    \"=\\n\",\n    \"\\\\frac{1}{ \\\\sum_{k=1}^{N}{e^{z_k} }}\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"e^{z_1} \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"e^{z_{N}} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix} \\\\tag{2}\\n\",\n    \"\\\\end{align}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Which shows the output is a vector of probabilities. The first entry is the probability the input is the first category given the input $\\\\mathbf{x}$ and parameters $\\\\mathbf{w}$ and $\\\\mathbf{b}$.  \\n\",\n    \"Let's create a NumPy implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_softmax(z):\\n\",\n    \"    ez = np.exp(z)              #element-wise exponenial\\n\",\n    \"    sm = ez/np.sum(ez)\\n\",\n    \"    return(sm)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below, vary the values of the `z` inputs using the sliders.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_softmax(my_softmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you are varying the values of the z's above, there are a few things to note:\\n\",\n    \"* the exponential in the numerator of the softmax magnifies small differences in the values \\n\",\n    \"* the output values sum to one\\n\",\n    \"* the softmax spans all of the outputs. A change in `z0` for example will change the values of `a0`-`a3`. Compare this to other activations such as ReLU or Sigmoid which have a single input and single output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Cost\\n\",\n    \"<center> <img  src=\\\"./images/C2_W2_SoftMaxCost.png\\\" width=\\\"400\\\" />    <center/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The loss function associated with Softmax, the cross-entropy loss, is:\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"  L(\\\\mathbf{a},y)=\\\\begin{cases}\\n\",\n    \"    -log(a_1), & \\\\text{if $y=1$}.\\\\\\\\\\n\",\n    \"        &\\\\vdots\\\\\\\\\\n\",\n    \"     -log(a_N), & \\\\text{if $y=N$}\\n\",\n    \"  \\\\end{cases} \\\\tag{3}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\n\",\n    \"Where y is the target category for this example and $\\\\mathbf{a}$ is the output of a softmax function. In particular, the values in $\\\\mathbf{a}$ are probabilities that sum to one.\\n\",\n    \">**Recall:** In this course, Loss is for one example while Cost covers all examples. \\n\",\n    \" \\n\",\n    \" \\n\",\n    \"Note in (3) above, only the line that corresponds to the target contributes to the loss, other lines are zero. To write the cost equation we need an 'indicator function' that will be 1 when the index matches the target and zero otherwise. \\n\",\n    \"    $$\\\\mathbf{1}\\\\{y == n\\\\} = =\\\\begin{cases}\\n\",\n    \"    1, & \\\\text{if $y==n$}.\\\\\\\\\\n\",\n    \"    0, & \\\\text{otherwise}.\\n\",\n    \"  \\\\end{cases}$$\\n\",\n    \"Now the cost is:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"J(\\\\mathbf{w},b) = - \\\\left[ \\\\sum_{i=1}^{m} \\\\sum_{j=1}^{N}  1\\\\left\\\\{y^{(i)} == j\\\\right\\\\} \\\\log \\\\frac{e^{z^{(i)}_j}}{\\\\sum_{k=1}^N e^{z^{(i)}_k} }\\\\right] \\\\tag{4}\\n\",\n    \"\\\\end{align}\\n\",\n    \"\\n\",\n    \"Where $m$ is the number of examples, $N$ is the number of outputs. This is the average of all the losses.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Tensorflow\\n\",\n    \"This lab will discuss two ways of implementing the softmax, cross-entropy loss in Tensorflow, the 'obvious' method and the 'preferred' method. The former is the most straightforward while the latter is more numerically stable.\\n\",\n    \"\\n\",\n    \"Let's start by creating a dataset to train a multiclass classification model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# make  dataset for example\\n\",\n    \"centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\\n\",\n    \"X_train, y_train = make_blobs(n_samples=2000, centers=centers, cluster_std=1.0,random_state=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### The *Obvious* organization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model below is implemented with the softmax as an activation in the final Dense layer.\\n\",\n    \"The loss function is separately specified in the `compile` directive. \\n\",\n    \"\\n\",\n    \"The loss function is `SparseCategoricalCrossentropy`. This loss is described in (3) above. In this model, the softmax takes place in the last layer. The loss function takes in the softmax output which is a vector of probabilities. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'softmax')    # < softmax activation here\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Because the softmax is integrated into the output layer, the output is a vector of probabilities.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"p_nonpreferred = model.predict(X_train)\\n\",\n    \"print(p_nonpreferred [:2])\\n\",\n    \"print(\\\"largest value\\\", np.max(p_nonpreferred), \\\"smallest value\\\", np.min(p_nonpreferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Preferred <img align=\\\"Right\\\" src=\\\"./images/C2_W2_softmax_accurate.png\\\"  style=\\\" width:400px; padding: 10px 20px ; \\\">\\n\",\n    \"Recall from lecture, more stable and accurate results can be obtained if the softmax and loss are combined during training.   This is enabled by the 'preferred' organization shown here.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the preferred organization the final layer has a linear activation. For historical reasons, the outputs in this form are referred to as *logits*. The loss function has an additional argument: `from_logits = True`. This informs the loss function that the softmax operation should be included in the loss calculation. This allows for an optimized implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"preferred_model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'linear')   #<-- Note\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"preferred_model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),  #<-- Note\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"preferred_model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Output Handling\\n\",\n    \"Notice that in the preferred model, the outputs are not probabilities, but can range from large negative numbers to large positive numbers. The output must be sent through a softmax when performing a prediction that expects a probability. \\n\",\n    \"Let's look at the preferred model outputs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"p_preferred = preferred_model.predict(X_train)\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {p_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(p_preferred), \\\"smallest value\\\", np.min(p_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output predictions are not probabilities!\\n\",\n    \"If the desired output are probabilities, the output should be be processed by a [softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sm_preferred = tf.nn.softmax(p_preferred).numpy()\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {sm_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(sm_preferred), \\\"smallest value\\\", np.min(sm_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To select the most likely category, the softmax is not required. One can find the index of the largest output using [np.argmax()](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for i in range(5):\\n\",\n    \"    print( f\\\"{p_preferred[i]}, category: {np.argmax(p_preferred[i])}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## SparseCategorialCrossentropy or CategoricalCrossEntropy\\n\",\n    \"Tensorflow has two potential formats for target values and the selection of the loss defines which is expected.\\n\",\n    \"- SparseCategorialCrossentropy: expects the target to be an integer corresponding to the index. For example, if there are 10 potential target values, y would be between 0 and 9. \\n\",\n    \"- CategoricalCrossEntropy: Expects the target value of an example to be one-hot encoded where the value at the target index is 1 while the other N-1 entries are zero. An example with 10 potential target values, where the target is 2 would be [0,0,1,0,0,0,0,0,0,0].\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you \\n\",\n    \"- Became more familiar with the softmax function and its use in softmax regression and in softmax activations in neural networks. \\n\",\n    \"- Learned the preferred model construction in Tensorflow:\\n\",\n    \"    - No activation on the final layer (same as linear activation)\\n\",\n    \"    - SparseCategoricalCrossentropy loss function\\n\",\n    \"    - use from_logits=True\\n\",\n    \"- Recognized that unlike ReLU and Sigmoid, the softmax spans multiple outputs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/archive/C2_W2_SoftMax-Copy1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Softmax Function\\n\",\n    \"In this lab, we will explore the softmax function. This function is used in both Softmax Regression and in Neural Networks when solving Multiclass Classification problems.  \\n\",\n    \"\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_Softmax_Header.PNG\\\" width=\\\"600\\\" />  <center/>\\n\",\n    \"\\n\",\n    \"  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from IPython.display import display, Markdown, Latex\\n\",\n    \"from sklearn.datasets import make_blobs\\n\",\n    \"%matplotlib widget\\n\",\n    \"from matplotlib.widgets import Slider\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_utils_softmax import plt_softmax\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> **Note**: Normally, in this course, the notebooks use the convention of starting counts with 0 and ending with N-1,  $\\\\sum_{i=0}^{N-1}$, while lectures start with 1 and end with N,  $\\\\sum_{i=1}^{N}$. This is because code will typically start iteration with 0 while in lecture, counting 1 to N leads to cleaner, more succinct equations. This notebook has more equations than is typical for a lab and thus  will break with the convention and will count 1 to N.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Softmax Function\\n\",\n    \"In both softmax regression and neural networks with Softmax outputs, N outputs are generated and one output is selected as the predicted category. In both cases a vector $\\\\mathbf{z}$ is generated by a linear function which is applied to a softmax function. The softmax function converts $\\\\mathbf{z}$  into a probability distribution as described below. After applying softmax, each output will be between 0 and 1 and the outputs will add to 1, so that they can be interpreted as probabilities. The larger inputs  will correspond to larger output probabilities.\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_SoftmaxReg_NN.png\\\" width=\\\"600\\\" />  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The softmax function can be written:\\n\",\n    \"$$a_j = \\\\frac{e^{z_j}}{ \\\\sum_{k=1}^{N}{e^{z_k} }} \\\\tag{1}$$\\n\",\n    \"The output $\\\\mathbf{a}$ is a vector of length N, so for softmax regression, you could also write:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\mathbf{a}(x) =\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"P(y = 1 | \\\\mathbf{x}; \\\\mathbf{w},b) \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"P(y = N | \\\\mathbf{x}; \\\\mathbf{w},b)\\n\",\n    \"\\\\end{bmatrix}\\n\",\n    \"=\\n\",\n    \"\\\\frac{1}{ \\\\sum_{k=1}^{N}{e^{z_k} }}\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"e^{z_1} \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"e^{z_{N}} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix} \\\\tag{2}\\n\",\n    \"\\\\end{align}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Which shows the output is a vector of probabilities. The first entry is the probability the input is the first category given the input $\\\\mathbf{x}$ and parameters $\\\\mathbf{w}$ and $\\\\mathbf{b}$.  \\n\",\n    \"Let's create a NumPy implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_softmax(z):\\n\",\n    \"    ez = np.exp(z)              #element-wise exponenial\\n\",\n    \"    sm = ez/np.sum(ez)\\n\",\n    \"    return(sm)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below, vary the values of the `z` inputs using the sliders.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_softmax(my_softmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you are varying the values of the z's above, there are a few things to note:\\n\",\n    \"* the exponential in the numerator of the softmax magnifies small differences in the values \\n\",\n    \"* the output values sum to one\\n\",\n    \"* the softmax spans all of the outputs. A change in `z0` for example will change the values of `a0`-`a3`. Compare this to other activations such as ReLU or Sigmoid which have a single input and single output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Cost\\n\",\n    \"<center> <img  src=\\\"./images/C2_W2_SoftMaxCost.png\\\" width=\\\"400\\\" />    <center/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The loss function associated with Softmax, the cross-entropy loss, is:\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"  L(\\\\mathbf{a},y)=\\\\begin{cases}\\n\",\n    \"    -log(a_1), & \\\\text{if $y=1$}.\\\\\\\\\\n\",\n    \"        &\\\\vdots\\\\\\\\\\n\",\n    \"     -log(a_N), & \\\\text{if $y=N$}\\n\",\n    \"  \\\\end{cases} \\\\tag{3}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\n\",\n    \"Where y is the target category for this example and $\\\\mathbf{a}$ is the output of a softmax function. In particular, the values in $\\\\mathbf{a}$ are probabilities that sum to one.\\n\",\n    \">**Recall:** In this course, Loss is for one example while Cost covers all examples. \\n\",\n    \" \\n\",\n    \" \\n\",\n    \"Note in (3) above, only the line that corresponds to the target contributes to the loss, other lines are zero. To write the cost equation we need an 'indicator function' that will be 1 when the index matches the target and zero otherwise. \\n\",\n    \"    $$\\\\mathbf{1}\\\\{y == n\\\\} = =\\\\begin{cases}\\n\",\n    \"    1, & \\\\text{if $y==n$}.\\\\\\\\\\n\",\n    \"    0, & \\\\text{otherwise}.\\n\",\n    \"  \\\\end{cases}$$\\n\",\n    \"Now the cost is:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"J(\\\\mathbf{w},b) = - \\\\left[ \\\\sum_{i=1}^{m} \\\\sum_{j=1}^{N}  1\\\\left\\\\{y^{(i)} == j\\\\right\\\\} \\\\log \\\\frac{e^{z^{(i)}_j}}{\\\\sum_{k=1}^N e^{z^{(i)}_k} }\\\\right] \\\\tag{4}\\n\",\n    \"\\\\end{align}\\n\",\n    \"\\n\",\n    \"Where $m$ is the number of examples, $N$ is the number of outputs. This is the average of all the losses.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Tensorflow\\n\",\n    \"This lab will discuss two ways of implementing the softmax, cross-entropy loss in Tensorflow, the 'obvious' method and the 'preferred' method. The former is the most straightforward while the latter is more numerically stable.\\n\",\n    \"\\n\",\n    \"Let's start by creating a dataset to train a multiclass classification model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# make  dataset for example\\n\",\n    \"centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\\n\",\n    \"X_train, y_train = make_blobs(n_samples=2000, centers=centers, cluster_std=1.0,random_state=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### The *Obvious* organization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model below is implemented with the softmax as an activation in the final Dense layer.\\n\",\n    \"The loss function is separately specified in the `compile` directive. \\n\",\n    \"\\n\",\n    \"The loss function `SparseCategoricalCrossentropy`. The loss described in (3) above. In this model, the softmax takes place in the last layer. The loss function takes in the softmax output which is a vector of probabilities. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'softmax')    # < softmax activation here\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Because the softmax is integrated into the output layer, the output is a vector of probabilities.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"p_nonpreferred = model.predict(X_train)\\n\",\n    \"print(p_nonpreferred [:2])\\n\",\n    \"print(\\\"largest value\\\", np.max(p_nonpreferred), \\\"smallest value\\\", np.min(p_nonpreferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Preferred <img align=\\\"Right\\\" src=\\\"./images/C2_W2_softmax_accurate.png\\\"  style=\\\" width:400px; padding: 10px 20px ; \\\">\\n\",\n    \"Recall from lecture, more stable and accurate results can be obtained if the softmax and loss are combined during training.   This is enabled by the 'preferred' organization shown here.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the preferred organization the final layer has a linear activation. For historical reasons, the outputs in this form are referred to as *logits*. The loss function has an additional argument: `from_logits = True`. This informs the loss function that the softmax operation should be included in the loss calculation. This allows for an optimized implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"preferred_model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'linear')   #<-- Note\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"preferred_model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),  #<-- Note\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"preferred_model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Output Handling\\n\",\n    \"Notice that in the preferred model, the outputs are not probabilities, but can range from large negative numbers to large positive numbers. The output must be sent through a softmax when performing a prediction that expects a probability. \\n\",\n    \"Let's look at the preferred model outputs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"p_preferred = preferred_model.predict(X_train)\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {p_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(p_preferred), \\\"smallest value\\\", np.min(p_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output predictions are not probabilities!\\n\",\n    \"If the desired output are probabilities, the output should be be processed by a [softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sm_preferred = tf.nn.softmax(p_preferred).numpy()\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {sm_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(sm_preferred), \\\"smallest value\\\", np.min(sm_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To select the most likely category, the softmax is not required. One can find the index of the largest output using [np.argmax()](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for i in range(5):\\n\",\n    \"    print( f\\\"{p_preferred[i]}, category: {np.argmax(p_preferred[i])}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## SparseCategorialCrossentropy or CategoricalCrossEntropy\\n\",\n    \"Tensorflow has two potential formats for target values and the selection of the loss defines which is expected.\\n\",\n    \"- SparseCategorialCrossentropy: expects the target to be an integer corresponding to the index. For example, if there are 10 potential target values, y would be between 0 and 9. \\n\",\n    \"- CategoricalCrossEntropy: Expects the target value of an example to be one-hot encoded where the value at the target index is 1 while the other N-1 entries are zero. An example with 10 potential target values, where the target is 2 would be [0,0,1,0,0,0,0,0,0,0].\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you \\n\",\n    \"- Became more familiar with the softmax function and its use in softmax regression and in softmax activations in neural networks. \\n\",\n    \"- Learned the preferred model construction in Tensorflow:\\n\",\n    \"    - No activation on the final layer (same as linear activation)\\n\",\n    \"    - SparseCategoricalCrossentropy loss function\\n\",\n    \"    - use from_logits=True\\n\",\n    \"- Recognized that unlike ReLU and Sigmoid, the softmax spans multiple outputs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/archive/C2_W2_SoftMax-Copy2.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Softmax Function\\n\",\n    \"In this lab, we will explore the softmax function. This function is used in both Softmax Regression and in Neural Networks when solving Multiclass Classification problems.  \\n\",\n    \"\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_Softmax_Header.PNG\\\" width=\\\"600\\\" />  <center/>\\n\",\n    \"\\n\",\n    \"  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from IPython.display import display, Markdown, Latex\\n\",\n    \"from sklearn.datasets import make_blobs\\n\",\n    \"%matplotlib widget\\n\",\n    \"from matplotlib.widgets import Slider\\n\",\n    \"from lab_utils_common import dlc\\n\",\n    \"from lab_utils_softmax import plt_softmax\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"> **Note**: Normally, in this course, the notebooks use the convention of starting counts with 0 and ending with N-1,  $\\\\sum_{i=0}^{N-1}$, while lectures start with 1 and end with N,  $\\\\sum_{i=1}^{N}$. This is because code will typically start iteration with 0 while in lecture, counting 1 to N leads to cleaner, more succinct equations. This notebook has more equations than is typical for a lab and thus  will break with the convention and will count 1 to N.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Softmax Function\\n\",\n    \"In both softmax regression and neural networks with Softmax outputs, N outputs are generated and one output is selected as the predicted category. In both cases a vector $\\\\mathbf{z}$ is generated by a linear function which is applied to a softmax function. The softmax function converts $\\\\mathbf{z}$  into a probability distribution as described below. After applying softmax, each output will be between 0 and 1 and the outputs will add to 1, so that they can be interpreted as probabilities. The larger inputs  will correspond to larger output probabilities.\\n\",\n    \"<center>  <img  src=\\\"./images/C2_W2_SoftmaxReg_NN.png\\\" width=\\\"600\\\" />  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The softmax function can be written:\\n\",\n    \"$$a_j = \\\\frac{e^{z_j}}{ \\\\sum_{k=1}^{N}{e^{z_k} }} \\\\tag{1}$$\\n\",\n    \"The output $\\\\mathbf{a}$ is a vector of length N, so for softmax regression, you could also write:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"\\\\mathbf{a}(x) =\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"P(y = 1 | \\\\mathbf{x}; \\\\mathbf{w},b) \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"P(y = N | \\\\mathbf{x}; \\\\mathbf{w},b)\\n\",\n    \"\\\\end{bmatrix}\\n\",\n    \"=\\n\",\n    \"\\\\frac{1}{ \\\\sum_{k=1}^{N}{e^{z_k} }}\\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"e^{z_1} \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"e^{z_{N}} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix} \\\\tag{2}\\n\",\n    \"\\\\end{align}\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Which shows the output is a vector of probabilities. The first entry is the probability the input is the first category given the input $\\\\mathbf{x}$ and parameters $\\\\mathbf{w}$ and $\\\\mathbf{b}$.  \\n\",\n    \"Let's create a NumPy implementation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def my_softmax(z):\\n\",\n    \"    ez = np.exp(z)              #element-wise exponenial\\n\",\n    \"    sm = ez/np.sum(ez)\\n\",\n    \"    return(sm)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below, vary the values of the `z` inputs using the sliders.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_softmax(my_softmax)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you are varying the values of the z's above, there are a few things to note:\\n\",\n    \"* the exponential in the numerator of the softmax magnifies small differences in the values \\n\",\n    \"* the output values sum to one\\n\",\n    \"* the softmax spans all of the outputs. A change in `z0` for example will change the values of `a0`-`a3`. Compare this to other activations such as ReLU or Sigmoid which have a single input and single output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Cost\\n\",\n    \"<center> <img  src=\\\"./images/C2_W2_SoftMaxCost.png\\\" width=\\\"400\\\" />    <center/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The loss function associated with Softmax, the cross-entropy loss, is:\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"  L(\\\\mathbf{a},y)=\\\\begin{cases}\\n\",\n    \"    -log(a_1), & \\\\text{if $y=1$}.\\\\\\\\\\n\",\n    \"        &\\\\vdots\\\\\\\\\\n\",\n    \"     -log(a_N), & \\\\text{if $y=N$}\\n\",\n    \"  \\\\end{cases} \\\\tag{3}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\n\",\n    \"Where y is the target category for this example and $\\\\mathbf{a}$ is the output of a softmax function. In particular, the values in $\\\\mathbf{a}$ are probabilities that sum to one.\\n\",\n    \">**Recall:** In this course, Loss is for one example while Cost covers all examples. \\n\",\n    \" \\n\",\n    \" \\n\",\n    \"Note in (3) above, only the line that corresponds to the target contributes to the loss, other lines are zero. To write the cost equation we need an 'indicator function' that will be 1 when the index matches the target and zero otherwise. \\n\",\n    \"    $$\\\\mathbf{1}\\\\{y == n\\\\} = =\\\\begin{cases}\\n\",\n    \"    1, & \\\\text{if $y==n$}.\\\\\\\\\\n\",\n    \"    0, & \\\\text{otherwise}.\\n\",\n    \"  \\\\end{cases}$$\\n\",\n    \"Now the cost is:\\n\",\n    \"\\\\begin{align}\\n\",\n    \"J(\\\\mathbf{w},b) = - \\\\left[ \\\\sum_{i=1}^{m} \\\\sum_{j=1}^{N}  1\\\\left\\\\{y^{(i)} == j\\\\right\\\\} \\\\log \\\\frac{e^{z^{(i)}_j}}{\\\\sum_{k=1}^N e^{z^{(i)}_k} }\\\\right] \\\\tag{4}\\n\",\n    \"\\\\end{align}\\n\",\n    \"\\n\",\n    \"Where $m$ is the number of examples, $N$ is the number of outputs. This is the average of all the losses.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Tensorflow\\n\",\n    \"This lab will discuss two ways of implementing the softmax, cross-entropy loss in Tensorflow, the 'obvious' method and the 'preferred' method. The former is the most straightforward while the latter is more numerically stable.\\n\",\n    \"\\n\",\n    \"Let's start by creating a dataset to train a multiclass classification model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# make  dataset for example\\n\",\n    \"centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]\\n\",\n    \"X_train, y_train = make_blobs(n_samples=2000, centers=centers, cluster_std=1.0,random_state=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### The *Obvious* organization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model below is implemented with the softmax as an activation in the final Dense layer.\\n\",\n    \"The loss function is separately specified in the `compile` directive. \\n\",\n    \"\\n\",\n    \"The loss function is `SparseCategoricalCrossentropy`. This loss is described in (3) above. In this model, the softmax takes place in the last layer. The loss function takes in the softmax output which is a vector of probabilities. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'softmax')    # < softmax activation here\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Because the softmax is integrated into the output layer, the output is a vector of probabilities.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"p_nonpreferred = model.predict(X_train)\\n\",\n    \"print(p_nonpreferred [:2])\\n\",\n    \"print(\\\"largest value\\\", np.max(p_nonpreferred), \\\"smallest value\\\", np.min(p_nonpreferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Preferred <img align=\\\"Right\\\" src=\\\"./images/C2_W2_softmax_accurate.png\\\"  style=\\\" width:400px; padding: 10px 20px ; \\\">\\n\",\n    \"Recall from lecture, more stable and accurate results can be obtained if the softmax and loss are combined during training.   This is enabled by the 'preferred' organization shown here.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the preferred organization the final layer has a linear activation. For historical reasons, the outputs in this form are referred to as *logits*. The loss function has an additional argument: `from_logits = True`. This informs the loss function that the softmax operation should be included in the loss calculation. This allows for an optimized implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"preferred_model = Sequential(\\n\",\n    \"    [ \\n\",\n    \"        Dense(25, activation = 'relu'),\\n\",\n    \"        Dense(15, activation = 'relu'),\\n\",\n    \"        Dense(4, activation = 'linear')   #<-- Note\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"preferred_model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),  #<-- Note\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.001),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"preferred_model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=10\\n\",\n    \")\\n\",\n    \"        \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Output Handling\\n\",\n    \"Notice that in the preferred model, the outputs are not probabilities, but can range from large negative numbers to large positive numbers. The output must be sent through a softmax when performing a prediction that expects a probability. \\n\",\n    \"Let's look at the preferred model outputs:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"p_preferred = preferred_model.predict(X_train)\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {p_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(p_preferred), \\\"smallest value\\\", np.min(p_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The output predictions are not probabilities!\\n\",\n    \"If the desired output are probabilities, the output should be be processed by a [softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sm_preferred = tf.nn.softmax(p_preferred).numpy()\\n\",\n    \"print(f\\\"two example output vectors:\\\\n {sm_preferred[:2]}\\\")\\n\",\n    \"print(\\\"largest value\\\", np.max(sm_preferred), \\\"smallest value\\\", np.min(sm_preferred))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To select the most likely category, the softmax is not required. One can find the index of the largest output using [np.argmax()](https://numpy.org/doc/stable/reference/generated/numpy.argmax.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for i in range(5):\\n\",\n    \"    print( f\\\"{p_preferred[i]}, category: {np.argmax(p_preferred[i])}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## SparseCategorialCrossentropy or CategoricalCrossEntropy\\n\",\n    \"Tensorflow has two potential formats for target values and the selection of the loss defines which is expected.\\n\",\n    \"- SparseCategorialCrossentropy: expects the target to be an integer corresponding to the index. For example, if there are 10 potential target values, y would be between 0 and 9. \\n\",\n    \"- CategoricalCrossEntropy: Expects the target value of an example to be one-hot encoded where the value at the target index is 1 while the other N-1 entries are zero. An example with 10 potential target values, where the target is 2 would be [0,0,1,0,0,0,0,0,0,0].\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"In this lab you \\n\",\n    \"- Became more familiar with the softmax function and its use in softmax regression and in softmax activations in neural networks. \\n\",\n    \"- Learned the preferred model construction in Tensorflow:\\n\",\n    \"    - No activation on the final layer (same as linear activation)\\n\",\n    \"    - SparseCategoricalCrossentropy loss function\\n\",\n    \"    - use from_logits=True\\n\",\n    \"- Recognized that unlike ReLU and Sigmoid, the softmax spans multiple outputs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/autils.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.activations import linear, relu, sigmoid\n\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0', dldarkblue =  '#0D5BDC', dlmedblue='#4285F4')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0'; dldarkblue =  '#0D5BDC'; dlmedblue='#4285F4'\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\nplt.style.use('./deeplearning.mplstyle')\n\n\ndef load_data():\n    X = np.load(\"data/X.npy\")\n    y = np.load(\"data/y.npy\")\n    return X, y\n\ndef plt_act_trio():\n    X = np.linspace(-5,5,100)\n    fig,ax = plt.subplots(1,3, figsize=(6,2))\n    widgvis(fig)\n    ax[0].plot(X,tf.keras.activations.linear(X))\n    ax[0].axvline(0, lw=0.3, c=\"black\")\n    ax[0].axhline(0, lw=0.3, c=\"black\")\n    ax[0].set_title(\"Linear\")\n    ax[1].plot(X,tf.keras.activations.sigmoid(X))\n    ax[1].axvline(0, lw=0.3, c=\"black\")\n    ax[1].axhline(0, lw=0.3, c=\"black\")\n    ax[1].set_title(\"Sigmoid\")\n    ax[2].plot(X,tf.keras.activations.relu(X))\n    ax[2].axhline(0, lw=0.3, c=\"black\")\n    ax[2].axvline(0, lw=0.3, c=\"black\")\n    ax[2].set_title(\"ReLu\")\n    fig.suptitle(\"Common Activation Functions\", fontsize=14)\n    fig.tight_layout(pad=0.2)\n    plt.show()\n\ndef widgvis(fig):\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\ndef plt_ex1():\n    X = np.linspace(0,2*np.pi, 100)\n    y = np.cos(X)+1\n    y[50:100]=0\n    fig,ax = plt.subplots(1,1, figsize=(2,2))\n    widgvis(fig)\n    ax.set_title(\"Target\")\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"y\")\n    ax.plot(X,y)\n    fig.tight_layout(pad=0.1)\n    plt.show()\n    return(X,y)\n \ndef plt_ex2():\n    X = np.linspace(0,2*np.pi, 100)\n    y = np.cos(X)+1\n    y[0:49]=0\n    fig,ax = plt.subplots(1,1, figsize=(2,2))\n    widgvis(fig)\n    ax.set_title(\"Target\")\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"y\")\n    ax.plot(X,y)\n    fig.tight_layout(pad=0.1)\n    plt.show()\n    return(X,y)\n\ndef gen_data():\n    X = np.linspace(0,2*np.pi, 100)\n    y = np.cos(X)+1\n    X=X.reshape(-1,1)\n    return(X,y)\n\ndef plt_dual(X,y,yhat):\n    fig,ax = plt.subplots(1,2, figsize=(4,2))\n    widgvis(fig)\n    ax[0].set_title(\"Target\")\n    ax[0].set_xlabel(\"x\")\n    ax[0].set_ylabel(\"y\")\n    ax[0].plot(X,y)\n    ax[1].set_title(\"Prediction\")\n    ax[1].set_xlabel(\"x\")\n    ax[1].set_ylabel(\"y\")\n    ax[1].plot(X,y)\n    ax[1].plot(X,yhat)\n    fig.tight_layout(pad=0.1)\n    plt.show()\n\ndef plt_act1(X,y,z,a):\n    fig,ax = plt.subplots(1,3, figsize=(6,2.5))\n    widgvis(fig)\n    ax[0].plot(X,y,label=\"target\")\n    ax[0].axvline(0, lw=0.3, c=\"black\")\n    ax[0].axhline(0, lw=0.3, c=\"black\")\n    ax[0].set_title(\"y - target\")\n    ax[1].plot(X,y, label=\"target\")\n    ax[1].plot(X,z, c=dlc[\"dldarkred\"],label=\"z\")\n    ax[1].axvline(0, lw=0.3, c=\"black\")\n    ax[1].axhline(0, lw=0.3, c=\"black\")\n    ax[1].set_title(r\"$z = w \\cdot x+b$\")\n    ax[1].legend(loc=\"upper center\")\n    ax[2].plot(X,y, label=\"target\")\n    ax[2].plot(X,a, c=dlc[\"dldarkred\"],label=\"ReLu(z)\")\n    ax[2].axhline(0, lw=0.3, c=\"black\")\n    ax[2].axvline(0, lw=0.3, c=\"black\")\n    ax[2].set_title(\"max(0,z)\")\n    ax[2].legend()\n    fig.suptitle(\"Role of Non-Linear Activation\", fontsize=12)\n    fig.tight_layout(pad=0.22)\n    return(ax)\n\n\ndef plt_add_notation(ax):\n    ax[1].annotate(text = \"matches\\n here\", xy =(1.5,1.0), \n                   xytext = (0.1,-1.5), fontsize=9,\n                  arrowprops=dict(facecolor=dlc[\"dlpurple\"],width=2, headwidth=8))\n    ax[1].annotate(text = \"but not\\n here\", xy =(5,-2.5), \n                   xytext = (1,-3), fontsize=9,\n                  arrowprops=dict(facecolor=dlc[\"dlpurple\"],width=2, headwidth=8))\n    ax[2].annotate(text = \"ReLu\\n 'off'\", xy =(2.6,0), \n                   xytext = (0.1,0.1), fontsize=9,\n                  arrowprops=dict(facecolor=dlc[\"dlpurple\"],width=2, headwidth=8))\n\ndef compile_fit(model,X,y):\n    model.compile(\n        loss=tf.keras.losses.MeanSquaredError(),\n        optimizer=tf.keras.optimizers.Adam(0.01),\n    )\n\n    model.fit(\n        X,y,\n        epochs=100,\n        verbose = 0\n    )\n    l1=model.get_layer(\"l1\")\n    l2=model.get_layer(\"l2\")\n    w1,b1 = l1.get_weights()\n    w2,b2 = l2.get_weights()\n    return(w1,b1,w2,b2)\n\ndef plt_model(X,y,yhat_pre, yhat_post):\n    fig,ax = plt.subplots(1,3, figsize=(8,2))\n    widgvis(fig)\n    ax[0].set_title(\"Target\")\n    ax[0].set_xlabel(\"x\")\n    ax[0].set_ylabel(\"y\")\n    ax[0].plot(X,y)\n    ax[1].set_title(\"Prediction, pre-training\")\n    ax[1].set_xlabel(\"x\")\n    ax[1].set_ylabel(\"y\")\n    ax[1].plot(X,y)\n    ax[1].plot(X,yhat_pre)\n    ax[2].set_title(\"Prediction, post-training\")\n    ax[2].set_xlabel(\"x\")\n    ax[2].set_ylabel(\"y\")\n    ax[2].plot(X,y)\n    ax[2].plot(X,yhat_post)\n    fig.tight_layout(pad=0.1)\n    plt.show()\n\ndef display_errors(model,X,y):\n    f = model.predict(X)\n    yhat = np.argmax(f, axis=1)\n    doo = yhat != y[:,0]\n    idxs = np.where(yhat != y[:,0])[0]\n    if len(idxs) == 0:\n        print(\"no errors found\")\n    else:\n        cnt = min(8, len(idxs))\n        fig, ax = plt.subplots(1,cnt, figsize=(5,1.2))\n        fig.tight_layout(pad=0.13,rect=[0, 0.03, 1, 0.80]) #[left, bottom, right, top]\n        widgvis(fig)\n\n        for i in range(cnt):\n            j = idxs[i]\n            X_reshaped = X[j].reshape((20,20)).T\n\n            # Display the image\n            ax[i].imshow(X_reshaped, cmap='gray')\n\n            # Predict using the Neural Network\n            prediction = model.predict(X[j].reshape(1,400))\n            prediction_p = tf.nn.softmax(prediction)\n            yhat = np.argmax(prediction_p)\n\n            # Display the label above the image\n            ax[i].set_title(f\"{y[j,0]},{yhat}\",fontsize=10)\n            ax[i].set_axis_off()\n            fig.suptitle(\"Label, yhat\", fontsize=12)\n    return(len(idxs))\n\ndef display_digit(X):\n    \"\"\" display a single digit. The input is one digit (400,). \"\"\"\n    fig, ax = plt.subplots(1,1, figsize=(0.5,0.5))\n    widgvis(fig)\n    X_reshaped = X.reshape((20,20)).T\n    # Display the image\n    ax.imshow(X_reshaped, cmap='gray')\n    plt.show()\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/backprop/C2_W2_Backprop.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"84d26ba3\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Back propagation using a computation graph\\n\",\n    \"Working through this lab will give you insight into a key algorithm used by most machine learning frameworks. Gradient descent requires the derivative of the cost with respect to each parameter in the network.  Neural networks can have millions or even billions of parameters. The *back propagation* algorithm is used to compute those derivatives. *Computation graphs* are used to simplify the operation. Let's dig into this below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"55de2e8c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sympy import *\\n\",\n    \"import numpy as np\\n\",\n    \"import re\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib.widgets import TextBox\\n\",\n    \"from matplotlib.widgets import Button\\n\",\n    \"import ipywidgets as widgets\\n\",\n    \"from lab_utils_backprop import *\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d41bc53f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Computation Graph\\n\",\n    \"A computation graph simplifies the computation of complex derivatives by breaking them into smaller steps. Let's see how this works.\\n\",\n    \"\\n\",\n    \"Let's calculate the derivative of this slightly complex expression, $J = (2+3w)^2$. We would like to find the derivative of $J$ with respect to $w$ or $\\\\frac{\\\\partial J}{\\\\partial w}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"8ce63474\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<lab_utils_backprop.plt_network at 0x1eaac20e4d0>\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6be00cc3e09b4d7a815eeca7c1ce8804\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"image/png\": 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\",\n 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' width=600.5154639175257/>\\n\",\n       \"            </div>\\n\",\n       \"        \"\n      ],\n      \"text/plain\": [\n       \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_network(config_nw0, \\\"./images/C2_W2_BP_network0.PNG\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b8b71c21\",\n   \"metadata\": {},\n   \"source\": [\n    \"Above, you can see we broke the expression into two nodes which we can work on independently. If you already have a good understanding of the process from the lecture, you can go ahead and fill in the boxes in the diagram above. You will want to first fill in the blue boxes going left to right and then fill in the green boxes starting on the right and moving to the left.\\n\",\n    \"If you have the correct values, the values will show as green or blue. If the value is incorrect, it will be red. Note, the interactive graphic is not particularly robust. If you run into trouble with the interface, run the cell above again to restart.\\n\",\n    \"\\n\",\n    \"If you are unsure of the process, we will work this example step by step below.\\n\",\n    \"\\n\",\n    \"### Forward Propagation   \\n\",\n    \"Let's calculate the values in the forward direction.\\n\",\n    \"\\n\",\n    \">Just a note about this section. It uses global variables and reuses them as the calculation progresses. If you run cells out of order, you may get funny results. If you do, go back to this point and run them in order.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"ec4c88a7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a = 11, J = 121\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w = 3\\n\",\n    \"a = 2+3*w\\n\",\n    \"J = a**2\\n\",\n    \"print(f\\\"a = {a}, J = {J}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5e527b71\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can fill these values in the blue boxes above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ae042e5c\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"### Backprop\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W2_BP_network0_j.PNG\\\"     style=\\\" width:100px; padding: 10px 20px; \\\" > Backprop is the algorithm we use to calculate derivatives. As described in the lectures, backprop starts at the right and moves to the left. The first node to consider is $J = a^2 $ and the first step is to find $\\\\frac{\\\\partial J}{\\\\partial a}$ \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4e996ebf\",\n   \"metadata\": {},\n   \"source\": [\n    \"### $\\\\frac{\\\\partial J}{\\\\partial a}$ \\n\",\n    \"#### Arithmetically\\n\",\n    \"Find $\\\\frac{\\\\partial J}{\\\\partial a}$ by finding how $J$ changes as a result of a little change in $a$. This is described in detail in the derivatives optional lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"1a7c6040\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 121, J_epsilon = 121.02200099999999, dJ_da ~= k = 22.000999999988835 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a_epsilon = a + 0.001       # a epsilon\\n\",\n    \"J_epsilon = a_epsilon**2    # J_epsilon\\n\",\n    \"k = (J_epsilon - J)/0.001   # difference divided by epsilon\\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_da ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2f4fce8e\",\n   \"metadata\": {},\n   \"source\": [\n    \"$\\\\frac{\\\\partial J}{\\\\partial a}$ is 22 which is $2\\\\times a$. Our result is not exactly $2 \\\\times a$ because our epsilon value is not infinitesimally small. \\n\",\n    \"#### Symbolically\\n\",\n    \"Now, let's use SymPy to calculate derivatives symbolically as we did in the derivatives optional lab. We will prefix the name of the variable with an 's' to indicate this is a *symbolic* variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"3c80c857\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle a^{2}$\"\n      ],\n      \"text/plain\": [\n       \"a**2\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sw,sJ,sa = symbols('w,J,a')\\n\",\n    \"sJ = sa**2\\n\",\n    \"sJ\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"5083d723\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 121$\"\n      ],\n      \"text/plain\": [\n       \"121\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sJ.subs([(sa,a)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"1a5fb459\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 2 a$\"\n      ],\n      \"text/plain\": [\n       \"2*a\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_da = diff(sJ, sa)\\n\",\n    \"dJ_da\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"806f1e96\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, $\\\\frac{\\\\partial J}{\\\\partial a} = 2a$. When $a=11$, $\\\\frac{\\\\partial J}{\\\\partial a} = 22$. This matches our arithmetic calculation above.\\n\",\n    \"If you have not already done so, you can go back to the diagram above and fill in the value for $\\\\frac{\\\\partial J}{\\\\partial a}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8010c2b1\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"### $\\\\frac{\\\\partial J}{\\\\partial w}$ \\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W2_BP_network0_a.PNG\\\"     style=\\\" width:100px; padding: 10px 20px; \\\" >  Moving from right to left, the next value we would like to compute is $\\\\frac{\\\\partial J}{\\\\partial w}$. To do this, we first need to calculate $\\\\frac{\\\\partial a}{\\\\partial w}$ which describes how the output of this node, $a$, changes when the input $w$ changes a little bit.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f47785a7\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Arithmetically\\n\",\n    \"Find $\\\\frac{\\\\partial a}{\\\\partial w}$ by finding how $a$ changes as a result of a little change in $w$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"d4cd0777\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"a = 11, a_epsilon = 11.003, da_dw ~= k = 3.0000000000001137 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w_epsilon = w + 0.001       # a  plus a small value, epsilon\\n\",\n    \"a_epsilon = 2 + 3*w_epsilon\\n\",\n    \"k = (a_epsilon - a)/0.001   # difference divided by epsilon\\n\",\n    \"print(f\\\"a = {a}, a_epsilon = {a_epsilon}, da_dw ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fe63c675\",\n   \"metadata\": {},\n   \"source\": [\n    \"Calculated arithmetically,  $\\\\frac{\\\\partial a}{\\\\partial w} \\\\approx 3$. Let's try it with SymPy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"2d788139\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 3 w + 2$\"\n      ],\n      \"text/plain\": [\n       \"3*w + 2\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sa = 2 + 3*sw\\n\",\n    \"sa\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"6a068532\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 3$\"\n      ],\n      \"text/plain\": [\n       \"3\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"da_dw = diff(sa,sw)\\n\",\n    \"da_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"45bbe997\",\n   \"metadata\": {},\n   \"source\": [\n    \">The next step is the interesting part:\\n\",\n    \"> - We know that a small change in $w$ will cause $a$ to change by 3 times that amount.\\n\",\n    \"> - We know that a small change in $a$ will cause $J$ to change by $2\\\\times a$ times that amount. (a=11 in this example)    \\n\",\n    \" so, putting these together, \\n\",\n    \"> - We  know that a small change in $w$ will cause $J$ to change by $3 \\\\times 2\\\\times a$ times that amount.\\n\",\n    \"> \\n\",\n    \"> These cascading changes go by the name of *the chain rule*.  It can be written like this: \\n\",\n    \" $$\\\\frac{\\\\partial J}{\\\\partial w} = \\\\frac{\\\\partial a}{\\\\partial w} \\\\frac{\\\\partial J}{\\\\partial a} $$\\n\",\n    \" \\n\",\n    \"It's worth spending some time thinking this through if it is not clear. This is a key take-away.\\n\",\n    \" \\n\",\n    \" Let's try calculating it:\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"5e8e92cf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 6 a$\"\n      ],\n      \"text/plain\": [\n       \"6*a\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw = da_dw * dJ_da\\n\",\n    \"dJ_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"34d22689\",\n   \"metadata\": {},\n   \"source\": [\n    \"And $a$ is 11 in this example so $\\\\frac{\\\\partial J}{\\\\partial w} = 66$. We can check this arithmetically:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"d87f7986\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 121, J_epsilon = 121.06600900000001, dJ_dw ~= k = 66.0090000000082 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"w_epsilon = w + 0.001\\n\",\n    \"a_epsilon = 2 + 3*w_epsilon\\n\",\n    \"J_epsilon = a_epsilon**2\\n\",\n    \"k = (J_epsilon - J)/0.001   # difference divided by epsilon\\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_dw ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"71594a83\",\n   \"metadata\": {},\n   \"source\": [\n    \"OK! You can now fill the values for  $\\\\frac{\\\\partial a}{\\\\partial w}$ and $\\\\frac{\\\\partial J}{\\\\partial w}$ in  the diagram if you have not already done so. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"60f0cd23\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Another view**  \\n\",\n    \"One could visualize these cascading changes this way:  \\n\",\n    \"<img align=\\\"center\\\" src=\\\"./images/C2_W2_BP_network0_diff.PNG\\\"  style=\\\" width:500px; padding: 10px 20px; \\\" >  \\n\",\n    \"A small change in $w$ is multiplied by $\\\\frac{\\\\partial a}{\\\\partial w}$ resulting in a change that is 3 times as large. This larger change is then multiplied by $\\\\frac{\\\\partial J}{\\\\partial a}$ resulting in a change that is now $3 \\\\times 22 = 66$ times larger.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fcb4b3df\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Computation Graph of a Simple Neural Network\\n\",\n    \"Below is a graph of the neural network used in the lecture with different values. Try and fill in the values in the boxes. Note, the interactive graphic is not particularly robust. If you run into trouble with the interface, run the cell below again to restart.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"de708e2b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<lab_utils_backprop.plt_network at 0x1eaaaf5e5f0>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a1fb1868a11648108597a6a139264dce\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"image/png\": 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\",\n 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REREQkABeAiIiIiIiIiAaAAXERERERERCQAFICLiIiIiIiIBIACcBEREREREZEAUAAuIiIiIiIiEgAKwEVEREREREQCwFHfBZD6YZomO3fuJCIiAsMw6rs4IiIiIiJSiyzLYv/+/SQnJ2Ozqd+1oVAA3kzt3LmTtLS0+i6GiIiIiIjUoW3btpGamlrfxZDfKQBvpiIiIgD4fPZK2iXH13NpRERERESkNhXu38/AHhn++35pGBSAN1MHhp23S46nW3qLei6NiIiIiIjUpoKCYABNN21gNBlAREREREREJAAUgIuIiIiIiIgEgAJwERERERERkQBQAC4iIiIiIiISAArARURERKRZsiyLkpISfvnlFwoKCuq7OCLSDCgAFxEREZFmxbIsTNNkxYoVXH311dx4441kZ2fXd7FEpBnQMmQiIiIi0qxYlsX333/PrFmziIiIoKioqL6LJCLNhHrARURERKRZMQyDzp07c/fddzNs2LD6Lo6INCPqARcRERGRZsUwDNLS0uq7GCLSDKkHXERERERERCQAFICLiIiIiIiIBIACcBEREREREZEAUAAuIiIiIiIiEgBKwiYiIiIizc7evXtZtWoVkydPJisri08++YRTTz2V4447DputafRRbd++ncmTJ7Nnzx5ycnIYMWIEZ5xxRpN5fyKNkT59IiIiItLslJSUkJ+fz6hRo3jnnXfo0KEDWVlZWJZV30WrNWvWrKG8vJwHHniAYcOG8dRTT1FYWFjfxRJp1tQDLiIiItKM5LktSrwWdgPiXQY2w6jvIh3Csiyyyi28FtgNiHUaOGy1W87WrVvTunXroypTYWEhc+fOJSoqin79+tVqeY5VYWEhX331FQMHDiQtLQ273e5/bvjw4QwbNgzDMIiMjCQ6OhqHQ7f/IvVJPeAiIiIizci3ez2cMLOQkfOKyffUd2kq57XgjpWldPh+P7etKKXAU7+90pZlsX37dm666SaWL19Oenp6vZbnYMHBwSQnJ/Pggw/y6aef4vH8749qGAaGYZCTk8O0adO44447CAkJqcfSioiawESkWTowxNA0Tf//bTab/2alLhR7LabtdpNVbtEqxMbpLRy13qNTFcuysCzL/34Nw8But9fZe22qKqs3hmH4605TdqD+NIf32pjkuS2+2OXmklQnziP8PhmWYCfBZaNTuI1Q++G3rw92A/6c6ODbvR4mtHUR46zfOldUVMQjjzxC9+7dGT9+PEFBQfVanoM5HA6GDBlCUlIS119/Pa1bt+b4448HfJ/b/Px8XnnlFS644AIGDRqkz69IPVMALiLN0u7du/n8889ZvXo1ZWVl7Nixg3bt2nH99dfTvn37OrlBcRkQ4TC4flkpl6Y5Ob1FYL6CLcti586dvPnmm2zZsgW73U5JSQlDhw5l9OjRhIWFBaQcTUF2djaff/45K1as8NebVq1acf3119O5c+cmeWNrWRY5OTl89NFHbNiwgYkTJ6oHrQHZU2Zy7+pSzkt24jzCcY0lXthdZnJljJOgADUCHi0TmJvrJTXERtuwyht9jmaudk0/m7NmzWL16tX8/e9/rxh8WxbkeGBtMZSaEP3793rHUAirndYNyzQpWraMsm3biBw0CGdsLOVZWRTMmoUtJIToYcOwuVxkZGRw9tln89JLL3HccccRHBxMeXk5//znPxk6dCiDBg1i3bp1pKamEh4eXitlE5GjpwBcRJql6dOn88ILL/D666/To0cP1q5dy6WXXkpOTg5vvvlmnfRuOGwGbcNsOGzQJ8oesN7vwsJCrr32WmJjY3n00UeJiopi4cKFXHbZZeTl5XHbbbdVmDMoVfv555/5+9//zquvvkrfvn3ZvHkzo0ePZvfu3bz77ruEhobWdxFrjWVZFBUVMWPGDD788EO+/vprBgwYgGma9V20I5Kfn8/69espLCykZcuW2Gw2YmNjiY+Pr5Pj7Ss3WVFgYjegV5SdMIeB27RYku8lIchGeqiNfLfvdwPoHmknxgmUZEHeWghPg4jWYHkhZyWYbojrAXZXrZd1Wb6XYg/0jrKzssBLdrlF61BfGRsK04Kl+SZdI2zEuSov19pCk2uXllDsrXo/x0XZeKF7CCE1+Irzer18+OGHDB48mIiIiIpPbi6FF3ZARgj0i4BXdkCRCc+1q7UAvHDhQvYvXsyeSZNIuOACUm68EVtQELvefhurrIzo4cMB3yiuoUOH8q9//YuNGzfStWtXvvnmG1544QU+/vhj7HY7drud999/XwG4SD1SAC4izdIpp5xC79696datG3a7nX79+tGjRw9mzpxJeXl5nQ0vXFFg4jWhW2TgAt4lS5Ywa9YsPvroI1q1agXA8ccfT/fu3ZkxYwY33HBDkwoc69KgQYP48MMP6dGjBw6Hg549e9KvXz++/fZbSkpKmtx53L59O9u3b+fuu+9m79699V2cI7Z27VqeeOIJOnToQM+ePXn++ef59ttvefvttxk6dGilr/nll1944oknqt2v3W7nmmuu4eyzzz7kOY8JL2wq46dsDz8PCqdnlJ1ct8UF84u5NM3FY52DKTMtnlhfxooCLz8OCiPGzIfZd8DWbyCxP5z2Aaz/AJa+DOEpcNqHdRKAz8vzEucyWFZgMmlrOWsKTTLCbHzYN5RWDSQIzy632FjsZUTLqr+Lk4Nt3N8xCE81bUIxLoM/xu+lpaWsWbOmytfY7XY6d+7sT1aWm5vL+vXrGT58eMXGymIv/GMnJLng6iTfuPmPHRAJJDgP2W++22JzcfUNWPEug5TgilOhglq1IqRTJ0o3bSLv559JGjcOe3g4jogIokeMwDgoqVp6ejplZWWsWbOGLl260LlzZ95++23/aIHw8HCSkpKqLYOI1C0F4CJyzD7f5eazXW66RNi4uW0QQXaDNfu9vLy5nAltXXQIt/PdXjf/2e4mI8zG7RlBhNgr9vpalkVeXh55eXnVHstms9GiRYtaG/qalpZGWlqa//eysjIKCwvJyMio0wyxM/d5SAnx3RDeu6qUvWUmF6Q4OTXBUWkmYo/Hw+7du3G73dXuNyQkhBYtWlS6tqvD4cBut5OTk+N/zDRNPB4P8fHxja/327Jg33JY8Q+Iagc9bgSzHNa8C7mroM+9EHboDWZxcTF79uw57O5jY2OJioqq9Lnk5GSSk5P9v5eXl1NYWEjbtm1xuWo/UAIo3bKFHS+9hCMmhtRbbsEeFkbpli1sf/ZZLK+XVvfcQ1Bq6iGv279/P/v27at2mK5hGMTFxR3aq/f7cx07dqRjx46UlJQQFBSE11tNV2MDkZOTwy233MLAgQO54447cDgcbNq0ie+++67ajNfp6emMGTOm2n3bbDY6duxY6XMtggxGJTv5KduD9/dzviDPy54yi33lFqZlEecyaBlkkNLCQdswG+xYAulnQvKJMO9hWPk67F0Ip04CywTnob2U+W6LHPf//qY7SnyZwjOLTUId//sOSQwyCLUf+p1S6rVYmu/FApYXeJnUJ5Tv9nq4fWUJm4rNBhOALyvw9dIPiq36+zjSaXBKJYHu4Wzfvp3zzz+/ytEcUVFR/Pzzz/7vgfLycrxeL9HR0f/byLJgUwmsLoaJbXxzjMp/H47eJxwch57H+blerl5aXG3ZxqS5eKBjEAd/K7sSEwEI696d3O++wyovpywrC6u8nLgRIyq8Pjw8HKfTSUFBAQDt27enffv2hzkjIhJICsBF5JgNiLHz8NpS1heajG8ThMuymLHPwztbyxmZ5KBDuJ1ukXZ+zSkl1mkQXMV93euvv87zzz9f7bHCw8N55513OOGEE2r9fViWxdy5c1m/fj3PPvsswcHBtX4M8N34ri40iXUaPLq2jFYhNmbneFmYb9L9eDvJwYfeLO/du5eLL76YDRs2VLvvk046iTfffLPS+dw9e/bkz3/+M6+//jpt27alVatWfPnll+Tl5TFx4sQ6CxzrTEkWLH4GPEWwcTK0HAi7ZkHWEvAUQxVzPX/77TdGjx592N3ff//9XH/99YfdzrIslixZwuLFi5k4cWKdzKX3FBSw/YUX8Obns+vNNwnv2ZO4ESMIatUKyzQpmDsXWxXHnTp1Krfffnu1Q8btdjv/93//x0UXXVTp841tTrtlWXz99ddkZmbyj3/8A4fDgWma7Nixg/bt29OiRYsqX3u0S1L9kWEYRDkNPCaUmFDitfh8l5vUEBvrC73s98DeMpN1hSbPdw/2BVgpJ/lenL3U9++mz2D42xDZtsp6/P72ch5ZW+b/3WNBTrnF8DlFHPyKd/uEckrCobd5e8osVu43aeEyuL9DEPEug3ZhNgyg3GwY619blsXGIpNIp0FCUNV1cFuJySubyyirpl2obZiNa9JduA6a8tOuXTtWrVrlT0hZGafzCAL75UW+wLvd7w3DK4pgaylcVXkP87AEO2uGH9rYdTAbVS9RFH7ccZTv2oUnL4+c6dOJ+dOfcCYmNrrPqUhzpwBcRI5ZYpBBz0g7S/K9WJYvac60PR48Fv45efluC4fha9Wv6iZhwoQJXHPNNYc9XlVz1jweD1OnTmXt2rXVvj46OpoLL7ywQi/GgaVlJk6cyPXXX8+f//znOruZ2VZisma/b+jnY51d9Im2E+cyeGpDGVllFsmVxP0tW7Zk2rRph51363A4qhz+HBISwq233sq1117LxIkTadmyJT///DNnnXVWnSWcq1OWF7pdDa5omHoWrPgnOEJh6Cu+4bqOykdJDB48mNWrVx9290faALNr1y4eeeQRrrzySs4777xKRx/UlFVeTsK55xLUujX5M2dSuHgxsX/+MxgG7r17Sbz0UhwH98od5Nxzz+WMM8447DGaUkI1y7L44YcfyMjIIC0tDcMwKC0tZc6cOZx88snVvtc1a9bw5ZdfVjtiwG63c/LJJ9OnT59Kn49wGDhtsLvUYo7Xw/YSiwuSnfyQ5aHctPh4p4fjY+30iT6wAsHvn72wFAiKhvheENmmyuAb4IpWLi5O/V+j2YYiL2f/Vsz8oeGEHdQDXtX045xyi6wyk791CCHe5du+xGvhtBlEOhrGd0GZCTP2eekcYaNFNQG4DXAaBpat6r9ZZW/JMIyjang88NkuKyur+ESp6ftb2YBcD3ySBS4bpAX51lGzUeFvaTMMXDU4xa7ERJxxceTNmEHRsmW0fvjhQ76/D/TWa61vkYZLn04ROWY2wyDOZVDktfACP2d7yXdbJAYZlHgtPKbFxzvdnJLgoGtE1cFJcHDwEc25ripQNE2T7du3Hza4SkhIoLy83P+7ZVlkZ2dz991307t3b2688cYj6/U4RlnlvqGjN7YNon+MHQOIcho4DN/UwcoYhlHjZDn79u3jxhtv5JRTTuGOO+7A6XSyYMECxowZQ2xsLHfddVfjulkLS/L9lOVDeCrsWwZnfeULYKoJXBwOB5GRkYfd/eEaJCzLIjc3l/vuu4+MjAxuv/32OhtF4IyPJ2rwYLxFRYR06kTR8uWYZWWUbd2Kt6SE+HPPrfK1LpfriOpzo2uAOYzFixfTs2dPwPfdMG3aNJYsWcLf/vY3gCp7PfPz81m1alW1+7bb7Rx33HFVPh/nMgi2GSzK97Kl2GR8Gxebi00KvRYz9nmZvc/Dyz1CKvZwWpZvDrin2DeFwrKA3wPKSsoZbDcIPii4jnAYGIbvuyT8CALomTkeXDaD46J8jQCmZfHFbt80oY7hDWM6ituCNfu9nJnoPGTa0sFSQmw82rluRiwdLDo6mvj4eDIzM/1L8QHQMgiy3fDmLtjjhkg7hNpgYwksLYQRcbVaDltwMEFpaex89VXaPPoozkoSCh7I1ZCRkVGrxxaR2tOI7rhEpCGKcRkUeqDAbfGPLeWMbxPEy5vLWJzvpXOEnZ+yPbx1XChB1dxEvfzyy/zzn/+s9jjh4eG8+OKL9O/f/5DnXC4XN9xww1GXvbi4mEceeYSWLVty33331WnwbVkWP2V7iHAYnJHom+/tMS3m5XpoFWIjsYpenj179nD11VezadOmavc/aNAgnnvuuUp7wefOncvixYt5+umn/QFov379GDBgANOnT2f8+PHExMTU/E0Gms0BQTG++d/BcdUG3+BbRui666477G5vvfVWrrzyyiqfLysr44knniA0NJRHHnkkIOsB20JDCe/Rg9zvvsMsKSHrk0+IHzGC4N+T6lXm888/56GHHqp23rbdbuehhx5i5MiRdVHsehEZGUl+fj6lpaUsWbKEOXPmYBgGsbGxTJo0iVNOOYWUlJRDXjdgwAAGDBhQo2O3CbUR6TT4cpebQXEOTklw8MlONznlFn/fUMbtGUG+ud8A7iIo2AR562HnTOj4V1j1hi+/wa6ZkDEKQhNrVJ7KLMjzEuEw6BBuw7IsFuR5+Tnby93tg4ipSfdsLdpSbLKj1KJ/TN02CLjdbrZs2cLWrVtxu920adOGjIyMQ/JiBAcHc8YZZzB9+nSuv/563ygZw4DjI+Av8bCzHE6PhVa/93zvLIM/xdZ6eQ2HA2dCAqFduxI1ZMghDUmWZbFgwQISExMb5+gmkWZCAbiI1Ei8y0aZCR/vcBPpgDMTHXywvZz1RSaPrSvjylYuWodWfxMwYsSIanuVwBcodOjQodbK7fF4eOWVV8jJyeHFF18kMjISt9vNN998w/HHH18nSxUtyvPSJtRG+99vwHPcFrNzvIxKcZIQVPkIgZiYGB544AFKSkqq3Xd0dHSVgaBlWViWhcfjqfC4w+HA6XTWydDpOuctg+WvABbkb4L8jRCWDIbN1xNeia5du/Laa68ddtfp6elVH9br5Y033iAzM5OXX36Z6OhoPB4P3333Hb169aJly5bH9n4OwzAMwo47jl1vvUXBb79RmplJ8vjx1b5m4MCBvPzyy4fdd1NK0GQYBpdccgkPPvggZ555JgMGDODSSy/liy++4Oabb+byyy+vdh54TTltvl7wrDKLG9q4CLL5sloXey1OjLVzVuJBt1175sOPV0JsNxj0NHjLYdVbvsfSz6qyHtdEbrlvbrVhwMc73diBD3a4uaq1i0tS664B8miYvzdWxjoNBtRhAF5UVMRdd93Fzp07OfPMM9m4cSP33nsv999/PyNHjjwkeB05ciSTJ09mxowZnHLKKb7vzRgn3PKHBIh/S6+T8lqWRcHcuVgeD6m33oqtku/7wsJCPvzwQ8aMGVNny+2JSM0pABeRGkkK8g1Bf3d7Oe/2DiXM7hsK+dVuD+ckORiV4sReTSu8YRikp6dXG/TUhWXLlvHKK69w4YUX8umnnwKwe/duPvroI3755ZdaP96+cos1hSbxLgOPBfllJk+sKyM1xMbYVlUPXw4KCqJv3741OnbPnj3p0KEDkyZNon379oSFhbF8+XIWLFjAhAkTKs2A3WB53bDkObA8ULwb+j8IX50F694HTwl0vBRaVD4/NzY2lsGDB9fo8GvXruX5559nxIgRfPbZZwBkZ2fz7rvv8v3339do34cT0bs3Vnk52595hrZPPonjMKMWWrZsWSsNAh6Ph7y8PAoKCvB6vRQUFBAcHNwgs+cbhsFf//pXOnfujMfjoX///oSEhPDRRx9hmiZ9+vSp05EuNuCpLsEE2aBrhA3DMOgTbefDvqEMiHEQfPBIoKh2MOQliOsG4a18636f/hF4S6FFX7Af2ciKlGAbbx0XUmWSy4MF2+HpLsEE2Q32lpl4LZjYOZiuETYctvrvLd1bZrIgz8vknW7Gt3H556jXBbfbze7du7n77rvp168fHo+HlStX8s477zBixIhD6klSUhL33nsvL774IhEREfTp0ycgCSwtr9e3CkJ0NDnTptHq3ntx/eFzbVkWWVlZvPbaa6SkpHD++efXeblE5NgpABeRGukTbSctxMZlaS7/DWfrUBvdI2082DG4Rgln6tIPP/yAx+Phvffeq/B4bGxsnWRB31VmkRRkEOowuG91KbnlFiF2g3/0DCYtpG5PUmpqKm+//Tb//Oc/uemmm4iKiqK8vJz777+fUaNGNa4e8JLdsO49iOsOJ/4fOMKh7V982dC7XuMLZurQjBkzKCsr45NPPqnweHh4eJ0nM3NERhKUkkLsmWcSftxxARleWlJSwquvvsq8efPYuHEjNpuN2267jb59+zJu3LgG2XgTEhLCkCFDKjxW2dSVumAYBifGVby1ahFk44zESj5jEWm+nwPsLkg6+lUewh0GpyceWaNCiN1gkL98Da8BZWuJyd4yi0c6BXNCrB1nHTYKREVF8dFHH2Gz+a5bdrud8PDwQxOt/c4wDIYNG0ZCQgKffvop5eXlVa4pX5s8BQXkTJuG4XLR+oEHCOvZ85DP/v79+3n99ddJT0/nvPPOq5MVGUSk9hhWdek+pckqKCggKiqK5Zv30C297objSdNnWhalXl/i1wM9KOWmhWlBkK3hJngqLy+vdG1twzAICQmp9XJ7LQv374nMTQswwGn4MvQG4hwdGILudruxLKvxDj+3vOAp883/tv0edJhuMD2+AMZWt+3Kbre7QiK/A+qq3hxgud3s/eAD8n75hXbPP48jQIGvZVmUlpYekoXfZrMRFBTU+OqPSCUsy2LTpk1ceOGF3HXXXZx//vlVfpYPfJcahhGQ5JWWZWGWlmLYbBiuylcTsSyL8vLyxvmdLnXqwP1+fn7+ESUhlcBQD7iI1IjNMAj9wzeJqwEMZTwcl8sV0PWv7YZBfY7YNQwDp9NZp8NvA8Kwg/MPiebsLt9PAATyHFqWRfHq1XgLCylZu5Z9U6fS9umnsdcwK/7RONCwINJUWZbF/v37eeqppzj11FM555xzqm1IO/BdGiiGYWA/zGfQMIyAJIMUkdqhAFxERKSByvrwQ3a9/TbRQ4fS5sknCWrdusGOKhFpbA6M8Hj22WeJi4vj3nvvbVxLMopIo6RvGRERkQaq5VVXEX/eeQS3bo09KkrBt0gt8nq9vPbaa+zZs4fHH3+c8PBwSkpK6nQ6iYiIJoqIiIg0QIZhENyqFeE9e+KIjlZAIFKLLMvihx9+YMaMGdx+++04nU527NjBww8/XGl+EBGR2qIkbM2UkrCJiIhIc5Wfn8+IESPYvXs3rVu3BnzraLvdbubMmRPQHCEidUVJ2BomDUEXERERkWbF4XBw0UUXHbKqQVpaWoNc415Emg4F4CIiIiLSrISFhTF+/Pj6LoaINEOaAy4iIiIiIiISAArARURERERERAJAAbiIiIiIiIhIACgAFxEREREREQkABeAiIiIiIiIiAaAAXERERERERCQAFICLiIiIiIiIBIACcBEREREREZEAUAAuIiIiIiIiEgAKwEVEREREREQCQAG4iIiIiIiISAAoABcREREREREJAAXgIiIiIiIiIgGgAFxEREREREQkABSAi4iIiIiIiASAAnARERERERGRAFAALiIiIiIiIhIACsBFREREREREAsBR3wUQETlalmVR5AWvFdjjGkC4A2yGEdgDS6PgtSye21hOTnmAKyZwegsHQ+J1SZfaY1kWhV4wA1+dCbZBkF3fs/I/u0tNXtpUTj1UR8a3cZEaoj5LqT26WotIo3TuvGKWF3gJ1C2aBYTYYf7QcOJcujGUQ5kW/GNzOX1j7LQIYB35LstDlNNQAC61ymPBsNlFbC8xA/Y9C76G1fs7BnFj26AAHlUauqxyi5c3lzE61YUjQBXSBN7bXs45SQ4F4FKrdLUWkUYpz23xeJdghsTZA3K8PWUWI+cW10tvkDQiBtye4aJvdOAur5ctKg7YsaR5yS23+EfPELpFBi74uGNlKcXegB1OGpFop8Gz3YIJDtDoCLdp8e1eT0COJc2LAnARabSSggzahQUmAA+2mWjkuYg0KwakhATuexYgIlDdmyIi9UTjKUREREREREQCQAG4iIiIiIiISAAoABcREREREREJAAXgIiIiIiIiIgGgAFxEREREREQkABSAi4iIiIiIiASAAnARERERERGRAFAALiIiIiIiIhIACsBFREREREREAkABuIiIiIiIiEgAKAAXERERERERCQAF4CLSJG3aBIMGQdeu8Je/gNtd3yWS5k51UpoC1WNpKFQXpbFSAC4iTZJlwaRJsHIlhIfD1Kn1XSJp7lQnpSlQPZaGQnVRGitHfRdARKQutGv3v/+Xl4PLVX9lEQHVSWkaVI+loVBdlMZKAbg0K5uLTP6zvZy1hSYOAwbHObgwxUm4w6jvokkd+eQT2LYNTjutvksi4qM6KU2B6rE0FKqL0tgoAJdm5fXMMlbtNzk+xsH8PA+3rijBa8HV6Wo2bYo8Hrj1VvjtN3Do204aANVJaQpUj6WhUF2UxkhVVZqVBzoGYwAuGxR4XAycUcjP2R4F4E3U7t2QlAQpKfVdEhEf1UlpClSPfSzTi2V6sExvfRelXhg2O4bNAYYNw6ifkYSqi9IYKQCXZiXE/r8LhMtmYQCRzprv17IsvKUFFO1ZQ0n2JrzlRTXfaaNi4AyNISS+HWEtO2HYXfV2MT5YTAw8/nh9l6JuWJaF5Sn7vc5txF2cW99FCji7K5yQ+DaEteyMPSiiQdS5w2nKdfJYWZaF6S6haPdqSrI34SnNr+8iBZwjOJKQ+LaEJXbG5gpt8HW5uddjy7Io37+HHbP+Qf6mORRnbcDyNq8U3IbNQUhCO6LaDCR18HW4IpPqpd4297oojZMCcGmyLMuq8PvBFwbTsvh6j4d95RYjWtYsArdMLwVbF7L560fwFO/DERKDYXcCBmAd7uU1VNvHOPb9me5S3IVZRKYPoPWpdxIcm17vN5H5+b4MqaecUq/FqHWWZVG6bzOZ3z1FwdYFOMPjsTmC/7BVw6kbdXUMy+vGXZyLKzyeNmc8QERabwybve6KVwuaap08VpZlUbx3LVumT6RozxqcYXHYHEGVbNm067PpLcdTtI+QuLakn34/YUld6/37szrNuR5blkXhjqWs+/RmnKExJA8cS2hixyrqbdNlessp3rOWPYs+ZsW/LqL9uc8S0apPwOttc66L0ngpAJcm651tbr7Y7QYL4oMMHuscTGKQb+W99UUmD68t5YIUJyfF1exjULxnLWs/uo74bmeRNOBygqKSsDn/GAw1bZZl4S0vojR7E5k//B/rJ99KlzH/xhESVa/lSk72XZibGk9Jvu/mLyyOLmP+TUhcG2yusAZ9w14XvO5SyvN3svPXf7Hmw+voevl/CEvsVN/FqlZTrZPHqnz/HtZ+OJ7Qlp3p+td3CY5thc0Z0qzqsm8EQClledvYPuM11nx4Ld2u+IDgmLT6LlqVmnM99pYWsPm/DxPZuh9tTv8bjpDI+i5SvYlI6UlclzPI/O5JNv/3Ibr8dRLOsNiAlqE510VpvLQOuDRZ/WPsjG3l4srWLi5McRHxe6bzdYVexi0poUOYjfs7BBFWgwzo3vJiMr9/isj040k/7V5C4ts0u+AbfKMLHEHhhKf0oP25z+ApyWfXvElYllnfRWtyLMtk19x/43UXkzHy74Qnd8ceFN6sApYD7M5gQuLb0ub0+4ls3ZfM757GW15c38WSI2R6PeyY8Sr24Agyzn6csJadsDeC4de1zTAM7K4QQlt0oO2IRwmKTmXbTy9gesrru2hSiYKtCyjL20Hq0BuadfB9gCM4gpTB4ykvyiZ/y9z6Lo5Io6AAXJqsLhF2zmrpZERLJ6ckOAi1G2wsMhm7uIQWQQYv9gghMahmN3qe4lwKd60macBlGHYlcgNwhsXTovcoctf/AgrAa51lmuSu/4WWfS7CGRZX38VpEAxHEEkDLqNo1wo8Jc1v/nBjZXnLyNs0i6QBf8UerEAGwO4KI2nAZeRvmYvpLqnv4kglinavJiShHUERifVdlAbDFR5PWGIninatqO+iiDQKCsClWXlsbSlL8r2E2w1e2FjGPavKeGRtKbtLjy1Q9JQV4i7cS2hCRrPrtamKYRiEJXWlePeaQ+bhSy2wTIr3rGnwc0QDyTAMQhLaU1awpxkmQGy8LK+b4qyNhLXsrLr8O8MwCGvZidKcLZiesvoujlTCXbQPe3AkRjOb810dw+7CERKNu2hffRdFpFHQHHBpVgbEOoh2+m70yk0AC4dpHHsqHsvEMj3NLvnK4didwQG5eXRbUG4GJsgvb0BtCaanrJKka82bzRGEZXrqf9SFBR7zyOqlhS9NV00F6CNQ6ywLLG85NkdIfRelQbE5gn/PqN0A/rCqz1VSo9H/BPJclJtgM+q+kliA12wQn0JpghSAS7Nyrdb7bjIM4OolJYQEKOm114KyxnJnKPXHgPPnF+MK4PiyrHKL7pENO/u7NFKqzw3KwaPKmmMDwO5Si54/76+Vhp4jtb1E132pfQrARaRRmtQnhGJPYI9pNyDG2fxueuTIOAz4ckAoZd7AHzs5RPVSapfqc8NSWlrKTz/9xPTp03n00UeJjGxeeRMywmz8NjS8XrqkO4Rrxq7ULgXgItLoGIZBx3D1kDQ132yDV1fASclwY3dwNLJ7HsMw6BKhelnXNmzYwOOPP05ycjKPPfZYfRenyVJ9bjh27NjBO++8w2+//UZmZiZebz20itSzELtBryjVx59//pnnnnsOy7K4/fbbGTJkSH0XSY5BI7u9kYao0A03zoTxMyDr96Sti7PhuhlQ5vXNoblvLlzxI2xUgmKRZqewsJDc3NzDbtcvAfLLIczpG20gUpm2bdvSsmVLnE5nfRdFJCAcDgeXX345Y8aMweGouu/MNE0+/vhjfvjhhxof0zRNZs6cyZNPPsmdd97JlClTmmXg39D06tWL/fv3Ex0dzYABA+q7OHKMFIBLjYXYoVUEfLYZCn5ftvSLzTB5I+woApsByWEwby+4GnnjpceEEo8veZBIIHhNKG3kde7777/njTfeOOx2xR5fg163WGiG0xubNMuCci+4ayFHnmmabNy4kd69e9d8ZyLVsCz4agtsK/R9F9eXxMREkpOTD7tdWVkZ7777LgkJCcd0nMLCQvbu3UtxcTFut5sPPviAP/3pT1x88cW89NJL5OTkHNN+xcdtQn6ZL6fMsfJ6vezYsYPTTz+doCAlAG6sNARdasxug47RvouTx/IF4fP2+oaP7iqC1uGwJhcuaQ+pYfVd2ppZlQPPLoOL28PgpNrbr2VZ7Nixg59//pk9e/aQkZHBGWecgcvlqrBNeXl5tS3QwcHB2Gz/a1cr9cI7a3z/H90BwhwweRO0jYQecfDpJtheCH/tCC3MnbBlGrgiod25sH8bbJkKGedD2OEv/FI3NhTAM0vgwgw4MQkO2+dnWrCmGJb/vhxXrAMSXdAjvMJmx1KfDvB6vaxevZrZs2ezf/9+hg4dSp8+fSrdFnwBk2ke/u51dwlggcsGLy+HSBec19bXIy6N3zfbYM5uGNsZ2kQc3WvLysqYPXs2K1asIDY2lk2bNtG+fftDtjuSeh0UFITdXklrsNeCJYWwrhicBoQ7ICPE91MDnv37yZk6FW9RES0uughbcDDZn31GaWYmLS+7DOcxBktS9yzghWWwvchXby/JqD5Lu9eEN1ZDdil0joZz2wauMdGyLObOnUtMTAzt2rU7pn3MmjWL22+/nfvvv58LL7yQZ599FpfLxeLFi4mMjFTAV0OFbrj8R980q0vaQ0KIr5PqaGRmZpKTk8Nxxx1XJ2WUwFAALrUiNsgXfHtMmLHT1yseGwRZpbAuHzYWwG3H1d2FyLJ8F8q67iUsM+HLLb4g9pRUuCIBYmp4TMuyWL58OY888ghXXnkl0dHRTJkyhVNPPbVCAO7xeJg8eTJr166tdD82m40bb7yR2NhY/2MOA3LKYNJaOLO1r4fxiUW+BoTj4n1/r083+i4E5G+C0CSYez+Ep8HSFyCkRY3em2k17p5boN7XICn2wJRN8PFGX527piM4q4plLQtm5cOkPTAqAULtMDETrk0+JAD3eDxMmTKFNWvWVLorm83GDTfcQFxcXIXHTdPks88+Y+rUqdx4441MnTqV3377jT59+tT4vc7Z5fuM/bgD+reAK36CNpFH19hlWvXbU1UjTbnX34I9JfD0Enhvva/R79JWR/b9UF5ezrPPPsvWrVuZMGECkydPJigoqMpevs8++4zVq1dX+lxISAhjxowhJSXlD+Wz4Its+DYXLm8JmaXw7DZ4LuMo3+ihyjIzCUpJYcOtt+JKSiL2zDMxPR72vv8+SePGVfoaC9hXCn8cbWwB7mpGAXutw/euuc3qz7vHOkyQeZhjHG7/FocfCeGuZvkn06z++LV5DixgdzGszYN758K/VsO1ufCnmMpfZxiQU+prQHz/lOrLUNs8Hg9Tp07ltNNOIzQ09Jj20bdvX1q1akVGRgaGYRAUFMSWLVt49tlnueGGG4iIqL7lbGUO/Lbu0MftRuXTiuyGrxPniLe3VR6wGoCzijG9DuPQ1xjG0W0Pvk6lyl5S1b4Mw9eOd7DcMliRA//dCv9eC1d1hgvaQWLIkd8fL126lDZt2pCYmHhkL5AGSQG41IouMb6ge2EW/LTD11L8wHzfl83rq2BUO2gVfvj9HCuvBZ9shGX76u4Y4LsQl3h8PctfbIZtmfBMDffp8Xh44oknGDZsGKeffjput5u+ffsSElKx18XpdHLJJZcc1b4dNuge6wu03abv/Oz6/WbCbfrez+gO0DIUCDsRygsgNBnmPQxdx0Hbc8B2bF8TuWXw5GKwahBU1GSYVm3x1nMjwt5SKPb66t3kTbAiCyaWVrFxoRf+tRvOiIVTYiDHAy4DOh16M+Z0Orn44ouPujw7d+7k2Wef5ZlnnqFPnz7+npaDe7/dbjdz5szxz/ueN28eO3fu5PPPPwd8PetDhgypcJNoWbAk2/c9cXUX32gNu+G7oTvSANy04IXlUHKUvasNgd1onMPubcaRtxsszvb9jbYV+hoCf94E9x7BSgbz589nypQpfPbZZ6SkpJCYmEjHjh2JiTk0CjIMg4suuujo3gTAljL4KAtuSIEBEb4PfowD4isOv/B6vSxcuJCdO3dWuathw4ZVyFAd1q0bltdLRL9+7J87l9gzz8STm0uLiy7CHl75hdFtwocbwB38x/d36E39wey2w+dPcNqqr2tVBSD+Y1QRHB3p/qsLlvz7sFddr+w2CKpm/7YqgroK+z/M5+3AObCACNf/XtM+ClqWVb/vUi/0iofByYH9TGdlZbFs2TJuueWWo1qi7OClzbKzsykoKPB/r+/atYsHH3yQSy65hGHDhh12XwkhkB576ONVNYpU+bhZeSOQp4rHTcs3VasylTUoWVbVjUBVNUB5TKjsJVXtq7LGsv1u3yhRz+/3Yy8u913rxnSs/nN9gNfrZcGCBXTo0IGwsEY+pLSZUwAutSLEAeFO+M863zDZAYkQ7YLPN/t6ws9te/TDbI6Gw+br1b340BGJtWpRlm8+WGwQ/KUNXBoH3vdrts/NmzezdetWTj75ZGw2G0FBQbRs2fKQ7bxeL2vWrGHfvspbGQzDqDRwbxHiu8Dll/l6u4cm+4bHrc3zBTx/H3jQ38YeBCHxYHNCm78cc/ANEBUEN3cHowbz/u0G9d4raD+KAKMuLNvny6cQEwQj28AV7aGokh4GANaUQL4H+kX4/qhLCiHSASmuQzY9kvrUp0+fQ3pS5syZQ3R0NF26dAEgOjq60tfGxsb6R3BER0dTUFDgb7F3uVyHDAHOLoW1+XB9N4hywaYC301Nt0pu5qpiM2BCdwhphCN6vVa9D7Y4JmY1PZUHs/CNxPl8M7SO8PX6jG4FeUsP/9off/yR3r17k5ycjMfjYdmyZfTt27fSKQ+WZbF69Wqys7Mr3ZfD4aB79+4Ve/IsCzaWgB3oGeYr7MYSaBUMf1iP2jAMoqOjqx3iXmmSLJuNqEGD2PfVV7izsihavJj0Rx7BqGwoPL5pGNd3A1cjbExqSkwLUsLgjFa+v8eQJMj6HsqqSCjrtWBBFvSMr7yRoaSkhGXLllFWVnkUHx4eTq9evSoNoE3TZP/+/ZSVleF2uys8Z1kW3333Hb169TqqnlHTNFmyZAmTJ0/G6/Wyd+9ekpOTiYiIwO12M3HiRBITEwkNDeVf//oXJ598MhkZVY8KaRECGfFHfPhmZ08xvLsOIpxwUXv4awdfo87hGowO2L9/P6tXr9b87yZAAbjUCgPffM2Ccl/vd0wQpIbD/L0w+TTfc01BhBPGdYHz2/ousGV7YXENo7N9+/bhcDhISEjAsizy8vIICwurMPz8gOLiYgoKCirdj81mq3SebUaU7ybi32t9/28VDi+tgJeWw+WdICkM8JT47ii2fAnl+WCZ4C0F+7FPvrUBoU6wNfLEe/UtwuW78Tu3rS8YtZnwa1V1br8Hgm0QZvcF4l/vg/Rg3918JUpKSqqsT4ZhVFqftm/fTkxMDBEREZimSU5ODnFxcRVuGA8EOQfs2rULgIEDB1b5PncV+4Zudoz2xT8/7PDljMiIqvIllbIbjW/5MmjEF+Mj/HxbFqSFw129fMPPO0aDtwR+O4Lvz82bN9O5c2cANm3axHfffVdtL3d135NOp7Py4Hl9CcQ5IcIOe93wYx6cEOkb2nUQm81Ghw4dDl/oPzAMg+C2bXHv28eed94h+pRTcFbS0CoNiwE81t9Xd0Mdvh7trGq2L/HAhny4rmvlz5umSWFhISUlJZUfr4qe65ycHF5//XXmzJlDUFAQjz76KCeffDLnnnsuAEVFRUyfPp3rr7++2izpf7RixQruvPNO7rnnHgYNGsRFF13EwIEDsdlsWJbFeeedR3FxMUVFRaSnp9OiRc2mpTV3QXa4raevI6RD9NF3TOXm5rJx40b69+9/VKMcpOFptNd8aVicdnh8gC/wTg71XaSu6eL7aR9d36WrPRlRvvcJvvd4mJFoRyQtLY3Q0FAWLFhAbGwsM2fOZOzYsYcE4Ha7nX79+h31/kOdviBuRQ68d4pvaPj6PPhzK19rPgBZi2HGjZDQG/o9CD9eCTt+gqAYSB5c8zcpxywjEh7+/c9uGGBWNzkzzulrbVlZBPvcsN8LLVy+ea1DoyDif1/5drudvn37HnV5evTowQ8//MCKFSvYtWsX27Zt48orr6zxzcDaPMgrh+X7YHMBvLMW7ujlmxsnTcOZrXy5KGz46vKRLmjUtWtXVq5cycqVK5k3bx7guxGdNWsWJ554YoVtD4wEOmodQmBWHqwogl8LfF2ZRSZMz4HBUb5GrRoKSk3FvXcvxevXkzx+vG6gGwHDgE5VzPeuzNw9vgbETtGVPx8WFsbw4cOPuhwxMTHceeedVT6/bt06SkpK6NWrl/+x0tJSCgoKKCsrIygoiNjY2ArBeXl5Oa+//jp9+/Zl6NChWJaFZVn07t0bwzCw2WxHNOxcjlyUyzfFCo5tesLatWuxLItOnTrVbsEk4BSAS62wG3BqWsXHBjbBxv26uF9KSUnhySefZObMmSQlJXHFFVcQFXWU3X7VcBhw+3G+jKyJIb7O0L+f4OvF9y8LF54KXa7yzfkOiYd+f4PSXEjUGpP17dA6V00l7B7mS7iW7Yb+kdA5zBdQJDohpHa6hU866SS8Xi8zZsygc+fOjB49+rCBhNPpPOxwubRweOlEX+LGEg+8MtiXqV8xStNgHGbecHWuvPJKJk+ezObNmxk1ahRxcXHk5OTU3hq4hgHHR0KJCRtLYUSc77O0tQxaBUFozT87lmVhlpYSlJpK2u23YzvGJFnScFmWL+lsi2CIDa58m5ycHD744APy8ysfw96yZUuuuOKKQ75TDcOo8nvWNE0+//xzTjnlFP8UtB07dvDUU0+RkJCAw+Fg5syZjBw5kiuuuMIfhBcWFrJs2TLuvfde7HY7mzZtYv/+/aSnp6txqI7U5LRalsWCBQvo2rVrhRwT0jgpABepZ4Zh0LNnT3r27Fkn+3fY4LKO//s9NtiXebOCiFbQ/br//d7uvDopi9QxmwEn/6G7plvtJmpxOBycdtppnHbaaUf8mtNOO41TTqk+JfDxiYCSukolYmJiuOqqq/y/n3POObV/kDA7nHVQxv+kIKh6xsQRszwe8n/9FWd0NNufe47k8eMJ+T3DtDQtZSbM3OVbYSS2ivbGqKgoRo8eXWUOgaMZPn5AVlYWS5cu5bnnnvPnRTBNk27dunHppZcSHBxMWFgY//rXvzjnnHP8qwd4vV5KS0uJiorC7Xbz+eefU1xcTEpKCqZpVrmspNSPkpISfvvtNwYNGlTpFEVpXBSAi4hIndLNgjRX3uJitj7+OJ7cXFJuuIHY00/HUGDTJJV7fas2jO9WdU+n3W6vNHFlTcybN48WLVqQlva/YYhpaWlcffXVgK/n1OVyERYWViHAj4iIYPDgwbz77rts27aN2NhY3G43H330EQMHDtQw5wbCsixM02T27Nnk5uYycuTIQ5KYSuOjAFxERESkDtjDwsh48UVsLhdBqalVZj2Xxm9xNhR7YHjK4betLW63m6lTp3L22WdX2Xuek5PD9OnTufjiiytMbwsODuaBBx4gMzOTxMREoqOjOf744wkNDa0QzEv9Kioq4rXXXqOgoIDnn3/en5BSGjcF4CI1YRiADcs60nRCzYNletXLU4cMmx3LVJ2rwPL+PqxXQ3sbE8OwY1lHsBh4I2XY7YS2P7r1MS3TC4YN1eWGyREUQUl5Maa3HJvdRakHFmbBC8vg/Ha+fBaBYFkWGzZsYPfu3Zx44omVTmsoLi7mueeeY8CAAVxyySWHDCuPioqiR48e/t+PNbgzvW685YU4IxrhGpANXHh4OHfccUd9F0Nqme6QRWrA7gzFGRpDae62+i5Kg1KSvZHg2HQM3UDWPsMgOLY1Jdkb67skDUppzjacoXHYnEqb3lgYdgdB0amUZG+q76I0KKX7NhMUlYRRg2Ugpe6EtuxEae5W3EU5ALhN2FcGE3rAA30gOICDHL7//nv69OlT6bB2t9vNa6+9htfr5cYbbyQ4uIrMcLXAU5JHSfYmwlp2qbNjiDQlCsBFasAREkVQTBr7Vk7DMptuL87R8JYXk738KyLT+4N6wWudYdiIbN2P7OVf4S0vru/iNAiW10P2ymkEx6XjCFF22MbCZncRntqT7GVfYnpqY1HHxs/0lJO1/EvCkrpic9ZdwCTHLiKtD1gmexZ+iOkpJ8IFZ6fDSckQ5gzcyg1lZWUsXLiQs88++5DnLMviv//9L9u2beO+++4jODiYH374gdzc3Fovh+l1s3fRJ5jlJUS17l/r+xdpijQEXaQG7MGRpJ00gfWf3U5oYmdiO52CzRGEYTSvwNOyLLBMvOVF7JzzFqW5W2n3lyfQEMo6YNhIOWEcK/59CTvnvEnS8Zdjd4WBYWt2mZUty8T0lJGz+lt2z3+PDuc/jz0oor6LJUfIsDtJG3oDKyf9ld3z3yOx9yhszlCoZsmlpsiyLMDCdJeQtfQzclZ/S+fRb2FzKABviFwRLWhzxoOsn3IbZXk7SOx7MSFxbbEFeMSCA4uXX/g/glxBmH9ojN29ezcvPPsUnTp14tUXn6W0rJTffvuN1157jcjQ2kmKaZpuSrM3s2fRx+xbOY12f3kSV1RyrexbpKkzLN83vzQzBQUFREVFsXzzHrqlt6jv4jRqpqeMXb/9m+0zXiGsZRfC03rjDImu72IFmEVZwS7yN83BdJeSfvr9xHY6tVndRAeSZVnkrP6GzdMnYg8KJarNCQRFtqS5NXi4S3LZv20RxbvXkDr0BpIGXIbNoYzrjYllmmQt+5zM757CFZFIZPoAXOHNbx6puyibgsz5lOZuo9Xw20jsfQGGTX0kDZVleijcsZzM75+meO86yguzoQHlgvG4PeTk5OA1vRy4LtjtduLi4rDba6mDwLDjCo8nJL4drU+5k4jU4zDsqrMNzYH7/fz8fK0f3oAoAG+mFIDXLsv0UrRrJTlrf6BwxzI8JXn1XaTAMgxckS2JSD2OuC5nEBSdquC7jlmWRVnuNvatms7+7Ysp378HAvB1bgG5ZbCjCLYWQosQ6JtQP6G/IzSa8JSexHYcTljLrkr810hZlkVJ9kZyVn/L/u1LcBdlB6QuNyTOsDjCU48jrtOfCGmR0exGUTVGlmVhlhfhLsnHW1YEmPVdpAAzsAeF4QiJxu4K0zW/gVIA3jApAG+mFICLyNFatg8eXQBr8iC7FPLK4OmBcEM1696KiIhI/VAA3jBprIiIiByR1uFgM2Bljq8nvGsMjGyj4FtERETkSGmMk4iIHJZlAQZ0joFWvwfiV3SClLD6LpmIiIhI46EAXEREqmVZsK0QXlkOg5Ng0nAY3BJGtq3vkomIiIg0LhqCLiIilbIs31DzhVkwLRMubg/to3zPvT0MWkdo+LmIiIjI0VAALiIih7AsKDfhyy2wuxjGd4OE4P8F3G2Uy0VERETkqCkAFxGRCiwL9pXCRxsgPgTGdoYQu3q7RURERGpKAbiIiPiZFmzI9wXfw1OgfyI4lC1EREREpFYoABcRaUAsy2Kf2yKv3MIEwh0GLYIMHAHofi73wi87Yd5euLQDpGuOt4iIiEitUgAuItJAWJbFrBwv964qJcdtYVm+5b4e6BjEBclOjDqMhveXw+RNUOKFm3pAmEPBt4iIiEht08BCEZEGosgLz2woo0WQwS8nhvHLiWF0j7TzyuZyrDo6pmX5kqy9uhLigmFcZwXfIiIiInVFPeAiIg2E14JCj0WrUBvhdgOXDWKcsLusbo5nWrBsH3yxGc5Ohx7xYFfgLSIiIlJnFICLiDQQEQ4Y29rFvatLuW1FKfFBBr/lenmkUzC1GRdbFrhNmL4N1ubCVZ0hOUy93iIiIiJ1TQG4iEgDYTMMTklw8HO2k092unFbMDTOTrdIe60eJ68Mpmz29XZf1w3CnbW6exERERGpguaAi4g0EEUei7+tKWPlfi+f9gvlw74h5Lstrl5Swr7yms8CtyzYmA+vrYR2kTC6g4JvERERkUBSD7iISAOxp8zis11unuoSzOA4X693kM3g4gXFrC40GRx07G2mXhNm7YY5u2FUhi8A15BzERERkcBSAC4i0kDYDQiywZJ8L39OdOCyGczP9RJiN4hzHVu0bFlQbsLnm2FHEVzbFaJdCr5FRERE6oMCcBGRBiI52OC+DsG8srmM/+7x4DTAaYN7OwTRMfzoe78tC/aVwr/XQlo4XNcVQvStLyIiIlJvdCsmItJAOG0GV7ZyMjLJQYnX91iIHWKdBvaj7LI2LViTC59ugj+lQt8W4FDWDxEREZF6pQBcRKQBcdoMWgTVbHy42/TN9Z69Cy7v6Ov91pBzERERkfqnAFxEpImwLCh0wycbfb/f0B0inAq+RURERBoKBeAiIk2AZcH2Inh/PfSMg1NSNeRcREREpKFRAC4i0sh5LViUBV9tgfPaQo849XqLiIiINEQKwEVEGrFSL/w3E9bnwfhukBii4FtERESkoVIALiLSCFkW5JbBpHUQH+yb7x3qUPAtIiIi0pApABcRaWQsCzL3w+RNvuXFBrXUfG8RERGRxkABuIhII+I1Yc4emLULLmgH7SLV6y0iIiLSWCgAFxFpJIrdvl7vQg9c0wVig+u7RCIiIiJyNBSAi4g0cJYFO4rgg/XQKgLGZoBTQ85FREREGh0F4CIiDZhpwaoc+GQjnJ0OvRLApiHnIiIiIo2SAnARkQbIssBjwffbYHE2jOsCKWGa7y0iIiLSmCkAFxFpYCwLij3w6SbAghu7Q7hTwbeIiIhIY6cAXESkgckqgXfWQs94ODlF871FREREmgoF4CIiDYzTDue1hfRIzfcWERERaUoUgIuINDAxQRDt0pBzERERkaZGAxtFRBogBd8iIiIiTY96wEVEGgDTghIPmECQDVz2+i6RiIiIiNQ29YCLiDQAxR54fBH0+Oj37OciIiIi0uQoABcRaQDCnXBpByjzQqvw+i6NiIiIiNQFBeAiIg3EwixfArYecfVdEhERERGpCwrARUQaiHl7oGssfLcdzv8GbpntWxNcRERERJoGBeAiIg1AQTmsyAWPCZ9vho7R8K81MC2zvksmIiIiIrVFWdBFRBqAPSWwMR/6t4DnB0GE05eMbXlOfZdMRERERGqLesBFRBqAnUW+ZcjGd4PYICjy+HrDk0Lru2QiIiIiUlsUgIuINAA/74DWETAw0ff7yhzwWjA0uX7LJSIiIiK1RwG4iEgDsCIH0sLBaQOPBR9ugONbQOeY+i6ZiIiIiNQWzQEXEalnOwphUwEEO2DaVpi/1zck/fEBvvXBRURERKRpUAAuIlLPIlzw4mCwG7CnGIYkwYTuEB9c3yUTERERkdqkAFxEpJ5FumBQy/ouhYiIiIjUNc0BFxEREREREQkABeAiIiIiIiIiAaAAXERERERERCQAFICLiIiIiIiIBIACcBEREREREZEAUAAuIiIiIiIiEgAKwEVEREREREQCQAG4iIiIiIiISAAoABcREREREREJAAXgIiIiIiIiIgGgAFxEREREREQkABSAi4iIiIiIiASAAnARERERERGRAFAALiIiIiIiIhIACsBFREREREREAkABuIiIiIiIiEgAKAAXERERERERCQAF4CIiIiIiIiIB4KjvAogEgmVZ7N+/nwULFvDrr7+Sn5/P4MGDOe2003C5XPVdPJEGwbIs8vPzmTdvHgsWLCA3N5dTTjmF4cOH43DociFNg9frZdOmTcyePZt169YRGhrK5ZdfTmpqapWvWb16Nc8//zzl5eU8+uij1W4rIofndrv58MMPKSgoYOzYsQQHB9d3kUQCRj3g0mw888wzZGdnM2HCBM4991weeughZs+eXd/FkmPkNX0/UnuKi4t5/vnnKSws5MYbb+Tss8/mnnvuYf78+ViWVd/FE6kVy5cv57XXXmPw4MHceeed5OXlcdddd1FUVFTla9q3b09UVBRz5swhLCwsgKWVY1Xuhdr82rIsi4KCArKzs6utK5ZlMWvWLP75z39SWlpaewVoJPbs2cNjjz3GLbfcwn/+8x9M89ALtdfr5euvv+aOO+7goYce4j//+Q9ut7vCNpZlUVJSwg8//MDEiRN55JFHmDVrFh6PJ1BvRaTOKACXZuPOO+/kvPPOIyIiguOOO44uXbowZcqUSi8O0vCtzIXF2bV7g9XchYSEcOedd3LOOecQERHBoEGDSE5OZvr06fVdNJFa07VrVx5//HHatm1LVFQU559/PkuXLmXz5s1VvsZut+N2u+nVqxchISEBLK0cq/fW1+7+PB4PU6ZMoW/fvrz11luVbmNZFsuXL+fqq6/m7rvv5p133qk2YPR4PMyfP58XX3yRt99+m61bt1bZ2GlZFhs3buSf//wnr776KkuWLKlw/2JZFjt37uTtt9/mpZdeYs6cOXi93kP24/V6+e2331i+fPlRnoEjExcXR3FxMZ9//jndu3fHMIxDtpkxYwaTJ09m4MCBDB8+nNmzZ/PNN99UeO+WZfGPf/yDhx56iEGDBmEYBhdeeCELFiyok3KLBJICcGkWDMMgNDQUwzAwTRPDMAgPD2fr1q0VWqgty8KyLEzT9P/Udc+fZVmYlhnwn8beo5lTCntL6rsUTYvNZvMHFwdu7KKjo1m7dm2F7Sr7jBz4CaT6+uyYjfyzU5Xdxc2jQcvhcBAUFOSvs2FhYeTm5rJv3z7/Nn+8Fuzfv59Vq1bRs2dPTVtqJNbmQW1WZ6fTyVlnnUVISAhdunQ55HnLsli/fj2PP/44vXr14rjjjmPRokVMnjy50oZ+0zT54osvuO+++2jdujWZmZlceumlVQbhq1at4qqrriIoKMg/beKnn37yP79r1y7Gjh1LQUEBiYmJ3HLLLXz88cf+fVmWRVZWFo899hgjR45k0aJFtXh2/sdut1NYWEifPn2qDMBN0+TJJ5+kbdu2pKam8tRTT2Gz2Q5prGjdujW33XYbQ4cO5cILL8Tr9SoAlyZBk/qkWfB6vcyYMYPPP/8ch8OB0+lk9erVREVFVdguMzOTjz/+mLy8PDZv3kxYWBh///vfiYmJqbOy/ZC9jJuXV96aXpeub3Mm17U5PeDHrS2GUbs3V+Kbk/fTTz/x1VdfERQUREhICOvXr6dNmzb+bSzL4tdff+WLL77ANE2cTicej4ekpCQmTJiA3W4PWHmXFmzh0oXPBex4B5ybPJBHOl0c8OPWtffWwXXdILSJ3xlkZWXxzjvvsG3bNkJDQyktLaWkpGJrXklJCZ9//jnz58/H6XSSk5PDunXruO2227DZ1HfRGBj83qB0aPx3zDIzMykpKaFz586HPHegB/ree+9lzpw5zJw5k4kTJ/Ltt9/idrsJCgqqsH1+fj7PPfccV111FWeffTYnnngi06ZN48MPP+SOO+6oELh6PB5eeOEFunTpwujRozEMg99++43nn3+eE088EafTydtvv43L5WLs2LGEhYWxdu1annnmGc466ywiIiLYtm0bb731FjExMZUGxbXF7XazaNEizjnnnCqPM3z48Aq/t2jRgjPPPLPCYzabjXPPPdf/e1lZGcHBwfTp06f2Cy0SYE38Miviuyh+8cUXPPXUUzzxxBMMHjyYRYsW8dJLL3HhhRf6E38cuLm65JJLuPHGG1m0aBFTp04lIiKiTsu3312Cw7Dzco9xdXqcgz294XOyyvMDdry6UHe3D82TaZp8/PHHPPfcc7z44ov069ePmTNn8uqrrzJ48GDA91n6/vvveeSRR3jwwQcZOnQoS5YsYciQITz77LMBD0yKvWXs95QyqfdN2OvwhvJgk7b9zM6SnIAcK9CKPGA28Vatffv2MWHCBBISEnjggQcICwvj5ptvBiAyMhKA8vJy/v73v7NixQqeeuopUlNTefTRRykvL6+057M2rd6/nZzy/XV6jMrEuiLoHNG0EssZ1LyRtrS0lK+++ooFCxZgs9nIzMykY8eO/rpyMJvNxkknneRvpATfcOxLLrmk0n1v2LCBtWvX0rNnTwzDIC4ujh49evDjjz9y6623Vvg+zcrKYt68eYwfP96fEPPEE0/kwQcfZMeOHcTHx/PLL7/Qq1cvwsLCMAyDoUOH8uKLL7Ju3Tr69OlDTEwMN910E0VFRfzzn/+s4Zmp2q5du9i9e3etBsput5vJkyczZswY+vbtW2v7FakvCsClycvKyuKRRx7h3HPP5aSTTsJms9G6dWucTift2rXDZrNRVlbGE088QWJiIueffz5Op5MTTjiBE044oU5big+IdIZyYlzd3tgdbNK2nwN2rLrUHIbLBsrWrVt56qmnuOyyyzj++OOx2WwkJSUREhJC27ZtAcjLy+OBBx7gL3/5C8OGDcNms2FZFqGhoQwYMCAgn5U/CrW7GBTXCYcRmJ73X/atZHPR3oAcK9A8JjiacMuWZVl88sknLF68mOnTp5OQkIBpmqSkpBAfH+8f6TRv3jzeeecdPvjgA3/dB+jcuTMtWrSo0zLes+pd5uWuJ9oZuERvee4i+sVk8MWAewN2zECo6Sgpj8fDyy+/zPLly3nsscew2WycddZZDB06tFbyABwYap6UlOR/rGXLlsyYMeOQIej5+fkUFhaSlJTk/56NjY2lpKSE3NxcIiIi2L17N2lpaf7nIyMjMQyD3bt3A/g7E6pLIFcb1q1bR3h4OK1bt66Va4JlWUybNo29e/fy1FNP4XQ6a6GUIvVLAbg0eb/88gvbt2/nz3/+s79FeevWrdhsNn/P3ubNm5k1axbPP/+8v3W5PoIJOXIagl67Zs2axd69e/nTn/6EYRhYlsWePXuw2Wz07t0b8AUmmzdvZuTIkf7P0pIlS0hKSiIjI6M+iy+1wML3uWqqysrK+M9//kO/fv1IT08HfCM/MjMz6dChA8nJyXi9XiZPnkyHDh3o2rUr4OsFXbhwIf379w/IFIsHOl3IuNan1vlxDvhX5vdM3dM059XWpJF27dq1vPfee7zzzjukpqaSlZWFy+WiV69etVIPDsx3Pnhf8fHxVc4XN02zQq94RESEP5eBaZp4PJ4KzwcHBxMeHh7QRLOmabJ48WKio6NrrbGqpKSESZMmcddddxEeHl4r+xSpb5rIJE2aaZqsXbuWxMREfyuzZVn89NNPpKWl+YcTrl+/ntLSUjp06KDAu5Hwz++TWrFq1SoiIyNp27atPwCfM2cOiYmJ/s/FggULiIqKIjo6GvD1iH/88cccf/zxygwtDd6+ffvYs2dPhaGx+fn5zJ49m9NOOw2Xy0VRURErV64kNTUVl8uFZVmsWLGCFStW0Ldv34BMs7BhYDdsAftpqte8mgxBN02Tr7/+mqSkJP934u7du9m1axc9evSolXN24Hv24PwDOTk5lQ5vP5BA9uCksYWFhYAv0D5QnuLiYv/zZWVllJaWBnTZPK/Xy+LFi+ndu3etHdcwDC666CLdn0mTogBcmjybzYbL5fK3Mufm5vLtt99y8cUX+4cclpSUYBgGdrsdy7LweDz8+uuv7N8f+Ll4cmTUA1677HY7drvd36NSUFDAV199xZgxY/yfk4KCAv/QSI/Hw2effcbatWvp2rUrOTk5bNmypR7fgUj1DMPAMAx/3g/Lsvjhhx8IDg5mxIgRgC+AKC0t9WdBLygo4L333sPhcNC6dWtWrlyp60IjUZNrhGmabN++nbi4OMLCwigvL+fDDz8kNDS01kb7pKenY1kW27ZtA3z1cevWrXTr1u2Qhp7o6GiioqLYtm2bv27u3buX8PBwEhISCA4OJiUlhczMTP/SY/v27cMwDFq1alUr5T0SxcXFLFmyhP79+9dasJyXl0dubi75+Y07b43IwRSAS5N2ICmK2+1m165d5Ofn8/rrrxMXF8fYsWP9QfmBnvCJEyfy/vvvc9NNN/HJJ580+qW6mrLaSLAj/3PSSScBsHHjRvLy8njuuefo0KGDP+MuQJ8+fdi7dy9vvvkmTzzxBMXFxSQnJzN//nweffRRCgoK6vEdiFQvMTGRgQMHsnLlSsrKyli2bBlvvfUW99xzjz9ICQsLo1OnTsyZM4e33nqLxx57jE6dOuF2u/n00095/fXX6/ldyJEyoEYXCcMw/IHftGnTWLNmDd27d8fr9bJ3b83zQLRt25Z27doxY8YM/z7XrVvH6aefjt1uJzMzk6lTp1JUVER8fDy9evVi9uzZlJaW4vF4mDlzJkOGDCEhIYGwsDAGDhzIkiVLyM3Nxev18ssvv9CvX79KA/CDlyarTVu2bKGoqIhu3bod8WsOF6hv2rSJu+++m5UrV9a0eCINhuaAS5PXr18/nnjiCT744AMAOnbsyKuvvkpcXJx/m06dOvHSSy/x9ttvM3nyZEaOHMmIESMqHQomDYMC8No1ePBgnnzySd544w1cLhft2rXjxhtvrPA5OeOMM7jtttuYO3cuF154ISNHjiQ8PJyvv/6a0aNHH9VNlzQ8ptW0W+VtNhsTJ07kvffe429/+xvh4eHcf//9DBw40N/j6HK5uPPOO3G73cyZM4fx48fTuXNn1q9fT1lZGXfeeafmoTYSNblG2O12TjjhBCZPnsyf//xnzj33XE4++WS+/PJLpk6dyvHHH1/jOc6xsbHcd999PPPMM4Ave3jfvn05++yzMQyDOXPm8MADD/Ddd9+Rnp7Orbfeym233cb9999PZGQkBQUFPP744/66O27cOJYtW8Z9991Heno6K1as4IEHHvAnLSstLWX+/Pn8+OOPbNu2jSlTphAbG8vxxx9PQkJCjd4L+IL5efPmkZ6eXiGxXHVuueWWw27Ts2dPfvrppwrLYYo0doalLr5mqaCggKioKJZv3kO39LrN6irV+2znbzy36StmnDgxYMe8esmrpIbE8UDHCwN2zNr22x7YWQQj2zTtxFFStTk5axi7+GWWD3shYFnQJ677hM1Fe3mz1/UBOV4g3fUrPHm8Pk/16Zy5T3BmYh+uTv9TwI75ZuZ3fLV7fpPLgv7QfLjzOAg9xqTZZWVlLF++HMuy6N69O6WlpSxbtoy2bduSkpJSZc+tZVns2LGD3NxcunXrVm0Pr2mabNq0iczMTKKioujatas/n8bu3bvZuHEjvXv3JiQkxJ8Yc+3atdhsNrp27UpsbGyF4+bm5rJq1So8Hg+dOnUiMTHRf/wDAfjB88htNhs9evSolQA8Ozuba665hv79+3PHHXcEfFlKqdyB+/38/Hx1KjUg6gEXkUZJPeAiIlKVml4jgoKCKqw5HRwczJAhQw5/XMMgNTWV1NTDr6tus9nIyMiodF55y5YtadmyZYX9/vGxPx43NjaWE088sdLng4OD/Su/1CbTNPn000/59ddf6du3L+PHj1fwLXIYCsBFpFFSFnQ5QB22IvJHaqQNDMMwOPfccxk5ciR2u12ZykWOgAJwEWmUdI0XEZGqGIYaaQPBMAwcDoUTIkdDnxiRGrIsC9M0D7udYRgallWL1LvReBzNZ+TAz1Ht/1gLJn4KVGruwPJQh0utc6z1XI6SlqoUkQZKAbhIDWVnZ3PGGWdQUlJS7XaDBw/mH//4R4BK1fSpd6Px2LVrF5dccglZWVnVbjd8+HCef/55BSb1wGuBQ+2DNeJ2uxk3bhwLFiyodrvWrVvzn//8p0ICLal9aqQVkYZKAbg0SatWrSInJ6dW9+l0OunTp88hQ62ioqJ46aWX8Hq91b4+JiamRsfflLuJMZ+NIa80j4zYDD694FOc9mNM79oE6Oaq9uTk5LBq1aoa78cwDHr16kVoaGiFx2NjY3n66acpLy+v9vVxcXEKvutJU/8sWZbFokWLDttQeiRsNhu9e/cmODi4wuMOh4Pbb7+d/Pz8al8fEhJCREREjcsh1avvPCGZmZkAtGrVSt9rlTBNk82bN+NyuUhNTdU5kmZFAbg0SS+99BIzZsyo1X1GR0czffr0Q26cXC4XAwcOrNVjVcayLCadM4l2se0YPWU0U9dNZWTnkXV+3IZMPeC1Y/ny5YwfP77G+7Hb7XzxxReHrNcaHBxM//79a7z/Y6GGKwHwer387W9/8wdFNeF0Opk6deohWa5tNhvdu3ev8f6ldhh1MAS9vLycf//730yfPp3LL7+cs88++5BtLMtiw4YNjBkzBrvdzrvvvkubNm2aTYBZWFjItm3bME2TuLi4CkuhHWBZFitWrGDUqFGkpaXx9ttvV5o13jRN9u3bR3Z2Ng6Hg+TkZEJDQ5vNuZSmSwG4NEmvvPLKYefhHa2q5uzl5eVxzz33UFZWVu3ru3fvzi233HLMx28X287//3JvOS6765j31RTUxc1VczVkyBCWLVtWK/uqLM9BdnY2jz/+OHl5edW+tlevXlx//fW1mitBDVcCvsahqVOn1tp1obI66na7eeaZZ1i3bl21r01MTOSee+45qjV51ZB09OoiRHM6nfzlL3/hoYce4tprrz3kecuy2LZtGxMnTiQnJ4f4+HjuvPNOnn/++WrXDm9K9u3bx7hx49i0aRNvvfUWp59++iHbrFmzhgcffBCAoqIiHnroISZOnEhiYqJ/G6/Xy8cff8zHH39M+/btmTdvHq1ateLll1/WetbS6CkAlyYpkMnOHA4Hbdu2xe12V7tdcnJyrRzvk5WfsC1/G6dlnFYr+2usNAS99hiGgd1ur7P9O51O0tPTKSwsrHa7pKSkY7pBre4VargSqLoBtbaPkZqaetiEg7GxsUf9eVND0tGriyHohmGwe/duIiIiaN++/SHPm6bJN998w0UXXYTL5aJNmzb06NGDqVOnMnbsWJzOpt9okpaWRkZGBunp6Zx22mmHfO7cbjdTp07lhhtu4N///jf9+/cnNTWV6dOnc9lll/m3M02TGTNmMGzYMK677jqmT5/O6NGjGTNmDKeeemqg35ZIrVIALlJD4eHh3HHHHQE5lsf0cOu3t/Lb2N9w2Jr3x7e+5/fJkYuKimLChAn1WgY1XFXPtMDW9Dvn6pTD4eDSSy+tk32rIeno1dYoKcuy2LhxI7NnzwZg8+bNtG7dutK8LjabjTFjxuByufj8889xOBycfvrplJeXV7pUl2VZlJeXs2DBAlauXAlA586dOf744xttsF5eXs7SpUu5+uqrK230cjgc3HDDDRiGwaRJk7Db7Zx99tmH5AhxOBw8/fTTuFwuHA4HLVq0wG63H5JjxLIsCgoKmD17NpmZmQQHB9OrVy969OihlWekwVLNlCbtwPJHa9eu5dNPP+XLL79k7969tT48PVB2F+4mKTyJlMiU+i5KvdMQ9Lpx4IZw4cKFfPzxx3z77bcUFhY26M/M4Up2oOFq8qjJzb7hqiqmBfZmEoBbloXX62XlypVMnjyZr776iuzs7AZdxw9QQ9KRq41GWsuymD59Ovfeey8pKSmkp6fz7rvvkp6eTnh4+KHHNAyCg4MrBH6GYRASElJpMOr1ennttdd444036NevHw6Hg0ceeaRR1MWqbN26lYKCAnr06FHpez5wPg5mt9sPecwwDCIiIggKCsI0TWbNmsXpp59O7969K2xXVFTE3Xffzdy5cxkyZAgbN27k9ddfbxbD/aXxUgAuTVp5eTlPPvkks2fPJi0tjUWLFnHttdeSnZ1d30U7JjHBMTw+/PH6LkaDoB7wupGVlcUDDzzAtm3bSEtL45NPPuHee+89bAbzhkwNV3Kw/fv389RTT7FgwQLS09P59ddfueGGG2p95Yzapoako1Mb05S2bdvGE088wdixYxk+fDjdu3cnNDSU/v37V9qjfbTKy8uZOnUqZ555Jr169WL06NG89dZbjbb3G3zzu6OiomjVqlWt7M+yLBYuXMiCBQt44oknDll9YM+ePSxYsIALLriArl27ctddd/G3v/1NAbg0aPoGlybNZrNx+umn061bN1wuF61bt+Yvf/kLP/zwAxdeeGGj+4LOL8tn0tJJnNL2lPouSr1TD3jdCAoK4q9//SsdO3bEZrMREhLCpZdeyooVK+jTp099F++YqOFKDuZwODjrrLPo0qULDoeDtLQ0TjrpJGbPns2IESMa7HVBDUlHp6bXCMuy+O9//4vL5WLQoEEYhkFOTg5ZWVn06tWrVsoYFBTEeeedx7Rp00hJSSEtLY20tLQGWwcPxzRNli5dSkJCAgkJCbWyz8LCQl588UWuuuqqSpd0a9myJYMHD+aDDz7gnHPOIT09naSkpFo5tkhdUQ+4NGlOp5PevXvjcvnmy4WFhREWFsbKlSsPmyinIUqOSGbSyEn1XYwGQUnY6kZUVBRdunTBbrdjGAYxMTGYpsmGDRvqu2hVOtyt6oGGKxGA0NBQevTo4e/BjI+Px+l0smrVqnouWfXUkHR0ajpKyjRNli9fTvv27QkLC8Pr9fL1118TGxtLenp6jct3YG75+vXradu2LZmZmWRlZdV4v/XJ4/Ewf/58+vXrR1BQUK3sMycnh5CQEHr16nVI8G2aJgsWLMDtdhMdHc3mzZspKCioleOK1CX1gEuTZpomGzduZPr06eTm5pKamkpxcTFlZWXs37+fl19+mfz8fG666SZSU1Nxu928//77hIaGcv7555Ofn88bb7zB3r17ufrqqyvNeir1wwBF4HXA7XazaNEiZs6cSVlZGYmJiZSWllbI8m+aJps2bWLGjBlkZmZSVlbGyJEjGTBgQL2U+XDVQA1XcjCv18uaNWv49ttv2b9/P126dKGkpKTCUpKWZbFr1y6+++47MjMzSUxMJDExEZfLxemnn14vyZ00Auro1bQH/MD3nmmarFixgs8++4zu3bsTFBSE2+2u0VDx0tJSHn30URITE7n33ntxOByNtuf7gP3797Ns2TLGjRtXa+8lMTGRRx99tNI591lZWdx9993cdNNNjBo1KiCrHYjUBvWAS5NlmibfffcdY8eOJSEhgWuuuYZ9+/axYMECYmJiCA8PJzU1lVdeeYWtW7cCvmQer7/+Ol999RVut5vIyEjCw8OZPHlyrbXmSu3QEPTa53a7efvtt7n33nvp27cvV155JfPnz2f37t3+dVdN0+Tnn3/m1ltvpXXr1lxxxRWsWrWqwc+flep5THA0gzsCy7L4+uuvueaaa0hPT+faa69l7dq1bNmyhdjYWP82a9euZdy4cRQWFnLttdeSkJDApZdeyvr16+vtBl8NSUenptcIm81Gq1at+OGHH7jvvvt4//336dq1K8HBwcyaNYv169fXuIwOh4PCwkLy8vLIysril19+YceOHTXeb31Zv349drudjh071to+v/vuO/r168eyZcsOec4wDBwOB/v27WP//v1s376d77//nv3799fa8UXqgnrApcnatWsX9913HyNHjuT888/H4XAwZMgQbDYbKSkpOBwOevXqRUREhP/LesOGDaxZs4b4+Hh/6/auXbu46KKLSEnRvLuGREPQa9/SpUt58sknefbZZxk6dCgA/fv3Z/LkySQmJgK+pEQ333wzDz30ECeffDKGYfDOO+8csjRMIKm/Q45UZmYmf/vb37jiiis4++yzsdvt/uGyqampABQXF3PfffeRkZHBVVddRVBQEGlpabhcLvr27asetkaipqOkDMNg7NixuN1ugoKCuPzyy8nKyuKTTz4hLCyMjIyMal8/aNAgEhISqqwvwcHBPPbYY0yePJkXX3yRuLg4evXqVenyZo3BgWRpaWlpxMfHH3Z7u93OkCFDaNu2bbXbpaSkMHLkSH8D2cESEhJ49dVX+eqrr3j22WdJSkqiX79+hyRqE2loFIBLk/XVV1+xbds2Ro0a5Z/rl52dTXR0NN27dwcgNjaW4OBgsrOzKSsr49NPP+VPf/oTWVlZuN1u9uzZw8qVK5k4caLWk2xglAW9dnm9Xv71r38RExPDsGHDMAwDy7LYt28fCQkJtGvXDtM0mTx5MpGRkf7GLKDebxhVDeRIWJbFl19+SV5eHiNGjMBut2NZFnl5ecTExNC+fXsMw2DJkiXMmjWL6dOn+0c+rVu3jri4OLp06VLP70KOVE0baQ3DICkpiYcfftj/WHJyMj179jyi148ZM+aw+09JSWHChAk1KGXDUVpaysyZMznxxBOJjo4+7PZOp5OxY8cedrvevXsfsvTYAYZh0L17d/89nUhjoQBcmiTTNPn111/p2LFjhWyYs2bNok2bNnTq1AnwBQ5BQUFkZ2ezcOFCcnJyOO+883jiiScoKSnhgw8+4KSTTqJDhw512utR4C5m1r7AJQDaXZZHakhcwI5XJzQEvVYVFhaybNkyBgwY4F+P1ev1MnPmTI4//nji4uL8N1gdO3astDeiPhR7y5m9bw32APVKZhY37iRJzd2vv/5Keno6aWlpgO9aMXfuXDIyMmjdujUAs2fPJjY21r+MUllZGV9++SUDBgyodB6qNEyGoUbaQPB6vaxcuZJffvkF0zQZN26cOixEDkMBuDRJHo+HwsJC4uLisNvtgK/3+8cff+TSSy/192qEhIQQHx/P9u3bWbNmDePGjcM0TQoLC5k9ezYrV67k//7v/+r0YhLhDMFjebl26T/q7BiVOaNF5S3KjYWGoNcut9tNWVkZsbGx2Gw2/zzY9evXc+ONNwK+z9WuXbvo3r27/zNRXFxMSUkJsbGxAR+aG2oPIsIRzPXL/hnQ456bPDCgx5Pak5eXR3x8vP+6kJubyzfffMOECROIiIgAfMPUD6wCYJom33//PTNmzODuu+/G7XZTWlrq31YaLuUJCQzLsigqKqJr165ccsklxMU18sZ9kQBQAC5NktPppG/fvnz77beUl5djs9n46KOPaNGiBRdddJE/UDAMg+TkZD766COuu+46+vTpw8aNG8nOzuaVV17h6aefPqKhVDUxPL4Hy05+vk6PURmjkc+cVe9G7YqMjKRTp05kZ2fj8Xhwu9288cYbDBs2jMGDB2MYBk6nk/bt2/Pjjz9y7rnn4vV6mTRpEv379+fiiy8OeADeMzK9Xj47TXHWuddqHllZBw4cyM8//4zb7cbj8fD222+TkZHBeeed59+mbdu2TJkyheXLl7N//34WLlxIfHw8hYWFvPDCCwwbNqxOM/7/e+uP/Jqzts72/0frinYS72p6DQpqpA0Mh8PBwIFqlBQ5GgrApUkyDIPLL7+czMxMnnzyScA31PCFF14gPj6+QqDQoUMHsrKyuOKKKzAMgxYtWhAbG8uFF15I79696zyoMAyj0QfD9UE3V7XL5XJx11138cwzzzBx4kSKi4uJj4/nuuuu8w9JDwoKYsKECdx6662cfvrpdOrUibFjx3LeeefVS2IqfXZqj2WBrRmcyrFjx7J7924eeughwDfy48UXX6wwpeL8889n+vTp/PWvf2XUqFHceuutZGdnM2nSJO67774q56PWhjFpJ7GucGed7b8yHcKTaR+edPgNGxnlCRGRhsqwLH09NUcFBQVERUWxfPMeuqW3qO/i1AnLsigrK6OoqAjDMAgLCztkKTHLsiguLsbr9RIREeEfcpiXl0dERESN1viUurUxH77fDld38fWGS81ZlkVJSQklJSXY7XbCwsIO+QxYlkVhYSFlZWUEBQURFham+X5NQG4p/HMV3NWr6X+eSktLKSoqwmaz+ev4wQ1IB4bUlpeX+68bJSUllJaWEhkZ6R++Lg3bKyvgrNbQuul17oscsQP3+/n5+f7lRKX+qQdcmizDMAgODq52OYoDgfnBbDZbg0kwJVXT/L7aZxgGoaGh1S4pZhgGERERmgMrjdaRXBf+mGwtJCTEPxJEGg9dI0SkIVK3hYg0ShqCLiIiVdEQdBFpqBSAi0ijpCRsIrXHoimmlpPmrKlPpRCRxksBuIg0SuoBF6k9HgvsuiOQJkQ94CLSUOlyKyKNkgGKwEVEpFJqpBWRhkoBuIg0SkrCJiIiVdE1QkQaKgXgItIoqXdDRESqoiHoItJQKQAXkUZJAbiIiFRH1wgRaYgUgItI46Qs6CK1xmuCXVmjpQmxaQi6iDRQCsBFpFFSD7hI7bHQsk3S9KiRVkQaIgXgItIoaX6fiIhURY20ItJQOeq7ACIix6KxZri1LIvy8nJKS0sxDIPQ0FAcjvr/KrYsi9LSUsrLy7HZbISGhmK32+u7WICvbGVlZZSVlTWocyYiDZdGdIhIQ6U7GBFplBpjD7jX62X27NksXLiQkpISli5dSr9+/bjhhhsIDg4GoLS0lI8//ph9+/Zhs9m48MILadmyZZ2Wq7S0lO+//55169ZRVFTEokWLuOCCCxg1alS9B7per5dZs2axaNEiSkpKWLJkCccffzzjx4/3n7OSkhI+/vhjcnJysNlsXHTRRSQmJtZruUWkfjXGa4SINA8KwEWkUWqMPeAFBQWsXLmSUaNGkZSUxMaNGznrrLPo168fQ4cOBcDhcFBWVsZ9993HuHHjCA8Pr/Ny7dmzh507d3LFFVcQFRXFDz/8wM0338yAAQNo165dnR+/OgUFBaxatcrfELF+/XpGjBhBv379GDx4MABOp5PS0lLuu+8+rrnmmoCcs6bGsjQnTZoWDUEXkYZK11sRaZQa4+jCmJgYrrvuOlJSUrDZbLRu3ZqWLVsyZ84c/zYOh4OWLVsSGhrKmDFjAhJMtm7dmquvvpqYmBhsNht9+/YlJyeHFStW1PmxD+fAOUtOTsZms5Genk5iYiK//vqrf5s/nrOwsLB6LHHj5LWUBV2aFkMrZYhIA6UecBFplBrrzVVxcTGZmZkUFRURFRWFzWZj9+7d/udN02TJkiUkJyeTmpoasHLl5+eTmZmJ2+0mKioKy7LIysqqsE1JSQlbt26loKCAmJgYgoODcblcJCQkYNThhMvi4mK2bNlCcXFxtecsNTWVlJSUOivHkcgp38/Got2H37CW2Q07PSJb47A1jHn7IvVNPeAi0lApABeRRqmx3VxZlsWOHTt44okncLlc9OnTh3nz5rFixQqOP/54/3ZlZWUsXbqUTp06ER0dHZByrVq1iieeeIK2bduSkZHBzz//TH5+PiEhIf7t9u7dy5NPPolhGPTt25eFCxfy9ddfc8899zB69Og6K9v27dt54oknCA4Opnfv3sydO5dVq1Zx4okn+rcrLS1l6dKldO7cOSDnrDrfZS3lisUvkRIcF7Bjeiwv+e4i1p/yGnGuiIAdV6RBa4TTlESkeVAALiKNUmMLwEtKSrjrrrswDIPXX3+dkJAQUlJSmDRpEunp6f7tCgsLWbJkCWPHjiUoKKjOy7V3716uu+46hg8fzr333ovD4aCoqIgPPvjA35tcXFzMAw88QFlZGS+++CLh4eFERETwzjvv0KlTpzorW3FxMXfeeSdOp5P/+7//IyQkhKSkpErP2dKlS7nmmmtwuVx1Vp4jNTCmI9MHPhiw420ryeaEmXcH7HgijYGNxjlKSkSaPgXgItIoNaYh6JZlMXPmTGbMmMF//vMfQkJCMAwDy7IwDKNCELt582ZycnIYMGBAnQ7rBt/Q7U8++YQ9e/Zw6aWX4nQ6sSwLy7KIioqidevWACxatIhp06bx+eef++ek79y5k6SkJDIyMuqknJZl8csvvzBr1izef//9CufMZrNVOGebNm0iJyeH/v371/k5OxIGBs4ADgV3GErnInII9YCLSAOlq7aINEqNqQfcsixmzJhBbGwsXbp08QeSq1atIiUlxZ9p3LIslixZQlhYGJ07d67zcnk8Hr777jvat2/vn2/u8XhYuXIlxx13HPHx8QD8+OOPJCQk0LVrV3/ZZ8yYQa9evYiKiqqTsh04Rnx8PJ07d8YwDEzTZOXKlaSmplY4Z4sXLyYiIiIg56ypMtG6ydK0NKZrhIg0LwrARaRRakxrvFqWxZYtW4iJiSEuzjc3uLS0lB9++IETTjjBv2a1x+Pht99+o2vXrkRE1P1cXtM02bRpE+3atfMPd8/Ly2PWrFmcfvrp/mziCxYsIDY2FrvdjmVZzJ07l++//96/DJhVB38I0zTZsmULsbGxxMbGAr5z9uOPPzJo0CASEhKA/52zbt26afmxGjBNsCkAlybEAEXgItIgKQAXkUapMa0DbhgGycnJWJaFaZpYlsXChQtZv34911xzDU6nE/Cteb1mzRp69uxZIQFaXbHZbKSmpuLxeABf0Pv1118THBzMqFGjsNl8l4iwsDCKi4spLS1lw4YNfPXVV5imSXJyMl999RUbNmyo9bIdOGemafrP2YIFC9i4cSNXX321/5zl5+ezdu1aevbsSXBwcK2XQ0Qap8Z0jRCR5kUBuIg0Wo3l5sowDEaNGkVQUBBff/01X3/9NS+99BIPP/wwPXv29G+3b98+Nm/eTN++fbHb634OsdPp5KqrrmLz5s3MnDmTTz75hClTpvDcc8/RsmVL/3bnnnsuGzZs4LzzzuP//u//OPvss0lOTuaxxx5j2bJlJCcn13rZbDYbF154IU6nk+nTp/Pf//6Xl19+mUcffbTCOcvOzmbLli0BO2ci0jhoCLqINFRKwiYijVJjGoJ+YOmu1157jfnz5+N0OnnyySdp3bq1v5cZfMnOAHr37h2wcp177rm0a9eOlStXEhUVxT//+U9atGhRIZnZ2WefTVxcHDk5OQwZMoSEhARef/11srOzOemkk+qkt94wDPr378+rr77K/PnzCQoK4umnn6ZVq1aHnDObzUavXr1qvQwi0ng1pmuEiDQvCsBFpFFqbAmjDMOgXbt2/uRhf1RYWMjkyZM5/fTTSUtLC2i5jjvuOI477rgqtwkKCmLYsGEVHuvfv38dl8xXtoyMDDIyMip9/sA5O/PMM/1J5OTYWPw+Z1akidAQdBFpqBSAi0ij1FSGF3o8HqZMmcLChQsJDw/nkUce8c9vlsp5PB4mT57MwoULiYqK4qGHHtI5qyGvBfYATUo7kAvB6/UCYLfbG8z0Acuy8Hg8mKaJzWbD4XBUu7Sd1+vF4/FgGAYOh6PC6IyGUL7mrilcIw7HNE1/Hg+73Y7NZlOdEGngFICLSKPUmNYBr47NZuPEE09k4MCBxMbGEhoa2iRunizLIjMzk+joaKKioiq8J8uyyM7OpqysjJSUlKN+vzabjcGDBzNo0CBiYmKazDmrb4E4g5ZlsXbtWmbMmMHu3bvZuHEjHTp04Oabb/Zn3fd4PMyYMYOdO3cCMGjQINq0aVPnZSsuLua7775j06ZN7Nixg+3bt3PbbbfRt2/fKuvXypUrueWWW9i3bx/Tpk0jJSWlzspXVFTEd999x8aNG9m9ezc7duzg9ttvp1evXv7ybd26ldmzZ+P1eomJieHMM89stp+N5jAEvbCwkKlTp7J+/Xr27NlDQkICt9xyC5GRkYAvsefUqVMxTZO0tDQGDRqEw1F3t/5ey8sXu+ZT7C2rs2NUJsIRwoiWfbEZSm0ljYMCcKk1Xq+XTZs2sWXLFoKDg+nZs6f/ItDQ5ObmsmLFCkpKSmjbti1t27at856LI1VUVMTKlSvJyckhLS2Njh071ukFs7E60APe2IfO2my2OkliVt9KS0v5+9//TmFhIc8++6x/+TWAvXv3ct111zFw4EBuueWWo67fTfWcNQdlZWV8++23nHTSSXTp0oVdu3YxcuRIOnTowAUXXAD4ph4UFhYyfvx4Bg8ezPDhwwNSttWrV7N7927Gjh1LcHAwL7/8Mg888ADvv/8+MTExlb6me/fu9O3bl5kzZ1a5TW1ZuXIle/fuZdy4cYSEhPDSSy9x9913M2XKFP8SfCEhIXzwwQfMnj2bV155pU7L09A19SHolmWxYsUKnE4nt99+O0VFRVx++eVMnz6dUaNGAeBwOJg+fTpff/01H330UZ3f55SbXm5a/iatQxMId9T9Sh4ABe5i9pbnc2ZibwXg0mjorl5qRXFxMS+99BKJiYkkJSXx/vvv85///Idnn33W36uxb98+HnvsMZYuXUqbNm147LHHSEpKCnhZv/32W+bNm0ffvn3Ztm0bTz/9NI8++igDBw4MeFn+aNGiRXzxxRf07duX0tJSbrnlFq6++mrOO+88fy/G8uXLmThxItnZ2QwcOJCHH364wTQeBFJTGYJeEzk5OYddAswwDNLS0ipkNQ+EkJAQbr75Zq677joefvhhHn30UaKiosjKyuLuu+/Gbrdz+eWX10vj0vr168nNza12G8Mw6NGjh399dKkdwcHBTJgwwf97UlISPXr04JtvvvEH4Ha7nTZt2uByubjssssCdp3o06cPffr08f8+aNAgXnvtNbZv315lcG2aJtu3b6dPnz51Pg2if///b+++w6MouzaA37Ml2XRID4QASUhCNXSISBBEOogUUQFpAqKiIE3BQpH6giC9FwsoVUQRkN5DTeiBBEIghJretsx8f+TLvOYlECFbw/27Li5ld7JzsuzsPOcp52lQqPZC48aNsXTpUiQkJKB69eoAAA8PD/j4+KBq1aro2LHjCzv6DZT+EXBBENCoUSP57yqVCoGBgdi9e7ecgGs0GpQtWxZNmzZFo0aNzNJWUAgCFr00GDVcK5r8XABwIuUq3jk1yyznIjIWJuBkFKIoIjIyEnXr1oVKpUKVKlXQoUMHHD9+XC7e5O7ujjp16mDp0qX44IMP4OPjY5FYPTw8MHDgQHh5eUGSJFy4cAELFy5Eo0aNLN5YcXJywnvvvSdPt3zw4AFmz56Ntm3bwtHREQBQtWpVlClTBsePH8f3339v8ZgtpbRMQS+J48eP45NPPnnqMUqlEmPGjMF7771npqj+Kzg4GIsXL0avXr3w1VdfYejQoZgwYQLS09OxcOFCeHp6mj0mAJgzZw527tz51GMKtj8zZ0E8SzLnpaTT6XD//n1kZWVBo9FAqVTi5s2bhY65ePEiVCoVatWqZba4JElCeno6Hj58CEmSkJeXh9TUVGRmZhY6Jjc3F/fu3YNOp4NWq0VcXBxatGhh8s6k/40vNzcXubm5hTqTdDodTp8+jcjISJPsTvA0D7UZeKjNMOs5AaCcxh3OKs1jj5f2O6MkSXj06BE2bdqE+Ph4lClTBgkJCXBzc5OPycrKwtWrV9GwYUNoNI+/RyaJyyxnIbJtTMDJKJydnQv1xLq6usLV1RUxMTFyAi4IAjIyMuDh4YGmTZtabNT2nyMcAFC5cmUcPHgQmZmZcHFxsUhMBUJDQ+X/lyQJgYGBuH37NpKTkxEYGAggf/ptamoqatSogdDQULMl4Om6bOQYtGY51z+VtXOGneLxrypbGQH//fff8eeff5boNcqXL4+xY8c+9m/dqlUrXLx4sdiff9K1tmPHDmzZsqVEsQH51/uECRMeGy0WBAGBgYGYN28e+vfvj127dsHDwwMrVqyAj49PkZ/d1NRUfP3119BqS/5Z69OnDxo2bPjY43PmzIH0L3pvTFEYLD4lHr0290JqbiqC3YOxodsGqJWWLyCnFwGVib9KJElCRkYGFixYgKtXr6JWrVq4ffs29u/fj6pVq8rHGQwGnDp1CoGBgfDy8jJtUP9PFEWcOHECc+fORVBQEMqWLYu9e/ciNzdX/hxIkoSbN29i1qxZ0Gg08Pf3x44dO3Dr1i2Eh4eb9LtYFEUcP34c8+bNQ3BwMNzd3bFz507k5eUVSvxv3LiB5ORki3Qoz43/A7PitsLFTFOPASBFm4lf6o9AB9/6jz1X2qegZ2RkYOTIkfD09MSYMWMQHx+PadOmoV+/fvIxqampuHHjBoYMGWK2Nldp7/ggMgYm4GQUoigiPj4e+/fvR3p6OsqVK4fc3FxkZ2fLx0iShEOHDiEsLAzu7u4WiVOSJGRlZWHfvn24fPky3N3dcffuXej1euh0OvkYvV6PU6dO4fTp00hPT4evry+6desmT6c3ldzcXBw9ehTR0dGwt7eXq5v+MxnJyMjAmTNn0Lt3b7NWDv7q8lqsSdxbZDJsKtkGLbY3/govu4c99pytjIA7OzvD29u7RK/xpOtFFEXk5uYW+/P29vZFNr6cnJxKHBuQ/zs+rbEfEhKC2rVr46effkL79u0REBDwxGMVCgW8vLzk67EknjTio9Pp5KrBT2OK4m6SJGHNG2sQ5B6Edze9i22x29C5amejnsNaSZKE2bNnY/fu3fj555/h5+eHK1euYPXq1XIHIwDk5OQgOjoaISEhZqkjIkkSrl27hg8//BCDBg1Cnz59AOQns87OzoUKWg0fPhwhISEYN24cHBwckJqaiqtXrz5xe0FjuXz5MoYMGYIPP/wQ7733HhQKBa5evYrz58/L9RUK1gRLkoSwsMe/M01NlCT0KN8Es2r0Nds5XzvyzROT7NI8BV2SJGzZsgUxMTHYunUrypYti6CgILi6uhbatjEhIQEpKSmoWbOmBaMlov/FBJxKTBRF7Nq1C1OnTsWgQYPQrFkzLFy4EFeuXCm0bi4lJQWxsbFo166dxbacuXv3LkaNGgV3d3cMGjQIN2/exPjx4+Hj4yNP18vJycG0adOQkZGB/v3748yZM1i6dCnefvttk8aWmZmJSZMm4caNGxg1ahQkScLAgQNhZ2cnTz8HgGvXriEtLQ316tUzaTz/K0/UoW9AC4wIfsMs55MkCc2PfAVREot83lZGwF999VW8+uqrJnntv/76C4MHD37qMUqlEl999RX69+//2HNNmjRBkyZNTBJbgezsbEyYMAHnzp3D8uXLMW3aNHh4eGDYsGGws7N77HhXV1eMGzfOpDENHTq02FkJarUa+/fvf2pnwfMIcv9voqY1aGGnfPw9KI0Kqp+vXr0aX375JcqVKwdBEKBWq6FQKAqNgKenp+PixYt44403zLa93NKlS6FQKNCtWzeo1Wro9Xqo1Wr4+fnB3d0dkiRh27ZtOH/+PGbOnAknJydIkoTbt2+jTp06chE0UzAYDFi0aBHs7e3l+CRJglqtRpkyZeTlXKIoIiYmBuXLl7dIfRUAUAlKsxXfAgDlU4pu2co94nmIoogtW7agUaNG8PPzgyAIePDgAbKysgq1DU6ePGnWmSRA6X3PiYyJCTiVWGJiIkaMGIEBAwagW7duUCqViIyMxOLFiwttyZKYmIi7d+8W2jLFnPR6Pb777jvcuHED3333HTw8PBAcHAxHR0c5ATcYDFixYgUOHTqEH3/8EX5+fggKCsKrr75aZLJgLJIkYe3atfjtt9+wdetWuQe7cuXKiI2NlUdAJUlCTEwMHB0dC40YmYuzygF+GtNW+i0gSiJUwpM7akpz4+rfioyMxO7du4s9zhij3M9Dq9Vi8eLF+PvvvzFv3jw0aNAAnp6eGDVqFHx9fdGzZ0+LdMZ9/fXX+Oyzz556jCAIJi1ct/7CeiSmJaJVcCuTncPaHD16FDqdDg0aNIAgCJAkCXFxcVAoFIWWCsTGxiIzMxP16z8+rdgU0tPTsX37drRu3VpeP2swGHDp0iWEh4fDw8MDOp0Of/zxB+rVqyd/LtLS0nDmzBm8/fbbJp3e++DBAxw4cABt27aVE32dTodz584hIiJCXjql1+tx5swZVKlSxWKzzKxJaZ6CLkkSLl26hHbt2snX0r59+xAaGiq3DfR6PY4fP4569eqZtP1CRM+OCTiViCiKWLduHTIzM/Hmm2/Kjem0tDQ4OzvLlVkLRj8EQUBYWJhFEvDr169j/fr1GDFihNw4yczMhE6nw8svvwwgf5R+9erVeO+99+RRBY1GY9K9XYH8atYrVqxAq1atEBgYCEEQYDAY8OjRI4SHh8tT3w0GA6Kjo+Hv72/2qtbWxlamoJuSi4tLoboB1kSv12Px4sX49ddfsWDBAjRo0AAKhQItW7bEpEmTMHr0aKjVarz11ltmT8JNfT0XRy/qMXzncBzrfwwqMy7psKR/7gtf0CFkMBiwe/duVKtWDZUqVZKPPXr0KLy9vVGxonmqKD98+BDp6emFpm0nJCQgOjoa8+bNg1KpRGpqKuLj41G/fn0olUpIkoS///4biYmJJu9UTk1NxcOHD1GlShU50b969Sri4+MxbNiwQsedPXsWo0aNstgsM2tS2u8RGo1GrruRmpqKLVu2oHfv3ihTpgyA/PbMjRs38Prrr/PzQGRlXow7P5lMXl4eTpw4gZo1a8pJrSiKOHToEGrWrAl/f3/52FOnTsHb21uu8G1OBSMtmZmZqFOnjtxYunTpEnJzc/HKK68AyG/UxMXFoXHjxmYtEpeUlISbN29i6NCh8nlv376Nmzdvom/f/66n0+l0OHHiBOrWrWvxgnGWZmsj4JIkISUlBVFRUYiKisKDBw/QsGFDdOnSxWzVaZ8mNzcXZ8+exbFjx5CYmAgPDw+8//77zz11UZIkuLq6Yt68eahbt658zSmVSrRt2xY6nQ55eXnFxhQdHY1jx44hISEBnp6eGDhwoMWqpxtLcmYy/Jz9UN7Vsh0B/2SQAKUJv/IEQYCHh0ehRDUxMRG7du3CF198IY88F3wOX3rpJZNO6/4nJycnODo6yoX59Ho9fvnlF4SHhyMyMlKOX6lUIicnBzqdDrGxsfj777/h4uICZ2dn/PLLL2jVqpWc/BiTo6NjofdCr9dj06ZNqFmzJl5++WX5Pb18+fJjU5BfdLZ0j3gWCoUC/fv3x/79+xEcHIzdu3cjJCQEPXr0kNsQd+/exaNHj1CjRo0XdrcUImvFBJxKRK/XIzMzE76+vnIl1qSkJERFReGjjz6Spz2JoojDhw+jfv36FpsKlZKSAnt7e7mBZDAYsHPnToSGhsoj9bdu3YIgCPIImSRJ8mi+KbeYycnJgV6vh6enpzyd7ODBg3BycsIrr7wi3zyTk5PlYkFke+bPn48aNWpg5MiRiIuLQ9++feHq6ooOHTpYOjTs2rULFy9exIABAyAIAj7++GNMnToVU6dOfa51uGq1Gr179waAxxp/KpUKb775ZpHPFSgYYbx06RL69u0LQRDw0UcfYfr06Zg0aZJNT6ksqymLyS0mWzqMQiTJ9NWLW7dujU2bNuHgwYMICAjAkiVL0L1790Kf/7S0NFy+fBndunUz2x7s3t7e6N69Ow4fPoxXXnkFJ06cwKlTpzB58mS5Y8DV1RXNmjXD6tWr0a9fP3h5eaFDhw7466+/MH78eDRp0sRkRTr9/PzQuXNnHD58GI0bN0ZUVBROnjxZKD5JkuRO7hdl67zilOYibIIg4P3330dUVBTi4+MRGRmJevXqFbpmzp8/D6VSaZGCfET0dEzAqUQcHBwQGhqK27dvQ6/XQ6FQ4JdffkH58uXRuXNnuXGdmJiIpKSkIgtBmUtQUBAcHR2RkZEBURQRGxuLHTt2YNy4cXIjpkKFCnKxnY4dO+LUqVNYv349pk6datJ1tD4+PvD19UVaWhpEUcS9e/ewZs0avP/++4WmYcbExECSJLPujWutBMH2RsFHjx4NlUoFhUKBkJAQhIeHY8uWLWjbtq3Fpwi2adMGbdu2lUdP2rdvj++++w4PHz587uUOTxt1+TcjMq1bt0abNm3kKb8dOnTA9OnTMWrUKJseBU/LS8Oa6DV4LfA1S4diNoIgoEqVKli+fDn27NmDe/fuYcCAAQgPDy/UwXPr1i0kJSWZfRbSmDFjsH//fuzatQu+vr5YunSp3CEK5HcajRgxAkFBQVAoFGjXrh1cXFwwffp02Nvbo1WrVibrpFWpVBg3bhz27t0rx7ds2bJC8eXl5eHUqVOoVq1aoeKnLzJbuz88C0EQYG9vL8/e+6eCPez/+OMPdO7c2eS7txDRs2MCTiWiUqkwZMgQTJw4EbNmzYJOp0NmZia+//57eaS5YIsXvV6PGjVqWCROQRBQq1Yt9O7dGytXrsSePXuQmJiI4cOH4/XXX5cbMbVq1cKAAQPw9ddfY9KkSWjatCk+/vhjkzf2K1SogE8//RTbtm1DQkICbt68ifbt26Nfv35ybKIo4syZM6hUqZK8Pv1FJ49w2MDsuoKKzwVb3omiCAcHB1y9ehW5ublyI0mSJBgMBhgMBgD5Uw1VKpXJpxAqlUoYDAZotVoIggAHBwd5bWxBAi5JkrxNX8F03YIq1sZWMOXXYDAgLy8PgiDAyckJiYmJhbZekyRJ3q6v4PeQJMks79nzKudSDms6r7F0GGYnCAKCgoKeuGWXXq/H5s2bERISgjp16pg1LkdHR7Rp0+apx5UtW7bQHssA0L17d1OGJnN0dES7du2KfE6SJCQmJuLo0aOYOHGivKOHNYlPiUevzb2QmpuKYPdgbOi2AWqlaSvcl+YibE8iiiL27duHXbt2wdXVFR9//LHVfg8SvciYgFOJhYSEYOHChUhJSYFSqYS7u3uhBoAkSTh27Bh8fHxQuXJli90MNBoNRo0ahfv370On08HV1RVubm6FkgdHR0eMHz8eQ4cOhSRJcHd3N0vvsUKhQO/evdG2bVvk5ubC0dERZcuWLTSikp2djWPHjqF27domWWdoi2ylgVWQJO7cuRNbtmyBq6srJEnCmTNn4OLiUqiTJTo6Gps2bYLBYMD58+fh5+eHOXPmmHSdeEFF3aVLlwLIv1bi4+PlToCCY9LT0/Hjjz8iKSkJqampiI6Oxpw5c1C3bl2TxHTlyhUsXboUBoMBDg4OSEhIeCymhIQELF26FDk5OXByckJaWhoyMjIwdepUdlTZCEmScOjQIRw9ehQXL17ErFmz+B33L927dw87duzAnj170Lt3b7zxxhtWmXBJkoQ1b6xBkHsQ3t30LrbFbkPnqp1Nek4BsI0bhBEJgoCXXnoJISEhclvMGj8PRC86JuBUYoIgwMXFpciiYAXFz3777TcMGDAAHh4eFojwv9RqNcqVK/fUYxwcHAoVjzMXpVL5xITBYDBg7969SEhIwPjx4822N661EwRAtJEG1saNG/Gf//wHs2bNQqNGjXDp0iUsWbIEnTp1kpPrw4cP45tvvsFXX32Fxo0b4+DBgzh37pzJk+8rV65g4MCBeOedd9CnTx9otVp069YNzs7OcvGn9PR0jBo1CmXLlsWoUaMAAN99953JktzY2Fj069cP7777Lvr27Yvs7Gy8/fbb8PLyktc53r59G0OGDEGLFi0waNAgqFQqeZSQCZxtCQgIkEeY/7dYm63Lzs7GgwcPUKFChcd+L51Oh1u3biEgIOC5lqE4OjoiPDwcERER8Pf3N9u6+WcV5P7fWQ9agxZ2StPXcCjNU9CfpKDY4bOSJAlHjhxBenr6E49xcHBAo0aNrKJoKJGtYwJOJpOVlYWFCxfi5s2b+OSTT9C1a9dS1agylxs3bmDx4sXIzc3FokWLWOH2HxSwjQbWvXv38O2336Jr166IiIiAQqGAj48P1Go1QkJCoFAokJaWhsmTJyMyMhJNmjSBUqlEixYt0KJFC5PGJooi5syZA3t7e/Tq1QuOjo5QKpXw8PCAXq+XO9Z+//13HD16FDt27JB3PJg4caJJYtJqtfj++++h0Wjw7rvvwtHREYIgoEyZMqhYsSLs7e0hiqJ8XQwePBhOTk7QarXIzs5Gq1atrDYRsVYGCVBa6OtZEASzbTlmCbt27cKMGTMwe/bsQt/fer0eq1atwtatW7Fy5crnWurk7OyMmjVrGjNck1p/YT0S0xLRKriVyc9lKzOkrMWiRYsQGxv7xOf9/PywbNmyYhPw//0ascTyAyJrxwScTMbR0RGfffaZ/PfSnHxLkoSoqChkZWUhMjKy0EiGJEm4evUqLly4gI4dOz7zKEfFihUxefJ/KyaX5vfxWdnKPq+HDh3C7du30bp1a3nJQ0JCAgCgadOmAPIr1l64cAGTJk0ya/GpO3fuYPv27fj444/l0W6dTof4+Hg0b94czs7OyMrKwrp169CiRYvn3pbsWTx48ABbt27FkCFD5AKJOTk5SEpKQmRkJJycnJCSkoJt27ahT58+cHR0BJA/In779m00btzY5DGWNhLyrydb9ODBAzx69EiuS1CUgt0tLFGQqnnz5ti7dy8+++wzLF++HMHBwTAYDPjtt9+wYMECTJw4Ue7UMidRFHHnzh1kZmY+9Tg7Ozv4+/uXeOaVXtRj+M7hONb/GFQK0zc/S2MV9EWLFmHXrl0leo1q1ao91nkqCALWrDFOXYr/fcstsfyAyNoxASeTeZESxYIk+9tvv8V3332H119/HQqFApIkISkpCR999BHq16+Pdu3aPXMC/iK9j89KgPVPQRdFEVevXoWXlxf8/PwA5H9e9uzZg4CAAFSrVg1AfoV7tVqNKlWqmPXf/NatW8jIyChUWf/KlStISEhA69atIQgCHj16hPj4eLz55ptmqdZ+69YtZGVlITw8XH4vrl+/jvj4eEycOBFKpRK3bt3CnTt3EBAQACB/NHHjxo3QaDQICQkxeYxPcznzFgZHLzLb+TL1udCLhuIPLKVWrVqFOXPmPDUBVygU+PHHH+UOL3NydnbGpEmTMHLkSHzwwQdYsGABoqOjMX78eEyYMAFt2rQxa6dbgby8PIwbN67YhK5SpUpYu3Ztibc3S85Mhp+zH8q7li/R6/xbAgDRLGcyn6pVq5b4NQruQ/8kSRIePnwoF7MsSkGNn2e9B1hi+QGRtWMCTlYtOTkZq1evRnZ29lOPq1u37nMlt8aiUCjQo0cPpKWlYdy4cXB1dUWjRo1w584dfPTRR6hQoQJGjBhh1r2Lc3NzsXbtWty4ceOpx/n4+KBXr15FruG3dgobmGJYsF1MwfZjAPDo0SPs2LED77zzjjzyVVBZ3GAwQJIkZGVl4dChQ2jatKk8wmsKarUaSqUS9vb2EAQBOp0OmzdvRqNGjVC7dm0A+Z0IBoMBopjfnDUYDDh79iycnJwQGhpq9A4DOzs7KBQKecStILmuV68eGjZsCCB/lF6v10MURUiShOjoaOzcuRO+vr7QaDQ4cuQIGjdubPYOrOouAXi/YkuznhP2QI0qb8DBChu2mZmZmDNnDrRabYlfq1OnTkVWRx86dCgGDx5c7M8XNXVWr9dj7ty5SE1NLXF8arUaQ4cOhaura6HHCyr4T5o0CUOHDkWXLl2Qk5OD0aNHo3379kXet/Ly8vD9998XOzr9b2g0GnzyySePfY9oNBosXLjwqUkXkH9/M8a637KaspjcYnLxBxqLjcyQehaRkZGIjIw0yWv36NED58+ff+LzFSpUwLZt24qt+/Gkb1xzLj8gsnZMwOmZiaIoN8SNSaFQPDYKoNfrcf/+/WIbIWlpaUWOfpgqVqVS+VjDXqVSYeDAgbh//z6GDBmC8ePHY8WKFbCzs8O0adOK3Ju1oDr200Zu/o2i3jtRFPHo0SPcvXv3qT+rUqmM9h6Ze62XLUwxFAQBzZo1w8qVK5GQkAA7OzssWrQIfn5+6Nu3r9z4rlevHnJycjBixAjUqlULUVFRqFq1Kpo1a2bS+EJDQ1GrVi2cOnUKdevWxa5du3DkyBHMmDFDTiQ8PT1Rq1YtzJ8/H+np6Xj48CGuXLmCSZMmmSSm4OBg1K5dG2fOnEGjRo2we/dunDhxAtOnT5eTiAoVKqBChQpYtmwZ4uLikJaWhkaNGmHdunWYOnUqwsPDLTIVvYZrAGq4Bpj9vNZKFEXcu3cPeXl5JX6tnJycIh//Z+fW0xSV6EqShPv37+PRo0cljs/Ozu6J36WCIMDd3R3du3fHH3/8gbCwMLRr1+6Je4cXxPW0olj/lqOj4xPjUqvV/2r/cmN0ZKXlpWFN9Bq8FvhaiV/r37CF+4M1Wbdu3b8aAS9OUW+5uZcfEFk7QSppy59sUnp6Otzc3HDu+l3UqOT9TD/7+++/Y8SIEUaPaf78+XjtNePemGfMmIFly5YZ9TUFQcDWrVuLnOZaMHL51VdfYfny5ahTpw5WrVqFgICAIhswBoMB7dq1w/Xr10sU07Bhw/7VCFBJfBC9CN72ZTA+rMcTj4l7FAcA8lqvrlW7PvdaL1ESUWvvMCx8aRBe8ahW5DGTTgFDawKu1jfwV4jBYMDBgwexe/duGAwG1KxZE61bt0aZMmXkz4VOp8PWrVuxefNm2Nvbo0ePHmjSpInJ9/SVJAnXr1/HL7/8gvT0dPj4+KB9+/YIDAwslNTExcVh/vz5SExMRJMmTdC1a1eUK1fOJCPMBTH9+uuvSEtLg6+vL9q1a4egoCD5fJIkYe/evVi5ciWqV6+OPn36IDMzE//5z38QERGBt956i4XYnsGJe8CNdKBrkO2tBZ8zZw4WLlxY7BT0ZcuW4eWXXzZjZP9VUGX6ww8/RNeuXREVFQVnZ2fMnj0bXl5eFllqlJOTg+HDh2PPnj1PPa5ixYpYsWJFsbuDfHVpLR5o07HgpUHGDPOpXj74OUZXeRMdfes/9tye20CeAWhTSvvDDAYDrl69isuXL8PT0xP16tWTZzL9r4SEBJw+fRpubm54+eWXTfbdmGPQInT3h/iz0TjUcP1vYcVb6bfw5i9vIur9KKOf80TKVbxzahYuNp8LNZP7xxS099PS0h6bnUOWw08qPbPGjRsbrVjHPxWV0GZmZuLs2bPQ6XRP/Vk/Pz+5mvQ/vfXWW0Zf8ycIwlMbInl5eUhJSYGnpyeys7ORkZHxxGMVCgWmT5/+xJGdf6uotXl6vR7nzp0rdmqls7MzatWqZZQbsrnXetnCGnAgf+SgWbNmTx3NVqvV6NKlC7p06WK+wJD/eQ4MDMTnn3/+1OOCgoIwa9Yss8Y0ZsyYpx7TvHlzNG/evNDjixaZb+11aSJKgNL8y5CNokuXLmjcuHGxRdhCQ0PNGNV/SZKE8+fP47PPPkPnzp3x2WefISkpCR9++CG+/vprTJkyxSLb5tnZ2WHYsGHo06fPU4/TaDRmKb5obKV5GzJRFLFkyRKoVCpUqVIFP/74I/bv349Ro0YVWSzP3t4e69atQ1paGjZv3mzS2IrqSjL78gMiK8cEnJ6Zp6fnc22X8jxu376NsWPHFptEdu7cGWPHjn0sAQ8ICJCLNJlDZmYmPv/8c8TFxWHTpk2YP38+Bg0ahBUrVhTZ+BMEoVDxK2PKycnB3LlzcerUqaceV6VKFSxcuNCoDSxzrfWyhTXgRGRa/v7+xY7OWtLly5cxZMgQtGnTBqNGjYKDgwOCgoIwf/58vPfee/jyyy8xZcoUeRcCc1EqlRYvWGhKAlCqbxARERGoWrUq7Ozs4Orqim7dumHgwIFF3ss9PDzg5uaGoKAg08+qKuIxcy8/ILJ2TMDJqoWEhGDv3r3FHmcNlcJzcnIwY8YMxMbGYvHixQgLC8O0adMwcuRIjBw5EvPmzTNrZ4CzszOWL19e7PpyY7935lzrxTV+RLbDYDDg5s2b2L9/P2JjY+Hm5oZevXrBz8/PKr7DRVHEgwcPcOjQIZw7dw46nQ5dunTBSy+9VKIq5SkpKXJy9M9iZoGBgZg/fz5Wrlz51CJ1BevoDx48iAsXLkAURXTv3h3VqlV7Ylx3797FnDlzcPfuXQwePBj16z8+Rbu0K837gAuCgKpVq+LBgwfQ6/Wws7NDWloa7ty5IyfgBoMBDx8+RE5ODhQKBY4dO4YJEyZYJN5yLuWwprPxZ04S2SobnXBG1kKSJOTk5CAlJQWpqanFVlN9VoIgyAXGnvZHEIRiG3CSJCE3N1eOtbhp7c/CYDBgzZo12L9/PxYsWICwsDAIgoAyZcpg2rRpUKvV+PLLL5GVlVVsjNnZ2Xj06BHS0tJgMDz/1kIF74kx3rtnYc6tZgTBNqagE1H+Vnvz5s1DkyZN8Nlnn+HOnTsYO3ZssbtcmMu9e/cwZcoUeHt7Y+TIkQgKCsLQoUNx69atEr1u48aN8dFHHz1WSbxgBtTMmTOLLNJZICkpCdOmTUNAQABGjhwJPz8/fPzxx08tsOnl5YXq1atj69atRqlgbotKawetJElIT0/Ht99+i8mTJ2P9+vWYOnUqDAaD3CGTl5eHJUuWYPz48diwYQPGjBmDu3fvIiwszMLRExHAEXAqAa1Wi/379+PcuXPQarU4efIkIiMjMWjQILNut/VvSJKEAwcOIDo6GllZWYiJiUHNmjUxbNgwo0zHUigUaNGiBVq2bInKlSvLCW1B5dt58+YhLi7uqefKy8vDjh07EB8fj/T0dJw5cwadO3fGu+++a7Ht1Z6HOdd6KVB6RziIzEmUTF98rXr16pg4caJcyb5bt27o0aMHkpOTERQUVMxPm56XlxcmTpwIJycnCIKAzp07Y/LkyYiKiirR7KWndXIKglDs97uvry8mTZoER0dHCIKArl27YsqUKTh79myRezoD+fckSZLg7e2NSpUqPXfstqy0joAbDAbMmTMHFy5cwOLFi1G2bFlMnz4du3fvhp+fHyRJwvr16/Hnn39i6dKl8Pb2xtdff41KlSqZbfkgET0dE3B6bmlpabh27Rp69eoFT09PnDlzBr1790bjxo1Rr149S4dXSFZWFi5duoQ333wT5cqVQ3x8PDp16oT69eujZcuS79krCAKCg4Of+Jyfn98TG0oFHj16hMTERPTt2xcuLi7Yu3cvRo8ejUaNGtnUOj1zrvUqrQ0sInMzSIDSxAm4nZ0d1Gq1vCWWq6srsrOzcfv2bTkBL1gyU/Dffyaupp6mrlQq4eTkBEmSIEkSnJ2doVQqER8fX+i4f8b4vzGZIkaVSgWlUinH5eLiAkEQio3ryJEjaNCggcnX/Fqr0lqE7fbt21i6dCnmz58PDw8PSJKEvLw8hISEwN7eHpmZmViwYAGGDRsGHx8f6PV6pKSkICQkhFWwiawEE3B6bl5eXvjggw/kv9eoUQMGgwFnzpyxugTc2dm50DZdfn5+qFy5Mvbv32+UBNwY/Pz88OGHH8p/r1atGtLT03Hp0iVUqVLFKtZI/hvmXOtVWqcYEpU2kiTh4cOH+OGHHxAXFwdnZ2fo9XrodLpCdSpSU1Pxyy+/4MqVK3BwcIBCoUBqaio+/vhjk1cxz8nJwaZNm3DkyBE4OjqiTJkyjxUALdhS8M8//4QkSbCzs4NWq0VYWBj69u1rku/p7Oxs/Prrrzhx4oQcV2ZmZqFj9Ho99uzZg507d8px7ty5E5988kmJ1q/butJ4fzh9+jScnZ0RHh4OIH824oULF9CwYUNoNBqcPHkSOTk5qFWrFgRBQEZGBk6fPo3u3bubbXbiwYcXcTPngVnOFZuZZJbzEBkTE3AqkczMTFy/fh3Z2dnw8fGBJEm4f/8+cnNzcf78eUiSVGiLq0uXLsHNzQ1+fn7Iy8uTC8pUrVrVpBVgC9aq37hxA5mZmfIoR3JyMgwGAy5evIicnBwEBwfD3d0dkiThzp07SEtLQ9WqVaHX6xEbG4uMjAxUqlQJPj4+JokzLS0N169fh06ng1qthiRJePCg8E0sNzcXCQkJSE1NhUKhkKeV2UqCbkyCAIiWDoKIipWSkoIhQ4bA29sb33zzDTQaDQYNGgStVgtfX18A/91FQqFQ4KuvvoK7uzuGDBmCI0eOYNy4cSaNT6fT4T//+Q8OHTqEuXPnonLlyliyZAkePXokxyeKIn7//Xd89913mDhxIiIiIrB371506NABK1euNMl3sFarxaRJk3Du3DnMmTMH/v7+mD17NjIyMgrFtWHDBixevBhTp05FnTp1sGHDBqxcuRK1atV6YRPw0jpDKjU1FV5eXnBycgKQv8d3bGwsPv74Y6hUKmRmZkKpVEKj0cBgMGDr1q24fPkyGjRoYPLYFBAQ7loJqxOLL55rTC+5VoZQ5AZoRNaJCTg9t7i4OEyaNAleXl4IDw/Hjz/+iFu3bsHBwQFarRaLFi3Cr7/+iiNHjqBGjRrIyMhAv3798Prrr+Prr7+GTqfDggULsHPnTuzevdtk06wLkulJkyZBo9Ggbt26OHHiBE6dOoXu3btDkiTs2bMHn3/+ORYvXoxevXpBFEXMmDEDp0+fxq5duyAIAv7++2+MHTsWP/30Ezp27Gj0GGNiYjB9+nQEBwcjJCQEO3bsQFJSkjx9sKDDYMaMGfD29oavry9mzZqFMWPG4O233zZqPLaCI+BE1k8URaxduxbnz5/H1q1b4enpCUmS5KU5BdNit23bhgMHDuDPP/+UO3QlSUK1atXg7e1tsvgkScLJkyexYsUKLFiwQJ5xVKFCBWg0GlSsWBFAfmXxb775BoMGDUKTJk3kxLZs2bKoU6eO0RNwSZJw+PBhrF27FitWrJDriwQEBMDe3l5el56YmIhJkyZh1KhRqF+/PgRBgJ2dHTw8PF7oolul9f5QsWJFaLVaZGdnw8HBAT/88ANefvllNGzYEADg4OCAR48e4dChQ5AkCfHx8VCpVPD29paXJahUpmn+2ylU+K3hFyZ57eK8iIMQZLuYgNNzSU1NxaeffopKlSph4sSJsLOzg6OjI5YvXw5/f3+4uLjg7bffxsaNG5GTkyPfBBISEpCcnAxJkuDk5IQqVapAFEW5gWMKWq0Wo0ePhsFgwOLFi+Hs7IzKlStj+fLlqFy5MlQqFTp27Ii5c+fKsWZnZ8s3r8zMTLi7uyM0NBTh4eFo1KiRUeMr6CAYOnQomjdvjrFjx0KpVCIvLw9r165F+fL51cQzMjIwbNgwhISE4NNPP4W9vT0qVKggV1x/EXEfcCLrl5eXh59//hm1a9eWC4Ll5eXhzp07qFu3Ltzc3KDT6fDzzz+jadOmqFChAgRBQE5ODs6ePYs+ffqYfBR38+bNcHZ2RsOGDSEIAiRJQmJiIvz9/VG5cmUAwKFDh3D//n106NBBjufs2bMICAgwyT1MkiRs3LgRXl5eqF27NgRBgCiKSEhIQFBQECpUqAAA2LdvHzIzM9GmTRu5+NqpU6cQHBxc5J7QppRtyMOd3BSznU8rPnnnldK6Brxx48Z44403MHPmTNjb28PNzQ0TJkyAWq0GANSqVQvNmzfH+PHj0bNnT/Tt2xdHjhzBpk2b0KpVK5MWdX1R2yJEz4oJOD0zSZKwc+dOxMTEyMl3weNOTk4IDAyEIAjw9/cHANy5cweSJGHXrl3w9/dHUlKSnOQePnwYY8eOlaeomyLWgwcPYteuXVi3bh1cXFzkx1UqFapVqwYAcHFxQdmyZZGUlARRFHHo0CE4ODggMzMTmZmZcHFxwY4dO+SCc8a2efNmJCYmok+fPvLNURRF+Pn5ydMM9+7dizNnzmDKlCnytjItWrQweixPoxV1yNDnmOVcoiRBlJ4+wVwAtyEjMgZRMt2+pBkZGYiPj0e3bt3kkbeUlBScOnUKH330ERwcHJCcnIz4+Hg0adJEToD37t2L27dvo27duiaKLJ8kSYiOjkaVKlXk7cC0Wi0OHjyI8PBw+Tv46NGj8PHxgaurq7ymfdOmTXjllVdMsrZWFEXExMQgNDQUbm5uAPKXIB0+fBj169eHh4cHAODIkSMoV64cnJ2d5ZlSf/zxB7p3727WhMheocJvyVH4695ps50zx6CDSij6k1tac0GNRoMRI0bIe8fb2dlBqVTK/9bOzs5YsGCBvD+4UqnEtm3bAAD29vZMkomsABNwei67du2Cn58fQkND5V75ixcvolKlSvJIgKurKxwcHJCVlYW4uDicO3cOPXr0wK+//gpRFLF9+3ZUq1YNtWvXNlmcoihi37598PLykgv4SJKE8+fPw93dHbVq1QKQP2XL2dkZWVlZSE9Px88//4x+/fph7ty5yM7OxsWLF3H37l188cUXJhmJ+f3331G9enX4+/tDEAQYDAZcuHABwcHBCAgIgF6vx2+//YYaNWqYbP15cZSCAguv/4U1ifvMds6H2oynruoqrWv8iMxNRP6MElMoaPD/cznNvn37oFar0alTJwiCAK1WKycUQP50759//hmenp7w9fVFTEwMwsLCTFZESqFQyNujSZKEGzdu4MSJE1i0aJF8zrS0NPl5vV6P9evXIz4+HoMHD8bdu3dhMBjkjmdjx1XQKXH16lWcO3cOo0aNkkc8CwrFSZIEnU6HX3/9Fbdv30ZYWBiSkpKgUCiK3YXDGD4J6oABFc1f1LSM2qnIx0vrFHRBEKBSqZ44jbxgCcI/r5UXdS94ImvFBJyeS1xcHCpVqiR/qefm5mLHjh1o0aIF3N3dAeT3yvr4+CApKQmrV69Gly5doFQqce/ePVy/fh1btmzBxIkTTTb6DeRXgk1KSoKPj49c5C0nJwe7du1C8+bN5VgdHR3h7u6OBw8eYOvWrahWrRoaNWqE2bNn486dO1i3bh0GDBggjzgYU1ZWFu7evYsWLVrIyX1GRgZ27tyJgQMHwsHBAampqYiPj0fDhg0LNWIB8035+iq0O4YGtjfLuf6pgsOT33MFSmcDi6g0cXNzQ4sWLXDx4kXk5ubi0qVLWLVqFb788kuUK1cOAODp6YmKFSti27Zt8PDwwKVLl1C7dm0cPXoUK1euhEKhwJdffmmS+ARBQPv27bFu3TpkZGQgIyMDU6ZMQY8ePdCkSRP5uAYNGmDz5s1YuXIl0tLS4OPjAy8vLxw5cgSnTp3Cxx9/bNS4FAoF2rVrh127diEjIwMpKSmYOnUq+vfvj/r168vHNWzYELt378bKlStx7949+Pv7w93dHXv37sWBAwcwfPhwo8b1JM4qDZxV1pPoldYp6ERk+5iA03MJCAiQt0EpGM1IT09H37595SRSrVbD09MT27ZtQ+3atdGiRQtcvnwZ6enpGD9+PLp37y6vrTMVhUIBX19f3LhxQ05Yz5w5gytXrmDFihVy8l8wZf7w4cPIysrCnDlzoFKpoNVqsXTpUtSoUQOvvPKKSZJdBwcHeHl5QafTAcgftd+6dSs8PDzw5ptvAsjfB9bZ2Rk3b96UK5xu374dAPDmm2+aJQn3ti8Db/syJj/PsxAEJuBE1k6tVmPKlClYtWoVxo0bB1dXV3z55Zdo1KiRvOTGyckJEydOxLRp0xAVFYWPP/4Y7u7uuHDhApRKJT7++GOTrV0VBAG9e/eGUqnEhAkToNFo0LZtW7Rr167QHtpdu3ZFfHw8du/ejV69eqFDhw7Q6XQ4cuQIRo4cieDgYKPGpVAoMGjQIDg4OMhxde/eHa1bty7Ucd2zZ08kJiZi586dcqHT9PR0nD17FqNGjZKLtb1oOEOKiKyVIElsvr6I0tPT4ebmhnPX76JGpWerLitJEg4cOIAJEyZgzJgxyMrKwg8//ID3338fLVu2lBtJubm56NOnD44dO4bt27ejatWquHLlCpo1a4Z33nkH3377rVmmRZ09exYjRozABx98AI1Gg7Vr16Jjx47yiHyBmTNnYuLEiVi1ahU6duwIg8GABg0awMfHBz/88IPJitlIkoQtW7Zg0aJFGDt2LJKSkrBhwwZ8+umniIiIgEKhgCiK+OmnnzB27FhUqlQJrq6u8PX1xeeffy6vuX8RLbwAtKoABLpaLoZsPbD6MnAjw3Ix0IvNzR4YXgvQlKBLfV8SkK0D2gSU3rWz9GI5eQ+ITwe6G7dfhMimFLT309LS5B0nyPKYgL+gSpKAA/lJ45UrVxAdHQ0HBwfUqVMH5cuXL5QIGgwGHD9+HKIoonHjxlAqlcjIyMD+/fsRERGBsmXLmiVxlCQJN2/eRFRUFFQqFWrXro2KFSs+du7Y2FjExsaiZcuWsLe3hyRJ2L59O0JDQ02e5IqiiPPnz+PChQtwcXFBw4YNH9vbW6/X4/jx40hISEC5cuXQqFGjF76gyqILQEt/IMjNcjEUfIPyi5QspeAboCRfBftu53cmMQGn0uLUPeBaOvAWE3B6gTEBt05MwF9QJU3AiazBkotA8/JAsAUTcKLS4O9b+ZXQW/ozAafS4fR9IDYNeCuIn2l6cTEBt06m3ViTiMiEuA0ZkXExUaHShENMRGSNmIATkc1ikR0iIioK+5KIyFoxAScim8VtyIiIqEjMwInISjEBJyKbJQicgk5ERI/jPuBEZK2YgBORzWIDi6jkJAkwiICSI4ZUynCGFBFZIybgRGSzmIATGYcEQMEEnEoRfpyJyFoxAScimyUIHOEgIqIiMAMnIivFBJyIbJaCVdCJiKgInCFFRNaKCTgR2SzuA05EREREtoQJOBHZLO4DTmQcehZho1KGI+BEZK2YgBORzeI+4ETGIYFLZqkU4v2BiKwQE3AislmCAIiWDoKIiKwOR8CJyFqpLB0AEdHzEsARcGPR6/XIysqCXq+Hg4MDHBwcIAgcEyUiG8WvLyKyUkzAichmcRuykpMkCbdv38Zvv/0GrVaLxMRE3Lp1C99++y2Cg4OtLgmXJAl5os7sI1sCBNgrVFb3fhBR0TgCTkTWigk4EdksBaegG0V0dDRq1KiBiIgI6HQ6DB8+HDNnzsScOXNgb29v6fAKyRN1aHpoLO7mpZr1vAEOXtjz8kSoBaVZz0tEJcAMnIisEBNwIrJZnIJecoIgoF27dvLf1Wo16tati+XLlyM9PR1eXl4WjO5xEoDkvFTMqdkfVZz8zHLO8+k38eXln1GaW/MGCVCyKgyVIhwBJyJrxQSciGwWtyEzDoPBgMTERCQnJ0Oj0SAvLw8pKSnQarXyMaIoIjk5GYmJiVAqlfDz80N2djYqV64Mlcr8t5IqTn6o4VrRLOfKMWiLP8jGSRKXzFIpww80EVkpJuBEZLO4DVnJabVarF69Gnv27MFrr72GvLw8LFu2DEqlEgpF/pCoXq/Hli1bsHnzZkRGRsLOzg4jRoyAWq3G1q1bLZKAExE9TUH+zS32iMjasNVERDaL25CVjCiK+O233zB79mz88MMPqF27NjIyMrBx40YYDAY4ODgAAA4cOIBvvvkGK1euRN26dSGKImbNmoWmTZtCo9FY+LcgIioa+2eJyBpxxRcR2SyuAS+ZrKwszJw5E82aNUOtWrXkCt+iKKJSpUpwcnJCTk4OZs6ciddeew21a9eGQqHAo0ePkJOTg5dfftkicXM0i4iKw+8JIrJWTMCJyGaxyE7JXL16FVevXkWLFi3kaeSpqam4fv06mjVrBrVajZs3byI2NhaRkZFQKpWQJAkXLlyAIAioXr26ReLmv7nxiVL+rgJEpQk7aInIGjEBJyKbxX3ASyYpKQlarRZBQUEA8vfYPnz4MARBQLNmzQAAycnJuH//PlxdXQEA2dnZWLZsGdzd3VGhQgX558yJeaLxiRKg5BtLpYjAzzMRWSkm4ERksxTgGvCS8PHxgZ2dHfR6PYD80e+1a9eif//+KF++PADAzs4OSqUSaWlpyMzMxLp165Camgo3Nzfcu3cPGzZsMHsCzj4XIvo3+F1BRNaICTgR2SyOgJdMjRo10LlzZ2zZsgWHDh3CjBkzEBwcjI8++ghqtRoAUKVKFdSpUwdffPEF+vTpA3t7ezRr1gwxMTGYMGECgoOD5bXjRETWgt9KRGStWAWdiGwW9wEvGY1Ggzlz5uDIkSNISkrCG2+8gfDwcDn5BgAPDw8sXboUJ0+eROXKlREeHo60tDSEhISgZs2aqFy5MhNwIrJKvD8QkTViAk5ENkuB/LWr9HwEQYCTkxNatmz51GMqVaqESpUqyY+5u7ujU6dOZoiQzIWXEZU27BYkImvFKehEZLM4Ak5kHAYJULFFQKUJlygRkZXi7ZaIbBb3AScyDl5GVNpwBJyIrBWnoBORzWIRNvpf8Snx6LW5F1JzUxHsHowN3TZArVQX/4NEVOrw9kBE1ogJOBHZLG5DZr0MBgOuXLmClJSUpx5nb2+PmjVrwt7e3ijnlSQJa95YgyD3ILy76V1si92GzlU7G+W1ich2cASciKwVE3AislkcAbdeWq0Ws2fPxsGDB596nJ+fH3788UeUK1fOKOcNcg/6bwwGLeyUdkZ5XSKyLUzAichaMQEnIpslgFMMn1dmZiY+//xz5OTklPi1evbsiWbNmhV6TKPRYNGiRZD+RQ+JQmH8ciTrL6xHYloiWgW3Mvprl0aiBCiZsVBpwg5aIrJSTMCJyGYpBG5D9rwUCgUqVqyIvLy8Er+Wi4tLkY/n5uZCr9c/9WcLtkIz5l7ielGP4TuH41j/Y1ApeJv7N0Qpf0YJUWnC2wMRWSO2TIjIZnEE/Pk5OjpixIgRJnv9nJwcvP/++9i7d+9Tjytfvjw2b94Mf39/o507OTMZfs5+KO9a3mivSUS2hf1JRGStmIATkc3iGnDrpdFoMGPGDGRnZz/1OJVKBR8fH6Oeu6ymLCa3mGzU1yQiIiIyBibgRGSzOAJuvRQKhdEKqz2rtLw0rIleg9cCX7PI+YnI8go6aHmPICJrwwSciGyWIHAbMmPIy8vDmTNnEBUVhRs3bsDJyQlDhgyBn58fACArKwsLFy7Enj170K5dOwwePBhKpdLCUT9ZOZdyWNN5jaXDsCkSOGWXShd+nonIWhm/9CwRkZkowCnoJSVJEg4ePIh9+/bhnXfewddff43k5GRMnDgRubm5APLXi3ft2hWXL1+GSqUySdXyZ8GGtfEZREDFN5ZKGd4eiMgaMQEnIpvFNeDG8eqrr2LkyJHw9PSEq6srOnTogAMHDuDhw4cA8iuVp6enIysrC/Xr1zdqxfLnwX9yIiIislWcgk5ENotrwEtOEAQoFAro9XpotVoIggAHBwckJSUVKqAWExMDHx8fBAYGIi8vD6Iowt7eHoIgWDwhJyL6X7w/EJG1YgJORDZL4D7gJSZJEmJjY7F48WKIoghHR0ckJCQU2r9bFEUcPnwYtWrVwsmTJzFjxgzcunULX375Jd566y0LRk9ERERkW5iAE5HNUoAjHCV1/fp1DBgwAF26dMHAgQOh0+nw3nvvoWzZsnBwcAAApKen4/Lly/Dz88PBgwfx5ZdfYsaMGZg5cybat28PZ2dns8e978F5xGffM8u5rmUmmeU8RGQ8XKJERNaKCTgR2ax/bjPDSdDPTq/XY/78+ZAkCb1794ajoyPy8vJQpkwZVKhQAU5OTgCAhw8f4urVqwgICMAnn3yCsmXL4syZM5g9ezYePXpk1gRcIQioWyYI624fMts5AaCOWxCEUvwp00uAklVhqBSRr1beIIjIyjABJyKbJa/xYwPruaSmpmLz5s3o2bMn3N3dAQBarRaJiYmoW7cuXF1dAQCXL19GdnY2Bg8eDHd3d0iSBJ1OBycnJ7i4uJg1ZnuFGpvqjzbrOQuU5rXuksRLiIiIyBzY301ENksQOAW9JJKSkpCRkYHw8HD5sZs3b+LixYto1aqVvNf3sWPHULlyZbz00ksA8teER0VFoV69eihTpozZ4y4o/GbuP0RkO1iEjYisFRNwIrJtbGE9N3t7eygUCqhU+ZOhDAYDNm7ciFq1aqFJkyYAgLy8PBw/fhy1atWCnZ0dJEnCxYsXcf78ebzzzjuWDJ+I6MnYZ0ZEVooJOBHZLI5wlEzFihXRuHFjnD59GhkZGdi+fTsOHDiAiRMnylPL7927h1u3buHRo0fIzMxEXFwcpk2bhr59+6Jp06YcGSYiq8T7AxFZK64BJyKbxdSvZOzt7TFv3jz8+uuvmDJlCnx9fbFw4UIEBwfLibUgCBgwYADKli2L2bNnQ6lUon///oiIiICdnZ2FfwMyFgn5SzqIShVm4ERkhZiAE5Ht4hrwEhEEAf7+/hg+fPgTjynueSodDBKgZAJOpQhHwInIWnEKOhHZLAHc55WIiIiIbAcTcCKyWRywIyKionCXDCKyVkzAich2sYFFRERERDaECTgR2SyOgBMRUVEKliixk5aIrA0TcCKyWWxgERmHXgRU7NEiIiIyOSbgRERExCklVKpwDTgRWSsm4ERks9jAIiIiIiJbwgSciGwW93klIqKicJtKIrJWTMCJyLaxgUVEREXhsgoiskJMwInIZnEEnMg4RAlQMlmhUkQAeIMgIqvEBJyIbJbAhIHIKEQJUPB6otKENUKIyEoxAScim8YGFhER/S/2JxGRtWICTkQ2i0V2iIjoSXh7ICJrxASciGwWp6ATEVFRuAaciKwVE3AismlsXxERUVF4fyAia6SydABERM+rYADcFhpZkiTBYDAgMzMTOp0OGo0Gzs7OEKxgGF+SJOTk5CA7OxuCIMDFxQV2dnaWDkuWl5eHzMxMSJIEZ2dn2NvbW8X7VpoYJPbIU+nCrwgislZMwInIZtnKGnBJknD//n1s2rQJeXl5uHPnDi5duoSvv/4atWvXlpPJuLg4rF+/Hnq9HpUqVUKXLl3g4OBg0thEUcTu3btx8eJF5OXl4fTp06hRowZGjBgBjUZj0nMXR5IknD59GocOHYJOp8Ply5dhb2+PCRMmwMPDAwCg1WqxdetWXL58GUqlEu3bt0fNmjUtGrctYhV0Ko1s4PZARC8gJuBEZLtsKGG4dOkSAgMD0axZMwDAhAkTMHXqVKxcuRJOTk4AAG9vb5w4cQJ///03Nm3aBHt7e5PHlZWVhRs3buCtt96Ct7c34uLi0KlTJzRu3BgtWrQw+fmLc/bsWbRt2xZBQUF4+PAh3nrrLaxfvx6DBg2CIAhQqVTw9/dH79690aZNGwwaNMjsMR54cAFXMm+b/bwd/RrAx76M2c9LZAts6PZARC8YJuBEZLME2MYIhyAIiIyMLPRY/fr1sXbtWqSnp8sJuEajgZOTExo0aIAGDRpAoTD9pGAXFxe8//778t99fX0REBCAgwcPWjwBFwQB/fv3l//u5uaG0NBQ7N+/H/3794darYZCoUCZMmVgZ2eH7t27w93d3exxrk7ci6OPriDEuZxZzicB2HM/BjVcKzIBJ3oaW7hBENELhwk4EdksOQGXYPXDHaIo4vbt20hMTIRarUZWVhYePnwIrVYrH5ORkYFr166hcePGJp96XkCSJGRnZ+PatWvIyMiAs7MzRFHEvXv3Ch2Xk5ODuLg4pKamokyZMnBxcYFKpUK5cuVMuh5bq9UiLi4ODx48gIuLC0RRRHp6OrRaLdRqNQAgJiYGarUa4eHhJoujOD0rROKLkK5mOZdeNKDG3k/Mci4iW2UrHbRE9OJhzRUiIhPT6XRYu3YtPvvsM1y4cAHnzp3D/PnzoVQqoVQq5eMePHiA+Ph4NGzYECqV6ftHJUlCYmIiPvnkE/z888+4c+cOVq1ahbNnz8qj8gBw9+5dfPHFF1i9ejWSk5Oxbt06tG7dGn/++adJY3v06BG++uorzJ49G3fu3MFvv/2G7du3w87OTn7fRFHEsWPHEBQUBF9fX5PFU9oZJEBp5Z1YRM9EyE/AmYQTkbXhCDgR2SxBsP4ibJIkYd++fZgwYQKWLFmCpk2bIjc3F9u3b0daWlqhQmcxMTEwGAxmKyKWk5ODzz77DE5OTvjqq6/g6OgIHx8fLFu2DJUqVQKQX4F8woQJSE9Px4IFC+Ds7AxXV1csW7YM1apVM1lsBoMB//nPfxAVFYVffvkFnp6eSEpKwnfffQdfX195fXxmZibOnTuHmjVrmm3WQGkkgVWjqXThx5mIrBVHwInIZtlCAys3Nxdz5szBSy+9hEaNGkEQBHlLsuDgYDlplCQJUVFR8Pf3R0BAgMnjkiQJe/fuxYEDBzBw4EA4OTlBEASIogi1Wo1q1arJVch37NiBwYMHw9nZGQCQnJwMX19fVK5c2WTTz69cuYJVq1ahf//+8PT0hCAIMBgMUCgUCA8Pl8+bkpKCuLg41KlTR56STkRERGStmIATkU2z8gFwJCcn48SJE3j11VcLjdrGxcUhIiICjo6OAPJHo6Ojo9GgQQOz7MFtMBiwb98+lC9fHsHBwQDyk/Lo6Gh4e3ujRo0aAICjR4/Czs4OtWrVkpPgI0eOIDAwEN7e3iaJTZIkHD16FABQt25dOdm+ePEilEolXn75ZfnYGzduIC0tDXXq1OHe4EQk4xpwIrJWTMCJyGbZQr5179496HQ6BAYGAshPLmNiYvDgwQO0adNGThrv37+PGzduoFatWmZZ/20wGJCcnAwfHx95vXdWVhZ2796Nli1bws3NDQBw7tw52NnZwcnJSR4R37ZtGyIjI6FUKiGZYA2AKIq4desWPD095Tj0ej3++usv1KtXDxUrVpSPjYqKgpubm9yJQEQkYwZORFaICTgR2ayCEQ5rbmN5enpCo9FAp9MBALKzs7F69Wr06NEDoaGh8nE3b95Eamqq2UZylUolfH19IYoiRFEEAJw4cQIJCQl4//335VF4JycnaLVaedR+586dyM3NhZ+fH7Zs2YILFy4YPTaFQoHy5csDgBxbXFwcDh06hCFDhshT4fV6PY4ePYp69erBxcXF6HG8SCQJUNhAhxbRvyUI1n1vIKIXFxNwIrJp1l6ELSAgAL1798b27dtx4MABzJ49Gy4uLhgzZkyhqeanT5+Gi4sLqlSpYpa4VCoV3nnnHYiiiL/++gubN2/G8uXLMWHChEJF4Nq3b4+0tDS0adMGU6dORdu2bVG5cmVMmzYNMTExcrE2YxIEAW3atEH58uWxfft2/PXXX5g1axb69OmD119/Xe6gePDgAa5fv446deqYZdZAacYq6ERERObBFgsR2SxbyBdUKhW++eYbnDx5EsnJyWjevDlq164trwcH8qeDHzhwAPXq1UPZsmXNFlt4eDiWL1+OU6dOQa1WY8qUKfD395cTXEEQ0KJFC2zYsAEPHjxAgwYN4OXlhRUrViA5ORkRERGFtiszpvLly2PNmjU4fvw4cnJyMGLEiMemmSckJODevXto0KAB138TUSFcA05E1ooJOBHZLFuYYigIAjQaDZo0aVLk85Ik4dy5c7h8+TImT54MhcJ8E5MEQUBAQMBTq66r1Wo0bty40GM1a9Y0+VZpgiDAy8sL7du3f+w5SZKg1+uxefNmhIWFoX79+kzAiYiIyCYwASci22btGfhTJCUlYf369Th79iwGDhxYqCibrTMYDADy15oX9ZwkSVAqlc/8+4qiiF27duHAgQO4c+cOvvvuO3lNOBFRAQHWv0SJiF5MTMCJyGbZ+hRDT09PvPXWW+jZsyfc3NyKTFZtkV6vx7x58+Dm5obevXsX+r10Oh1+/PFHZGVl4YMPPnjm31kQBDRs2BAvvfQSnJ2d5f3LrVV8Sjx6be6F1NxUBLsHY0O3DVAruV85kclZ79cCEb3gWISNiGyWLUxBfxo7Ozv4+vrCw8MDKpXqmRJJSZL+9R9zUygUqFChAqZPn46NGzfKo+GiKOL333/HnDlzEBYW9lzT7QVBQJkyZeDr6wtnZ+dnTr7N/b5JkoQ1b6zBhSEX4GznjG2x24zyusYmSsxXqHTh55mIrBVHwImIbFBUVBQ+/fTTYo8bPHgw3nvvPdMH9A8KhQKdOnWCXq/HpEmToNFo0KZNG2zbtg2TJ0/G559/jldffdUiI9c//fQT5s+f/9Rj7O3tMWnSpCeu238WQe5B8v9rDVrYKe2ecrTlGCRAyS55KmVsuYOWiEovJuBEZLOseY1fQkIC0tPTS/w6Tk5OCAwMfOzxwMBAjBs3rtif/+de4/+UlJSEhw8fljg+Ozs7hISEPJZMq1QqdOnSBampqRg9ejSOHj2KLVu2YPjw4ejWrVuRo9+5ubm4evVqiWMCgEqVKhW5N3jjxo2LrTSvUCiMvh3c+gvrkZiWiFbBrYz6ukRUNI6AE5G1YgJORDbLmhtY3377LXbs2FHi12nYsCF+/fXXxx738vJCu3btnvt1ly5dihUrVpQkNAD5HQG7du0qch9ulUqFfv36ITY2FjNmzMDQoUPRu3fvJ049v3nzJjp16iRPWS+J1atXo1mzZo89HhQUhKCgoMd/wIT0oh7Ddw7Hsf7HoFLwtktkFoL1dtAS0YuNLQEisl1WvAZ83rx5EEWxxK/zpGQ1NjYWy5cvL/bnW7VqhebNmz/2+BdffIHRo0eXOD5BEJ5YSE0URZw4cQJ79uxBx44dsXfvXpw4cQIRERFF/l7BwcG4ePFiiWMC8rdPK8q+ffuwffv2p/6sSqVCz549UbVqVaPEkpyZDD9nP5R3LW+U1yOi4skdtNZ6kyCiFxYTcCKyWQIApZUOg9vZmXatryRJ0Ol0xR73pE4AtVr9xCTVGCRJwoULF/DBBx+gU6dO+PTTTzFz5kwMHToUq1atQs2aNR+btq5QKKDRaEwWE5D/fpTkfXseZTVlMbnFZKO9nikEurIqK5UuSgGoUia/WCcRkTVhAk5ENquMPdCvqvUm4aYUGhqKWbNmWTqMIkmShEuXLuGjjz7CG2+8gVGjRsHR0RFffPEFDAYDPvjgAyxbtgxhYWFmL8TWvHnzImcEmFJaXhrWRK/Ba4GvmfW8z+LdKizCRqWLSgF0DXwx7w9EZN14uyUim6UUADc7jnBYG71ej9WrV+PVV1/FqFGj5L26nZycMHr0aFSvXh0rVqyAXq+3dKhmUc6lHNZ0XmPpMJ5IEAANu+OpFLJT8v5ARNaHt1wiIhOSJAlpaWk4evQozp49i9u3byMsLAx9+/aFk5MTgPyK5DNmzMClS5fwxRdfoGnTpmaN7/79+zhy5AjOnDmD+/fvIyIiAt26dYO9vT0AIDk5Gd988w0SEhIwduzYYrfnUqvV+Pzzz2Fvbw8HB4dCz5UtWxYzZ85ETk5OkYXbCuh0Oly8eBGHDx/G9evXIUkSBgwYgNDQUItsX0ZERERkDBwBJyIyIYPBgJ9//hkpKSn45JNPMGLECPz6669Yt26dvM7Yz88Pbdu2xenTp+Hq6mrW+CRJwrx58+Qp4oMGDcJ3332Hffv2ycf4+Pigffv2OHHiRLFbeBUoU6bMY8l3ARcXF3h7ez81kT537hw2btyIDh06YNKkSXBzc8OIESOQlZX1TL8fERERkTXhCDgRkQkplUoMGjQICoUCgiCgYsWKaNSoEXbs2IF3330XGo0GgiDg5s2b8Pb2RnBwsFnjEwQBX3/9tRxfaGgowsLCsG3bNrz22mtQKpVyfBUrVkSFChXMEld4eDheeuklOa433ngD33//PW7cuIEaNWqYJQYiIiIiY+MIOBGRCQmCAIVCAa1Wi6ysLOTm5sLR0REJCQlyNW5JknD8+HE0atQI9vb2yM7ORk5ODiQzbGIrCAIEQUBeXh6ysrKg1Wphb2+PpKQkaLVaAPlruqOiotCgQQM5vtzcXJPGJwgCJElCTk4OsrKyYG9vD71ej9u3bxc6Tq/XIzs7G1lZWcjLy0NeXp5R9hEnIiIiMgWOgBMRmZDBYMC+ffuwbt06uLu7QxAEHDlyRB7ZBYCMjAxER0fj3XffxQ8//IC5c+fCwcEBc+fORd26dU0WmyRJ0Ov12LJlC3bs2AEvLy/k5uYiOjoagYGBcnxpaWm4du0aunfvjp9++gmzZ8+Gq6sr5s6di/DwcKOvyZYkCUlJSZg3bx4yMjLg6uqK1NRUGAyGQnuO379/H0uWLMHdu3dRpkwZPHr0CImJiZgyZQqqVatm1JiIiIiIjIEj4EREJiKKInbu3Ilhw4bhrbfewqRJkzBgwADcuXMH5cqVk5PJW7duISMjA1evXkVWVhbmzp2LlJQULF682KSjzJIkYfXq1Zg5cyY+/PBDTJw4Eb169cKlS5fg5eUl72V+9+5d3Lp1C3FxccjNzcX8+fNx9+5drF692iRxPXjwAO+//z70ej2+/fZbfPPNNzAYDFAoFPDx8QEAZGZmYtiwYUhNTcXEiRMxbtw4KJVKXLt2DW5ubiaJqziSJJnlDxEREdkujoATEZlIWloapkyZgmbNmqFp06ZQq9VwdnaGk5MTKlasCLVaDUmScO3aNSQmJiIwMBCDBg0CANSuXRsJCQnIy8uDRqMxSXy3bt3C9OnT8eGHH8oj2Z6enlCr1QgJCYFCkd9He/nyZdy7dw+BgYEYMGAAVCoVqlevjsuXLxs9JlEUsWbNGty4cQMLFiyQk2l3d3e4uLjAz88PkiRhy5YtiImJweTJk+Hm5gadToe8vDyEhobCy8vL6HEVZ9GNHdiafMJMZ5OQkH3PTOciIiIiY2ICTkRkIufOnUNMTAzGjh0rjyanpqYiJSUFERERUKlUkCQJx44dQ2BgIHr27Ak7Ozvk5eVBq9XCw8PDZMk3AOzZswepqal47bXX5Gnk8fHxUKlUhbYaO3ToEMLCwtC7d2/Y2dlBFEXk5ubC39/f6DFlZGRg69atqF+/PsqVKwcA0Gq1uHbtGl5++WW4uLhAkiRs2LAB9evXl4vC5eTk4MyZM+jRo4f8XpvLJ4Ht0aVcY7OeEwDCnMub/ZxERERUMkzAiYhMJDY2FoIgFFqPfPz4cSgUCjRunJ+wGQwGHDlyBJGRkfD09AQApKenIyYmBmPGjDFZbAaDAVevXkX58uXl84qiiF27diE0NBRVqlQBAGRnZ+PkyZNo0aKFvEXa/fv3cf36dfTs2dPoceXm5uL69eto164d1Go1AODOnTu4dOkSPvnkE6jVaqSnp+PixYvo1auXHPfff/+N2NhY+X01p1pulVDLrZLZz0tERES2h2vAiYhMxNHREYIgyFPNMzMzsXHjRrz77rvw9fUFACQlJSEhIQE1a9YEkJ9M/vnnn3Bzc0OLFi1MFpsgCHBwcIBKpZKnmt+9exf79u1Dnz594OLiIseXmJiIqlWrQqFQyPGVLVsWzZo1M3pcCoUCjo6OUKvVciX0AwcOwM7ODh07doRCoYBOp4MoitDr9ZAkCfHx8di0aRPc3d3h4+ODw4cPc79wIiIiskocASciMpGIiAj4+/sjKioKTZo0wfLly+Ho6IiPPvpITnqvXbuG1NRUJCcnIzMzEydPnsS6deswdepUk+65rVAo8Prrr+PXX3/F9evXodPpMGfOHNSsWRM9evSAQqGAJEm4ceMG7t69i7t37yI7OxvHjh3DunXrMHnyZJQvX97oFdDd3NzQunVrxMTEICUlBRcuXMDq1asxefJkeHt7A8hfDx4eHo6NGzdCpVLh0aNHiIyMxJ49ezBjxgwEBASgXr16Ro2LiIiIyBgEiSVVX0jp6elwc3PDuet3UaOSt6XDISqVRFHEhQsX8Ntvv0Gr1aJKlSpo06YNPDw85MT1+PHjOHv2rLz1lru7O15//XWEhYXJSbqp6PV6HDlyBHv37oUoiqhTpw5atGgBJycnefQ5KioKZ86cgSAIuHXrFtzd3dG6dWuEhoaaLL6UlBSsX78eCQkJ8PT0RPPmzVGjRg25arwkSbh69SoWLlwINzc39O3bF66urpg5cyZ8fX3Rq1cvi1VCJyIishYF7f20tDR5GRlZHhPwFxQTcCIiIiKi0osJuHXiGnAiIiIiIiIiM2ACTkRERERERGQGTMCJiIiIiIiIzIAJOBEREREREZEZMAEnIiIiIiIiMgMm4ERERERERERmwASciIiIiIiIyAyYgBMRERERERGZgcrSAZBl3UnPhcujHEuHQURERERERpSRzja+NWIC/oKSJAkA0Ob7o1DYOVo4GiIiIiIiMiZJm53/3/9v95N1YAL+gsrIyAAAGJb3gMHCsRARERERkWlkZGTAzc3N0mHQ/xMkdom8kERRRFJSElxcXCAIgqXDISIiIiIiI5IkCRkZGShXrhwUCpb+shZMwImIiIiIiIjMgF0hRERERERERGbABJyIiIiIiIjIDJiAExEREREREZkBE3AiIiIiIiIiM+A2ZERERGQR3JGDjO1FqvrM64eM7UW6fiyJCTgRERFZRFJSEipUqGDpMKgUSkxMhL+/v6XDMCleP2QqL8L1Y0lMwImIiMgiXFxcAAD79++Hs7OzhaOh0iAzMxORkZHyZ6s04/VDxvYiXT+WxASciIiILKJg2qyzszMTCDKqF2FKNq8fMpUX4fqxJE7uJyIiIiIiIjIDJuBEREREREREZsAEnIiIiIiIiMgMmIATERERERERmQETcCIiIiIiIiIzYAJOREREREREZAZMwImIiIiIiIjMgAk4ERERERERkRkwASciIiIqJZo3b45Vq1aV6DVu3bqF0NBQXLp0CQBw/PhxhIaGIj093QgREj2fTZs2oV69ek89Zu7cuejUqZPJY/nfa+LfxEZUgAk4ERER0VOMGTMGoaGhCA0NRfXq1dG8eXNMnz4deXl5lg7tuSUnJ6NGjRpo3769pUOhF9yYMWMwZMiQxx7/3yS3bdu22LFjh9niOnPmDKpWrYqBAwea7Zz0YmACTkRERFSMV155BYcOHcLff/+NL774Ar/88gu+//57S4f13DZt2oTWrVsjMzMT0dHRlg6HqFgajQYeHh5mO9+GDRvQs2dPnDhxAnfv3jXbean0YwJOREREVAw7Ozt4eXnBz88Pr732GiIiInDkyBH5eVEUsXjxYjRv3hy1atVCx44d8ddff8nPNW3aFD///HOh17x48SLCwsJw+/ZtAEB6ejrGjh2LRo0aoU6dOujduzcuX74sH3/z5k188MEHiIiIQO3atdGlS5dCMfxbkiRh06ZN6NSpE9q3b48NGzY8z1tCZFZFTfNesmSJfD188cUXRc5KWb9+Pdq0aYOaNWuidevW+Omnn4o9V1ZWFv7880+8/fbbaNasGTZv3my034OICTgRERHRM4iNjcWZM2egVqvlxxYvXowtW7Zg/Pjx+OOPP9CnTx+MHDkSUVFRUCgUaNeuHbZt21bodX7//XfUqVMH5cuXBwB88sknePjwIZYuXYpNmzahevXqeO+995CamgoAyM7ORmRkJFatWoXNmzfjlVdeweDBg5GUlPRM8R87dgy5ubmIiIhAx44d8ccffyA7O7tkbwqRmf3555+YO3cuhg0bho0bN8LLy+uxTq6tW7dizpw5GDZsGP78808MHz4c33//fbEJ9fbt2xEYGIjAwEB07NgRGzduhCRJpvx16AWisnQARERERNZu3759qF27NvR6PbRaLRQKBb788ksAgFarxeLFi7Fy5UrUrl0bAFChQgWcOnUKv/zyCxo0aICOHTti5cqVSEpKQrly5SCKIv744w988MEHAICTJ08iJiYGR48ehZ2dHQBg9OjR+Pvvv7Fjxw689dZbCAsLQ1hYmBzTp59+ir///ht79uxBz549//XvsmHDBrRt2xZKpRIhISGoUKEC/vrrL7z55pvGeruInknB9fVPBoPhqT+zZs0adO3aFd26dQMADBs2DEePHi00Cj537lyMGTMGr7/+OoD86/LatWv45Zdf0Llz5ye+9oYNG9CxY0cA+ctPMjIyEBUVhYYNGz7X70f0T0zAiYiIiIrRsGFDfPPNN8jJycGqVaugVCrRqlUrAEBCQgJycnLQr1+/Qj+j0+lQtWpVAEDVqlURFBSEbdu2YeDAgYiKisKjR4/QunVrAMCVK1eQnZ39WAM/NzcXN2/eBJA/LXbevHnYt28f7t+/D4PBgNzc3GcaAU9PT8euXbsKjRR27NgRGzZsYAJOFlNwff1TdHQ0Ro4c+cSfiYuLQ48ePQo9Fh4ejuPHjwPInzFy8+ZNjB07Vu4sAwC9Xg8XF5cnvm58fDzOnTuH+fPnAwBUKhXatm2LDRs2MAEno2ACTkRERFQMBwcHVKxYEQAwefJkdOrUCevXr0e3bt3k6duLFy+Gj49PoZ8rGM0GgA4dOuD333/HwIEDsW3bNjRp0gRly5YFkJ9ce3l54Ycffnjs3AXJwrRp03DkyBGMHj0aAQEB0Gg0GDp0KHQ63b/+PX7//Xfk5eWhe/fu8mOSJEEURVy/fh2VK1f+169FZCz/vL4KJCcnl+g1C67LiRMn4qWXXir0nELx5FW4GzZsgF6vxyuvvCI/JkkS7Ozs8NVXXz01eSf6N5iAExERET0DhUKBQYMGYerUqejQoQOCgoJgZ2eHpKQkNGjQ4Ik/1759e8yePRvnz5/Hjh07MH78ePm56tWr48GDB1AqlfD39y/y58+cOYPOnTujZcuWAPKT9oICbv/Wxo0b0a9fv8em344fPx4bN27EiBEjnun1iCwlKCgI0dHReOONN+TH/lnR39PTE97e3khMTJSnkxdHr9fjt99+w5gxY/Dyyy8Xeu7DDz/Etm3b8PbbbxslfnpxsQgbERER0TNq3bo1FAoFfvrpJzg7O6Nfv36YMmUKNm/ejJs3b+LChQv44YcfChV78vf3R+3atTF27FgYDAY0b95cfi4iIgLh4eH48MMPcejQIdy6dQunT5/Gd999h3PnzgEAKlasiF27duHSpUu4fPkyPvvsM4ii+K9jvnTpEi5cuICuXbsiJCSk0J927dphy5Yt0Ov1xnuTiEyod+/e2LhxIzZu3Ijr16/j+++/x9WrVwsdM3ToUCxZsgRr1qzB9evXceXKFWzcuBErV64s8jX37duHtLS0Iq+R119/nTsGkFEwASciIiJ6RiqVCj179sSyZcuQnZ2NTz/9FEOGDMHixYvRtm1bDBgwAPv27XtsNLtDhw64fPkyWrZsCY1GIz8uCAKWLFmC+vXr4/PPP0fr1q0xfPhw3L59G56engCAMWPGwNXVFT169MDgwYPxyiuvoHr16v865g0bNiA4OBhBQUGPPdeyZUs8fPgQ+/fvf853hMi82rZtiyFDhmDGjBl48803kZSU9NjodLdu3TBp0iRs2rQJHTp0QK9evbB58+YnzjLZsGEDIiIiipxm3qpVK5w/f77Q1oBEz0OQWFOfiIiILCA9PR1ubm44deoUnJ2dLR0OlQKZmZmoW7cu0tLS4OrqaulwTIrXDxnbi3T9WBJHwImIiIiIiIjMgAk4ERERERERkRkwASciIiIiIiIyAybgRERERERERGbABJyIiIiIiIjIDJiAExEREREREZkBE3AiIiIiIiIiM2ACTkRERERERGQGTMCJiIiIiIiIzIAJOBEREREREZEZMAEnIiIiIiIiMgMm4ERERERERERmwASciIiIiIiIyAxUlg6AiIiIXkySJAEAMjMzLRwJlRYFn6WCz1ZpxuuHjO1Fun4siQk4ERERWURGRgYAIDIy0sKRUGmTkZEBNzc3S4dhUrx+yFRehOvHkgSJXRxERERkAaIoIikpCS4uLhAEwdLhUCkgSRIyMjJQrlw5KBSle6Ulrx8ythfp+rEkJuBEREREREREZsCuDSIiIiIiIiIzYAJOREREREREZAZMwImIiIiIiIjMgAk4ERERERERkRkwASciIiIiIiIyAybgRERERERERGbABJyIiIiIiIjIDP4PPYmpsmdO3EcAAAAASUVORK5CYII=' width=992.2644163150492/>\\n\",\n       \"            </div>\\n\",\n       \"        \"\n      ],\n      \"text/plain\": [\n       \"Canvas(footer_visible=False, header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_network(config_nw1, \\\"./images/C2_W2_BP_network1.PNG\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"eeb31b52\",\n   \"metadata\": {},\n   \"source\": [\n    \"Below, we will go through the computations required to fill in the above computation graph in detail. We start with the forward path.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9113fa00\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Forward propagation\\n\",\n    \"The calculations in the forward path are the ones you have recently learned for neural networks. You can compare the values below to those you calculated for the diagram above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"95c103dc\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J=4.5, d=3, a=4, c=-4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Inputs and parameters\\n\",\n    \"x = 2\\n\",\n    \"w = -2\\n\",\n    \"b = 8\\n\",\n    \"y = 1\\n\",\n    \"# calculate per step values   \\n\",\n    \"c = w * x\\n\",\n    \"a = c + b\\n\",\n    \"d = a - y\\n\",\n    \"J = d**2/2\\n\",\n    \"print(f\\\"J={J}, d={d}, a={a}, c={c}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"48b0aad1\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Backward propagation (Backprop)\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W2_BP_network1_jdsq.PNG\\\"     style=\\\" width:100px; padding: 10px 20px; \\\" > As described in the lectures, backprop starts at the right and moves to the left. The first node to consider is $J = \\\\frac{1}{2}d^2 $ and the first step is to find $\\\\frac{\\\\partial J}{\\\\partial d}$ \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"72ec88e0\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"### $\\\\frac{\\\\partial J}{\\\\partial d}$ \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a13afd71\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Arithmetically\\n\",\n    \"Find $\\\\frac{\\\\partial J}{\\\\partial d}$ by finding how $J$ changes as a result of a little change in $d$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"ef040cc3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 4.5, J_epsilon = 4.5030005, dJ_dd ~= k = 3.0004999999997395 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"d_epsilon = d + 0.001\\n\",\n    \"J_epsilon = d_epsilon**2/2\\n\",\n    \"k = (J_epsilon - J)/0.001   # difference divided by epsilon\\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_dd ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1ab8a185\",\n   \"metadata\": {},\n   \"source\": [\n    \"$\\\\frac{\\\\partial J}{\\\\partial d}$ is 3, which is the value of $d$. Our result is not exactly $d$ because our epsilon value is not infinitesimally small. \\n\",\n    \"#### Symbolically\\n\",\n    \"Now, let's use SymPy to calculate derivatives symbolically, as we did in the derivatives optional lab. We will prefix the name of the variable with an 's' to indicate this is a *symbolic* variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"e8dd3647\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle \\\\frac{d^{2}}{2}$\"\n      ],\n      \"text/plain\": [\n       \"d**2/2\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sx,sw,sb,sy,sJ = symbols('x,w,b,y,J')\\n\",\n    \"sa, sc, sd = symbols('a,c,d')\\n\",\n    \"sJ = sd**2/2\\n\",\n    \"sJ\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"dd1e4fd9\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle \\\\frac{9}{2}$\"\n      ],\n      \"text/plain\": [\n       \"9/2\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sJ.subs([(sd,d)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"776cd71c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle d$\"\n      ],\n      \"text/plain\": [\n       \"d\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dd = diff(sJ, sd)\\n\",\n    \"dJ_dd\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d2f57fe8\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, $\\\\frac{\\\\partial J}{\\\\partial d}$ = d. When $d=3$, $\\\\frac{\\\\partial J}{\\\\partial d}$ = 3. This matches our arithmetic calculation above.\\n\",\n    \"If you have not already done so, you can go back to the diagram above and fill in the value for $\\\\frac{\\\\partial J}{\\\\partial d}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cead3d50\",\n   \"metadata\": {},\n   \"source\": [\n    \"### $\\\\frac{\\\\partial J}{\\\\partial a}$ \\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W2_BP_network1_d.PNG\\\"     style=\\\" width:100px; padding: 10px 20px; \\\" >  Moving from right to left, the next value we would like to compute is $\\\\frac{\\\\partial J}{\\\\partial a}$. To do this, we first need to calculate $\\\\frac{\\\\partial d}{\\\\partial a}$ which describes how the output of this node changes when the input $a$ changes a little bit. (Note, we are not interested in how the output changes when $y$ changes since $y$ is not a parameter.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b689d707\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Arithmetically\\n\",\n    \"Find $\\\\frac{\\\\partial d}{\\\\partial a}$ by finding how $d$ changes as a result of a little change in $a$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"de061567\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"d = 3, d_epsilon = 3.0010000000000003, dd_da ~= k = 1.000000000000334 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a_epsilon = a + 0.001         # a  plus a small value\\n\",\n    \"d_epsilon = a_epsilon - y\\n\",\n    \"k = (d_epsilon - d)/0.001   # difference divided by epsilon\\n\",\n    \"print(f\\\"d = {d}, d_epsilon = {d_epsilon}, dd_da ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6dad3683\",\n   \"metadata\": {},\n   \"source\": [\n    \"Calculated arithmetically,  $\\\\frac{\\\\partial d}{\\\\partial a} \\\\approx 1$. Let's try it with SymPy.\\n\",\n    \"#### Symbolically\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"d8dbaf35\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle a - y$\"\n      ],\n      \"text/plain\": [\n       \"a - y\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"sd = sa - sy\\n\",\n    \"sd\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"d8028fcf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 1$\"\n      ],\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dd_da = diff(sd,sa)\\n\",\n    \"dd_da\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"75e9c210\",\n   \"metadata\": {},\n   \"source\": [\n    \"Calculated arithmetically,  $\\\\frac{\\\\partial d}{\\\\partial a}$ also equals 1.  \\n\",\n    \">The next step is the interesting part, repeated again in this example:\\n\",\n    \"> - We know that a small change in $a$ will cause $d$ to change by 1 times that amount.\\n\",\n    \"> - We know that a small change in $d$ will cause $J$ to change by $d$ times that amount. (d=3 in this example)    \\n\",\n    \" so, putting these together, \\n\",\n    \"> - We  know that a small change in $a$ will cause $J$ to change by $1\\\\times d$ times that amount.\\n\",\n    \"> \\n\",\n    \">This is again *the chain rule*.  It can be written like this: \\n\",\n    \" $$\\\\frac{\\\\partial J}{\\\\partial a} = \\\\frac{\\\\partial d}{\\\\partial a} \\\\frac{\\\\partial J}{\\\\partial d} $$\\n\",\n    \" \\n\",\n    \" Let's try calculating it:\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"fd330aaa\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle d$\"\n      ],\n      \"text/plain\": [\n       \"d\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_da = dd_da * dJ_dd\\n\",\n    \"dJ_da\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9434b28a\",\n   \"metadata\": {},\n   \"source\": [\n    \"And $d$ is 3 in this example so $\\\\frac{\\\\partial J}{\\\\partial a} = 3$. We can check this arithmetically:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"ea02e4ac\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 4.5, J_epsilon = 4.503000500000001, dJ_da ~= k = 3.0005000000006277 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a_epsilon = a + 0.001\\n\",\n    \"d_epsilon = a_epsilon - y\\n\",\n    \"J_epsilon = d_epsilon**2/2\\n\",\n    \"k = (J_epsilon - J)/0.001   \\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_da ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e7911e9a\",\n   \"metadata\": {},\n   \"source\": [\n    \"OK, they match! You can now fill the values for  $\\\\frac{\\\\partial d}{\\\\partial a}$ and $\\\\frac{\\\\partial J}{\\\\partial a}$ in the diagram if you have not already done so. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"41a4a2ca\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"> **The steps in backprop**   \\n\",\n    \">Now that you have worked through several nodes, we can write down the basic method:\\\\\\n\",\n    \"> working right to left, for each node:\\n\",\n    \">- calculate the local derivative(s) of the node\\n\",\n    \">- using the chain rule, combine with the derivative of the cost with respect to the node to the right.   \\n\",\n    \"\\n\",\n    \"The 'local derivative(s)' are the derivative(s) of the output of the current node with respect to all inputs or parameters.\\n\",\n    \"\\n\",\n    \"Let's continue the job. We'll be a bit less verbose now that you are familiar with the method.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6a3b7f1b\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"### $\\\\frac{\\\\partial J}{\\\\partial c}$,  $\\\\frac{\\\\partial J}{\\\\partial b}$\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W2_BP_network1_a.PNG\\\"     style=\\\" width:100px; padding: 10px 20px; \\\" >The next node has two derivatives of interest. We need to calculate  $\\\\frac{\\\\partial J}{\\\\partial c}$ so we can propagate to the left. We also want to calculate   $\\\\frac{\\\\partial J}{\\\\partial b}$. Finding the derivative of the cost with respect to the parameters $w$ and $b$ is the object of backprop. We will find the local derivatives,  $\\\\frac{\\\\partial a}{\\\\partial c}$ and  $\\\\frac{\\\\partial a}{\\\\partial b}$ first and then combine those with the derivative coming from the right, $\\\\frac{\\\\partial J}{\\\\partial a}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"b6f8480d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle b + c$\"\n      ],\n      \"text/plain\": [\n       \"b + c\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# calculate the local derivatives da_dc, da_db\\n\",\n    \"sa = sc + sb\\n\",\n    \"sa\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"517d398f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1 1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"da_dc = diff(sa,sc)\\n\",\n    \"da_db = diff(sa,sb)\\n\",\n    \"print(da_dc, da_db)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"e5c4b3a4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dJ_dc = d,  dJ_db = d\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dJ_dc = da_dc * dJ_da\\n\",\n    \"dJ_db = da_db * dJ_da\\n\",\n    \"print(f\\\"dJ_dc = {dJ_dc},  dJ_db = {dJ_db}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"99c8f9f5\",\n   \"metadata\": {},\n   \"source\": [\n    \"And in our example, d = 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"32d0a738\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"###  $\\\\frac{\\\\partial J}{\\\\partial w}$\\n\",\n    \"<img align=\\\"left\\\" src=\\\"./images/C2_W2_BP_network1_c.PNG\\\"     style=\\\" width:100px; padding: 10px 20px; \\\" > The last node in this example calculates `c`. Here, we are interested in how J changes with respect to the parameter w. We will not back propagate to the input $x$, so we are not interested in $\\\\frac{\\\\partial J}{\\\\partial x}$. Let's start by calculating $\\\\frac{\\\\partial c}{\\\\partial w}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"2f67c3e4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle w x$\"\n      ],\n      \"text/plain\": [\n       \"w*x\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# calculate the local derivative\\n\",\n    \"sc = sw * sx\\n\",\n    \"sc\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"bd3b112f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle x$\"\n      ],\n      \"text/plain\": [\n       \"x\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dc_dw = diff(sc,sw)\\n\",\n    \"dc_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1ef136e9\",\n   \"metadata\": {},\n   \"source\": [\n    \"This derivative is a bit more exciting than the last one. This will vary depending on the value of $x$. This is 2 in our example.\\n\",\n    \"\\n\",\n    \"Combine this with $\\\\frac{\\\\partial J}{\\\\partial c}$ to find $\\\\frac{\\\\partial J}{\\\\partial w}$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"ca72dfef\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle d x$\"\n      ],\n      \"text/plain\": [\n       \"d*x\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw = dc_dw * dJ_dc\\n\",\n    \"dJ_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"8419a99d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"dJ_dw = 2*d\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"dJ_dw = {dJ_dw.subs([(sd,d),(sx,x)])}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f1cd855a\",\n   \"metadata\": {},\n   \"source\": [\n    \"$d=3$,  so $\\\\frac{\\\\partial J}{\\\\partial w} = 6$ for our example.   \\n\",\n    \"Let's test this arithmetically:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"5b38a46a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 4.5, J_epsilon = 4.506002, dJ_dw ~= k = 6.001999999999619 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"J_epsilon = ((w+0.001)*x+b - y)**2/2\\n\",\n    \"k = (J_epsilon - J)/0.001  \\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_dw ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dab9f839\",\n   \"metadata\": {},\n   \"source\": [\n    \"They match! Great. You can add $\\\\frac{\\\\partial J}{\\\\partial w}$ to the diagram above and our analysis is complete.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b8c85458\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"You've worked through an example of back propagation using a computation graph. You can apply this to larger examples by following the same node by node approach. \"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/backprop/C2_W2_Derivatives.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"55ca2cda\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"# Optional Lab - Derivatives\\n\",\n    \"This lab will give you a more intuitive understanding of derivatives. It will show you a simple way of calculating derivatives arithmetically. It will also introduce you to a handy Python library that allows you to calculate derivatives symbolically.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"9201a0da\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sympy import symbols, diff\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"69ac228f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Informal definition of derivatives\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b116fb76\",\n   \"metadata\": {},\n   \"source\": [\n    \"The formal definition of derivatives can be a bit daunting with limits and values 'going to zero'. The idea is really much simpler. \\n\",\n    \"\\n\",\n    \"The derivative of a function describes how the output of a function changes when there is a small change in an input variable.\\n\",\n    \"\\n\",\n    \"Let's use the cost function $J(w)$ as an example. The cost $J$ is the output and $w$ is the input variable.  \\n\",\n    \"Let's give a 'small change' a name *epsilon* or $\\\\epsilon$. We use these Greek letters because it is traditional in mathematics to use *epsilon*($\\\\epsilon$) or *delta* ($\\\\Delta$) to represent a small value. You can think of it as representing 0.001 or some other small value.  \\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"\\\\text{if } w \\\\uparrow \\\\epsilon \\\\text{ causes }J(w) \\\\uparrow \\\\text{by }k \\\\times \\\\epsilon \\\\text{ then}  \\\\\\\\\\n\",\n    \"\\\\frac{\\\\partial J(w)}{\\\\partial w} = k \\\\tag{1}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"This just says if you change the input to the function $J(w)$ by a little bit and the output changes by $k$ times that little bit, then the derivative of $J(w)$ is equal to $k$.\\n\",\n    \"\\n\",\n    \"Let's try this out.  Let's look at the derivative of the function $J(w) = w^2$ at the point $w=3$ and $\\\\epsilon = 0.001$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"3296f0d0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 9, J_epsilon = 9.006001, dJ_dw ~= k = 6.001000 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"J = (3)**2\\n\",\n    \"J_epsilon = (3 + 0.001)**2\\n\",\n    \"k = (J_epsilon - J)/0.001    # difference divided by epsilon\\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_dw ~= k = {k:0.6f} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b8e9c557\",\n   \"metadata\": {},\n   \"source\": [\n    \"We have increased the input value a little bit (0.001), causing the output to change from 9 to 9.006001, an increase of 6 times the input increase. Referencing (1) above, this says that $k=6$, so $\\\\frac{\\\\partial J(w)}{\\\\partial w} \\\\approx 6$. If you are familiar with calculus, you know, written symbolically,  $\\\\frac{\\\\partial J(w)}{\\\\partial w} = 2 w$. With $w=3$ this is 6. Our calculation above is not exactly 6 because to be exactly correct $\\\\epsilon$ would need to be [infinitesimally small](https://www.dictionary.com/browse/infinitesimally) or really, really small. That is why we use the symbols $\\\\approx$ or ~= rather than =. Let's see what happens if we make $\\\\epsilon$ smaller.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"1f2c157c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 9, J_epsilon = 9.000000006, dJ_dw ~= k = 6.000000496442226 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"J = (3)**2\\n\",\n    \"J_epsilon = (3 + 0.000000001)**2\\n\",\n    \"k = (J_epsilon - J)/0.000000001\\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_dw ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5348b172\",\n   \"metadata\": {},\n   \"source\": [\n    \"The value gets close to exactly 6 as we reduce the size of $\\\\epsilon$. Feel free to try reducing the value further.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"29ba8ff4\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Finding symbolic derivatives\\n\",\n    \"In backprop it is useful to know the derivative of simple functions at any input value. Put another way, we would like to know the 'symbolic' derivative rather than the 'arithmetic' derivative. An example of a symbolic derivative is,  $\\\\frac{\\\\partial J(w)}{\\\\partial w} = 2 w$, the derivative of $J(w) = w^2$ above.  With the symbolic derivative you can find the value of the derivative at any input value $w$.  \\n\",\n    \"\\n\",\n    \"If you have taken a calculus course, you are familiar with the many [differentiation rules](https://en.wikipedia.org/wiki/Differentiation_rules#Power_laws,_polynomials,_quotients,_and_reciprocals) that mathematicians have developed to solve for a derivative given an expression. Well, it turns out this process has been automated with symbolic differentiation programs. An example of this in python is the [SymPy](https://www.sympy.org/en/index.html) library. Let's take a look at how to use this.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fe31ab09\",\n   \"metadata\": {},\n   \"source\": [\n    \"### $J = w^2$\\n\",\n    \"Define the python variables and their symbolic names.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"59f44e5f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"J, w = symbols('J, w')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"31dfc16a\",\n   \"metadata\": {},\n   \"source\": [\n    \"Define and print the expression. Note SymPy produces a [latex](https://en.wikibooks.org/wiki/LaTeX/Mathematics) string which generates a nicely readable equation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"0cf67456\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle w^{2}$\"\n      ],\n      \"text/plain\": [\n       \"w**2\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"J=w**2\\n\",\n    \"J\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6793c270\",\n   \"metadata\": {},\n   \"source\": [\n    \"Use SymPy's `diff` to differentiate the expression for $J$ with respect to $w$. Note the result matches our earlier example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"64ae7fd1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 2 w$\"\n      ],\n      \"text/plain\": [\n       \"2*w\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw = diff(J,w)\\n\",\n    \"dJ_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"264eb419\",\n   \"metadata\": {},\n   \"source\": [\n    \"Evaluate the derivative at a few points by 'substituting' numeric values for the symbolic values. In the first example, $w$ is replaced by $2$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"8d03247c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 4$\"\n      ],\n      \"text/plain\": [\n       \"4\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw.subs([(w,2)])    # derivative at the point w = 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"ff4238e7\",\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 6$\"\n      ],\n      \"text/plain\": [\n       \"6\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw.subs([(w,3)])    # derivative at the point w = 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"8ae2e2df\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle -6$\"\n      ],\n      \"text/plain\": [\n       \"-6\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw.subs([(w,-3)])    # derivative at the point w = -3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"737ddb5e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## $J = 2w$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"4906fb6d\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"w, J = symbols('w, J')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"4be899dd\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 2 w$\"\n      ],\n      \"text/plain\": [\n       \"2*w\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"J = 2 * w\\n\",\n    \"J\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"df81d60f\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 2$\"\n      ],\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw = diff(J,w)\\n\",\n    \"dJ_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"1c070c62\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 2$\"\n      ],\n      \"text/plain\": [\n       \"2\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw.subs([(w,-3)])    # derivative at the point w = -3\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a7893ca0\",\n   \"metadata\": {},\n   \"source\": [\n    \"Compare this with the arithmetic calculation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"920dcbff\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 6, J_epsilon = 6.002, dJ_dw ~= k = 1.9999999999997797 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"J = 2*3\\n\",\n    \"J_epsilon = 2*(3 + 0.001)\\n\",\n    \"k = (J_epsilon - J)/0.001\\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_dw ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"510cd1c0\",\n   \"metadata\": {},\n   \"source\": [\n    \"For the function $J=2w$, it is easy to see that any change in $w$ will result in 2 times that amount of change in the output $J$, regardless of the starting value of $w$. Our NumPy and arithmetic results confirm this. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e2013c16\",\n   \"metadata\": {},\n   \"source\": [\n    \"## $J = w^3$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"775604c1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"J, w = symbols('J, w')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"08e5961f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle w^{3}$\"\n      ],\n      \"text/plain\": [\n       \"w**3\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"J=w**3\\n\",\n    \"J\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"304c645d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 3 w^{2}$\"\n      ],\n      \"text/plain\": [\n       \"3*w**2\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw = diff(J,w)\\n\",\n    \"dJ_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"9e59dbdd\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle 12$\"\n      ],\n      \"text/plain\": [\n       \"12\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw.subs([(w,2)])   # derivative at the point w=2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ecae9e21\",\n   \"metadata\": {},\n   \"source\": [\n    \"Compare this with the arithmetic calculation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"19b12f78\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 8, J_epsilon = 8.012006000999998, dJ_dw ~= k = 12.006000999997823 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"J = (2)**3\\n\",\n    \"J_epsilon = (2+0.001)**3\\n\",\n    \"k = (J_epsilon - J)/0.001\\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_dw ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"07c5d254\",\n   \"metadata\": {},\n   \"source\": [\n    \"## $J = \\\\frac{1}{w}$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"fb286c54\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"J, w = symbols('J, w')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"f153494c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle \\\\frac{1}{w}$\"\n      ],\n      \"text/plain\": [\n       \"1/w\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"J= 1/w\\n\",\n    \"J\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"e9029dd2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle - \\\\frac{1}{w^{2}}$\"\n      ],\n      \"text/plain\": [\n       \"-1/w**2\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw = diff(J,w)\\n\",\n    \"dJ_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"2fd3cfa4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle - \\\\frac{1}{4}$\"\n      ],\n      \"text/plain\": [\n       \"-1/4\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw.subs([(w,2)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"487e67c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"Compare this with the arithmetic calculation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"9f251b52\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 0.5, J_epsilon = 0.49975012493753124, dJ_dw ~= k = -0.2498750624687629 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"J = 1/2\\n\",\n    \"J_epsilon = 1/(2+0.001)\\n\",\n    \"k = (J_epsilon - J)/0.001\\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_dw ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4b06bbe8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## $J = \\\\frac{1}{w^2}$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"9b7e3772\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"J, w = symbols('J, w')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dc112bce\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you have time, try to repeat the above steps on the function  $J = \\\\frac{1}{w^2}$ and evaluate at w=4\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"71e89c66\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"J, w = symbols('J, w')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"18764f0f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle \\\\frac{1}{w^{2}}$\"\n      ],\n      \"text/plain\": [\n       \"w**(-2)\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"J= 1/(w**2)\\n\",\n    \"J\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"1dc0dedf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle - \\\\frac{2}{w^{3}}$\"\n      ],\n      \"text/plain\": [\n       \"-2/w**3\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw = diff(J,w)\\n\",\n    \"dJ_dw\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"578cbd71\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/latex\": [\n       \"$\\\\displaystyle - \\\\frac{1}{32}$\"\n      ],\n      \"text/plain\": [\n       \"-1/32\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"dJ_dw.subs([(w,4)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2cf1879d\",\n   \"metadata\": {},\n   \"source\": [\n    \"Compare this with the arithmetic calculation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"7b68f746\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"J = 0.0625, J_epsilon = 0.06246876171484496, dJ_dw ~= k = -0.031238285155041345 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"J = 1/4**2\\n\",\n    \"J_epsilon = 1/(4+0.001)**2\\n\",\n    \"k = (J_epsilon - J)/0.001\\n\",\n    \"print(f\\\"J = {J}, J_epsilon = {J_epsilon}, dJ_dw ~= k = {k} \\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ed0df8ec\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python \\n\",\n    \"J= 1/w**2\\n\",\n    \"dJ_dw = diff(J,w)\\n\",\n    \"dJ_dw.subs([(w,4)])\\n\",\n    \"```\\n\",\n    \"  \\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a7e9b47d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations!\\n\",\n    \"If you have run through the above examples, you understand a derivative describes the change in the output of a function that is a result of a small change in an input to that function. You also can use *SymPy* in python to find the symbolic derivative of functions.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.10\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/backprop/lab_utils_backprop.py",
    "content": "from sympy import *\r\nimport numpy as np\r\nimport re\r\n\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.widgets import TextBox\r\nfrom matplotlib.widgets import Button\r\nimport ipywidgets as widgets\r\n\r\ndef widgvis(fig):\r\n    fig.canvas.toolbar_visible = False\r\n    fig.canvas.header_visible = False\r\n    fig.canvas.footer_visible = False\r\n    \r\ndef between(a, b, x):\r\n    ''' determine if a point x is between a and b. a may be greater or less than b '''\r\n    if a > b:\r\n        return b <= x <= a\r\n    if b > a:\r\n        return a <= x <= b\r\n    \r\ndef near(pt, alist, dist=15):\r\n    for a in alist:\r\n        x, y = a.ao.get_position()  #(bot left, bot right) data coords, not relative\r\n        x = x - 5  \r\n        y = y + 2.5\r\n        if 0 < (pt[0] - x) < 25 and 0 < (y - pt[1]) < 25:\r\n            return(True, a)\r\n    return(False,None)\r\n\r\ndef inboxes(pt, boxlist):\r\n    ''' returns true if pt is within one of the boxes in boxlist '''\r\n    #with out:\r\n    #    print(f\" inboxes:{boxlist}, {pt}\")\r\n    for b in boxlist:\r\n        if b.inbox(pt):\r\n            return(True, b)\r\n    return(False, None)\r\n\r\n\r\nclass avalue():\r\n    ''' one of the values on the figure that can be filled in '''\r\n    def __init__(self, value, pt, cl):\r\n        self.value = value\r\n        self.cl = cl   # color\r\n        self.pt = pt   # point\r\n    \r\n    def add_anote(self, ax):\r\n        self.ax = ax\r\n        self.ao = self.ax.annotate(\"?\", self.pt, c=self.cl, fontsize='x-small')\r\n\r\nclass astring():\r\n    ''' a string that can be set visible or invisible '''\r\n    def __init__(self, ax, string, pt, cl):\r\n        self.string = string\r\n        self.cl = cl   # color\r\n        self.pt = pt   # point\r\n        self.ax = ax\r\n        self.ao = self.ax.annotate(self.string, self.pt, c=\"white\", fontsize='x-small')\r\n    \r\n    def astring_visible(self):\r\n        self.ao.set_color(self.cl)\r\n\r\n    def astring_invisible(self):\r\n        self.ao.set_color(\"white\")\r\n\r\n\r\nclass abox():\r\n    ''' one of the boxes in the graph that has a value '''\r\n    def __init__(self, ax, value, left, bottom, right, top, anpt, cl, adj_anote_obj):\r\n        self.ax = ax\r\n        self.value = value  # correct value for annotation\r\n        self.left = left\r\n        self.right = right \r\n        self.bottom = bottom\r\n        self.top = top\r\n        self.anpt= anpt # x,y where expression should be listed\r\n        self.cl = cl\r\n        self.ao = self.ax.annotate(\"?\", self.anpt, c=self.cl, fontsize='x-small')\r\n        self.astr = adj_anote_obj   # 2ndary text for marking edges or none\r\n            \r\n    def inbox(self, pt):\r\n        ''' true if point is within the box '''\r\n        #with out:   #debug\r\n        #    print(f\" b.inbox: {pt}\")\r\n        x, y = pt  \r\n        isbetween =  between(self.top, self.bottom, y) and between(self.left, self.right, x)\r\n        return isbetween\r\n    \r\n    def update_val(self, value, cl=None):\r\n        self.ao.set_text(value)\r\n        if cl:\r\n            self.ao.set_c(cl)\r\n        else:\r\n            self.ao.set_c(self.cl)\r\n            \r\n    def show_secondary(self):\r\n        if self.astr:  # if there is a 2ndary set of text\r\n            self.astr.ao.set_c(\"green\")\r\n\r\n    def clear_secondary(self):\r\n        if self.astr:  # if there is a 2ndary set of text\r\n            self.astr.ao.set_c(\"white\")\r\n\r\n            \r\n            \r\n## For debug, put this in the notebook being debugged and be sure to set the out=out parameter\r\n#out = widgets.Output(layout={'border': '1px solid black'})\r\n#out            \r\n\r\nclass plt_network():\r\n    \r\n    def __init__(self, fn, image, out=None):\r\n        self.out = out # debug\r\n        #with self.out:\r\n        #    print(\"hello world\")\r\n        img = plt.imread(image)\r\n        self.fig, self.ax = plt.subplots(figsize=self.sizefig(img))\r\n        boxes = fn(self.ax)\r\n        self.boxes = boxes\r\n        widgvis(self.fig)\r\n        self.ax.xaxis.set_visible(False)\r\n        self.ax.yaxis.set_visible(False)\r\n        self.ax.imshow(img)\r\n        self.fig.text(0.1,0.9, \"Click in boxes to fill in values.\")\r\n        self.glist = []  # place to stash global things \r\n        self.san = []    # selected annotation\r\n        \r\n        self.cid = self.fig.canvas.mpl_connect('button_press_event', self.onclick)\r\n        self.axreveal = plt.axes([0.55, 0.02, 0.15, 0.075]) #[left, bottom, width, height]\r\n        self.axhide   = plt.axes([0.76, 0.02, 0.15, 0.075])\r\n        self.breveal  = Button(self.axreveal, 'Reveal All')\r\n        self.breveal.on_clicked(self.reveal_values)\r\n        self.bhide    = Button(self.axhide, 'Hide All')\r\n        self.bhide.on_clicked(self.hide_values)\r\n        #plt.show()\r\n\r\n    def sizefig(self,img):\r\n        iy,ix,iz = np.shape(img)\r\n        if 10/5 < ix/iy:   # if x is the limiting size\r\n            figx = 10\r\n            figy = figx*iy/ix\r\n        else:\r\n            figy = 5\r\n            figx = figy*ix/iy\r\n        return(figx,figy)\r\n       \r\n    def updateval(self, event):\r\n        #with self.out:  #debug\r\n        #    print(event)\r\n        box = self.san[0]\r\n        num_format = re.compile(r\"[+-]?\\d+(?:\\.\\d+)?\")\r\n        isnumber = re.match(num_format,event)\r\n        if not isnumber:\r\n            box.update_val('?','red')\r\n        else:\r\n            #with self.out:\r\n            #    print(event)\r\n            newval = int(float(event)) if int(float(event)) == float(event) else float(event)\r\n            newval = round(newval,2)\r\n            #with self.out:\r\n            #    print(newval, box.value, type(newval), type(box.value))\r\n            if newval == box.value:\r\n                box.show_secondary()\r\n                box.update_val(round(newval,2))\r\n            else:\r\n                box.update_val(round(newval,2), 'red')\r\n                box.clear_secondary()\r\n        self.glist[0].remove()\r\n        self.glist.clear()\r\n        self.san.clear()\r\n\r\n    # collects all clicks within diagram and dispatches\r\n    def onclick(self, event):\r\n        #with self.out:\r\n        #    print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %\r\n        #          ('double' if event.dblclick else 'single', event.button,\r\n        #           event.x, event.y, event.xdata, event.ydata))\r\n        if len(self.san) != 0: # already waiting for new value\r\n            return\r\n        inbox, box = inboxes((event.xdata, event.ydata), self.boxes)\r\n        #with self.out:\r\n        #    print(f\" in box: {inbox, box}\")\r\n        if inbox:\r\n            self.san.append(box) \r\n            #an.set_text(an.get_text() + \"1\") # debug\r\n            graphBox = self.fig.add_axes([0.225, 0.02, 0.2, 0.075])  # [left, bottom, width, height]\r\n            txtBox = TextBox(graphBox, \"newvalue: \")\r\n            txtBox.on_submit(self.updateval)\r\n            self.glist.append(graphBox)\r\n            self.glist.append(txtBox)\r\n        return\r\n\r\n    def reveal_values(self, event):\r\n        for b in self.boxes:\r\n            b.update_val(b.value)\r\n            b.show_secondary()\r\n        plt.draw()\r\n\r\n    def hide_values(self, event):\r\n        for b in self.boxes:\r\n            b.update_val(\"?\")\r\n            b.clear_secondary()\r\n        plt.draw()\r\n        \r\n#--------------------------------------------------------------------------\r\n\r\n\r\ndef config_nw0(ax):\r\n    #\"./images/C2_W2_BP_network0.PNG\"\r\n\r\n    w = 3\r\n    a = 2+3*w\r\n    J = a**2\r\n    \r\n    pass             ; dJ_dJ  = 1\r\n    dJ_da = 2*a      ; dJ_da = dJ_dJ * dJ_da\r\n    da_dw = 3        ; dJ_dw = dJ_da * da_dw\r\n\r\n    box1 = abox(ax, round(a,2), 307,  140,  352, 100, (315, 128),'blue', None) # left, bottom, right, top, \r\n    box2 = abox(ax, round(J,2), 581,  138,  624, 100, (589, 128),'blue', None) \r\n\r\n    dJ_da_a = astring(ax, r\"$\\frac{\\partial J}{\\partial a}=$\"+f\"{dJ_da}\", (291,186), \"green\")\r\n    box3 = abox(ax, round(dJ_da,2), 545, 417, 588, 380, (553,407), 'green', dJ_da_a) \r\n\r\n    dJ_dw_a = astring(ax, r\"$\\frac{\\partial J}{\\partial w}=$\"+f\"{dJ_dw}\", (60,186), \"green\")\r\n    box4 = abox(ax, round(da_dw,2), 195, 421, 237, 380, (203,411), 'green', None)   \r\n    box5 = abox(ax, round(dJ_dw,2), 265, 515, 310, 475, (273,505), 'green', dJ_dw_a)   \r\n\r\n    boxes = [box1, box2, box3, box4, box5]\r\n    \r\n    return boxes   \r\n\r\ndef config_nw1(ax):\r\n    # \"./images/C2_W2_BP_Network1.PNG\"\r\n\r\n    x = 2\r\n    w = -2\r\n    b = 8\r\n    y = 1\r\n    \r\n    c = w * x\r\n    a = c + b\r\n    d = a - y\r\n    J = d**2/2\r\n    \r\n    pass             ; dJ_dJ = 1\r\n    dJ_dd = 2*d/2    ; dJ_dd = dJ_dJ * dJ_dd\r\n    dd_da = 1        ; dJ_da = dJ_dd * dd_da\r\n    da_db = 1        ; dJ_db = dJ_da * da_db\r\n    da_dc = 1        ; dJ_dc = dJ_da * da_dc\r\n    dc_dw = x        ; dJ_dw = dJ_dc * dc_dw\r\n    \r\n    box1 = abox(ax, round(c,2), 330,  162,  382, 114, (338, 150),'blue', None) # left, bottom, right, top, \r\n    box2 = abox(ax, round(a,2), 636,  162,  688, 114, (644, 150),'blue', None) \r\n    box3 = abox(ax, round(d,2), 964,  162, 1015, 114, (972, 150),'blue', None) \r\n    box4 = abox(ax, round(J,2), 1266, 162, 1315, 114, (1274,150),'blue', None) \r\n\r\n    dJ_dd_a = astring(ax, r\"$\\frac{\\partial J}{\\partial d}=$\"+f\"{dJ_dd}\", (967,208), \"green\")\r\n    box5 = abox(ax, round(dJ_dd,2), 1222, 488, 1275, 441, (1230,478), 'green', dJ_dd_a) \r\n\r\n    dJ_da_a = astring(ax, r\"$\\frac{\\partial J}{\\partial a}=$\"+f\"{dJ_da}\", (615,208), \"green\")\r\n    box6 = abox(ax, round(dd_da,2), 900, 383, 951,  333, (908,373), 'green', None)   \r\n    box7 = abox(ax, round(dJ_da,2), 988, 483, 1037, 441, (996,473), 'green', dJ_da_a)   \r\n\r\n    dJ_dc_a = astring(ax, r\"$\\frac{\\partial J}{\\partial c}=$\"+f\"{dJ_dc}\", (337,208), \"green\")\r\n    box8 = abox(ax, round(da_dc,2),  570, 380, 620, 333, (578,370), 'green', None)   \r\n    box9 = abox(ax, round(dJ_dc,2),  638, 467, 688, 419, (646,457), 'green', dJ_dc_a)   \r\n\r\n    dJ_db_a = astring(ax, r\"$\\frac{\\partial J}{\\partial b}=$\"+f\"{dJ_dc}\", (474,252), \"green\")\r\n    box10 = abox(ax, round(da_db,2), 563, 582, 615, 533, (571,572), 'green', None)   \r\n    box11 = abox(ax, round(dJ_db,2), 630, 677, 684, 630, (638,667), 'green', dJ_db_a)   \r\n\r\n    dJ_dw_a = astring(ax, r\"$\\frac{\\partial J}{\\partial w}=$\"+f\"{dJ_dw}\", (60,208), \"green\")\r\n    box12 = abox(ax, round(dc_dw,2), 191, 379, 341, 332, (199,369), 'green', None)   \r\n    box13 = abox(ax, round(dJ_dw,2), 266, 495, 319, 448, (274,485), 'green', dJ_dw_a)   \r\n\r\n    boxes = [box1, box2, box3, box4, box5, box6, box7, box8, box9, box10, box11, box12, box13]\r\n\r\n    return boxes   \r\n            \r\n#not used\r\ndef config_nw2():\r\n    x0 = 1\r\n    x1 = 2\r\n    w0 = -2\r\n    w1 = 3\r\n    b  = -4\r\n    y  = 1\r\n    d  = x0 * w0\r\n    e  = x1 * w1\r\n    f  = d+e+b\r\n    g  = -f\r\n    h  = np.exp(g)\r\n    i  = h+1\r\n    a  = 1/i\r\n    k  = y-a\r\n    L  = k**2\r\n\r\n    pass             ; dL_dL  = 1\r\n    dL_dk = 2*k      ; dL_dk  = dL_dL * dL_dk\r\n    dk_da = -1       ; dL_da  = dL_dk * dk_da\r\n    da_di = -1/i**2  ; dL_di  = dL_da * da_di\r\n    di_dh = 1        ; dL_dh  = dL_di * di_dh\r\n    dh_dg = exp(g)   ; dL_dg  = dL_dh * dh_dg\r\n    dg_df = -1       ; dL_df  = dL_dg * dg_df\r\n    df_dd = 1        ; dL_dd  = dL_df * df_dd\r\n    df_de = 2        ; dL_de  = dL_df * df_de\r\n    df_db = 1        ; dL_db  = dL_df * df_db\r\n    dd_dw0 = 1       ; dL_dw0 = dL_dd * dd_dw0\r\n    de_dw1 = 2       ; dL_dw1 = dL_de * de_dw1\r\n\r\n    an1 = avalue(round(d,2), (270,265), 'blue')\r\n    an2 = avalue(round(e,2), (270,350), 'blue')\r\n    an3 = avalue(round(f,2), (400,315), 'blue')\r\n    an4 = avalue(round(g,2), (540,315), 'blue')\r\n    an5 = avalue(round(h,2), (650,315), 'blue')\r\n    an6 = avalue(round(i,2), (760,315), 'blue')\r\n    an7 = avalue(round(a,2), (890,315), 'blue')\r\n    an8 = avalue(round(k,2), (1015,315), 'blue')\r\n    an9 = avalue(round(L,2), (1120,315), 'blue')\r\n    bn1 = avalue(round(dL_dd,2), (260,300),  'green')   #d\r\n    bn2 = avalue(round(dL_de,2), (270,385),  'green')   #e\r\n    bn3 = avalue(round(dL_df,2), (408,350),  'green')   #f\r\n    bn4 = avalue(round(dL_dg,2), (540,350),  'green')   #g\r\n    bn5 = avalue(round(dL_dh,2), (650,350),  'green')   #h\r\n    bn6 = avalue(round(dL_di,2), (760,350),  'green')   #i\r\n    bn7 = avalue(round(dL_da,2), (890,350),  'green')   #a\r\n    bn8 = avalue(round(dL_dk,2), (1015,350), 'green')   #k\r\n    bn9 = avalue(round(dL_dw0,2), (210,300), 'green')   #w0\r\n    bn10 = avalue(round(dL_dw1,2), (205,440),'green')   #w1\r\n    bn11 = avalue(round(dL_db,2), (345,385), 'green')   #b\r\n\r\n    anotes = [an1, an2, an3, an4, an5, an6, an7, an8, an9,\r\n              bn1, bn2, bn3, bn4, bn5, bn6, bn7, bn8, bn9, bn10, bn11]\r\n\r\n    box1 = abox(r\"$\\frac{\\partial v}{\\partial t}$\", 943, 347, 980, 310, (980,300))\r\n    boxes = [box1]\r\n\r\n    fn = \"./images/C2_W2_BP_bkground.PNG\"\r\n    return fn, anotes, boxes"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/deeplearning.mplstyle",
    "content": "# see https://matplotlib.org/stable/tutorials/introductory/customizing.html\nlines.linewidth: 4\nlines.solid_capstyle: butt\n\nlegend.fancybox: true\n\n# Verdana\" for non-math text,\n# Cambria Math\n\n#Blue (Crayon-Aqua) 0096FF\n#Dark Red C00000\n#Orange (Apple Orange) FF9300\n#Black 000000\n#Magenta FF40FF\n#Purple 7030A0\n\naxes.prop_cycle: cycler('color', ['0096FF', 'FF9300', 'FF40FF', '7030A0', 'C00000'])\n#axes.facecolor: f0f0f0 # grey\naxes.facecolor: ffffff  # white\naxes.labelsize: large\naxes.axisbelow: true\naxes.grid: False\naxes.edgecolor: f0f0f0\naxes.linewidth: 3.0\naxes.titlesize: x-large\n\npatch.edgecolor: f0f0f0\npatch.linewidth: 0.5\n\nsvg.fonttype: path\n\ngrid.linestyle: -\ngrid.linewidth: 1.0\ngrid.color: cbcbcb\n\nxtick.major.size: 0\nxtick.minor.size: 0\nytick.major.size: 0\nytick.minor.size: 0\n\nsavefig.edgecolor: f0f0f0\nsavefig.facecolor: f0f0f0\n\n#figure.subplot.left: 0.08\n#figure.subplot.right: 0.95\n#figure.subplot.bottom: 0.07\n\n#figure.facecolor: f0f0f0  # grey\nfigure.facecolor: ffffff  # white\n\n## ***************************************************************************\n## * FONT                                                                    *\n## ***************************************************************************\n## The font properties used by `text.Text`.\n## See https://matplotlib.org/api/font_manager_api.html for more information\n## on font properties.  The 6 font properties used for font matching are\n## given below with their default values.\n##\n## The font.family property can take either a concrete font name (not supported\n## when rendering text with usetex), or one of the following five generic\n## values:\n##     - 'serif' (e.g., Times),\n##     - 'sans-serif' (e.g., Helvetica),\n##     - 'cursive' (e.g., Zapf-Chancery),\n##     - 'fantasy' (e.g., Western), and\n##     - 'monospace' (e.g., Courier).\n## Each of these values has a corresponding default list of font names\n## (font.serif, etc.); the first available font in the list is used.  Note that\n## for font.serif, font.sans-serif, and font.monospace, the first element of\n## the list (a DejaVu font) will always be used because DejaVu is shipped with\n## Matplotlib and is thus guaranteed to be available; the other entries are\n## left as examples of other possible values.\n##\n## The font.style property has three values: normal (or roman), italic\n## or oblique.  The oblique style will be used for italic, if it is not\n## present.\n##\n## The font.variant property has two values: normal or small-caps.  For\n## TrueType fonts, which are scalable fonts, small-caps is equivalent\n## to using a font size of 'smaller', or about 83%% of the current font\n## size.\n##\n## The font.weight property has effectively 13 values: normal, bold,\n## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as\n## 400, and bold is 700.  bolder and lighter are relative values with\n## respect to the current weight.\n##\n## The font.stretch property has 11 values: ultra-condensed,\n## extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This\n## property is not currently implemented.\n##\n## The font.size property is the default font size for text, given in points.\n## 10 pt is the standard value.\n##\n## Note that font.size controls default text sizes.  To configure\n## special text sizes tick labels, axes, labels, title, etc., see the rc\n## settings for axes and ticks.  Special text sizes can be defined\n## relative to font.size, using the following values: xx-small, x-small,\n## small, medium, large, x-large, xx-large, larger, or smaller\n\n\nfont.family:  sans-serif\nfont.style:   normal\nfont.variant: normal\nfont.weight:  normal\nfont.stretch: normal\nfont.size:    8.0\n\nfont.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\nfont.sans-serif: Verdana, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\nfont.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive\nfont.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy\nfont.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n\n\n## ***************************************************************************\n## * TEXT                                                                    *\n## ***************************************************************************\n## The text properties used by `text.Text`.\n## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\n## for more information on text properties\n#text.color: black\n\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/lab_utils_common.py",
    "content": "\"\"\"\nlab_utils_common\n   contains common routines and variable definitions\n   used by all the labs in this week.\n   by contrast, specific, large plotting routines will be in separate files\n   and are generally imported into the week where they are used.\n   those files will import this file\n\"\"\"\nimport copy\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import FancyArrowPatch\nfrom ipywidgets import Output\nfrom matplotlib.widgets import Button, CheckButtons\n\nnp.set_printoptions(precision=2)\n\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0', dldarkblue =  '#0D5BDC', dlmedblue='#4285F4')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0'; dldarkblue =  '#0D5BDC'; dlmedblue='#4285F4'\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\nplt.style.use('./deeplearning.mplstyle')\n\ndef sigmoid(z):\n    \"\"\"\n    Compute the sigmoid of z\n\n    Parameters\n    ----------\n    z : array_like\n        A scalar or numpy array of any size.\n\n    Returns\n    -------\n     g : array_like\n         sigmoid(z)\n    \"\"\"\n    z = np.clip( z, -500, 500 )           # protect against overflow\n    g = 1.0/(1.0+np.exp(-z))\n\n    return g\n\n##########################################################\n# Regression Routines\n##########################################################\n\ndef predict_logistic(X, w, b):\n    \"\"\" performs prediction \"\"\"\n    return sigmoid(X @ w + b)\n\ndef predict_linear(X, w, b):\n    \"\"\" performs prediction \"\"\"\n    return X @ w + b\n\ndef compute_cost_logistic(X, y, w, b, lambda_=0, safe=False):\n    \"\"\"\n    Computes cost using logistic loss, non-matrix version\n\n    Args:\n      X (ndarray): Shape (m,n)  matrix of examples with n features\n      y (ndarray): Shape (m,)   target values\n      w (ndarray): Shape (n,)   parameters for prediction\n      b (scalar):               parameter  for prediction\n      lambda_ : (scalar, float) Controls amount of regularization, 0 = no regularization\n      safe : (boolean)          True-selects under/overflow safe algorithm\n    Returns:\n      cost (scalar): cost\n    \"\"\"\n\n    m,n = X.shape\n    cost = 0.0\n    for i in range(m):\n        z_i    = np.dot(X[i],w) + b                                             #(n,)(n,) or (n,) ()\n        if safe:  #avoids overflows\n            cost += -(y[i] * z_i ) + log_1pexp(z_i)\n        else:\n            f_wb_i = sigmoid(z_i)                                                   #(n,)\n            cost  += -y[i] * np.log(f_wb_i) - (1 - y[i]) * np.log(1 - f_wb_i)       # scalar\n    cost = cost/m\n\n    reg_cost = 0\n    if lambda_ != 0:\n        for j in range(n):\n            reg_cost += (w[j]**2)                                               # scalar\n        reg_cost = (lambda_/(2*m))*reg_cost\n\n    return cost + reg_cost\n\n\ndef log_1pexp(x, maximum=20):\n    ''' approximate log(1+exp^x)\n        https://stats.stackexchange.com/questions/475589/numerical-computation-of-cross-entropy-in-practice\n    Args:\n    x   : (ndarray Shape (n,1) or (n,)  input\n    out : (ndarray Shape matches x      output ~= np.log(1+exp(x))\n    '''\n\n    out  = np.zeros_like(x,dtype=float)\n    i    = x <= maximum\n    ni   = np.logical_not(i)\n\n    out[i]  = np.log(1 + np.exp(x[i]))\n    out[ni] = x[ni]\n    return out\n\n\ndef compute_cost_matrix(X, y, w, b, logistic=False, lambda_=0, safe=True):\n    \"\"\"\n    Computes the cost using  using matrices\n    Args:\n      X : (ndarray, Shape (m,n))          matrix of examples\n      y : (ndarray  Shape (m,) or (m,1))  target value of each example\n      w : (ndarray  Shape (n,) or (n,1))  Values of parameter(s) of the model\n      b : (scalar )                       Values of parameter of the model\n      verbose : (Boolean) If true, print out intermediate value f_wb\n    Returns:\n      total_cost: (scalar)                cost\n    \"\"\"\n    m = X.shape[0]\n    y = y.reshape(-1,1)             # ensure 2D\n    w = w.reshape(-1,1)             # ensure 2D\n    if logistic:\n        if safe:  #safe from overflow\n            z = X @ w + b                                                           #(m,n)(n,1)=(m,1)\n            cost = -(y * z) + log_1pexp(z)\n            cost = np.sum(cost)/m                                                   # (scalar)\n        else:\n            f    = sigmoid(X @ w + b)                                               # (m,n)(n,1) = (m,1)\n            cost = (1/m)*(np.dot(-y.T, np.log(f)) - np.dot((1-y).T, np.log(1-f)))   # (1,m)(m,1) = (1,1)\n            cost = cost[0,0]                                                        # scalar\n    else:\n        f    = X @ w + b                                                        # (m,n)(n,1) = (m,1)\n        cost = (1/(2*m)) * np.sum((f - y)**2)                                   # scalar\n\n    reg_cost = (lambda_/(2*m)) * np.sum(w**2)                                   # scalar\n\n    total_cost = cost + reg_cost                                                # scalar\n\n    return total_cost                                                           # scalar\n\ndef compute_gradient_matrix(X, y, w, b, logistic=False, lambda_=0):\n    \"\"\"\n    Computes the gradient using matrices\n\n    Args:\n      X : (ndarray, Shape (m,n))          matrix of examples\n      y : (ndarray  Shape (m,) or (m,1))  target value of each example\n      w : (ndarray  Shape (n,) or (n,1))  Values of parameters of the model\n      b : (scalar )                       Values of parameter of the model\n      logistic: (boolean)                 linear if false, logistic if true\n      lambda_:  (float)                   applies regularization if non-zero\n    Returns\n      dj_dw: (array_like Shape (n,1))     The gradient of the cost w.r.t. the parameters w\n      dj_db: (scalar)                     The gradient of the cost w.r.t. the parameter b\n    \"\"\"\n    m = X.shape[0]\n    y = y.reshape(-1,1)             # ensure 2D\n    w = w.reshape(-1,1)             # ensure 2D\n\n    f_wb  = sigmoid( X @ w + b ) if logistic else  X @ w + b      # (m,n)(n,1) = (m,1)\n    err   = f_wb - y                                              # (m,1)\n    dj_dw = (1/m) * (X.T @ err)                                   # (n,m)(m,1) = (n,1)\n    dj_db = (1/m) * np.sum(err)                                   # scalar\n\n    dj_dw += (lambda_/m) * w        # regularize                  # (n,1)\n\n    return dj_db, dj_dw                                           # scalar, (n,1)\n\ndef gradient_descent(X, y, w_in, b_in, alpha, num_iters, logistic=False, lambda_=0, verbose=True, Trace=True):\n    \"\"\"\n    Performs batch gradient descent to learn theta. Updates theta by taking\n    num_iters gradient steps with learning rate alpha\n\n    Args:\n      X (ndarray):    Shape (m,n)         matrix of examples\n      y (ndarray):    Shape (m,) or (m,1) target value of each example\n      w_in (ndarray): Shape (n,) or (n,1) Initial values of parameters of the model\n      b_in (scalar):                      Initial value of parameter of the model\n      logistic: (boolean)                 linear if false, logistic if true\n      lambda_:  (float)                   applies regularization if non-zero\n      alpha (float):                      Learning rate\n      num_iters (int):                    number of iterations to run gradient descent\n\n    Returns:\n      w (ndarray): Shape (n,) or (n,1)    Updated values of parameters; matches incoming shape\n      b (scalar):                         Updated value of parameter\n    \"\"\"\n    # An array to store cost J and w's at each iteration primarily for graphing later\n    J_history = []\n    w = copy.deepcopy(w_in)  #avoid modifying global w within function\n    b = b_in\n    w = w.reshape(-1,1)      #prep for matrix operations\n    y = y.reshape(-1,1)\n    last_cost = np.Inf\n\n    for i in range(num_iters):\n\n        # Calculate the gradient and update the parameters\n        dj_db,dj_dw = compute_gradient_matrix(X, y, w, b, logistic, lambda_)\n\n        # Update Parameters using w, b, alpha and gradient\n        w = w - alpha * dj_dw\n        b = b - alpha * dj_db\n\n        # Save cost J at each iteration\n        ccost = compute_cost_matrix(X, y, w, b, logistic, lambda_)\n        if Trace and i<100000:      # prevent resource exhaustion\n            J_history.append( ccost )\n\n        # Print cost every at intervals 10 times or as many iterations if < 10\n        if i% math.ceil(num_iters / 10) == 0:\n            if verbose: print(f\"Iteration {i:4d}: Cost {ccost}   \")\n            if verbose ==2: print(f\"dj_db, dj_dw = {dj_db: 0.3f}, {dj_dw.reshape(-1)}\")\n\n            if ccost == last_cost:\n                alpha = alpha/10\n                print(f\" alpha now {alpha}\")\n            last_cost = ccost\n\n    return w.reshape(w_in.shape), b, J_history  #return final w,b and J history for graphing\n\ndef zscore_normalize_features(X):\n    \"\"\"\n    computes  X, zcore normalized by column\n\n    Args:\n      X (ndarray): Shape (m,n) input data, m examples, n features\n\n    Returns:\n      X_norm (ndarray): Shape (m,n)  input normalized by column\n      mu (ndarray):     Shape (n,)   mean of each feature\n      sigma (ndarray):  Shape (n,)   standard deviation of each feature\n    \"\"\"\n    # find the mean of each column/feature\n    mu     = np.mean(X, axis=0)                 # mu will have shape (n,)\n    # find the standard deviation of each column/feature\n    sigma  = np.std(X, axis=0)                  # sigma will have shape (n,)\n    # element-wise, subtract mu for that column from each example, divide by std for that column\n    X_norm = (X - mu) / sigma\n\n    return X_norm, mu, sigma\n\n#check our work\n#from sklearn.preprocessing import scale\n#scale(X_orig, axis=0, with_mean=True, with_std=True, copy=True)\n\n######################################################\n# Common Plotting Routines\n######################################################\n\n\ndef plot_data(X, y, ax, pos_label=\"y=1\", neg_label=\"y=0\", s=80, loc='best' ):\n    \"\"\" plots logistic data with two axis \"\"\"\n    # Find Indices of Positive and Negative Examples\n    pos = y == 1\n    neg = y == 0\n    pos = pos.reshape(-1,)  #work with 1D or 1D y vectors\n    neg = neg.reshape(-1,)\n\n    # Plot examples\n    ax.scatter(X[pos, 0], X[pos, 1], marker='x', s=s, c = 'red', label=pos_label)\n    ax.scatter(X[neg, 0], X[neg, 1], marker='o', s=s, label=neg_label, facecolors='none', edgecolors=dlblue, lw=3)\n    ax.legend(loc=loc)\n\n    ax.figure.canvas.toolbar_visible = False\n    ax.figure.canvas.header_visible = False\n    ax.figure.canvas.footer_visible = False\n\ndef plt_tumor_data(x, y, ax):\n    \"\"\" plots tumor data on one axis \"\"\"\n    pos = y == 1\n    neg = y == 0\n\n    ax.scatter(x[pos], y[pos], marker='x', s=80, c = 'red', label=\"malignant\")\n    ax.scatter(x[neg], y[neg], marker='o', s=100, label=\"benign\", facecolors='none', edgecolors=dlblue,lw=3)\n    ax.set_ylim(-0.175,1.1)\n    ax.set_ylabel('y')\n    ax.set_xlabel('Tumor Size')\n    ax.set_title(\"Logistic Regression on Categorical Data\")\n\n    ax.figure.canvas.toolbar_visible = False\n    ax.figure.canvas.header_visible = False\n    ax.figure.canvas.footer_visible = False\n\n# Draws a threshold at 0.5\ndef draw_vthresh(ax,x):\n    \"\"\" draws a threshold \"\"\"\n    ylim = ax.get_ylim()\n    xlim = ax.get_xlim()\n    ax.fill_between([xlim[0], x], [ylim[1], ylim[1]], alpha=0.2, color=dlblue)\n    ax.fill_between([x, xlim[1]], [ylim[1], ylim[1]], alpha=0.2, color=dldarkred)\n    ax.annotate(\"z >= 0\", xy= [x,0.5], xycoords='data',\n                xytext=[30,5],textcoords='offset points')\n    d = FancyArrowPatch(\n        posA=(x, 0.5), posB=(x+3, 0.5), color=dldarkred,\n        arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n    )\n    ax.add_artist(d)\n    ax.annotate(\"z < 0\", xy= [x,0.5], xycoords='data',\n                 xytext=[-50,5],textcoords='offset points', ha='left')\n    f = FancyArrowPatch(\n        posA=(x, 0.5), posB=(x-3, 0.5), color=dlblue,\n        arrowstyle='simple, head_width=5, head_length=10, tail_width=0.0',\n    )\n    ax.add_artist(f)\n\n\n#-----------------------------------------------------\n# common interactive plotting routines\n#-----------------------------------------------------\n\nclass button_manager:\n    ''' Handles some missing features of matplotlib check buttons\n    on init:\n        creates button, links to button_click routine,\n        calls call_on_click with active index and firsttime=True\n    on click:\n        maintains single button on state, calls call_on_click\n    '''\n\n    #@output.capture()  # debug\n    def __init__(self,fig, dim, labels, init, call_on_click):\n        '''\n        dim: (list)     [leftbottom_x,bottom_y,width,height]\n        labels: (list)  for example ['1','2','3','4','5','6']\n        init: (list)    for example [True, False, False, False, False, False]\n        '''\n        self.fig = fig\n        self.ax = plt.axes(dim)  #lx,by,w,h\n        self.init_state = init\n        self.call_on_click = call_on_click\n        self.button  = CheckButtons(self.ax,labels,init)\n        self.button.on_clicked(self.button_click)\n        self.status = self.button.get_status()\n        self.call_on_click(self.status.index(True),firsttime=True)\n\n    #@output.capture()  # debug\n    def reinit(self):\n        self.status = self.init_state\n        self.button.set_active(self.status.index(True))      #turn off old, will trigger update and set to status\n\n    #@output.capture()  # debug\n    def button_click(self, event):\n        ''' maintains one-on state. If on-button is clicked, will process correctly '''\n        #new_status = self.button.get_status()\n        #new = [self.status[i] ^ new_status[i] for i in range(len(self.status))]\n        #newidx = new.index(True)\n        self.button.eventson = False\n        self.button.set_active(self.status.index(True))  #turn off old or reenable if same\n        self.button.eventson = True\n        self.status = self.button.get_status()\n        self.call_on_click(self.status.index(True))\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/lab_utils_multiclass.py",
    "content": "# C2_W1 Utilities\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_blobs\n\ndef sigmoid(x):\n    return 1 / (1 + np.exp(-x))\n\n# Plot  multi-class training points\ndef plot_mc_data(X, y, class_labels=None, legend=False,size=40):\n    classes = np.unique(y)\n    for i in classes:\n        label = class_labels[i] if class_labels else \"class {}\".format(i)\n        idx = np.where(y == i)\n        plt.scatter(X[idx, 0], X[idx, 1],  cmap=plt.cm.Paired,\n                    edgecolor='black', s=size, label=label)\n    if legend: plt.legend()\n        \n\n#Plot a multi-class categorical decision boundary\n# This version handles a non-vector prediction (adds a for-loop over points)\ndef plot_cat_decision_boundary(X,predict , class_labels=None, legend=False, vector=True):\n\n    # create a mesh to points to plot\n    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    h = max(x_max-x_min, y_max-y_min)/200\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                         np.arange(y_min, y_max, h))\n    points = np.c_[xx.ravel(), yy.ravel()]\n    print(\"points\", points.shape)\n    print(\"xx.shape\", xx.shape)\n\n    #make predictions for each point in mesh\n    if vector:\n        Z = predict(points)\n    else:\n        Z = np.zeros((len(points),))\n        for i in range(len(points)):\n            Z[i] = predict(points[i].reshape(1,2))\n    Z = Z.reshape(xx.shape)\n\n    #contour plot highlights boundaries between values - classes in this case\n    plt.figure()\n    plt.contour(xx, yy, Z, colors='g') \n    plt.axis('tight')"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/lab_utils_multiclass_TF.py",
    "content": "import matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib as mpl\nimport warnings\nfrom matplotlib import cm\nfrom matplotlib.patches import FancyArrowPatch\nfrom matplotlib.colors import ListedColormap, LinearSegmentedColormap\nimport matplotlib.colors as colors\nfrom lab_utils_common import dlc\nfrom matplotlib import cm\n\n\n\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0', dldarkblue =  '#0D5BDC')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0'; dldarkblue =  '#0D5BDC'\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\nplt.style.use('./deeplearning.mplstyle')\n\ndkcolors = plt.cm.Paired((1,3,7,9,5,11))\nltcolors = plt.cm.Paired((0,2,6,8,4,10))\ndkcolors_map = mpl.colors.ListedColormap(dkcolors)\nltcolors_map = mpl.colors.ListedColormap(ltcolors)\n\n#Plot a multi-class categorical decision boundary\n# This version handles a non-vector prediction (adds a for-loop over points)\ndef plot_cat_decision_boundary_mc(ax, X, predict , class_labels=None, legend=False, vector=True):\n\n    # create a mesh to points to plot\n    x_min, x_max = X[:, 0].min()- 0.5, X[:, 0].max()+0.5\n    y_min, y_max = X[:, 1].min()- 0.5, X[:, 1].max()+0.5\n    h = max(x_max-x_min, y_max-y_min)/100\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                         np.arange(y_min, y_max, h))\n    points = np.c_[xx.ravel(), yy.ravel()]\n    #print(\"points\", points.shape)\n    #print(\"xx.shape\", xx.shape)\n\n    #make predictions for each point in mesh\n    if vector:\n        Z = predict(points)\n    else:\n        Z = np.zeros((len(points),))\n        for i in range(len(points)):\n            Z[i] = predict(points[i].reshape(1,2))\n    Z = Z.reshape(xx.shape)\n\n    #contour plot highlights boundaries between values - classes in this case\n    ax.contour(xx, yy, Z, linewidths=1) \n    #ax.axis('tight')\n\n      \ndef plt_mc_data(ax, X, y, classes,  class_labels=None, map=plt.cm.Paired, \n                legend=False, size=50, m='o', equal_xy = False):\n    \"\"\" Plot multiclass data. Note, if equal_xy is True, setting ylim on the plot may not work \"\"\"\n    for i in range(classes):\n        idx = np.where(y == i)\n        col = len(idx[0])*[i]\n        label = class_labels[i] if class_labels else \"c{}\".format(i)\n        # this didn't work on coursera but did in local version\n        #ax.scatter(X[idx, 0], X[idx, 1],  marker=m,\n        #            c=col, vmin=0, vmax=map.N, cmap=map,\n        #            s=size, label=label)\n        ax.scatter(X[idx, 0], X[idx, 1],  marker=m,\n                    color=map(col), vmin=0, vmax=map.N, \n                    s=size, label=label)\n    if legend: ax.legend()\n    if equal_xy: ax.axis(\"equal\")\n\ndef plt_mc(X_train,y_train,classes, centers, std):\n    css = np.unique(y_train)\n    fig,ax = plt.subplots(1,1,figsize=(3,3))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n    plt_mc_data(ax, X_train,y_train,classes, map=dkcolors_map, legend=True, size=50, equal_xy = False)\n    ax.set_title(\"Multiclass Data\")\n    ax.set_xlabel(\"x0\")\n    ax.set_ylabel(\"x1\")\n    #for c in css:\n    #    circ = plt.Circle(centers[c], 2*std, color=dkcolors_map(c), clip_on=False, fill=False, lw=0.5)\n    #    ax.add_patch(circ)\n    plt.show()\n\ndef plt_cat_mc(X_train, y_train, model, classes):\n    #make a model for plotting routines to call\n    model_predict = lambda Xl: np.argmax(model.predict(Xl),axis=1)\n\n    fig,ax = plt.subplots(1,1, figsize=(3,3))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n \n    #add the original data to the decison boundary\n    plt_mc_data(ax, X_train,y_train, classes, map=dkcolors_map, legend=True)\n    #plot the decison boundary. \n    plot_cat_decision_boundary_mc(ax, X_train, model_predict, vector=True)\n    ax.set_title(\"model decision boundary\")\n\n    plt.xlabel(r'$x_0$');\n    plt.ylabel(r\"$x_1$\"); \n    plt.show()\n\n    \ndef plt_prob_z(ax,fwb, x0_rng=(-8,8), x1_rng=(-5,4)):\n    \"\"\" plots a decision boundary but include shading to indicate the probability\n        and adds a conouter to show where z=0\n    \"\"\"\n    #setup useful ranges and common linspaces\n    x0_space  = np.linspace(x0_rng[0], x0_rng[1], 40)\n    x1_space  = np.linspace(x1_rng[0], x1_rng[1], 40)\n\n    # get probability for x0,x1 ranges\n    tmp_x0,tmp_x1 = np.meshgrid(x0_space,x1_space)\n    z = np.zeros_like(tmp_x0)\n    c = np.zeros_like(tmp_x0)\n    for i in range(tmp_x0.shape[0]):\n        for j in range(tmp_x1.shape[1]):\n            x = np.array([[tmp_x0[i,j],tmp_x1[i,j]]])\n            z[i,j] = fwb(x)\n            c[i,j] = 0. if z[i,j] == 0 else 1.\n    with warnings.catch_warnings():  # suppress no contour warning\n        warnings.simplefilter(\"ignore\")\n        #ax.contour(tmp_x0, tmp_x1, c, colors='b', linewidths=1) \n        ax.contour(tmp_x0, tmp_x1, c, linewidths=1) \n\n    cmap = plt.get_cmap('Blues')\n    new_cmap = truncate_colormap(cmap, 0.0, 0.7)\n\n    pcm = ax.pcolormesh(tmp_x0, tmp_x1, z,\n                   norm=cm.colors.Normalize(vmin=np.amin(z), vmax=np.amax(z)),\n                   cmap=new_cmap, shading='nearest', alpha = 0.9)\n    ax.figure.colorbar(pcm, ax=ax)\n\ndef truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n    \"\"\" truncates color map \"\"\"\n    new_cmap = colors.LinearSegmentedColormap.from_list(\n        'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n        cmap(np.linspace(minval, maxval, n)))\n    return new_cmap\n\n\ndef plt_layer_relu(X, Y, W1, b1, classes):\n    nunits = (W1.shape[1])\n    Y = Y.reshape(-1,)\n    fig,ax = plt.subplots(1,W1.shape[1], figsize=(7,2.5))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\n    for i in range(nunits):\n        layerf= lambda x : np.maximum(0,(np.dot(x,W1[:,i]) + b1[i]))\n        plt_prob_z(ax[i], layerf)\n        plt_mc_data(ax[i], X, Y, classes, map=dkcolors_map,legend=True, size=50, m='o')\n        ax[i].set_title(f\"Layer 1 Unit {i}\")\n        ax[i].set_ylabel(r\"$x_1$\",size=10)\n        ax[i].set_xlabel(r\"$x_0$\",size=10)\n    fig.tight_layout()\n    plt.show()\n\n\ndef plt_output_layer_linear(X, Y, W, b, classes, x0_rng=None, x1_rng=None):\n    nunits = (W.shape[1])\n    Y = Y.reshape(-1,)\n    fig,ax = plt.subplots(2,int(nunits/2), figsize=(7,5))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n    for i,axi in enumerate(ax.flat):\n        layerf = lambda x : np.dot(x,W[:,i]) + b[i]\n        plt_prob_z(axi, layerf, x0_rng=x0_rng, x1_rng=x1_rng)\n        plt_mc_data(axi, X, Y, classes, map=dkcolors_map,legend=True, size=50, m='o')\n        axi.set_ylabel(r\"$a^{[1]}_1$\",size=9)\n        axi.set_xlabel(r\"$a^{[1]}_0$\",size=9)\n        axi.set_xlim(x0_rng)\n        axi.set_ylim(x1_rng)\n        axi.set_title(f\"Linear Output Unit {i}\")\n    fig.tight_layout()\n    plt.show()"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/lab_utils_relu.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.gridspec import GridSpec\nplt.style.use('./deeplearning.mplstyle')\nfrom matplotlib.widgets import Slider\nfrom lab_utils_common import dlc\n\ndef widgvis(fig):\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n    \n    \ndef plt_base(ax):\n    X = np.linspace(0, 3, 3*100)\n    y = np.r_[ -2*X[0:100]+2, 1*X[100:200]-3+2, 3*X[200:300]-7+2 ]\n    w00 = -2\n    b00 =  2\n    w01 =  0  #  1\n    b01 =  0  # -1\n    w02 =  0  #  2\n    b02 =  0  # -4\n    ax[0].plot(X, y, color = dlc[\"dlblue\"], label=\"target\")\n    arts = []\n    arts.extend( plt_yhat(ax[0], X, w00, b00, w01, b01, w02, b02) )\n    _ = plt_unit(ax[1], X, w00, b00)   #Fixed\n    arts.extend( plt_unit(ax[2], X, w01, b01) )\n    arts.extend( plt_unit(ax[3], X, w02, b02) )\n    return(X, arts)\n\ndef plt_yhat(ax, X, w00, b00, w01, b01, w02, b02):\n    yhat = np.maximum(0, np.dot(w00, X) + b00) + \\\n            np.maximum(0, np.dot(w01, X) + b01) + \\\n            np.maximum(0, np.dot(w02, X) + b02)\n    lp = ax.plot(X, yhat, lw=2, color = dlc[\"dlorange\"], label=\"a2\")\n    return(lp)\n\ndef plt_unit(ax, X, w, b):\n    z = np.dot(w,X) + b\n    yhat = np.maximum(0,z)\n    lpa = ax.plot(X, z,    dlc[\"dlblue\"], label=\"z\")\n    lpb = ax.plot(X, yhat, dlc[\"dlmagenta\"], lw=1, label=\"a\")\n    return([lpa[0], lpb[0]])\n\n# if output is need for debug, put this in a cell and call ahead of time. Output will be below that cell.\n#from ipywidgets import Output   #this line stays here\n#output = Output()               #this line stays here\n#display(output)                 #this line goes in notebook\n\ndef plt_relu_ex():\n    artists = []\n\n    fig = plt.figure()\n    fig.suptitle(\"Explore Non-Linear Activation\")\n\n    gs = GridSpec(3, 2, width_ratios=[2, 1], height_ratios=[1, 1, 1])\n    ax1 = fig.add_subplot(gs[0:2,0])\n    ax2 = fig.add_subplot(gs[0,1])\n    ax3 = fig.add_subplot(gs[1,1])\n    ax4 = fig.add_subplot(gs[2,1])\n    ax = [ax1,ax2,ax3,ax4]\n    \n    widgvis(fig)\n    #plt.subplots_adjust(bottom=0.35)\n\n    axb2 = fig.add_axes([0.15, 0.10, 0.30, 0.03]) # [left, bottom, width, height]\n    axw2 = fig.add_axes([0.15, 0.15, 0.30, 0.03])\n    axb1 = fig.add_axes([0.15, 0.20, 0.30, 0.03])\n    axw1 = fig.add_axes([0.15, 0.25, 0.30, 0.03])\n\n    sw1 = Slider(axw1, 'w1', -4.0, 4.0, valinit=0, valstep=0.1)\n    sb1 = Slider(axb1, 'b1', -4.0, 4.0, valinit=0, valstep=0.1)\n    sw2 = Slider(axw2, 'w2', -4.0, 4.0, valinit=0, valstep=0.1)\n    sb2 = Slider(axb2, 'b2', -4.0, 4.0, valinit=0, valstep=0.1)\n    \n    X,lp = plt_base(ax)\n    artists.extend( lp )\n    \n    #@output.capture()\n    def update(val):\n        #print(\"-----------\")\n        #print(f\"len artists {len(artists)}\", artists)\n        for i in range(len(artists)):\n            artist = artists[i]\n            #print(\"artist:\", artist)\n            artist.remove()\n        artists.clear()\n        #print(artists)\n        w00 = -2\n        b00 =  2\n        w01 =  sw1.val  #  1\n        b01 =  sb1.val  # -1\n        w02 =  sw2.val  #  2\n        b02 =  sb2.val  # -4\n        artists.extend(plt_yhat(ax[0], X, w00, b00, w01, b01, w02, b02))\n        artists.extend(plt_unit(ax[2], X, w01, b01) )\n        artists.extend(plt_unit(ax[3], X, w02, b02) )\n        #fig.canvas.draw_idle()\n        \n    sw1.on_changed(update)\n    sb1.on_changed(update)\n    sw2.on_changed(update)\n    sb2.on_changed(update)\n\n    ax[0].set_title(\" Match Target \")\n    ax[0].legend()\n    ax[0].set_xlabel(\"x\")\n    ax[1].set_title(\"Unit 0 (fixed) \")\n    ax[1].legend()\n    ax[2].set_title(\"Unit 1\")\n    ax[2].legend() \n    ax[3].set_title(\"Unit 2\")\n    ax[3].legend()\n    plt.tight_layout()\n\n    plt.show()\n    return([sw1,sw2,sb1,sb2,artists]) # returned to keep a live reference to sliders\n\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week2/optional-labs/lab_utils_softmax.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('./deeplearning.mplstyle')\nimport tensorflow as tf\nfrom IPython.display import display, Markdown, Latex\nfrom matplotlib.widgets import Slider\nfrom lab_utils_common import dlc\n\n\ndef plt_softmax(my_softmax):\n    fig, ax = plt.subplots(1,2,figsize=(8,4))\n    plt.subplots_adjust(bottom=0.35)\n\n    axz0 = fig.add_axes([0.15, 0.10, 0.30, 0.03]) # [left, bottom, width, height]\n    axz1 = fig.add_axes([0.15, 0.15, 0.30, 0.03])\n    axz2 = fig.add_axes([0.15, 0.20, 0.30, 0.03])\n    axz3 = fig.add_axes([0.15, 0.25, 0.30, 0.03])\n\n    z3 = Slider(axz3, 'z3', 0.1, 10.0, valinit=4, valstep=0.1)\n    z2 = Slider(axz2, 'z2', 0.1, 10.0, valinit=3, valstep=0.1)\n    z1 = Slider(axz1, 'z1', 0.1, 10.0, valinit=2, valstep=0.1)\n    z0 = Slider(axz0, 'z0', 0.1, 10.0, valinit=1, valstep=0.1)\n\n    z = np.array(['z0','z1','z2','z3'])\n    bar = ax[0].barh(z, height=0.6, width=[z0.val,z1.val,z2.val,z3.val], left=None, align='center')\n    bars = bar.get_children()\n    ax[0].set_xlim([0,10])\n    ax[0].set_title(\"z input to softmax\")\n\n    a = my_softmax(np.array([z0.val,z1.val,z2.val,z3.val]))\n    anames = np.array(['a0','a1','a2','a3'])\n    sbar = ax[1].barh(anames, height=0.6, width=a, left=None, align='center',color=dlc[\"dldarkred\"])\n    sbars = sbar.get_children()\n    ax[1].set_xlim([0,1])\n    ax[1].set_title(\"softmax(z)\")\n\n    def update(val):\n        bars[0].set_width(z0.val)\n        bars[1].set_width(z1.val)\n        bars[2].set_width(z2.val)\n        bars[3].set_width(z3.val)\n        a = my_softmax(np.array([z0.val,z1.val,z2.val,z3.val]))\n        sbars[0].set_width(a[0])\n        sbars[1].set_width(a[1])\n        sbars[2].set_width(a[2])\n        sbars[3].set_width(a[3])\n\n        fig.canvas.draw_idle()\n\n    z0.on_changed(update)\n    z1.on_changed(update)\n    z2.on_changed(update)\n    z3.on_changed(update)\n\n    plt.show()\n "
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/C2W3A1/.ipynb_checkpoints/C2_W3_Assignment-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Practice Lab: Advice for Applying Machine Learning\\n\",\n    \"In this lab, you will explore techniques to evaluate and improve your machine learning models.\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Evaluating a Learning Algorithm (Polynomial Regression)](#2)\\n\",\n    \"  - [ 2.1 Splitting your data set](#2.1)\\n\",\n    \"  - [ 2.2 Error calculation for model evaluation, linear regression](#2.2)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 2.3 Compare performance on training and test data](#2.3)\\n\",\n    \"- [ 3 - Bias and Variance<img align=\\\"Right\\\" src=\\\"./images/C2_W3_BiasVarianceDegree.png\\\"  style=\\\" width:500px; padding: 10px 20px ; \\\"> ](#3)\\n\",\n    \"  - [ 3.1 Plot Train, Cross-Validation, Test](#3.1)\\n\",\n    \"  - [ 3.2 Finding the optimal degree](#3.2)\\n\",\n    \"  - [ 3.3 Tuning Regularization.](#3.3)\\n\",\n    \"  - [ 3.4 Getting more data: Increasing Training Set Size (m)](#3.4)\\n\",\n    \"- [ 4 - Evaluating a Learning Algorithm (Neural Network)](#4)\\n\",\n    \"  - [ 4.1 Data Set](#4.1)\\n\",\n    \"  - [ 4.2 Evaluating categorical model by calculating classification error](#4.2)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"- [ 5 - Model Complexity](#5)\\n\",\n    \"  - [ Exercise 3](#ex03)\\n\",\n    \"  - [ 5.1 Simple model](#5.1)\\n\",\n    \"    - [ Exercise 4](#ex04)\\n\",\n    \"- [ 6 - Regularization](#6)\\n\",\n    \"  - [ Exercise 5](#ex05)\\n\",\n    \"- [ 7 - Iterate to find optimal regularization value](#7)\\n\",\n    \"  - [ 7.1 Test](#7.1)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://numpy.org/) is the fundamental package for scientific computing Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\\n\",\n    \"- [scikitlearn](https://scikit-learn.org/stable/) is a basic library for data mining\\n\",\n    \"- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.linear_model import LinearRegression, Ridge\\n\",\n    \"from sklearn.preprocessing import StandardScaler, PolynomialFeatures\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.metrics import mean_squared_error\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from tensorflow.keras.activations import relu,linear\\n\",\n    \"from tensorflow.keras.losses import SparseCategoricalCrossentropy\\n\",\n    \"from tensorflow.keras.optimizers import Adam\\n\",\n    \"\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"\\n\",\n    \"from public_tests_a1 import * \\n\",\n    \"\\n\",\n    \"tf.keras.backend.set_floatx('float64')\\n\",\n    \"from assigment_utils import *\\n\",\n    \"\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Evaluating a Learning Algorithm (Polynomial Regression)\\n\",\n    \"\\n\",\n    \"<img align=\\\"Right\\\" src=\\\"./images/C2_W3_TrainingVsNew.png\\\"  style=\\\" width:350px; padding: 10px 20px ; \\\"> Let's say you have created a machine learning model and you find it *fits* your training data very well. You're done? Not quite. The goal of creating the model was to be able to predict values for <span style=\\\"color:blue\\\">*new* </span> examples. \\n\",\n    \"\\n\",\n    \"How can you test your model's performance on new data before deploying it?   \\n\",\n    \"The answer has two parts:\\n\",\n    \"* Split your original data set into \\\"Training\\\" and \\\"Test\\\" sets. \\n\",\n    \"    * Use the training data to fit the parameters of the model\\n\",\n    \"    * Use the test data to evaluate the model on *new* data\\n\",\n    \"* Develop an error function to evaluate your model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Splitting your data set\\n\",\n    \"Lectures advised reserving 20-40% of your data set for testing. Let's use an `sklearn` function [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) to perform the split. Double-check the shapes after running the following cell.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Generate some data\\n\",\n    \"X,y,x_ideal,y_ideal = gen_data(18, 2, 0.7)\\n\",\n    \"print(\\\"X.shape\\\", X.shape, \\\"y.shape\\\", y.shape)\\n\",\n    \"\\n\",\n    \"#split the data using sklearn routine \\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=1)\\n\",\n    \"print(\\\"X_train.shape\\\", X_train.shape, \\\"y_train.shape\\\", y_train.shape)\\n\",\n    \"print(\\\"X_test.shape\\\", X_test.shape, \\\"y_test.shape\\\", y_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### 2.1.1 Plot Train, Test sets\\n\",\n    \"You can see below the data points that will be part of training (in red) are intermixed with those that the model is not trained on (test). This particular data set is a quadratic function with noise added. The \\\"ideal\\\" curve is shown for reference.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fig, ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"ax.plot(x_ideal, y_ideal, \\\"--\\\", color = \\\"orangered\\\", label=\\\"y_ideal\\\", lw=1)\\n\",\n    \"ax.set_title(\\\"Training, Test\\\",fontsize = 14)\\n\",\n    \"ax.set_xlabel(\\\"x\\\")\\n\",\n    \"ax.set_ylabel(\\\"y\\\")\\n\",\n    \"\\n\",\n    \"ax.scatter(X_train, y_train, color = \\\"red\\\",           label=\\\"train\\\")\\n\",\n    \"ax.scatter(X_test, y_test,   color = dlc[\\\"dlblue\\\"],   label=\\\"test\\\")\\n\",\n    \"ax.legend(loc='upper left')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Error calculation for model evaluation, linear regression\\n\",\n    \"When *evaluating* a linear regression model, you average the squared error difference of the predicted values and the target values.\\n\",\n    \"\\n\",\n    \"$$ J_\\\\text{test}(\\\\mathbf{w},b) = \\n\",\n    \"            \\\\frac{1}{2m_\\\\text{test}}\\\\sum_{i=0}^{m_\\\\text{test}-1} ( f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}_\\\\text{test}) - y^{(i)}_\\\\text{test} )^2 \\n\",\n    \"            \\\\tag{1}\\n\",\n    \"$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Below, create a function to evaluate the error on a data set for a linear regression model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED CELL: eval_mse\\n\",\n    \"def eval_mse(y, yhat):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculate the mean squared error on a data set.\\n\",\n    \"    Args:\\n\",\n    \"      y    : (ndarray  Shape (m,) or (m,1))  target value of each example\\n\",\n    \"      yhat : (ndarray  Shape (m,) or (m,1))  predicted value of each example\\n\",\n    \"    Returns:\\n\",\n    \"      err: (scalar)             \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = len(y)\\n\",\n    \"    err = 0.0\\n\",\n    \"    for i in range(m):\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    return(err)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_hat = np.array([2.4, 4.2])\\n\",\n    \"y_tmp = np.array([2.3, 4.1])\\n\",\n    \"eval_mse(y_hat, y_tmp)\\n\",\n    \"\\n\",\n    \"# BEGIN UNIT TEST\\n\",\n    \"test_eval_mse(eval_mse)   \\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def eval_mse(y, yhat):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculate the mean squared error on a data set.\\n\",\n    \"    Args:\\n\",\n    \"      y    : (ndarray  Shape (m,) or (m,1))  target value of each example\\n\",\n    \"      yhat : (ndarray  Shape (m,) or (m,1))  predicted value of each example\\n\",\n    \"    Returns:\\n\",\n    \"      err: (scalar)             \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = len(y)\\n\",\n    \"    err = 0.0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        err_i  = ( (yhat[i] - y[i])**2 ) \\n\",\n    \"        err   += err_i                                                                \\n\",\n    \"    err = err / (2*m)                    \\n\",\n    \"    return(err)\\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3 Compare performance on training and test data\\n\",\n    \"Let's build a high degree polynomial model to minimize training error. This will use the linear_regression functions from `sklearn`. The code is in the imported utility file if you would like to see the details. The steps below are:\\n\",\n    \"* create and fit the model. ('fit' is another name for training or running gradient descent).\\n\",\n    \"* compute the error on the training data.\\n\",\n    \"* compute the error on the test data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# create a model in sklearn, train on training data\\n\",\n    \"degree = 10\\n\",\n    \"lmodel = lin_model(degree)\\n\",\n    \"lmodel.fit(X_train, y_train)\\n\",\n    \"\\n\",\n    \"# predict on training data, find training error\\n\",\n    \"yhat = lmodel.predict(X_train)\\n\",\n    \"err_train = lmodel.mse(y_train, yhat)\\n\",\n    \"\\n\",\n    \"# predict on test data, find error\\n\",\n    \"yhat = lmodel.predict(X_test)\\n\",\n    \"err_test = lmodel.mse(y_test, yhat)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The computed error on the training set is substantially less than that of the test set. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(f\\\"training err {err_train:0.2f}, test err {err_test:0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following plot shows why this is. The model fits the training data very well. To do so, it has created a complex function. The test data was not part of the training and the model does a poor job of predicting on this data.  \\n\",\n    \"This model would be described as 1) is overfitting, 2) has high variance 3) 'generalizes' poorly.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# plot predictions over data range \\n\",\n    \"x = np.linspace(0,int(X.max()),100)  # predict values for plot\\n\",\n    \"y_pred = lmodel.predict(x).reshape(-1,1)\\n\",\n    \"\\n\",\n    \"plt_train_test(X_train, y_train, X_test, y_test, x, y_pred, x_ideal, y_ideal, degree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The test set error shows this model will not work well on new data. If you use the test error to guide improvements in the model, then the model will perform well on the test data... but the test data was meant to represent *new* data.\\n\",\n    \"You need yet another set of data to test new data performance.\\n\",\n    \"\\n\",\n    \"The proposal made during lecture is to separate data into three groups. The distribution of training, cross-validation and test sets shown in the below table is a typical distribution, but can be varied depending on the amount of data available.\\n\",\n    \"\\n\",\n    \"| data             | % of total | Description |\\n\",\n    \"|------------------|:----------:|:---------|\\n\",\n    \"| training         | 60         | Data used to tune model parameters $w$ and $b$ in training or fitting |\\n\",\n    \"| cross-validation | 20         | Data used to tune other model parameters like degree of polynomial, regularization or the architecture of a neural network.|\\n\",\n    \"| test             | 20         | Data used to test the model after tuning to gauge performance on new data |\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Let's generate three data sets below. We'll once again use `train_test_split` from `sklearn` but will call it twice to get three splits:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Generate  data\\n\",\n    \"X,y, x_ideal,y_ideal = gen_data(40, 5, 0.7)\\n\",\n    \"print(\\\"X.shape\\\", X.shape, \\\"y.shape\\\", y.shape)\\n\",\n    \"\\n\",\n    \"#split the data using sklearn routine \\n\",\n    \"X_train, X_, y_train, y_ = train_test_split(X,y,test_size=0.40, random_state=1)\\n\",\n    \"X_cv, X_test, y_cv, y_test = train_test_split(X_,y_,test_size=0.50, random_state=1)\\n\",\n    \"print(\\\"X_train.shape\\\", X_train.shape, \\\"y_train.shape\\\", y_train.shape)\\n\",\n    \"print(\\\"X_cv.shape\\\", X_cv.shape, \\\"y_cv.shape\\\", y_cv.shape)\\n\",\n    \"print(\\\"X_test.shape\\\", X_test.shape, \\\"y_test.shape\\\", y_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Bias and Variance<img align=\\\"Right\\\" src=\\\"./images/C2_W3_BiasVarianceDegree.png\\\"  style=\\\" width:500px; padding: 10px 20px ; \\\"> \\n\",\n    \" Above, it was clear the degree of the polynomial model was too high. How can you choose a good value? It turns out, as shown in the diagram, the training and cross-validation performance can provide guidance. By trying a range of degree values, the training and cross-validation performance can be evaluated. As the degree becomes too large, the cross-validation performance will start to degrade relative to the training performance. Let's try this on our example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.1\\\"></a>\\n\",\n    \"### 3.1 Plot Train, Cross-Validation, Test\\n\",\n    \"You can see below the datapoints that will be part of training (in red) are intermixed with those that the model is not trained on (test and cv).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"fig, ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"ax.plot(x_ideal, y_ideal, \\\"--\\\", color = \\\"orangered\\\", label=\\\"y_ideal\\\", lw=1)\\n\",\n    \"ax.set_title(\\\"Training, CV, Test\\\",fontsize = 14)\\n\",\n    \"ax.set_xlabel(\\\"x\\\")\\n\",\n    \"ax.set_ylabel(\\\"y\\\")\\n\",\n    \"\\n\",\n    \"ax.scatter(X_train, y_train, color = \\\"red\\\",           label=\\\"train\\\")\\n\",\n    \"ax.scatter(X_cv, y_cv,       color = dlc[\\\"dlorange\\\"], label=\\\"cv\\\")\\n\",\n    \"ax.scatter(X_test, y_test,   color = dlc[\\\"dlblue\\\"],   label=\\\"test\\\")\\n\",\n    \"ax.legend(loc='upper left')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.2\\\"></a>\\n\",\n    \"### 3.2 Finding the optimal degree\\n\",\n    \"In previous labs, you found that you could create a model capable of fitting complex curves by utilizing a polynomial (See Course1, Week2 Feature Engineering and Polynomial Regression Lab).  Further, you demonstrated that by increasing the *degree* of the polynomial, you could *create* overfitting. (See Course 1, Week3, Over-Fitting Lab). Let's use that knowledge here to test our ability to tell the difference between over-fitting and under-fitting.\\n\",\n    \"\\n\",\n    \"Let's train the model repeatedly, increasing the degree of the polynomial each iteration. Here, we're going to use the [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression) linear regression model for speed and simplicity.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"max_degree = 9\\n\",\n    \"err_train = np.zeros(max_degree)    \\n\",\n    \"err_cv = np.zeros(max_degree)      \\n\",\n    \"x = np.linspace(0,int(X.max()),100)  \\n\",\n    \"y_pred = np.zeros((100,max_degree))  #columns are lines to plot\\n\",\n    \"\\n\",\n    \"for degree in range(max_degree):\\n\",\n    \"    lmodel = lin_model(degree+1)\\n\",\n    \"    lmodel.fit(X_train, y_train)\\n\",\n    \"    yhat = lmodel.predict(X_train)\\n\",\n    \"    err_train[degree] = lmodel.mse(y_train, yhat)\\n\",\n    \"    yhat = lmodel.predict(X_cv)\\n\",\n    \"    err_cv[degree] = lmodel.mse(y_cv, yhat)\\n\",\n    \"    y_pred[:,degree] = lmodel.predict(x)\\n\",\n    \"    \\n\",\n    \"optimal_degree = np.argmin(err_cv)+1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<font size=\\\"4\\\">Let's plot the result:</font>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_optimal_degree(X_train, y_train, X_cv, y_cv, x, y_pred, x_ideal, y_ideal, \\n\",\n    \"                   err_train, err_cv, optimal_degree, max_degree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The plot above demonstrates that separating data into two groups, data the model is trained on and data the model has not been trained on, can be used to determine if the model is underfitting or overfitting. In our example, we created a variety of models varying from underfitting to overfitting by increasing the degree of the polynomial used. \\n\",\n    \"- On the left plot, the solid lines represent the predictions from these models. A polynomial model with degree 1 produces a straight line that intersects very few data points, while the maximum degree hews very closely to every data point. \\n\",\n    \"- on the right:\\n\",\n    \"    - the error on the trained data (blue) decreases as the model complexity increases as expected\\n\",\n    \"    - the error of the cross-validation data decreases initially as the model starts to conform to the data, but then increases as the model starts to over-fit on the training data (fails to *generalize*).     \\n\",\n    \"    \\n\",\n    \"It's worth noting that the curves in these examples as not as smooth as one might draw for a lecture. It's clear the specific data points assigned to each group can change your results significantly. The general trend is what is important.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.3\\\"></a>\\n\",\n    \"### 3.3 Tuning Regularization.\\n\",\n    \"In previous labs, you have utilized *regularization* to reduce overfitting. Similar to degree, one can use the same methodology to tune the regularization parameter lambda ($\\\\lambda$).\\n\",\n    \"\\n\",\n    \"Let's demonstrate this by starting with a high degree polynomial and varying the regularization parameter.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lambda_range = np.array([0.0, 1e-6, 1e-5, 1e-4,1e-3,1e-2, 1e-1,1,10,100])\\n\",\n    \"num_steps = len(lambda_range)\\n\",\n    \"degree = 10\\n\",\n    \"err_train = np.zeros(num_steps)    \\n\",\n    \"err_cv = np.zeros(num_steps)       \\n\",\n    \"x = np.linspace(0,int(X.max()),100) \\n\",\n    \"y_pred = np.zeros((100,num_steps))  #columns are lines to plot\\n\",\n    \"\\n\",\n    \"for i in range(num_steps):\\n\",\n    \"    lambda_= lambda_range[i]\\n\",\n    \"    lmodel = lin_model(degree, regularization=True, lambda_=lambda_)\\n\",\n    \"    lmodel.fit(X_train, y_train)\\n\",\n    \"    yhat = lmodel.predict(X_train)\\n\",\n    \"    err_train[i] = lmodel.mse(y_train, yhat)\\n\",\n    \"    yhat = lmodel.predict(X_cv)\\n\",\n    \"    err_cv[i] = lmodel.mse(y_cv, yhat)\\n\",\n    \"    y_pred[:,i] = lmodel.predict(x)\\n\",\n    \"    \\n\",\n    \"optimal_reg_idx = np.argmin(err_cv) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_tune_regularization(X_train, y_train, X_cv, y_cv, x, y_pred, err_train, err_cv, optimal_reg_idx, lambda_range)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Above, the plots show that as regularization increases, the model moves from a high variance (overfitting) model to a high bias (underfitting) model. The vertical line in the right plot shows the optimal value of lambda. In this example, the polynomial degree was set to 10. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3.4\\\"></a>\\n\",\n    \"### 3.4 Getting more data: Increasing Training Set Size (m)\\n\",\n    \"When a model is overfitting (high variance), collecting additional data can improve performance. Let's try that here.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, y_train, X_cv, y_cv, x, y_pred, err_train, err_cv, m_range,degree = tune_m()\\n\",\n    \"plt_tune_m(X_train, y_train, X_cv, y_cv, x, y_pred, err_train, err_cv, m_range, degree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The above plots show that when a model has high variance and is overfitting, adding more examples improves performance. Note the curves on the left plot. The final curve with the highest value of $m$ is a smooth curve that is in the center of the data. On the right, as the number of examples increases, the performance of the training set and cross-validation set converge to similar values. Note that the curves are not as smooth as one might see in a lecture. That is to be expected. The trend remains clear: more data improves generalization. \\n\",\n    \"\\n\",\n    \"> Note that adding more examples when the model has high bias (underfitting) does not improve performance.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Evaluating a Learning Algorithm (Neural Network)\\n\",\n    \"Above, you tuned aspects of a polynomial regression model. Here, you will work with a neural network model. Let's start by creating a classification data set. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.1\\\"></a>\\n\",\n    \"### 4.1 Data Set\\n\",\n    \"Run the cell below to generate a data set and split it into training, cross-validation (CV) and test sets. In this example, we're increasing the percentage of cross-validation data points for emphasis.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Generate and split data set\\n\",\n    \"X, y, centers, classes, std = gen_blobs()\\n\",\n    \"\\n\",\n    \"# split the data. Large CV population for demonstration\\n\",\n    \"X_train, X_, y_train, y_ = train_test_split(X,y,test_size=0.50, random_state=1)\\n\",\n    \"X_cv, X_test, y_cv, y_test = train_test_split(X_,y_,test_size=0.20, random_state=1)\\n\",\n    \"print(\\\"X_train.shape:\\\", X_train.shape, \\\"X_cv.shape:\\\", X_cv.shape, \\\"X_test.shape:\\\", X_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt_train_eq_dist(X_train, y_train,classes, X_cv, y_cv, centers, std)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Above, you can see the data on the left. There are six clusters identified by color. Both training points (dots) and cross-validataion points (triangles) are shown. The interesting points are those that fall in ambiguous locations where either cluster might consider them members. What would you expect a neural network model to do? What would be an example of overfitting? underfitting?  \\n\",\n    \"On the right is an example of an 'ideal' model, or a model one might create knowing the source of the data. The lines represent 'equal distance' boundaries where the distance between center points is equal. It's worth noting that this model would \\\"misclassify\\\" roughly 8% of the total data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.2\\\"></a>\\n\",\n    \"### 4.2 Evaluating categorical model by calculating classification error\\n\",\n    \"The evaluation function for categorical models used here is simply the fraction of incorrect predictions:  \\n\",\n    \"$$ J_{cv} =\\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\n\",\n    \"\\\\begin{cases}\\n\",\n    \"    1, & \\\\text{if $\\\\hat{y}^{(i)} \\\\neq y^{(i)}$}\\\\\\\\\\n\",\n    \"    0, & \\\\text{otherwise}\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Below, complete the routine to calculate classification error. Note, in this lab, target values are the index of the category and are not [one-hot encoded](https://en.wikipedia.org/wiki/One-hot).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED CELL: eval_cat_err\\n\",\n    \"def eval_cat_err(y, yhat):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculate the categorization error\\n\",\n    \"    Args:\\n\",\n    \"      y    : (ndarray  Shape (m,) or (m,1))  target value of each example\\n\",\n    \"      yhat : (ndarray  Shape (m,) or (m,1))  predicted value of each example\\n\",\n    \"    Returns:|\\n\",\n    \"      cerr: (scalar)             \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = len(y)\\n\",\n    \"    incorrect = 0\\n\",\n    \"    for i in range(m):\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    return(cerr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"y_hat = np.array([1, 2, 0])\\n\",\n    \"y_tmp = np.array([1, 2, 3])\\n\",\n    \"print(f\\\"categorization error {np.squeeze(eval_cat_err(y_hat, y_tmp)):0.3f}, expected:0.333\\\" )\\n\",\n    \"y_hat = np.array([[1], [2], [0], [3]])\\n\",\n    \"y_tmp = np.array([[1], [2], [1], [3]])\\n\",\n    \"print(f\\\"categorization error {np.squeeze(eval_cat_err(y_hat, y_tmp)):0.3f}, expected:0.250\\\" )\\n\",\n    \"\\n\",\n    \"# BEGIN UNIT TEST  \\n\",\n    \"test_eval_cat_err(eval_cat_err)\\n\",\n    \"# END UNIT TEST\\n\",\n    \"# BEGIN UNIT TEST  \\n\",\n    \"test_eval_cat_err(eval_cat_err)\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def eval_cat_err(y, yhat):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculate the categorization error\\n\",\n    \"    Args:\\n\",\n    \"      y    : (ndarray  Shape (m,) or (m,1))  target value of each example\\n\",\n    \"      yhat : (ndarray  Shape (m,) or (m,1))  predicted value of each example\\n\",\n    \"    Returns:|\\n\",\n    \"      cerr: (scalar)             \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = len(y)\\n\",\n    \"    incorrect = 0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        if yhat[i] != y[i]:    # @REPLACE\\n\",\n    \"            incorrect += 1     # @REPLACE\\n\",\n    \"    cerr = incorrect/m         # @REPLACE\\n\",\n    \"    return(cerr)                                    \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"5\\\"></a>\\n\",\n    \"## 5 - Model Complexity\\n\",\n    \"Below, you will build two models. A complex model and a simple model. You will evaluate the models to determine if they are likely to overfit or underfit.\\n\",\n    \"\\n\",\n    \"###  5.1 Complex model\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex03\\\"></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"Below, compose a three-layer model:\\n\",\n    \"* Dense layer with 120 units, relu activation\\n\",\n    \"* Dense layer with 40 units, relu activation\\n\",\n    \"* Dense layer with 6 units and a linear activation (not softmax)  \\n\",\n    \"Compile using\\n\",\n    \"* loss with `SparseCategoricalCrossentropy`, remember to use  `from_logits=True`\\n\",\n    \"* Adam optimizer with learning rate of 0.01.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED CELL: model\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"  \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"\\n\",\n    \"    ], name=\\\"Complex\\\"\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    loss=None,\\n\",\n    \"    optimizer=None,\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model.fit(\\n\",\n    \"    X_train, y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"model_test(model, classes, X_train.shape[1]) \\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"Summary should match this (layer instance names may increment )\\n\",\n    \"```\\n\",\n    \"Model: \\\"Complex\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"L1 (Dense)                   (None, 120)               360       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L2 (Dense)                   (None, 40)                4840      \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L3 (Dense)                   (None, 6)                 246       \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 5,446\\n\",\n    \"Trainable params: 5,446\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\\n\",\n    \"  <details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"  \\n\",\n    \"```python\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        Dense(120, activation = 'relu', name = \\\"L1\\\"),      \\n\",\n    \"        Dense(40, activation = 'relu', name = \\\"L2\\\"),         \\n\",\n    \"        Dense(classes, activation = 'linear', name = \\\"L3\\\")  \\n\",\n    \"    ], name=\\\"Complex\\\"\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),          \\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.01),   \\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")                                  \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#make a model for plotting routines to call\\n\",\n    \"model_predict = lambda Xl: np.argmax(tf.nn.softmax(model.predict(Xl)).numpy(),axis=1)\\n\",\n    \"plt_nn(model_predict,X_train,y_train, classes, X_cv, y_cv, suptitle=\\\"Complex Model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This model has worked very hard to capture outliers of each category. As a result, it has miscategorized some of the cross-validation data. Let's calculate the classification error.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"training_cerr_complex = eval_cat_err(y_train, model_predict(X_train))\\n\",\n    \"cv_cerr_complex = eval_cat_err(y_cv, model_predict(X_cv))\\n\",\n    \"print(f\\\"categorization error, training, complex model: {training_cerr_complex:0.3f}\\\")\\n\",\n    \"print(f\\\"categorization error, cv,       complex model: {cv_cerr_complex:0.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"5.1\\\"></a>\\n\",\n    \"### 5.1 Simple model\\n\",\n    \"Now, let's try a simple model\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex04\\\"></a>\\n\",\n    \"### Exercise 4\\n\",\n    \"\\n\",\n    \"Below, compose a two-layer model:\\n\",\n    \"* Dense layer with 6 units, relu activation\\n\",\n    \"* Dense layer with 6 units and a linear activation. \\n\",\n    \"Compile using\\n\",\n    \"* loss with `SparseCategoricalCrossentropy`, remember to use  `from_logits=True`\\n\",\n    \"* Adam optimizer with learning rate of 0.01.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C4\\n\",\n    \"# GRADED CELL: model_s\\n\",\n    \"\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model_s = Sequential(\\n\",\n    \"    [\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"      \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"Simple\\\"\\n\",\n    \")\\n\",\n    \"model_s.compile(\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    loss=None,\\n\",\n    \"    optimizer=None,\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"\\n\",\n    \"# BEGIN UNIT TEST\\n\",\n    \"model_s.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model_s.summary()\\n\",\n    \"\\n\",\n    \"model_s_test(model_s, classes, X_train.shape[1])\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"Summary should match this (layer instance names may increment )\\n\",\n    \"```\\n\",\n    \"Model: \\\"Simple\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"L1 (Dense)                   (None, 6)                 18        \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L2 (Dense)                   (None, 6)                 42        \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 60\\n\",\n    \"Trainable params: 60\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\\n\",\n    \"  <details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"  \\n\",\n    \"```python\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model_s = Sequential(\\n\",\n    \"    [\\n\",\n    \"        Dense(6, activation = 'relu', name=\\\"L1\\\"),            # @REPLACE\\n\",\n    \"        Dense(classes, activation = 'linear', name=\\\"L2\\\")     # @REPLACE\\n\",\n    \"    ], name = \\\"Simple\\\"\\n\",\n    \")\\n\",\n    \"model_s.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),     # @REPLACE\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.01),     # @REPLACE\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model_s.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")                                   \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#make a model for plotting routines to call\\n\",\n    \"model_predict_s = lambda Xl: np.argmax(tf.nn.softmax(model_s.predict(Xl)).numpy(),axis=1)\\n\",\n    \"plt_nn(model_predict_s,X_train,y_train, classes, X_cv, y_cv, suptitle=\\\"Simple Model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This simple models does pretty well. Let's calculate the classification error.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"training_cerr_simple = eval_cat_err(y_train, model_predict_s(X_train))\\n\",\n    \"cv_cerr_simple = eval_cat_err(y_cv, model_predict_s(X_cv))\\n\",\n    \"print(f\\\"categorization error, training, simple model, {training_cerr_simple:0.3f}, complex model: {training_cerr_complex:0.3f}\\\" )\\n\",\n    \"print(f\\\"categorization error, cv,       simple model, {cv_cerr_simple:0.3f}, complex model: {cv_cerr_complex:0.3f}\\\" )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Our simple model has a little higher classification error on training data but does better on cross-validation data than the more complex model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"6\\\"></a>\\n\",\n    \"## 6 - Regularization\\n\",\n    \"As in the case of polynomial regression, one can apply regularization to moderate the impact of a more complex model. Let's try this below.\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex05\\\"></a>\\n\",\n    \"### Exercise 5\\n\",\n    \"\\n\",\n    \"Reconstruct your complex model, but this time include regularization.\\n\",\n    \"Below, compose a three-layer model:\\n\",\n    \"* Dense layer with 120 units, relu activation, `kernel_regularizer=tf.keras.regularizers.l2(0.1)`\\n\",\n    \"* Dense layer with 40 units, relu activation, `kernel_regularizer=tf.keras.regularizers.l2(0.1)`\\n\",\n    \"* Dense layer with 6 units and a linear activation. \\n\",\n    \"Compile using\\n\",\n    \"* loss with `SparseCategoricalCrossentropy`, remember to use  `from_logits=True`\\n\",\n    \"* Adam optimizer with learning rate of 0.01.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C5\\n\",\n    \"# GRADED CELL: model_r\\n\",\n    \"\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model_r = Sequential(\\n\",\n    \"    [\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        \\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"    ], name= None\\n\",\n    \")\\n\",\n    \"model_r.compile(\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    loss=None,\\n\",\n    \"    optimizer=None,\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model_r.fit(\\n\",\n    \"    X_train, y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model_r.summary()\\n\",\n    \"\\n\",\n    \"model_r_test(model_r, classes, X_train.shape[1]) \\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"Summary should match this (layer instance names may increment )\\n\",\n    \"```\\n\",\n    \"Model: \\\"ComplexRegularized\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"L1 (Dense)                   (None, 120)               360       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L2 (Dense)                   (None, 40)                4840      \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L3 (Dense)                   (None, 6)                 246       \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 5,446\\n\",\n    \"Trainable params: 5,446\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\\n\",\n    \"  <details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"  \\n\",\n    \"```python\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model_r = Sequential(\\n\",\n    \"    [\\n\",\n    \"        Dense(120, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(0.1), name=\\\"L1\\\"), \\n\",\n    \"        Dense(40, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(0.1), name=\\\"L2\\\"),  \\n\",\n    \"        Dense(classes, activation = 'linear', name=\\\"L3\\\")  \\n\",\n    \"    ], name=\\\"ComplexRegularized\\\"\\n\",\n    \")\\n\",\n    \"model_r.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.01),                             \\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model_r.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")                                   \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#make a model for plotting routines to call\\n\",\n    \"model_predict_r = lambda Xl: np.argmax(tf.nn.softmax(model_r.predict(Xl)).numpy(),axis=1)\\n\",\n    \" \\n\",\n    \"plt_nn(model_predict_r, X_train,y_train, classes, X_cv, y_cv, suptitle=\\\"Regularized\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The results look very similar to the 'ideal' model. Let's check classification error.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"training_cerr_reg = eval_cat_err(y_train, model_predict_r(X_train))\\n\",\n    \"cv_cerr_reg = eval_cat_err(y_cv, model_predict_r(X_cv))\\n\",\n    \"test_cerr_reg = eval_cat_err(y_test, model_predict_r(X_test))\\n\",\n    \"print(f\\\"categorization error, training, regularized: {training_cerr_reg:0.3f}, simple model, {training_cerr_simple:0.3f}, complex model: {training_cerr_complex:0.3f}\\\" )\\n\",\n    \"print(f\\\"categorization error, cv,       regularized: {cv_cerr_reg:0.3f}, simple model, {cv_cerr_simple:0.3f}, complex model: {cv_cerr_complex:0.3f}\\\" )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The simple model is a bit better in the training set than the regularized model but it worse in the cross validation set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"7\\\"></a>\\n\",\n    \"## 7 - Iterate to find optimal regularization value\\n\",\n    \"As you did in linear regression, you can try many regularization values. This code takes several minutes to run. If you have time, you can run it and check the results. If not, you have completed the graded parts of the assignment!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tf.random.set_seed(1234)\\n\",\n    \"lambdas = [0.0, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3]\\n\",\n    \"models=[None] * len(lambdas)\\n\",\n    \"for i in range(len(lambdas)):\\n\",\n    \"    lambda_ = lambdas[i]\\n\",\n    \"    models[i] =  Sequential(\\n\",\n    \"        [\\n\",\n    \"            Dense(120, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(lambda_)),\\n\",\n    \"            Dense(40, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(lambda_)),\\n\",\n    \"            Dense(classes, activation = 'linear')\\n\",\n    \"        ]\\n\",\n    \"    )\\n\",\n    \"    models[i].compile(\\n\",\n    \"        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"        optimizer=tf.keras.optimizers.Adam(0.01),\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    models[i].fit(\\n\",\n    \"        X_train,y_train,\\n\",\n    \"        epochs=1000\\n\",\n    \"    )\\n\",\n    \"    print(f\\\"Finished lambda = {lambda_}\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plot_iterate(lambdas, models, X_train, y_train, X_cv, y_cv)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As regularization is increased, the performance of the model on the training and cross-validation data sets converge. For this data set and model, lambda > 0.01 seems to be a reasonable choice.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"7.1\\\"></a>\\n\",\n    \"### 7.1 Test\\n\",\n    \"Let's try our optimized models on the test set and compare them to 'ideal' performance. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"plt_compare(X_test,y_test, classes, model_predict_s, model_predict_r, centers)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Our test set is small and seems to have a number of outliers so classification error is high. However, the performance of our optimized models is comparable to ideal performance.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Congratulations! \\n\",\n    \"You have become familiar with important tools to apply when evaluating your machine learning models. Namely:  \\n\",\n    \"* splitting data into trained and untrained sets allows you to differentiate between underfitting and overfitting\\n\",\n    \"* creating three data sets, Training, Cross-Validation and Test allows you to\\n\",\n    \"    * train your parameters $W,B$ with the training set\\n\",\n    \"    * tune model parameters such as complexity, regularization and number of examples with the cross-validation set\\n\",\n    \"    * evaluate your 'real world' performance using the test set.\\n\",\n    \"* comparing training vs cross-validation performance provides insight into a model's propensity towards overfitting (high variance) or underfitting (high bias)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/C2W3A1/C2_W3_Assignment.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Practice Lab: Advice for Applying Machine Learning\\n\",\n    \"In this lab, you will explore techniques to evaluate and improve your machine learning models.\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Evaluating a Learning Algorithm (Polynomial Regression)](#2)\\n\",\n    \"  - [ 2.1 Splitting your data set](#2.1)\\n\",\n    \"  - [ 2.2 Error calculation for model evaluation, linear regression](#2.2)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 2.3 Compare performance on training and test data](#2.3)\\n\",\n    \"- [ 3 - Bias and Variance<img align=\\\"Right\\\" src=\\\"./images/C2_W3_BiasVarianceDegree.png\\\"  style=\\\" width:500px; padding: 10px 20px ; \\\"> ](#3)\\n\",\n    \"  - [ 3.1 Plot Train, Cross-Validation, Test](#3.1)\\n\",\n    \"  - [ 3.2 Finding the optimal degree](#3.2)\\n\",\n    \"  - [ 3.3 Tuning Regularization.](#3.3)\\n\",\n    \"  - [ 3.4 Getting more data: Increasing Training Set Size (m)](#3.4)\\n\",\n    \"- [ 4 - Evaluating a Learning Algorithm (Neural Network)](#4)\\n\",\n    \"  - [ 4.1 Data Set](#4.1)\\n\",\n    \"  - [ 4.2 Evaluating categorical model by calculating classification error](#4.2)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"- [ 5 - Model Complexity](#5)\\n\",\n    \"  - [ Exercise 3](#ex03)\\n\",\n    \"  - [ 5.1 Simple model](#5.1)\\n\",\n    \"    - [ Exercise 4](#ex04)\\n\",\n    \"- [ 6 - Regularization](#6)\\n\",\n    \"  - [ Exercise 5](#ex05)\\n\",\n    \"- [ 7 - Iterate to find optimal regularization value](#7)\\n\",\n    \"  - [ 7.1 Test](#7.1)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://numpy.org/) is the fundamental package for scientific computing Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\\n\",\n    \"- [scikitlearn](https://scikit-learn.org/stable/) is a basic library for data mining\\n\",\n    \"- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"%matplotlib widget\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.linear_model import LinearRegression, Ridge\\n\",\n    \"from sklearn.preprocessing import StandardScaler, PolynomialFeatures\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.metrics import mean_squared_error\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras.models import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense\\n\",\n    \"from tensorflow.keras.activations import relu,linear\\n\",\n    \"from tensorflow.keras.losses import SparseCategoricalCrossentropy\\n\",\n    \"from tensorflow.keras.optimizers import Adam\\n\",\n    \"\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"\\n\",\n    \"from public_tests_a1 import * \\n\",\n    \"\\n\",\n    \"tf.keras.backend.set_floatx('float64')\\n\",\n    \"from assigment_utils import *\\n\",\n    \"\\n\",\n    \"tf.autograph.set_verbosity(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Evaluating a Learning Algorithm (Polynomial Regression)\\n\",\n    \"\\n\",\n    \"<img align=\\\"Right\\\" src=\\\"./images/C2_W3_TrainingVsNew.png\\\"  style=\\\" width:350px; padding: 10px 20px ; \\\"> Let's say you have created a machine learning model and you find it *fits* your training data very well. You're done? Not quite. The goal of creating the model was to be able to predict values for <span style=\\\"color:blue\\\">*new* </span> examples. \\n\",\n    \"\\n\",\n    \"How can you test your model's performance on new data before deploying it?   \\n\",\n    \"The answer has two parts:\\n\",\n    \"* Split your original data set into \\\"Training\\\" and \\\"Test\\\" sets. \\n\",\n    \"    * Use the training data to fit the parameters of the model\\n\",\n    \"    * Use the test data to evaluate the model on *new* data\\n\",\n    \"* Develop an error function to evaluate your model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Splitting your data set\\n\",\n    \"Lectures advised reserving 20-40% of your data set for testing. Let's use an `sklearn` function [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) to perform the split. Double-check the shapes after running the following cell.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X.shape (18,) y.shape (18,)\\n\",\n      \"X_train.shape (12,) y_train.shape (12,)\\n\",\n      \"X_test.shape (6,) y_test.shape (6,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Generate some data\\n\",\n    \"X,y,x_ideal,y_ideal = gen_data(18, 2, 0.7)\\n\",\n    \"print(\\\"X.shape\\\", X.shape, \\\"y.shape\\\", y.shape)\\n\",\n    \"\\n\",\n    \"#split the data using sklearn routine \\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.33, random_state=1)\\n\",\n    \"print(\\\"X_train.shape\\\", X_train.shape, \\\"y_train.shape\\\", y_train.shape)\\n\",\n    \"print(\\\"X_test.shape\\\", X_test.shape, \\\"y_test.shape\\\", y_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"#### 2.1.1 Plot Train, Test sets\\n\",\n    \"You can see below the data points that will be part of training (in red) are intermixed with those that the model is not trained on (test). This particular data set is a quadratic function with noise added. The \\\"ideal\\\" curve is shown for reference.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"88e274385b3e45af965334a00a9fa97b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"ax.plot(x_ideal, y_ideal, \\\"--\\\", color = \\\"orangered\\\", label=\\\"y_ideal\\\", lw=1)\\n\",\n    \"ax.set_title(\\\"Training, Test\\\",fontsize = 14)\\n\",\n    \"ax.set_xlabel(\\\"x\\\")\\n\",\n    \"ax.set_ylabel(\\\"y\\\")\\n\",\n    \"\\n\",\n    \"ax.scatter(X_train, y_train, color = \\\"red\\\",           label=\\\"train\\\")\\n\",\n    \"ax.scatter(X_test, y_test,   color = dlc[\\\"dlblue\\\"],   label=\\\"test\\\")\\n\",\n    \"ax.legend(loc='upper left')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Error calculation for model evaluation, linear regression\\n\",\n    \"When *evaluating* a linear regression model, you average the squared error difference of the predicted values and the target values.\\n\",\n    \"\\n\",\n    \"$$ J_\\\\text{test}(\\\\mathbf{w},b) = \\n\",\n    \"            \\\\frac{1}{2m_\\\\text{test}}\\\\sum_{i=0}^{m_\\\\text{test}-1} ( f_{\\\\mathbf{w},b}(\\\\mathbf{x}^{(i)}_\\\\text{test}) - y^{(i)}_\\\\text{test} )^2 \\n\",\n    \"            \\\\tag{1}\\n\",\n    \"$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Below, create a function to evaluate the error on a data set for a linear regression model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED CELL: eval_mse\\n\",\n    \"def eval_mse(y, yhat):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculate the mean squared error on a data set.\\n\",\n    \"    Args:\\n\",\n    \"      y    : (ndarray  Shape (m,) or (m,1))  target value of each example\\n\",\n    \"      yhat : (ndarray  Shape (m,) or (m,1))  predicted value of each example\\n\",\n    \"    Returns:\\n\",\n    \"      err: (scalar)             \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = len(y)\\n\",\n    \"    err = 0.0\\n\",\n    \"    for i in range(m):\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"        err+=(y[i]-yhat[i])**2\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    err/= 2*m\\n\",\n    \"    return(err)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001B[92m All tests passed.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"y_hat = np.array([2.4, 4.2])\\n\",\n    \"y_tmp = np.array([2.3, 4.1])\\n\",\n    \"eval_mse(y_hat, y_tmp)\\n\",\n    \"\\n\",\n    \"# BEGIN UNIT TEST\\n\",\n    \"test_eval_mse(eval_mse)   \\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def eval_mse(y, yhat):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculate the mean squared error on a data set.\\n\",\n    \"    Args:\\n\",\n    \"      y    : (ndarray  Shape (m,) or (m,1))  target value of each example\\n\",\n    \"      yhat : (ndarray  Shape (m,) or (m,1))  predicted value of each example\\n\",\n    \"    Returns:\\n\",\n    \"      err: (scalar)             \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = len(y)\\n\",\n    \"    err = 0.0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        err_i  = ( (yhat[i] - y[i])**2 ) \\n\",\n    \"        err   += err_i                                                                \\n\",\n    \"    err = err / (2*m)                    \\n\",\n    \"    return(err)\\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3 Compare performance on training and test data\\n\",\n    \"Let's build a high degree polynomial model to minimize training error. This will use the linear_regression functions from `sklearn`. The code is in the imported utility file if you would like to see the details. The steps below are:\\n\",\n    \"* create and fit the model. ('fit' is another name for training or running gradient descent).\\n\",\n    \"* compute the error on the training data.\\n\",\n    \"* compute the error on the test data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# create a model in sklearn, train on training data\\n\",\n    \"degree = 10\\n\",\n    \"lmodel = lin_model(degree)\\n\",\n    \"lmodel.fit(X_train, y_train)\\n\",\n    \"\\n\",\n    \"# predict on training data, find training error\\n\",\n    \"yhat = lmodel.predict(X_train)\\n\",\n    \"err_train = lmodel.mse(y_train, yhat)\\n\",\n    \"\\n\",\n    \"# predict on test data, find error\\n\",\n    \"yhat = lmodel.predict(X_test)\\n\",\n    \"err_test = lmodel.mse(y_test, yhat)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The computed error on the training set is substantially less than that of the test set. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"training err 58.01, test err 171215.01\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"training err {err_train:0.2f}, test err {err_test:0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The following plot shows why this is. The model fits the training data very well. To do so, it has created a complex function. The test data was not part of the training and the model does a poor job of predicting on this data.  \\n\",\n    \"This model would be described as 1) is overfitting, 2) has high variance 3) 'generalizes' poorly.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c010af15af364ac0aec92a37f9045c41\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# plot predictions over data range \\n\",\n    \"x = np.linspace(0,int(X.max()),100)  # predict values for plot\\n\",\n    \"y_pred = lmodel.predict(x).reshape(-1,1)\\n\",\n    \"\\n\",\n    \"plt_train_test(X_train, y_train, X_test, y_test, x, y_pred, x_ideal, y_ideal, degree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The test set error shows this model will not work well on new data. If you use the test error to guide improvements in the model, then the model will perform well on the test data... but the test data was meant to represent *new* data.\\n\",\n    \"You need yet another set of data to test new data performance.\\n\",\n    \"\\n\",\n    \"The proposal made during lecture is to separate data into three groups. The distribution of training, cross-validation and test sets shown in the below table is a typical distribution, but can be varied depending on the amount of data available.\\n\",\n    \"\\n\",\n    \"| data             | % of total | Description |\\n\",\n    \"|------------------|:----------:|:---------|\\n\",\n    \"| training         | 60         | Data used to tune model parameters $w$ and $b$ in training or fitting |\\n\",\n    \"| cross-validation | 20         | Data used to tune other model parameters like degree of polynomial, regularization or the architecture of a neural network.|\\n\",\n    \"| test             | 20         | Data used to test the model after tuning to gauge performance on new data |\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Let's generate three data sets below. We'll once again use `train_test_split` from `sklearn` but will call it twice to get three splits:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X.shape (40,) y.shape (40,)\\n\",\n      \"X_train.shape (24,) y_train.shape (24,)\\n\",\n      \"X_cv.shape (8,) y_cv.shape (8,)\\n\",\n      \"X_test.shape (8,) y_test.shape (8,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Generate  data\\n\",\n    \"X,y, x_ideal,y_ideal = gen_data(40, 5, 0.7)\\n\",\n    \"print(\\\"X.shape\\\", X.shape, \\\"y.shape\\\", y.shape)\\n\",\n    \"\\n\",\n    \"#split the data using sklearn routine \\n\",\n    \"X_train, X_, y_train, y_ = train_test_split(X,y,test_size=0.40, random_state=1)\\n\",\n    \"X_cv, X_test, y_cv, y_test = train_test_split(X_,y_,test_size=0.50, random_state=1)\\n\",\n    \"print(\\\"X_train.shape\\\", X_train.shape, \\\"y_train.shape\\\", y_train.shape)\\n\",\n    \"print(\\\"X_cv.shape\\\", X_cv.shape, \\\"y_cv.shape\\\", y_cv.shape)\\n\",\n    \"print(\\\"X_test.shape\\\", X_test.shape, \\\"y_test.shape\\\", y_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Bias and Variance<img align=\\\"Right\\\" src=\\\"./images/C2_W3_BiasVarianceDegree.png\\\"  style=\\\" width:500px; padding: 10px 20px ; \\\"> \\n\",\n    \" Above, it was clear the degree of the polynomial model was too high. How can you choose a good value? It turns out, as shown in the diagram, the training and cross-validation performance can provide guidance. By trying a range of degree values, the training and cross-validation performance can be evaluated. As the degree becomes too large, the cross-validation performance will start to degrade relative to the training performance. Let's try this on our example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"3.1\\\"></a>\\n\",\n    \"### 3.1 Plot Train, Cross-Validation, Test\\n\",\n    \"You can see below the datapoints that will be part of training (in red) are intermixed with those that the model is not trained on (test and cv).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"cda8aa7e3b7e4ceda731131452a46be3\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig, ax = plt.subplots(1,1,figsize=(4,4))\\n\",\n    \"ax.plot(x_ideal, y_ideal, \\\"--\\\", color = \\\"orangered\\\", label=\\\"y_ideal\\\", lw=1)\\n\",\n    \"ax.set_title(\\\"Training, CV, Test\\\",fontsize = 14)\\n\",\n    \"ax.set_xlabel(\\\"x\\\")\\n\",\n    \"ax.set_ylabel(\\\"y\\\")\\n\",\n    \"\\n\",\n    \"ax.scatter(X_train, y_train, color = \\\"red\\\",           label=\\\"train\\\")\\n\",\n    \"ax.scatter(X_cv, y_cv,       color = dlc[\\\"dlorange\\\"], label=\\\"cv\\\")\\n\",\n    \"ax.scatter(X_test, y_test,   color = dlc[\\\"dlblue\\\"],   label=\\\"test\\\")\\n\",\n    \"ax.legend(loc='upper left')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"3.2\\\"></a>\\n\",\n    \"### 3.2 Finding the optimal degree\\n\",\n    \"In previous labs, you found that you could create a model capable of fitting complex curves by utilizing a polynomial (See Course1, Week2 Feature Engineering and Polynomial Regression Lab).  Further, you demonstrated that by increasing the *degree* of the polynomial, you could *create* overfitting. (See Course 1, Week3, Over-Fitting Lab). Let's use that knowledge here to test our ability to tell the difference between over-fitting and under-fitting.\\n\",\n    \"\\n\",\n    \"Let's train the model repeatedly, increasing the degree of the polynomial each iteration. Here, we're going to use the [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression) linear regression model for speed and simplicity.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max_degree = 9\\n\",\n    \"err_train = np.zeros(max_degree)    \\n\",\n    \"err_cv = np.zeros(max_degree)      \\n\",\n    \"x = np.linspace(0,int(X.max()),100)  \\n\",\n    \"y_pred = np.zeros((100,max_degree))  #columns are lines to plot\\n\",\n    \"\\n\",\n    \"for degree in range(max_degree):\\n\",\n    \"    lmodel = lin_model(degree+1)\\n\",\n    \"    lmodel.fit(X_train, y_train)\\n\",\n    \"    yhat = lmodel.predict(X_train)\\n\",\n    \"    err_train[degree] = lmodel.mse(y_train, yhat)\\n\",\n    \"    yhat = lmodel.predict(X_cv)\\n\",\n    \"    err_cv[degree] = lmodel.mse(y_cv, yhat)\\n\",\n    \"    y_pred[:,degree] = lmodel.predict(x)\\n\",\n    \"    \\n\",\n    \"optimal_degree = np.argmin(err_cv)+1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<font size=\\\"4\\\">Let's plot the result:</font>\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f000bfc6c27a4c58a42e76429748721c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_optimal_degree(X_train, y_train, X_cv, y_cv, x, y_pred, x_ideal, y_ideal, \\n\",\n    \"                   err_train, err_cv, optimal_degree, max_degree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The plot above demonstrates that separating data into two groups, data the model is trained on and data the model has not been trained on, can be used to determine if the model is underfitting or overfitting. In our example, we created a variety of models varying from underfitting to overfitting by increasing the degree of the polynomial used. \\n\",\n    \"- On the left plot, the solid lines represent the predictions from these models. A polynomial model with degree 1 produces a straight line that intersects very few data points, while the maximum degree hews very closely to every data point. \\n\",\n    \"- on the right:\\n\",\n    \"    - the error on the trained data (blue) decreases as the model complexity increases as expected\\n\",\n    \"    - the error of the cross-validation data decreases initially as the model starts to conform to the data, but then increases as the model starts to over-fit on the training data (fails to *generalize*).     \\n\",\n    \"    \\n\",\n    \"It's worth noting that the curves in these examples as not as smooth as one might draw for a lecture. It's clear the specific data points assigned to each group can change your results significantly. The general trend is what is important.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"3.3\\\"></a>\\n\",\n    \"### 3.3 Tuning Regularization.\\n\",\n    \"In previous labs, you have utilized *regularization* to reduce overfitting. Similar to degree, one can use the same methodology to tune the regularization parameter lambda ($\\\\lambda$).\\n\",\n    \"\\n\",\n    \"Let's demonstrate this by starting with a high degree polynomial and varying the regularization parameter.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"lambda_range = np.array([0.0, 1e-6, 1e-5, 1e-4,1e-3,1e-2, 1e-1,1,10,100])\\n\",\n    \"num_steps = len(lambda_range)\\n\",\n    \"degree = 10\\n\",\n    \"err_train = np.zeros(num_steps)    \\n\",\n    \"err_cv = np.zeros(num_steps)       \\n\",\n    \"x = np.linspace(0,int(X.max()),100) \\n\",\n    \"y_pred = np.zeros((100,num_steps))  #columns are lines to plot\\n\",\n    \"\\n\",\n    \"for i in range(num_steps):\\n\",\n    \"    lambda_= lambda_range[i]\\n\",\n    \"    lmodel = lin_model(degree, regularization=True, lambda_=lambda_)\\n\",\n    \"    lmodel.fit(X_train, y_train)\\n\",\n    \"    yhat = lmodel.predict(X_train)\\n\",\n    \"    err_train[i] = lmodel.mse(y_train, yhat)\\n\",\n    \"    yhat = lmodel.predict(X_cv)\\n\",\n    \"    err_cv[i] = lmodel.mse(y_cv, yhat)\\n\",\n    \"    y_pred[:,i] = lmodel.predict(x)\\n\",\n    \"    \\n\",\n    \"optimal_reg_idx = np.argmin(err_cv) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"59b0ef32dc274e06aeea8ee585bd4a24\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.close(\\\"all\\\")\\n\",\n    \"plt_tune_regularization(X_train, y_train, X_cv, y_cv, x, y_pred, err_train, err_cv, optimal_reg_idx, lambda_range)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Above, the plots show that as regularization increases, the model moves from a high variance (overfitting) model to a high bias (underfitting) model. The vertical line in the right plot shows the optimal value of lambda. In this example, the polynomial degree was set to 10. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"3.4\\\"></a>\\n\",\n    \"### 3.4 Getting more data: Increasing Training Set Size (m)\\n\",\n    \"When a model is overfitting (high variance), collecting additional data can improve performance. Let's try that here.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4a765c45096c4dcdad44e7d493dc5d56\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X_train, y_train, X_cv, y_cv, x, y_pred, err_train, err_cv, m_range,degree = tune_m()\\n\",\n    \"plt_tune_m(X_train, y_train, X_cv, y_cv, x, y_pred, err_train, err_cv, m_range, degree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The above plots show that when a model has high variance and is overfitting, adding more examples improves performance. Note the curves on the left plot. The final curve with the highest value of $m$ is a smooth curve that is in the center of the data. On the right, as the number of examples increases, the performance of the training set and cross-validation set converge to similar values. Note that the curves are not as smooth as one might see in a lecture. That is to be expected. The trend remains clear: more data improves generalization. \\n\",\n    \"\\n\",\n    \"> Note that adding more examples when the model has high bias (underfitting) does not improve performance.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Evaluating a Learning Algorithm (Neural Network)\\n\",\n    \"Above, you tuned aspects of a polynomial regression model. Here, you will work with a neural network model. Let's start by creating a classification data set. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"4.1\\\"></a>\\n\",\n    \"### 4.1 Data Set\\n\",\n    \"Run the cell below to generate a data set and split it into training, cross-validation (CV) and test sets. In this example, we're increasing the percentage of cross-validation data points for emphasis.  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"X_train.shape: (400, 2) X_cv.shape: (320, 2) X_test.shape: (80, 2)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Generate and split data set\\n\",\n    \"X, y, centers, classes, std = gen_blobs() \\n\",\n    \"\\n\",\n    \"# split the data. Large CV population for demonstration\\n\",\n    \"X_train, X_, y_train, y_ = train_test_split(X,y,test_size=0.50, random_state=1)\\n\",\n    \"X_cv, X_test, y_cv, y_test = train_test_split(X_,y_,test_size=0.20, random_state=1)\\n\",\n    \"print(\\\"X_train.shape:\\\", X_train.shape, \\\"X_cv.shape:\\\", X_cv.shape, \\\"X_test.shape:\\\", X_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"34f17b03f3be496a95b69145568c14be\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_train_eq_dist(X_train, y_train,classes, X_cv, y_cv, centers, std)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Above, you can see the data on the left. There are six clusters identified by color. Both training points (dots) and cross-validataion points (triangles) are shown. The interesting points are those that fall in ambiguous locations where either cluster might consider them members. What would you expect a neural network model to do? What would be an example of overfitting? underfitting?  \\n\",\n    \"On the right is an example of an 'ideal' model, or a model one might create knowing the source of the data. The lines represent 'equal distance' boundaries where the distance between center points is equal. It's worth noting that this model would \\\"misclassify\\\" roughly 8% of the total data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"4.2\\\"></a>\\n\",\n    \"### 4.2 Evaluating categorical model by calculating classification error\\n\",\n    \"The evaluation function for categorical models used here is simply the fraction of incorrect predictions:  \\n\",\n    \"$$ J_{cv} =\\\\frac{1}{m}\\\\sum_{i=0}^{m-1} \\n\",\n    \"\\\\begin{cases}\\n\",\n    \"    1, & \\\\text{if $\\\\hat{y}^{(i)} \\\\neq y^{(i)}$}\\\\\\\\\\n\",\n    \"    0, & \\\\text{otherwise}\\n\",\n    \"\\\\end{cases}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Below, complete the routine to calculate classification error. Note, in this lab, target values are the index of the category and are not [one-hot encoded](https://en.wikipedia.org/wiki/One-hot).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED CELL: eval_cat_err\\n\",\n    \"def eval_cat_err(y, yhat):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculate the categorization error\\n\",\n    \"    Args:\\n\",\n    \"      y    : (ndarray  Shape (m,) or (m,1))  target value of each example\\n\",\n    \"      yhat : (ndarray  Shape (m,) or (m,1))  predicted value of each example\\n\",\n    \"    Returns:|\\n\",\n    \"      cerr: (scalar)             \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = len(y)\\n\",\n    \"    incorrect = 0\\n\",\n    \"    for i in range(m):\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"        if(yhat[i]!=y[i]):\\n\",\n    \"            incorrect+=1\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    cerr=incorrect/m\\n\",\n    \"    return(cerr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"categorization error 0.333, expected:0.333\\n\",\n      \"categorization error 0.250, expected:0.250\\n\",\n      \"\\u001B[92m All tests passed.\\n\",\n      \"\\u001B[92m All tests passed.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"y_hat = np.array([1, 2, 0])\\n\",\n    \"y_tmp = np.array([1, 2, 3])\\n\",\n    \"print(f\\\"categorization error {np.squeeze(eval_cat_err(y_hat, y_tmp)):0.3f}, expected:0.333\\\" )\\n\",\n    \"y_hat = np.array([[1], [2], [0], [3]])\\n\",\n    \"y_tmp = np.array([[1], [2], [1], [3]])\\n\",\n    \"print(f\\\"categorization error {np.squeeze(eval_cat_err(y_hat, y_tmp)):0.3f}, expected:0.250\\\" )\\n\",\n    \"\\n\",\n    \"# BEGIN UNIT TEST  \\n\",\n    \"test_eval_cat_err(eval_cat_err)\\n\",\n    \"# END UNIT TEST\\n\",\n    \"# BEGIN UNIT TEST  \\n\",\n    \"test_eval_cat_err(eval_cat_err)\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def eval_cat_err(y, yhat):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculate the categorization error\\n\",\n    \"    Args:\\n\",\n    \"      y    : (ndarray  Shape (m,) or (m,1))  target value of each example\\n\",\n    \"      yhat : (ndarray  Shape (m,) or (m,1))  predicted value of each example\\n\",\n    \"    Returns:|\\n\",\n    \"      cerr: (scalar)             \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    m = len(y)\\n\",\n    \"    incorrect = 0\\n\",\n    \"    for i in range(m):\\n\",\n    \"        if yhat[i] != y[i]:    # @REPLACE\\n\",\n    \"            incorrect += 1     # @REPLACE\\n\",\n    \"    cerr = incorrect/m         # @REPLACE\\n\",\n    \"    return(cerr)                                    \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"5\\\"></a>\\n\",\n    \"## 5 - Model Complexity\\n\",\n    \"Below, you will build two models. A complex model and a simple model. You will evaluate the models to determine if they are likely to overfit or underfit.\\n\",\n    \"\\n\",\n    \"###  5.1 Complex model\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex03\\\"></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"Below, compose a three-layer model:\\n\",\n    \"* Dense layer with 120 units, relu activation\\n\",\n    \"* Dense layer with 40 units, relu activation\\n\",\n    \"* Dense layer with 6 units and a linear activation (not softmax)  \\n\",\n    \"Compile using\\n\",\n    \"* loss with `SparseCategoricalCrossentropy`, remember to use  `from_logits=True`\\n\",\n    \"* Adam optimizer with learning rate of 0.01.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED CELL: model\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        tf.keras.layers.Dense(120, activation=\\\"relu\\\"),\\n\",\n    \"        tf.keras.layers.Dense(40, activation=\\\"relu\\\"),\\n\",\n    \"        tf.keras.layers.Dense(6, activation=\\\"linear\\\")\\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name=\\\"Complex\\\"\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    loss=SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(lr=0.01),\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.1106\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4281\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3345\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2896\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2867\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2918\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2497\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2298\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2307\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2071\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2115\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2070\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2366\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2261\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2224\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2055\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2044\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2006\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2168\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2047\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2237\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2497\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2113\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2025\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2107\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2000\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1935\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1963\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2188\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2424\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1969\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1950\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1904\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2173\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2074\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1768\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1794\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1733\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1955\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1870\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2128\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1987\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1895\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2073\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2148\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1774\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1886\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1763\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1769\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1763\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2020\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1889\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2035\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1761\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1838\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1774\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1953\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1882\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1860\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1919\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1848\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1630\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1616\\n\",\n      \"Epoch 64/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2008\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1936\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1824\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2092\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2287\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1877\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1716\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1917\\n\",\n      \"Epoch 72/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1703\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1750\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1836\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1696\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1542\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1715\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1545\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1593\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1844\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1881\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1696\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1614\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1762\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1779\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1658\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1614\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1639\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1629\\n\",\n      \"Epoch 90/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1475\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1452\\n\",\n      \"Epoch 92/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1473\\n\",\n      \"Epoch 93/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1490\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1650\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1706\\n\",\n      \"Epoch 96/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1704\\n\",\n      \"Epoch 97/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1764\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1855\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1685\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1569\\n\",\n      \"Epoch 101/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1645\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1737\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1935\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1600\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1483\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1555\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1678\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1435\\n\",\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1419\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1494\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.1538\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1682\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1687\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1436\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1366\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1485\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1400\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1357\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1444\\n\",\n      \"Epoch 120/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1403\\n\",\n      \"Epoch 121/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1465\\n\",\n      \"Epoch 122/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1549\\n\",\n      \"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1402\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1337\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1422\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1560\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1319\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1389\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1404\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1299\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1247\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1244\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1260\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1158\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1343\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1306\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1294\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1297\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1342\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1255\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1232\\n\",\n      \"Epoch 142/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1199\\n\",\n      \"Epoch 143/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1192\\n\",\n      \"Epoch 144/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1192\\n\",\n      \"Epoch 145/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1342\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1477\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1780\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1673\\n\",\n      \"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1402\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1292\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1296\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1221\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1300\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1316\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1274\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1192\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1266\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1185\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1197\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1148\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1137\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1427\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1420\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1327\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1276\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1099\\n\",\n      \"Epoch 167/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1205\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1307\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1476\\n\",\n      \"Epoch 170/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1673\\n\",\n      \"Epoch 171/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1349\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1183\\n\",\n      \"Epoch 173/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1225\\n\",\n      \"Epoch 174/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1276\\n\",\n      \"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1029\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1134\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1081\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1245\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1346\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1233\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1113\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1040\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1155\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 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193/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0991\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0890\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0880\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1006\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0974\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1141\\n\",\n      \"Epoch 199/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1423\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1381\\n\",\n      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0.1178\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1017\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1051\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1014\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1096\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1087\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1047\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1044\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1044\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1006\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1093\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1041\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0956\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1109\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1041\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1000\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0968\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0951\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1092\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1041\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1032\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1153\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1237\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0978\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1074\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1059\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1122\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0974\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0879\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0913\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0831\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0752\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0733\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0886\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0837\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0866\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0933\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0976\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1150\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0904\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1073\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1296\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1022\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0987\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0846\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0813\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0924\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0799\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0947\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0956\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0788\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1018\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0942\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 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271/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0726\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0984\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1074\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0836\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0783\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0799\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1225\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1017\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0990\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1014\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0808\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0798\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0847\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0755\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0631\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0651\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0602\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0733\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0659\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0682\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0745\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0848\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0701\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0828\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0741\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0890\\n\",\n      \"Epoch 297/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0800\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0803\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0765\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0733\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0544\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0718\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0877\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0687\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0671\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0575\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0773\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0779\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0696\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0883\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0880\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0707\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0603\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0772\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0660\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0586\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0618\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0588\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0674\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0598\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0670\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0970\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1366\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1148\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0837\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0749\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0746\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0698\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0691\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0541\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0558\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0653\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0593\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0606\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0696\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0713\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0628\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0752\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0723\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0647\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0688\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0793\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0595\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0528\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0552\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0534\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0471\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0491\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0524\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0696\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0690\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0864\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0999\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1094\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1189\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1059\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0655\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0652\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0544\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0545\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0549\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0581\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0506\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0579\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0583\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0607\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0428\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0495\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0721\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0817\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0588\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0516\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0526\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0463\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0447\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0441\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0422\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0391\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0343\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0461\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0442\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0496\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0509\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0479\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0520\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0391\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0394\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0510\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0525\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0666\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.0490\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0551\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0689\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0663\\n\",\n      \"Epoch 395/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0844\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0704\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0700\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0591\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0586\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0628\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1717\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1648\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1616\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1326\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1367\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1098\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1122\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1798\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1268\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1123\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0720\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0774\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0661\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0720\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0580\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0572\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0586\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0546\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.0573\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0721\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0658\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0686\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0491\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0647\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0465\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0435\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0362\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0411\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0374\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0412\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0391\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0412\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0479\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0436\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0482\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0420\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0347\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0390\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0328\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0371\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0334\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0348\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0370\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0408\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0329\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0318\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0391\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0408\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0346\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0340\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0332\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0325\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0406\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0394\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0584\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0440\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0412\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0468\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0373\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0329\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0390\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0284\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0310\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0348\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0302\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0348\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0350\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0347\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0305\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0369\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0436\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0543\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0477\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0630\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1523\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3248\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1600\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1623\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1206\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0955\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1595\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1626\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1170\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1481\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0686\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0590\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0651\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0575\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0593\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0539\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0451\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0436\\n\",\n      \"Epoch 493/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0484\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0639\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0497\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0787\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0805\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0639\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0504\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0478\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0466\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0419\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0365\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0352\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0368\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0337\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0375\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0317\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0318\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0364\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0337\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0290\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0317\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0320\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0271\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0343\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0308\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0388\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0444\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0381\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0356\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0324\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0292\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0308\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0308\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0365\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0351\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0305\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.0320\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0351\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0290\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0329\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0387\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0431\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0414\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0318\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0285\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0278\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0274\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0338\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0262\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0283\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0265\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0267\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0278\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0256\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0302\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0323\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0262\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0288\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0283\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0315\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0411\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0376\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0346\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0296\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0307\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0270\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0268\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0303\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0251\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0267\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0249\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0265\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0297\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 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575/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0772\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0691\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0770\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0637\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0528\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0371\\n\",\n      \"Epoch 581/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0356\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0431\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0300\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0309\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0307\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0321\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0266\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0274\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0276\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0267\\n\",\n      \"Epoch 591/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0305\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0278\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0343\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0262\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0254\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0251\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0241\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0269\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0287\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0257\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0254\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0232\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0281\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0254\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 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618/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0241\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0253\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0290\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0456\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0647\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1078\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1180\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0837\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0510\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0333\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0327\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0389\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0347\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0342\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0272\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0240\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0235\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0243\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0225\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0222\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0223\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0215\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0248\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0257\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0213\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 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653/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0242\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0239\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0218\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0227\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0265\\n\",\n      \"Epoch 659/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0257\\n\",\n      \"Epoch 660/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0233\\n\",\n      \"Epoch 661/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0246\\n\",\n      \"Epoch 662/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0313\\n\",\n      \"Epoch 663/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0238\\n\",\n      \"Epoch 664/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0277\\n\",\n      \"Epoch 665/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0205\\n\",\n      \"Epoch 666/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0238\\n\",\n      \"Epoch 667/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0249\\n\",\n      \"Epoch 668/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0441\\n\",\n      \"Epoch 669/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0441\\n\",\n      \"Epoch 670/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0305\\n\",\n      \"Epoch 671/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0323\\n\",\n      \"Epoch 672/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0356\\n\",\n      \"Epoch 673/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0670\\n\",\n      \"Epoch 674/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1732\\n\",\n      \"Epoch 675/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0889\\n\",\n      \"Epoch 676/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1098\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0468\\n\",\n      \"Epoch 678/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0532\\n\",\n      \"Epoch 679/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0577\\n\",\n      \"Epoch 680/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0880\\n\",\n      \"Epoch 681/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1123\\n\",\n      \"Epoch 682/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1581\\n\",\n      \"Epoch 683/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1343\\n\",\n      \"Epoch 684/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1065\\n\",\n      \"Epoch 685/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1236\\n\",\n      \"Epoch 686/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1184\\n\",\n      \"Epoch 687/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1218\\n\",\n      \"Epoch 688/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1673\\n\",\n      \"Epoch 689/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1437\\n\",\n      \"Epoch 690/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0897\\n\",\n      \"Epoch 691/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0665\\n\",\n      \"Epoch 692/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0579\\n\",\n      \"Epoch 693/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0563\\n\",\n      \"Epoch 694/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0425\\n\",\n      \"Epoch 695/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0441\\n\",\n      \"Epoch 696/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0411\\n\",\n      \"Epoch 697/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0429\\n\",\n      \"Epoch 698/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0347\\n\",\n      \"Epoch 699/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0367\\n\",\n      \"Epoch 700/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0311\\n\",\n      \"Epoch 701/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0333\\n\",\n      \"Epoch 702/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0308\\n\",\n      \"Epoch 703/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0287\\n\",\n      \"Epoch 704/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0297\\n\",\n      \"Epoch 705/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0282\\n\",\n      \"Epoch 706/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0263\\n\",\n      \"Epoch 707/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0286\\n\",\n      \"Epoch 708/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0275\\n\",\n      \"Epoch 709/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0274\\n\",\n      \"Epoch 710/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0252\\n\",\n      \"Epoch 711/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0277\\n\",\n      \"Epoch 712/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0261\\n\",\n      \"Epoch 713/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0311\\n\",\n      \"Epoch 714/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0265\\n\",\n      \"Epoch 715/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0281\\n\",\n      \"Epoch 716/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0275\\n\",\n      \"Epoch 717/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0264\\n\",\n      \"Epoch 718/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0240\\n\",\n      \"Epoch 719/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0234\\n\",\n      \"Epoch 720/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0284\\n\",\n      \"Epoch 721/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0311\\n\",\n      \"Epoch 722/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0244\\n\",\n      \"Epoch 723/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0249\\n\",\n      \"Epoch 724/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0269\\n\",\n      \"Epoch 725/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0224\\n\",\n      \"Epoch 726/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0238\\n\",\n      \"Epoch 727/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0234\\n\",\n      \"Epoch 728/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0223\\n\",\n      \"Epoch 729/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0220\\n\",\n      \"Epoch 730/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0268\\n\",\n      \"Epoch 731/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0363\\n\",\n      \"Epoch 732/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0300\\n\",\n      \"Epoch 733/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0208\\n\",\n      \"Epoch 734/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0254\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0264\\n\",\n      \"Epoch 736/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0230\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0224\\n\",\n      \"Epoch 738/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0270\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0257\\n\",\n      \"Epoch 740/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0228\\n\",\n      \"Epoch 741/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0249\\n\",\n      \"Epoch 742/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0241\\n\",\n      \"Epoch 743/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0210\\n\",\n      \"Epoch 744/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0216\\n\",\n      \"Epoch 745/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0208\\n\",\n      \"Epoch 746/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0227\\n\",\n      \"Epoch 747/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0193\\n\",\n      \"Epoch 748/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0241\\n\",\n      \"Epoch 749/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0217\\n\",\n      \"Epoch 750/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0248\\n\",\n      \"Epoch 751/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0203\\n\",\n      \"Epoch 752/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0194\\n\",\n      \"Epoch 753/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0252\\n\",\n      \"Epoch 754/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0203\\n\",\n      \"Epoch 755/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0206\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0192\\n\",\n      \"Epoch 757/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0213\\n\",\n      \"Epoch 758/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0206\\n\",\n      \"Epoch 759/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 760/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0227\\n\",\n      \"Epoch 761/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0204\\n\",\n      \"Epoch 762/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0219\\n\",\n      \"Epoch 763/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0266\\n\",\n      \"Epoch 764/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0699\\n\",\n      \"Epoch 765/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0436\\n\",\n      \"Epoch 766/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0451\\n\",\n      \"Epoch 767/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1029\\n\",\n      \"Epoch 768/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1082\\n\",\n      \"Epoch 769/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0924\\n\",\n      \"Epoch 770/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0936\\n\",\n      \"Epoch 771/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0690\\n\",\n      \"Epoch 772/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0589\\n\",\n      \"Epoch 773/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0519\\n\",\n      \"Epoch 774/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0714\\n\",\n      \"Epoch 775/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1015\\n\",\n      \"Epoch 776/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0932\\n\",\n      \"Epoch 777/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1891\\n\",\n      \"Epoch 778/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1356\\n\",\n      \"Epoch 779/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1081\\n\",\n      \"Epoch 780/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0973\\n\",\n      \"Epoch 781/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0768\\n\",\n      \"Epoch 782/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0761\\n\",\n      \"Epoch 783/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1075\\n\",\n      \"Epoch 784/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0789\\n\",\n      \"Epoch 785/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0467\\n\",\n      \"Epoch 786/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0394\\n\",\n      \"Epoch 787/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0360\\n\",\n      \"Epoch 788/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0324\\n\",\n      \"Epoch 789/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0329\\n\",\n      \"Epoch 790/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0291\\n\",\n      \"Epoch 791/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0283\\n\",\n      \"Epoch 792/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0291\\n\",\n      \"Epoch 793/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0261\\n\",\n      \"Epoch 794/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0294\\n\",\n      \"Epoch 795/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0250\\n\",\n      \"Epoch 796/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0292\\n\",\n      \"Epoch 797/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0286\\n\",\n      \"Epoch 798/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0271\\n\",\n      \"Epoch 799/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0307\\n\",\n      \"Epoch 800/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0298\\n\",\n      \"Epoch 801/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0371\\n\",\n      \"Epoch 802/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 803/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0274\\n\",\n      \"Epoch 804/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0266\\n\",\n      \"Epoch 805/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0260\\n\",\n      \"Epoch 806/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0254\\n\",\n      \"Epoch 807/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0252\\n\",\n      \"Epoch 809/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0280\\n\",\n      \"Epoch 810/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0249\\n\",\n      \"Epoch 811/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0255\\n\",\n      \"Epoch 812/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 813/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0310\\n\",\n      \"Epoch 814/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 815/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0246\\n\",\n      \"Epoch 816/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0263\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0328\\n\",\n      \"Epoch 818/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 819/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0250\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 821/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0252\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0256\\n\",\n      \"Epoch 823/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0299\\n\",\n      \"Epoch 824/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0312\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0243\\n\",\n      \"Epoch 826/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0263\\n\",\n      \"Epoch 827/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0233\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0246\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0262\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0238\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0221\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0240\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0248\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0253\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0340\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0229\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0294\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0286\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0268\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0283\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0271\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0235\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0300\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0246\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0244\\n\",\n      \"Epoch 849/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0219\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0244\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0257\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0220\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0221\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0256\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0211\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0227\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0252\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0224\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0214\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0204\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0228\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0206\\n\",\n      \"Epoch 864/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0198\\n\",\n      \"Epoch 865/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0200\\n\",\n      \"Epoch 866/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0273\\n\",\n      \"Epoch 867/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0271\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0217\\n\",\n      \"Epoch 869/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0231\\n\",\n      \"Epoch 870/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0325\\n\",\n      \"Epoch 871/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0354\\n\",\n      \"Epoch 872/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0321\\n\",\n      \"Epoch 873/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0216\\n\",\n      \"Epoch 874/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0201\\n\",\n      \"Epoch 875/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0218\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0217\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0275\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0305\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0440\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0466\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0729\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0460\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0439\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0811\\n\",\n      \"Epoch 885/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0291\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0309\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0289\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0294\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0283\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0240\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0232\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0225\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0196\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0218\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0189\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0221\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0204\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0200\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0208\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0205\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0199\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0298\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0185\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0290\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0272\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0237\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0190\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0210\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0189\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0199\\n\",\n      \"Epoch 911/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0688\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1337\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1883\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2096\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1323\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0795\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1167\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0621\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0929\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0352\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0303\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0287\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0457\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0712\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0553\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0385\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0311\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0394\\n\",\n      \"Epoch 929/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0261\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0346\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0332\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0322\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0311\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0493\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0289\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0325\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0255\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0210\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0235\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0371\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0300\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0265\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0327\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0367\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0307\\n\",\n      \"Epoch 947/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0376\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0375\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0350\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0284\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0293\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0374\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0353\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0395\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0405\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0432\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0234\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0266\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0213\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0200\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0203\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0190\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0239\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0240\\n\",\n      \"Epoch 965/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0261\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0197\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0206\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0188\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0200\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0169\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0161\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0176\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0218\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0161\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0203\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0384\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0292\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0234\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0522\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0851\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0541\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0380\\n\",\n      \"Epoch 983/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0328\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0276\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0227\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0235\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0287\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0170\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0166\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0175\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0149\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0152\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0153\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0142\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0199\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0231\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0199\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0188\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0155\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0172\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f9a783a0390>\"\n      ]\n     },\n     \"execution_count\": 37,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model.fit(\\n\",\n    \"    X_train, y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"Complex\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" dense (Dense)               (None, 120)               360       \\n\",\n      \"                                                                 \\n\",\n      \" dense_1 (Dense)             (None, 40)                4840      \\n\",\n      \"                                                                 \\n\",\n      \" dense_2 (Dense)             (None, 6)                 246       \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 5,446\\n\",\n      \"Trainable params: 5,446\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\",\n      \"\\u001B[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"model_test(model, classes, X_train.shape[1]) \\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"Summary should match this (layer instance names may increment )\\n\",\n    \"```\\n\",\n    \"Model: \\\"Complex\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"L1 (Dense)                   (None, 120)               360       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L2 (Dense)                   (None, 40)                4840      \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L3 (Dense)                   (None, 6)                 246       \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 5,446\\n\",\n    \"Trainable params: 5,446\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\\n\",\n    \"  <details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"  \\n\",\n    \"```python\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model = Sequential(\\n\",\n    \"    [\\n\",\n    \"        Dense(120, activation = 'relu', name = \\\"L1\\\"),      \\n\",\n    \"        Dense(40, activation = 'relu', name = \\\"L2\\\"),         \\n\",\n    \"        Dense(classes, activation = 'linear', name = \\\"L3\\\")  \\n\",\n    \"    ], name=\\\"Complex\\\"\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),          \\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.01),   \\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")                                  \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1c69f50c315541d982b9970d86f57006\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#make a model for plotting routines to call\\n\",\n    \"model_predict = lambda Xl: np.argmax(tf.nn.softmax(model.predict(Xl)).numpy(),axis=1)\\n\",\n    \"plt_nn(model_predict,X_train,y_train, classes, X_cv, y_cv, suptitle=\\\"Complex Model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"This model has worked very hard to capture outliers of each category. As a result, it has miscategorized some of the cross-validation data. Let's calculate the classification error.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"categorization error, training, complex model: 0.003\\n\",\n      \"categorization error, cv,       complex model: 0.122\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"training_cerr_complex = eval_cat_err(y_train, model_predict(X_train))\\n\",\n    \"cv_cerr_complex = eval_cat_err(y_cv, model_predict(X_cv))\\n\",\n    \"print(f\\\"categorization error, training, complex model: {training_cerr_complex:0.3f}\\\")\\n\",\n    \"print(f\\\"categorization error, cv,       complex model: {cv_cerr_complex:0.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"5.1\\\"></a>\\n\",\n    \"### 5.1 Simple model\\n\",\n    \"Now, let's try a simple model\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex04\\\"></a>\\n\",\n    \"### Exercise 4\\n\",\n    \"\\n\",\n    \"Below, compose a two-layer model:\\n\",\n    \"* Dense layer with 6 units, relu activation\\n\",\n    \"* Dense layer with 6 units and a linear activation. \\n\",\n    \"Compile using\\n\",\n    \"* loss with `SparseCategoricalCrossentropy`, remember to use  `from_logits=True`\\n\",\n    \"* Adam optimizer with learning rate of 0.01.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C4\\n\",\n    \"# GRADED CELL: model_s\\n\",\n    \"\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model_s = Sequential(\\n\",\n    \"    [\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        tf.keras.layers.Dense(6, activation=\\\"relu\\\"),\\n\",\n    \"        tf.keras.layers.Dense(6, activation=\\\"linear\\\")\\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"    ], name = \\\"Simple\\\"\\n\",\n    \")\\n\",\n    \"model_s.compile(\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    loss=SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(lr=0.01),\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 885us/step - loss: 1.7306\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 1.4468\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 865us/step - loss: 1.2902\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 1.1367\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.9710\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 858us/step - loss: 0.7947\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.6499\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.5378\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.4652\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.4184\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.3860\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.3641\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.3487\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.3316\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.3201\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.3110\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.3026\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.2953\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.2880\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.2824\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.2768\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.2716\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.2690\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.2618\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.2606\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.2560\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.2516\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.2500\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.2497\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.2424\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.2406\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 884us/step - loss: 0.2386\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 928us/step - loss: 0.2371\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 908us/step - loss: 0.2355\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.2328\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 915us/step - loss: 0.2311\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 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46/1000\\n\",\n      \"13/13 [==============================] - 0s 936us/step - loss: 0.2198\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.2188\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 896us/step - loss: 0.2156\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.2156\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.2165\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.2155\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.2130\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.2121\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.2122\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.2105\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.2116\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.2121\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.2084\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.2122\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.2101\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.2095\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.2092\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 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72/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.2061\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 935us/step - loss: 0.2075\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.2067\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.2039\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.2036\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.2062\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.2017\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.2044\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.2055\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1999\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.2028\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.2019\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.2042\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.2016\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.2068\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.2005\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.2011\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 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98/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.2031\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1991\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.2006\\n\",\n      \"Epoch 101/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.2010\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.2018\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.2026\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1988\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1974\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1966\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1963\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1969\\n\",\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1987\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1978\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1962\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1979\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1944\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 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\"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1960\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1973\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1961\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1957\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1949\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1946\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1944\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1969\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1926\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1925\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 890us/step - loss: 0.1933\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 881us/step - loss: 0.1942\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 924us/step - loss: 0.1976\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 0s 975us/step - loss: 0.1939\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 957us/step - loss: 0.1931\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 923us/step - loss: 0.1947\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.1941\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 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\"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1913\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1914\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1944\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1920\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.1949\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 979us/step - loss: 0.1904\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 934us/step - loss: 0.1917\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 914us/step - loss: 0.1898\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 976us/step - loss: 0.1913\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 925us/step - loss: 0.1905\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 937us/step - loss: 0.1898\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1910\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 912us/step - loss: 0.1913\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1930\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1913\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1907\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1910\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 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\"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1876\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1861\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1922\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1977\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1881\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1894\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1906\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1894\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1872\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1893\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1885\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1867\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1866\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1884\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 871us/step - loss: 0.1907\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1890\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1880\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1863\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1904\\n\",\n      \"Epoch 194/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1857\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1859\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1856\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1879\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1884\\n\",\n      \"Epoch 199/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1894\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1860\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1869\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1837\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1861\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1869\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1846\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1881\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1841\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1902\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1850\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1883\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1863\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1856\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1860\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1890\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1855\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1891\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1834\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1887\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1857\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1844\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1846\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1843\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1878\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1884\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1851\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1844\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1824\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1849\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1879\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1860\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1834\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1882\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1851\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1874\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1822\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1841\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1876\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1923\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1867\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1832\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1863\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1978\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1946\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1871\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1826\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1850\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1836\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1820\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1857\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 781us/step - loss: 0.1829\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1838\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1828\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1842\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1832\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1830\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1830\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1833\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1826\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1796\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 852us/step - loss: 0.1876\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1819\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1826\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1827\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1820\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1831\\n\",\n      \"Epoch 266/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1805\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1835\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1812\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1817\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1836\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1801\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1868\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1869\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1815\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1847\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1787\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1841\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1804\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1861\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1816\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1797\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1807\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1815\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1822\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1813\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1815\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1829\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1849\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1805\\n\",\n      \"Epoch 290/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 885us/step - loss: 0.1807\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1801\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1793\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1815\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1784\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1867\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1805\\n\",\n      \"Epoch 297/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1855\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1816\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1798\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1817\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1823\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.1878\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 917us/step - loss: 0.1788\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 899us/step - loss: 0.1850\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 898us/step - loss: 0.1827\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 910us/step - loss: 0.1818\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.1811\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 912us/step - loss: 0.1827\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 900us/step - loss: 0.1814\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 903us/step - loss: 0.1854\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 912us/step - loss: 0.1785\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 917us/step - loss: 0.1831\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 880us/step - loss: 0.1775\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1820\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.1801\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1792\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1847\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.1841\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 876us/step - loss: 0.1811\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1841\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1785\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1815\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1792\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1829\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1800\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1783\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1797\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1846\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1790\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1815\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1801\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1803\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1824\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1849\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.1835\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1797\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1805\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1796\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.1807\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1794\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 938us/step - loss: 0.1808\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 912us/step - loss: 0.1790\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 941us/step - loss: 0.1797\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 930us/step - loss: 0.1804\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 933us/step - loss: 0.1838\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 916us/step - loss: 0.1832\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 930us/step - loss: 0.1819\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 935us/step - loss: 0.1800\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 919us/step - loss: 0.1789\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 910us/step - loss: 0.1787\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1784\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1846\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1826\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1802\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1792\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1786\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1802\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1781\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1800\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1821\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1789\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.1798\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1815\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1799\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1811\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1785\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1776\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 943us/step - loss: 0.1784\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 905us/step - loss: 0.1819\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 924us/step - loss: 0.1771\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 936us/step - loss: 0.1799\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 923us/step - loss: 0.1780\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 916us/step - loss: 0.1773\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 933us/step - loss: 0.1769\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1770\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1766\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.1768\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.1794\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1799\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1768\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1805\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1782\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1843\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1763\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 777us/step - loss: 0.1790\\n\",\n      \"Epoch 386/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1781\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1771\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1809\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1807\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1792\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1767\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1767\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1763\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1768\\n\",\n      \"Epoch 395/1000\\n\",\n      \"13/13 [==============================] - 0s 875us/step - loss: 0.1789\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 909us/step - loss: 0.1801\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1805\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 891us/step - loss: 0.1783\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1775\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 904us/step - loss: 0.1796\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 891us/step - loss: 0.1776\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 906us/step - loss: 0.1771\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 898us/step - loss: 0.1765\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 883us/step - loss: 0.1775\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 896us/step - loss: 0.1753\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 880us/step - loss: 0.1759\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 890us/step - loss: 0.1776\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1779\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 905us/step - loss: 0.1759\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1798\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 875us/step - loss: 0.1807\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1778\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 887us/step - loss: 0.1771\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1760\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1760\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1782\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1756\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1762\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1756\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1773\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1761\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1753\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1777\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 880us/step - loss: 0.1754\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1779\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 918us/step - loss: 0.1781\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 894us/step - loss: 0.1739\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 888us/step - loss: 0.1757\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 913us/step - loss: 0.1755\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 855us/step - loss: 0.1775\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1775\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1773\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1777\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.1781\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1761\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 896us/step - loss: 0.1775\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1788\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1762\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1752\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1742\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 858us/step - loss: 0.1765\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1776\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1755\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1773\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1763\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1764\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1792\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1746\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1752\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.1773\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1772\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 865us/step - loss: 0.1764\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1754\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1748\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1752\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1753\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1785\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1744\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1758\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1759\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1750\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1745\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1792\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1752\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1756\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1752\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1774\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1748\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1767\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1813\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1793\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1748\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1762\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1822\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1788\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1760\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1758\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 781us/step - loss: 0.1763\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1751\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1749\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1742\\n\",\n      \"Epoch 482/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1745\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1763\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1767\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1780\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1739\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1781\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1755\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1766\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.1783\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1769\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1752\\n\",\n      \"Epoch 493/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1772\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1739\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1750\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 777us/step - loss: 0.1798\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1744\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 780us/step - loss: 0.1750\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1750\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1735\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1783\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1749\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1749\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1741\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1767\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1752\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1764\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1719\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 865us/step - loss: 0.1791\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1746\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1786\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1737\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1781\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 862us/step - loss: 0.1766\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1730\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1738\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1729\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1747\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1759\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1748\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1762\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1750\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 944us/step - loss: 0.1751\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 958us/step - loss: 0.1747\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 953us/step - loss: 0.1739\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 936us/step - loss: 0.1731\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 924us/step - loss: 0.1783\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 910us/step - loss: 0.1810\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 898us/step - loss: 0.1770\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 939us/step - loss: 0.1740\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 946us/step - loss: 0.1743\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 920us/step - loss: 0.1759\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 937us/step - loss: 0.1786\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 904us/step - loss: 0.1766\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 929us/step - loss: 0.1755\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1749\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1713\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1774\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1741\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1774\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1734\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1754\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.1735\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 958us/step - loss: 0.1758\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 923us/step - loss: 0.1723\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1786\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1743\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1750\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1747\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1768\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1732\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1736\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1725\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1748\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1733\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1727\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1754\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1781\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1805\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1764\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1784\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1715\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.1730\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 919us/step - loss: 0.1733\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 902us/step - loss: 0.1718\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 898us/step - loss: 0.1750\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1751\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1728\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1730\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 852us/step - loss: 0.1761\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 889us/step - loss: 0.1798\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 935us/step - loss: 0.1762\\n\",\n      \"Epoch 573/1000\\n\",\n      \"13/13 [==============================] - 0s 916us/step - loss: 0.1727\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 906us/step - loss: 0.1722\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1717\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 899us/step - loss: 0.1730\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 898us/step - loss: 0.1751\\n\",\n      \"Epoch 578/1000\\n\"\n     ]\n    },\n   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586/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1730\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1728\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1718\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1710\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1787\\n\",\n      \"Epoch 591/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1789\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1745\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1775\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1727\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1738\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1746\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1734\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1738\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1707\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1735\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 782us/step - loss: 0.1731\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1727\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1722\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1720\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1747\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1770\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 782us/step - loss: 0.1741\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 781us/step - loss: 0.1748\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1731\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1743\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1725\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1706\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1732\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1746\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1729\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1711\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1722\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1802\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1725\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1773\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1710\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1746\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1728\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1709\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1776\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.1717\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1728\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 782us/step - loss: 0.1711\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1732\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1719\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1711\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1752\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 781us/step - loss: 0.1731\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1758\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1713\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1744\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1728\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1725\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1718\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1732\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1736\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1700\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1705\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1725\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1711\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1723\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1719\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1718\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1740\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.1737\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1705\\n\",\n      \"Epoch 652/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1699\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1712\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1704\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1705\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1701\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1701\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1739\\n\",\n      \"Epoch 659/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1712\\n\",\n      \"Epoch 660/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1697\\n\",\n      \"Epoch 661/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1718\\n\",\n      \"Epoch 662/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1720\\n\",\n      \"Epoch 663/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1725\\n\",\n      \"Epoch 664/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1694\\n\",\n      \"Epoch 665/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1700\\n\",\n      \"Epoch 666/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1740\\n\",\n      \"Epoch 667/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1693\\n\",\n      \"Epoch 668/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1722\\n\",\n      \"Epoch 669/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1732\\n\",\n      \"Epoch 670/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1704\\n\",\n      \"Epoch 671/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1696\\n\",\n      \"Epoch 672/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1733\\n\",\n      \"Epoch 673/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1726\\n\",\n      \"Epoch 674/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1740\\n\",\n      \"Epoch 675/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1699\\n\",\n      \"Epoch 676/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1712\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1711\\n\",\n      \"Epoch 678/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1718\\n\",\n      \"Epoch 679/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1795\\n\",\n      \"Epoch 680/1000\\n\",\n      \"13/13 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\"Epoch 689/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1705\\n\",\n      \"Epoch 690/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1698\\n\",\n      \"Epoch 691/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1721\\n\",\n      \"Epoch 692/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1712\\n\",\n      \"Epoch 693/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1716\\n\",\n      \"Epoch 694/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1692\\n\",\n      \"Epoch 695/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1718\\n\",\n      \"Epoch 696/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1704\\n\",\n      \"Epoch 697/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1711\\n\",\n      \"Epoch 698/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1708\\n\",\n      \"Epoch 699/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1702\\n\",\n      \"Epoch 700/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1737\\n\",\n      \"Epoch 701/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1720\\n\",\n      \"Epoch 702/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1701\\n\",\n      \"Epoch 703/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1710\\n\",\n      \"Epoch 704/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1690\\n\",\n      \"Epoch 705/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1719\\n\",\n      \"Epoch 706/1000\\n\",\n      \"13/13 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\"Epoch 715/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1704\\n\",\n      \"Epoch 716/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1749\\n\",\n      \"Epoch 717/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1676\\n\",\n      \"Epoch 718/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1713\\n\",\n      \"Epoch 719/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1690\\n\",\n      \"Epoch 720/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1700\\n\",\n      \"Epoch 721/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1713\\n\",\n      \"Epoch 722/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1712\\n\",\n      \"Epoch 723/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1697\\n\",\n      \"Epoch 724/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1718\\n\",\n      \"Epoch 725/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1741\\n\",\n      \"Epoch 726/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1719\\n\",\n      \"Epoch 727/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1716\\n\",\n      \"Epoch 728/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1713\\n\",\n      \"Epoch 729/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1694\\n\",\n      \"Epoch 730/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1764\\n\",\n      \"Epoch 731/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1758\\n\",\n      \"Epoch 732/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1735\\n\",\n      \"Epoch 733/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1700\\n\",\n      \"Epoch 734/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1698\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1699\\n\",\n      \"Epoch 736/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1716\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1701\\n\",\n      \"Epoch 738/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1720\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1737\\n\",\n      \"Epoch 740/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1730\\n\",\n      \"Epoch 741/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1700\\n\",\n      \"Epoch 742/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1684\\n\",\n      \"Epoch 743/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1713\\n\",\n      \"Epoch 744/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1695\\n\",\n      \"Epoch 745/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1715\\n\",\n      \"Epoch 746/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1690\\n\",\n      \"Epoch 747/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1706\\n\",\n      \"Epoch 748/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1687\\n\",\n      \"Epoch 749/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1694\\n\",\n      \"Epoch 750/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1700\\n\",\n      \"Epoch 751/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1697\\n\",\n      \"Epoch 752/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1696\\n\",\n      \"Epoch 753/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1707\\n\",\n      \"Epoch 754/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1719\\n\",\n      \"Epoch 755/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1716\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1766\\n\",\n      \"Epoch 757/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1752\\n\",\n      \"Epoch 758/1000\\n\",\n      \"13/13 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\"Epoch 767/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1705\\n\",\n      \"Epoch 768/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1699\\n\",\n      \"Epoch 769/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1701\\n\",\n      \"Epoch 770/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1693\\n\",\n      \"Epoch 771/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1708\\n\",\n      \"Epoch 772/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1693\\n\",\n      \"Epoch 773/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1697\\n\",\n      \"Epoch 774/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1712\\n\",\n      \"Epoch 775/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1704\\n\",\n      \"Epoch 776/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1681\\n\",\n      \"Epoch 777/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1704\\n\",\n      \"Epoch 778/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1721\\n\",\n      \"Epoch 779/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.1706\\n\",\n      \"Epoch 780/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1747\\n\",\n      \"Epoch 781/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1722\\n\",\n      \"Epoch 782/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1714\\n\",\n      \"Epoch 783/1000\\n\",\n      \"13/13 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835us/step - loss: 0.1668\\n\",\n      \"Epoch 801/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1703\\n\",\n      \"Epoch 802/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1683\\n\",\n      \"Epoch 803/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1704\\n\",\n      \"Epoch 804/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1701\\n\",\n      \"Epoch 805/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1691\\n\",\n      \"Epoch 806/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1712\\n\",\n      \"Epoch 807/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1679\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1688\\n\",\n      \"Epoch 809/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1704\\n\",\n      \"Epoch 810/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1699\\n\",\n      \"Epoch 811/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1693\\n\",\n      \"Epoch 812/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1678\\n\",\n      \"Epoch 813/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1694\\n\",\n      \"Epoch 814/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1676\\n\",\n      \"Epoch 815/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1698\\n\",\n      \"Epoch 816/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1717\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1712\\n\",\n      \"Epoch 818/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1681\\n\",\n      \"Epoch 819/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1723\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1733\\n\",\n      \"Epoch 821/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1692\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1745\\n\",\n      \"Epoch 823/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1762\\n\",\n      \"Epoch 824/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1713\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1697\\n\",\n      \"Epoch 826/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1698\\n\",\n      \"Epoch 827/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1720\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.1696\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1707\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1693\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.1691\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.1689\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 0s 885us/step - loss: 0.1716\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1669\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1683\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1673\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1684\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1688\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 855us/step - loss: 0.1695\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.1689\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.1702\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1711\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.1689\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1682\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1694\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1678\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 852us/step - loss: 0.1693\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1707\\n\",\n      \"Epoch 849/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.1699\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1683\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1688\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1751\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 852us/step - loss: 0.1707\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1680\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1688\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1690\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.1676\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.1720\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1691\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 [==============================] - 0s 852us/step - loss: 0.1692\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1705\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.1675\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1715\\n\",\n      \"Epoch 864/1000\\n\",\n      \"13/13 [==============================] - 0s 852us/step - loss: 0.1684\\n\",\n      \"Epoch 865/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.1703\\n\",\n      \"Epoch 866/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1702\\n\",\n      \"Epoch 867/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1695\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1728\\n\",\n      \"Epoch 869/1000\\n\",\n      \"13/13 [==============================] - 0s 892us/step - loss: 0.1682\\n\",\n      \"Epoch 870/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.1681\\n\",\n      \"Epoch 871/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.1684\\n\",\n      \"Epoch 872/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1680\\n\",\n      \"Epoch 873/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1720\\n\",\n      \"Epoch 874/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1705\\n\",\n      \"Epoch 875/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1686\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.1676\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1750\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1728\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1733\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1690\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1721\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1754\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1727\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1697\\n\",\n      \"Epoch 885/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1670\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1675\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1723\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1701\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1677\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1712\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 945us/step - loss: 0.1684\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1695\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1680\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1694\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1683\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1694\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1714\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1682\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1704\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1664\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1683\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1682\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1669\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1688\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1686\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1739\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1693\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1689\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1673\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1700\\n\",\n      \"Epoch 911/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1672\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1672\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1702\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1662\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1716\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1669\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1704\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1659\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1725\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1718\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1670\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1695\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1670\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1672\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1685\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1681\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1698\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1660\\n\",\n      \"Epoch 929/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1704\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1678\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1703\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1700\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1699\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 875us/step - loss: 0.1691\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 877us/step - loss: 0.1689\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1680\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1701\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1681\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1693\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1703\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 924us/step - loss: 0.1674\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1667\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1682\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1706\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1679\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1647\\n\",\n      \"Epoch 947/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1759\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1712\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1679\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1669\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1733\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1662\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1751\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1705\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1661\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1658\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1676\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1718\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1644\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1697\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1654\\n\",\n      \"Epoch 962/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1667\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1757\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 871us/step - loss: 0.1661\\n\",\n      \"Epoch 965/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1713\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.1671\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1697\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1716\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 882us/step - loss: 0.1688\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 941us/step - loss: 0.1672\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 887us/step - loss: 0.1664\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 879us/step - loss: 0.1684\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.1660\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 868us/step - loss: 0.1678\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1675\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1710\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1722\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1648\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1716\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 888us/step - loss: 0.1666\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 930us/step - loss: 0.1666\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.1696\\n\",\n      \"Epoch 983/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.1703\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 855us/step - loss: 0.1655\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1658\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1691\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 904us/step - loss: 0.1665\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 916us/step - loss: 0.1680\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 948us/step - loss: 0.1682\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 947us/step - loss: 0.1664\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 904us/step - loss: 0.1682\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1685\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1672\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 858us/step - loss: 0.1660\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.1705\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1678\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1689\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1701\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1711\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 884us/step - loss: 0.1628\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f9a6c7769d0>\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import logging\\n\",\n    \"logging.getLogger(\\\"tensorflow\\\").setLevel(logging.ERROR)\\n\",\n    \"\\n\",\n    \"# BEGIN UNIT TEST\\n\",\n    \"model_s.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"Simple\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" dense_3 (Dense)             (None, 6)                 18        \\n\",\n      \"                                                                 \\n\",\n      \" dense_4 (Dense)             (None, 6)                 42        \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 60\\n\",\n      \"Trainable params: 60\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\",\n      \"\\u001B[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model_s.summary()\\n\",\n    \"\\n\",\n    \"model_s_test(model_s, classes, X_train.shape[1])\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"Summary should match this (layer instance names may increment )\\n\",\n    \"```\\n\",\n    \"Model: \\\"Simple\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"L1 (Dense)                   (None, 6)                 18        \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L2 (Dense)                   (None, 6)                 42        \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 60\\n\",\n    \"Trainable params: 60\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\\n\",\n    \"  <details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"  \\n\",\n    \"```python\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model_s = Sequential(\\n\",\n    \"    [\\n\",\n    \"        Dense(6, activation = 'relu', name=\\\"L1\\\"),            # @REPLACE\\n\",\n    \"        Dense(classes, activation = 'linear', name=\\\"L2\\\")     # @REPLACE\\n\",\n    \"    ], name = \\\"Simple\\\"\\n\",\n    \")\\n\",\n    \"model_s.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),     # @REPLACE\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.01),     # @REPLACE\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model_s.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")                                   \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"5ef37b7aaa7147abbdc694e9f4b4ac83\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#make a model for plotting routines to call\\n\",\n    \"model_predict_s = lambda Xl: np.argmax(tf.nn.softmax(model_s.predict(Xl)).numpy(),axis=1)\\n\",\n    \"plt_nn(model_predict_s,X_train,y_train, classes, X_cv, y_cv, suptitle=\\\"Simple Model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"This simple models does pretty well. Let's calculate the classification error.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"categorization error, training, simple model, 0.062, complex model: 0.003\\n\",\n      \"categorization error, cv,       simple model, 0.087, complex model: 0.122\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"training_cerr_simple = eval_cat_err(y_train, model_predict_s(X_train))\\n\",\n    \"cv_cerr_simple = eval_cat_err(y_cv, model_predict_s(X_cv))\\n\",\n    \"print(f\\\"categorization error, training, simple model, {training_cerr_simple:0.3f}, complex model: {training_cerr_complex:0.3f}\\\" )\\n\",\n    \"print(f\\\"categorization error, cv,       simple model, {cv_cerr_simple:0.3f}, complex model: {cv_cerr_complex:0.3f}\\\" )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Our simple model has a little higher classification error on training data but does better on cross-validation data than the more complex model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"6\\\"></a>\\n\",\n    \"## 6 - Regularization\\n\",\n    \"As in the case of polynomial regression, one can apply regularization to moderate the impact of a more complex model. Let's try this below.\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex05\\\"></a>\\n\",\n    \"### Exercise 5\\n\",\n    \"\\n\",\n    \"Reconstruct your complex model, but this time include regularization.\\n\",\n    \"Below, compose a three-layer model:\\n\",\n    \"* Dense layer with 120 units, relu activation, `kernel_regularizer=tf.keras.regularizers.l2(0.1)`\\n\",\n    \"* Dense layer with 40 units, relu activation, `kernel_regularizer=tf.keras.regularizers.l2(0.1)`\\n\",\n    \"* Dense layer with 6 units and a linear activation. \\n\",\n    \"Compile using\\n\",\n    \"* loss with `SparseCategoricalCrossentropy`, remember to use  `from_logits=True`\\n\",\n    \"* Adam optimizer with learning rate of 0.01.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C5\\n\",\n    \"# GRADED CELL: model_r\\n\",\n    \"\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model_r = Sequential(\\n\",\n    \"    [\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        tf.keras.layers.Dense(120, activation=\\\"relu\\\", kernel_regularizer=tf.keras.regularizers.l2(0.1)),\\n\",\n    \"        tf.keras.layers.Dense(40, activation=\\\"relu\\\", kernel_regularizer=tf.keras.regularizers.l2(0.1)),\\n\",\n    \"        tf.keras.layers.Dense(6, activation=\\\"linear\\\")\\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"    ], name= None\\n\",\n    \")\\n\",\n    \"model_r.compile(\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    loss=SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(lr=0.01),\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 4.4464\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.7086\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.3465\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 1.0870\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.0137\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9718\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9481\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.8934\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8171\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7715\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7611\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7521\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7430\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7474\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7045\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7056\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7182\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7126\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6868\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6733\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6572\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6630\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6508\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6395\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6603\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7651\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6369\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6122\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6002\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6216\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6096\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6260\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6151\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6551\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6538\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6324\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5940\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5739\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5686\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5697\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5845\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5564\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5791\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5855\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5822\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5683\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5278\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5762\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5532\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5313\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5409\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5302\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5362\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5209\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5680\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5131\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5216\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5181\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5470\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5524\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step 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1ms/step - loss: 0.5156\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5115\\n\",\n      \"Epoch 72/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5003\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4989\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5097\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5001\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5060\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4977\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5227\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 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97/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4709\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4669\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4961\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4954\\n\",\n      \"Epoch 101/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4874\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4759\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4739\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4682\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5125\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4548\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4610\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4702\\n\",\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4565\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4568\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4550\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4541\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4450\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4411\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4398\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4482\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4724\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4591\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4686\\n\",\n      \"Epoch 120/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4736\\n\",\n      \"Epoch 121/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5020\\n\",\n      \"Epoch 122/1000\\n\",\n      \"13/13 [==============================] - 0s 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140/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4627\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4332\\n\",\n      \"Epoch 142/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4201\\n\",\n      \"Epoch 143/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4340\\n\",\n      \"Epoch 144/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4382\\n\",\n      \"Epoch 145/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4264\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4260\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4603\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4396\\n\",\n      \"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4239\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4208\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4169\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4201\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4391\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4230\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4316\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4312\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4280\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4210\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4066\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4302\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4433\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4284\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4102\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4265\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4454\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 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175/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4200\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4431\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4323\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4162\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4214\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4130\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4324\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4232\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4093\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4030\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4055\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4087\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4134\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4165\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3974\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3971\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4116\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4153\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4132\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4158\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4026\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3953\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4191\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3963\\n\",\n      \"Epoch 199/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4080\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4032\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4268\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3954\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3980\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4088\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4571\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4315\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4097\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4166\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4393\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4124\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4216\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4118\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4038\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4036\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3945\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4068\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3940\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4194\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3976\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3994\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3873\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4067\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4034\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4393\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4334\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4213\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4377\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3912\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4028\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4112\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4021\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4107\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3893\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3889\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3881\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3966\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3954\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4168\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4049\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3863\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3890\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3908\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3888\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3984\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3993\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4078\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3814\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3897\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3995\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3910\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4142\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4036\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3950\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4073\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4041\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3808\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4020\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3885\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3947\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3841\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4000\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4665\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4367\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3957\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3989\\n\",\n      \"Epoch 266/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4251\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4346\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4114\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3832\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3787\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3874\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3891\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4039\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3776\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3903\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3870\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3825\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3812\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4026\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3938\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3764\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3800\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3876\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3853\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4070\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3956\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3915\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3877\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3760\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3892\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3911\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3697\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3800\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4007\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4066\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3768\\n\",\n      \"Epoch 297/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3841\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3884\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3926\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4250\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3915\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3894\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3858\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 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313/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3863\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3843\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3822\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3789\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3808\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3742\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3791\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3836\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3935\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3927\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4023\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4109\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3989\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3860\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3807\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3919\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3763\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3669\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3715\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3724\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4101\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3930\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3933\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3975\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4038\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3737\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3719\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3868\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3792\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3749\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3693\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3644\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3633\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3662\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3888\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4182\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3776\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4027\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3697\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3903\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3757\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3691\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3733\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3651\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3814\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3961\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3892\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3938\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4104\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4556\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4061\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3714\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3674\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3638\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3693\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3912\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3991\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3732\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3608\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3611\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3791\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3565\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3797\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3772\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3616\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3748\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3832\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3814\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4119\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3712\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3780\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3642\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3681\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3574\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3764\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3717\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3674\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3531\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3664\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3819\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3605\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3635\\n\",\n      \"Epoch 395/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3932\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3799\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3915\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3771\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3753\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3727\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3584\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3613\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3600\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3617\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3545\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3600\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3698\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3630\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3818\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3842\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3936\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3794\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3626\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3576\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3730\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3806\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3915\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3629\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3673\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3534\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3874\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3942\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3729\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3723\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3682\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3655\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3641\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3707\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3673\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3631\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3523\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3592\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3893\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3961\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4097\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3961\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3837\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3836\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3501\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3474\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3626\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3807\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3725\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3662\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3735\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3537\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3685\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3609\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3533\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3551\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3492\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3630\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3763\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3718\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3727\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3628\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3558\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3812\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3643\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3624\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3632\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3509\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3559\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3718\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3495\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3765\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3667\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4002\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4147\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3473\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3688\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4113\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4088\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3998\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3723\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3604\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3805\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3670\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3594\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3609\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3550\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3755\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3802\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3782\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3808\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3564\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3470\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3539\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3401\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3561\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3693\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3690\\n\",\n      \"Epoch 493/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3510\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3548\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3525\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3736\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4008\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3497\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3444\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3610\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3546\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3586\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3814\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3645\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3684\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3834\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3581\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3402\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3503\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3488\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3514\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3611\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3482\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3461\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3535\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3595\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3676\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3638\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3670\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3616\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3475\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3659\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3748\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3416\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3484\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3559\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3420\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3476\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3793\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3642\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3761\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3456\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3398\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3614\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3618\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3422\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4039\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3591\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3597\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3934\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4010\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3746\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3709\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3576\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3510\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3669\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3648\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3654\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3436\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3411\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3460\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3460\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3396\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3513\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3890\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3884\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3706\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3578\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3826\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3486\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3443\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3528\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3515\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3615\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3448\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3620\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3439\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3493\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3499\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3386\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3667\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3514\\n\",\n      \"Epoch 573/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3500\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3619\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3435\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3396\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3557\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4221\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3583\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3376\\n\",\n      \"Epoch 581/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3628\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3540\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3571\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3818\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3954\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3669\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3536\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3407\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3348\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3374\\n\",\n      \"Epoch 591/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n   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- 0s 1ms/step - loss: 0.3419\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3529\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3345\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3436\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3594\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3504\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3590\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3738\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3654\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 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617/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3287\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3673\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4033\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3884\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3619\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3834\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3413\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3359\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3319\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3425\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3567\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3715\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3719\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3774\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3697\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3777\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3753\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3749\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3667\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3486\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3488\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3443\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3455\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3583\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3428\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3522\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 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652/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3329\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3478\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3550\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3478\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3361\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3457\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3430\\n\",\n      \"Epoch 659/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3480\\n\",\n      \"Epoch 660/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3667\\n\",\n      \"Epoch 661/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3403\\n\",\n      \"Epoch 662/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3545\\n\",\n      \"Epoch 663/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3889\\n\",\n      \"Epoch 664/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3568\\n\",\n      \"Epoch 665/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3541\\n\",\n      \"Epoch 666/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3520\\n\",\n      \"Epoch 667/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3340\\n\",\n      \"Epoch 668/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3299\\n\",\n      \"Epoch 669/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3509\\n\",\n      \"Epoch 670/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3352\\n\",\n      \"Epoch 671/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3466\\n\",\n      \"Epoch 672/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3784\\n\",\n      \"Epoch 673/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4029\\n\",\n      \"Epoch 674/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4009\\n\",\n      \"Epoch 675/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3426\\n\",\n      \"Epoch 676/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3406\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3369\\n\",\n      \"Epoch 678/1000\\n\",\n      \"13/13 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687/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3424\\n\",\n      \"Epoch 688/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3321\\n\",\n      \"Epoch 689/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3976\\n\",\n      \"Epoch 690/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3724\\n\",\n      \"Epoch 691/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3471\\n\",\n      \"Epoch 692/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3554\\n\",\n      \"Epoch 693/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3445\\n\",\n      \"Epoch 694/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3483\\n\",\n      \"Epoch 695/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3390\\n\",\n      \"Epoch 696/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3378\\n\",\n      \"Epoch 697/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3355\\n\",\n      \"Epoch 698/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3517\\n\",\n      \"Epoch 699/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3456\\n\",\n      \"Epoch 700/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3493\\n\",\n      \"Epoch 701/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3460\\n\",\n      \"Epoch 702/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3256\\n\",\n      \"Epoch 703/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3269\\n\",\n      \"Epoch 704/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3510\\n\",\n      \"Epoch 705/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3470\\n\",\n      \"Epoch 706/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3533\\n\",\n      \"Epoch 707/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3518\\n\",\n      \"Epoch 708/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3458\\n\",\n      \"Epoch 709/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3581\\n\",\n      \"Epoch 710/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3513\\n\",\n      \"Epoch 711/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3361\\n\",\n      \"Epoch 712/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3854\\n\",\n      \"Epoch 713/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3573\\n\",\n      \"Epoch 714/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3398\\n\",\n      \"Epoch 715/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3291\\n\",\n      \"Epoch 716/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3360\\n\",\n      \"Epoch 717/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3615\\n\",\n      \"Epoch 718/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3587\\n\",\n      \"Epoch 719/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4233\\n\",\n      \"Epoch 720/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4165\\n\",\n      \"Epoch 721/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3999\\n\",\n      \"Epoch 722/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3667\\n\",\n      \"Epoch 723/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3688\\n\",\n      \"Epoch 724/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3474\\n\",\n      \"Epoch 725/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3534\\n\",\n      \"Epoch 726/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3492\\n\",\n      \"Epoch 727/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3512\\n\",\n      \"Epoch 728/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3524\\n\",\n      \"Epoch 729/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3441\\n\",\n      \"Epoch 730/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3547\\n\",\n      \"Epoch 731/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3466\\n\",\n      \"Epoch 732/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3483\\n\",\n      \"Epoch 733/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3376\\n\",\n      \"Epoch 734/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3519\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3520\\n\",\n      \"Epoch 736/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3650\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3722\\n\",\n      \"Epoch 738/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3423\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3472\\n\",\n      \"Epoch 740/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3422\\n\",\n      \"Epoch 741/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3447\\n\",\n      \"Epoch 742/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3786\\n\",\n      \"Epoch 743/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3409\\n\",\n      \"Epoch 744/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3318\\n\",\n      \"Epoch 745/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3281\\n\",\n      \"Epoch 746/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3304\\n\",\n      \"Epoch 747/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3277\\n\",\n      \"Epoch 748/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3441\\n\",\n      \"Epoch 749/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3797\\n\",\n      \"Epoch 750/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3511\\n\",\n      \"Epoch 751/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3599\\n\",\n      \"Epoch 752/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4169\\n\",\n      \"Epoch 753/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4063\\n\",\n      \"Epoch 754/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3516\\n\",\n      \"Epoch 755/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3407\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 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765/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3551\\n\",\n      \"Epoch 766/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3396\\n\",\n      \"Epoch 767/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3339\\n\",\n      \"Epoch 768/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3589\\n\",\n      \"Epoch 769/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3521\\n\",\n      \"Epoch 770/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3301\\n\",\n      \"Epoch 771/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3454\\n\",\n      \"Epoch 772/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3471\\n\",\n      \"Epoch 773/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3825\\n\",\n      \"Epoch 774/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3659\\n\",\n      \"Epoch 775/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3377\\n\",\n      \"Epoch 776/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3882\\n\",\n      \"Epoch 777/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3705\\n\",\n      \"Epoch 778/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3279\\n\",\n      \"Epoch 779/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3339\\n\",\n      \"Epoch 780/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3435\\n\",\n      \"Epoch 781/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3393\\n\",\n      \"Epoch 782/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3259\\n\",\n      \"Epoch 783/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3296\\n\",\n      \"Epoch 784/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3298\\n\",\n      \"Epoch 785/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3286\\n\",\n      \"Epoch 786/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3392\\n\",\n      \"Epoch 787/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3368\\n\",\n      \"Epoch 788/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3307\\n\",\n      \"Epoch 789/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3382\\n\",\n      \"Epoch 790/1000\\n\",\n      \"13/13 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799/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3608\\n\",\n      \"Epoch 800/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3491\\n\",\n      \"Epoch 801/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3315\\n\",\n      \"Epoch 802/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3287\\n\",\n      \"Epoch 803/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3276\\n\",\n      \"Epoch 804/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3280\\n\",\n      \"Epoch 805/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3504\\n\",\n      \"Epoch 806/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3500\\n\",\n      \"Epoch 807/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3403\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3552\\n\",\n      \"Epoch 809/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3773\\n\",\n      \"Epoch 810/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3458\\n\",\n      \"Epoch 811/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3324\\n\",\n      \"Epoch 812/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3241\\n\",\n      \"Epoch 813/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3331\\n\",\n      \"Epoch 814/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3376\\n\",\n      \"Epoch 815/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3443\\n\",\n      \"Epoch 816/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3452\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3625\\n\",\n      \"Epoch 818/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3543\\n\",\n      \"Epoch 819/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3300\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3694\\n\",\n      \"Epoch 821/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3836\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3472\\n\",\n      \"Epoch 823/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3578\\n\",\n      \"Epoch 824/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3510\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 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834/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3518\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3238\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3339\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3339\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3434\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3268\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3740\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3566\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3545\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3543\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3347\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3272\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3351\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3570\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3441\\n\",\n      \"Epoch 849/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3220\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3376\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3364\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3501\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3658\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3400\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3381\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3374\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3421\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3686\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3783\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 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869/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3430\\n\",\n      \"Epoch 870/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3601\\n\",\n      \"Epoch 871/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3625\\n\",\n      \"Epoch 872/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3410\\n\",\n      \"Epoch 873/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3373\\n\",\n      \"Epoch 874/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3479\\n\",\n      \"Epoch 875/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3524\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3360\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3316\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3564\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3425\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3270\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3594\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3598\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4354\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3778\\n\",\n      \"Epoch 885/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3704\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3419\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3491\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3509\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3373\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3713\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3285\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3294\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3340\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3266\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3464\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3392\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3304\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3448\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3721\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3583\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3743\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3616\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3491\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3283\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3386\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3571\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3552\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3694\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4247\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3797\\n\",\n      \"Epoch 911/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3910\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3706\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3323\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3561\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3473\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3535\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3453\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3378\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3582\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3751\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3452\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3507\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3225\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3479\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3356\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3285\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3434\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3272\\n\",\n      \"Epoch 929/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3504\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3919\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4201\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3934\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3428\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3645\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3348\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3342\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3461\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3503\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3471\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3407\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3188\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3240\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3440\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3599\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3812\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3393\\n\",\n      \"Epoch 947/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3357\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3297\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3231\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3178\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3111\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3343\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3389\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3572\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3215\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3439\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3319\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3322\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3159\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3218\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3287\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3196\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3408\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3208\\n\",\n      \"Epoch 965/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3241\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3396\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3292\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3362\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3865\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3795\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3494\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3260\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3279\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3238\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3419\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3488\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3278\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3219\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3267\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3458\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3263\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3288\\n\",\n      \"Epoch 983/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3174\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3339\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3361\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3253\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3248\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3199\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3323\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3463\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3422\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3354\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3225\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3282\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3532\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3445\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3738\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3308\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3505\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3514\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f9a684e19d0>\"\n      ]\n     },\n     \"execution_count\": 47,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model_r.fit(\\n\",\n    \"    X_train, y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")\\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"sequential\\\"\\n\",\n      \"_________________________________________________________________\\n\",\n      \" Layer (type)                Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \" dense_5 (Dense)             (None, 120)               360       \\n\",\n      \"                                                                 \\n\",\n      \" dense_6 (Dense)             (None, 40)                4840      \\n\",\n      \"                                                                 \\n\",\n      \" dense_7 (Dense)             (None, 6)                 246       \\n\",\n      \"                                                                 \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 5,446\\n\",\n      \"Trainable params: 5,446\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\",\n      \"ddd\\n\",\n      \"\\u001B[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# BEGIN UNIT TEST\\n\",\n    \"model_r.summary()\\n\",\n    \"\\n\",\n    \"model_r_test(model_r, classes, X_train.shape[1]) \\n\",\n    \"# END UNIT TEST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"Summary should match this (layer instance names may increment )\\n\",\n    \"```\\n\",\n    \"Model: \\\"ComplexRegularized\\\"\\n\",\n    \"_________________________________________________________________\\n\",\n    \"Layer (type)                 Output Shape              Param #   \\n\",\n    \"=================================================================\\n\",\n    \"L1 (Dense)                   (None, 120)               360       \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L2 (Dense)                   (None, 40)                4840      \\n\",\n    \"_________________________________________________________________\\n\",\n    \"L3 (Dense)                   (None, 6)                 246       \\n\",\n    \"=================================================================\\n\",\n    \"Total params: 5,446\\n\",\n    \"Trainable params: 5,446\\n\",\n    \"Non-trainable params: 0\\n\",\n    \"_________________________________________________________________\\n\",\n    \"```\\n\",\n    \"  <details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for more hints</b></font></summary>\\n\",\n    \"  \\n\",\n    \"```python\\n\",\n    \"tf.random.set_seed(1234)\\n\",\n    \"model_r = Sequential(\\n\",\n    \"    [\\n\",\n    \"        Dense(120, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(0.1), name=\\\"L1\\\"), \\n\",\n    \"        Dense(40, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(0.1), name=\\\"L2\\\"),  \\n\",\n    \"        Dense(classes, activation = 'linear', name=\\\"L3\\\")  \\n\",\n    \"    ], name=\\\"ComplexRegularized\\\"\\n\",\n    \")\\n\",\n    \"model_r.compile(\\n\",\n    \"    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \\n\",\n    \"    optimizer=tf.keras.optimizers.Adam(0.01),                             \\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model_r.fit(\\n\",\n    \"    X_train,y_train,\\n\",\n    \"    epochs=1000\\n\",\n    \")                                   \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c167b1cc4971485e87c3a7e9974d0d24\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"#make a model for plotting routines to call\\n\",\n    \"model_predict_r = lambda Xl: np.argmax(tf.nn.softmax(model_r.predict(Xl)).numpy(),axis=1)\\n\",\n    \" \\n\",\n    \"plt_nn(model_predict_r, X_train,y_train, classes, X_cv, y_cv, suptitle=\\\"Regularized\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The results look very similar to the 'ideal' model. Let's check classification error.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"categorization error, training, regularized: 0.072, simple model, 0.062, complex model: 0.003\\n\",\n      \"categorization error, cv,       regularized: 0.066, simple model, 0.087, complex model: 0.122\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"training_cerr_reg = eval_cat_err(y_train, model_predict_r(X_train))\\n\",\n    \"cv_cerr_reg = eval_cat_err(y_cv, model_predict_r(X_cv))\\n\",\n    \"test_cerr_reg = eval_cat_err(y_test, model_predict_r(X_test))\\n\",\n    \"print(f\\\"categorization error, training, regularized: {training_cerr_reg:0.3f}, simple model, {training_cerr_simple:0.3f}, complex model: {training_cerr_complex:0.3f}\\\" )\\n\",\n    \"print(f\\\"categorization error, cv,       regularized: {cv_cerr_reg:0.3f}, simple model, {cv_cerr_simple:0.3f}, complex model: {cv_cerr_complex:0.3f}\\\" )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The simple model is a bit better in the training set than the regularized model but it worse in the cross validation set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"7\\\"></a>\\n\",\n    \"## 7 - Iterate to find optimal regularization value\\n\",\n    \"As you did in linear regression, you can try many regularization values. This code takes several minutes to run. If you have time, you can run it and check the results. If not, you have completed the graded parts of the assignment!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": [],\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.1106\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4281\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3345\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2896\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2867\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2918\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2497\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2298\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2307\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2071\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2115\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2070\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2366\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2261\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2224\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2055\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2044\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2006\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2168\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2047\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2237\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2497\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2113\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2025\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2107\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2000\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1935\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1963\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2188\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2424\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1969\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1950\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1904\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2173\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2074\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1768\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1794\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1733\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1955\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1870\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2128\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1987\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1895\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2073\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2148\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1774\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1886\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1763\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1769\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1763\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2020\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1889\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2035\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1761\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1838\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1774\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1953\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1882\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1860\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1919\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1848\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1630\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1616\\n\",\n      \"Epoch 64/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2008\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1936\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1824\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2092\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2287\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.0913\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0831\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0752\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0733\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0886\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0837\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0866\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0933\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0976\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1150\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0904\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1073\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1296\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1022\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0987\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0846\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0813\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0924\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0799\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0947\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0956\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0788\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1018\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0942\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0780\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0821\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0795\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0924\\n\",\n      \"Epoch 266/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0948\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0767\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0720\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0742\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0747\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0726\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0984\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1074\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0836\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0783\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0799\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1225\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1017\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0990\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1014\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0808\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0798\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0847\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0755\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0631\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.0651\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0602\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0733\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0659\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.0682\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0745\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0848\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0701\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0828\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0741\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0890\\n\",\n      \"Epoch 297/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0800\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0803\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0765\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0733\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0544\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0718\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0877\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0687\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0671\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0575\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0773\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0779\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0696\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0883\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0880\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0707\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0603\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0772\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0660\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0586\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0618\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0588\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0674\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0598\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0670\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0970\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1366\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1148\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0837\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0749\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0746\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0698\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0691\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0541\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0558\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0653\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0593\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0606\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0696\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0713\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0628\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0752\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0723\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0647\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0688\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0793\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0595\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0528\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0552\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0534\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0471\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0491\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0524\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0696\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0690\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0864\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0999\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1094\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1189\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1059\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0655\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0652\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0544\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0545\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0549\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0581\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0506\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0579\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0583\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0607\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0428\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0495\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0721\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0817\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0588\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0516\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0526\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0463\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0447\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0441\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0422\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0391\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0343\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0461\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0442\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0496\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0509\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0479\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0520\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0391\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0394\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0510\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0525\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0666\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0490\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0551\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0689\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0663\\n\",\n      \"Epoch 395/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0844\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0704\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0700\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0591\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0586\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0628\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1717\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1648\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1616\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1326\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1367\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1098\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1122\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1798\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1268\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1123\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0720\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0774\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0661\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0720\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0580\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0572\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0586\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0546\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0573\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0721\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0658\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0686\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0491\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0647\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0465\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0435\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0362\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0411\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0374\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0412\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0391\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0412\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0479\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0436\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0482\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0420\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0347\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0390\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0328\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0371\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0334\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0348\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0370\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0408\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0329\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0318\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0391\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0408\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0346\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0340\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0332\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0325\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0406\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0394\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0584\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0440\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0412\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0468\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0373\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0329\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0390\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0284\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0310\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0348\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0302\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0348\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0350\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0347\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0305\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0369\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0436\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0543\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0477\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0630\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1523\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3248\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1600\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1623\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1206\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0955\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1595\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1626\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1170\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1481\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0686\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0590\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0651\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0575\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0593\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0539\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0451\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0436\\n\",\n      \"Epoch 493/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0484\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0639\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0497\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0787\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0805\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0639\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0504\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0478\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0466\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0419\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0365\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0352\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0368\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0337\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0375\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0317\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0318\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0364\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0337\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0290\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0317\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0320\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0271\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0343\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0308\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0388\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0444\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0381\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0356\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0324\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0292\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0308\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0308\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0365\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0351\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0305\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0320\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0351\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0290\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0329\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0387\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0431\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0414\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0318\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0285\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0278\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0274\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0338\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0262\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0283\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0265\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0267\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0278\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0256\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0302\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0323\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0262\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0288\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0283\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0315\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0411\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0376\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0346\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0296\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0307\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0270\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0268\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0303\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0251\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0267\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0249\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0265\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0297\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0338\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0432\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0483\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1205\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1063\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1035\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1415\\n\",\n      \"Epoch 573/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1534\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1474\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0772\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0691\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0770\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0637\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0528\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0371\\n\",\n      \"Epoch 581/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0356\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0431\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0300\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0309\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0307\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0321\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0266\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0274\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0276\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0267\\n\",\n      \"Epoch 591/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0305\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0278\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0343\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0262\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0254\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0251\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0241\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0269\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0287\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0257\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0254\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0232\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0281\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0247\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0254\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0237\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0253\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0256\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0235\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0290\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0236\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0249\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0253\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0231\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0241\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0253\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0290\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0456\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0647\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1078\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1180\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0837\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0510\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0333\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0327\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0389\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0347\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0342\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0272\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0240\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0235\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0243\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0225\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0222\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.0223\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0215\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0248\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0257\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0213\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0277\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0266\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0320\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0269\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0357\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0321\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0255\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0287\\n\",\n      \"Epoch 652/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0251\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0242\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0239\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0218\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0227\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.0291\\n\",\n      \"Epoch 791/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0283\\n\",\n      \"Epoch 792/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0291\\n\",\n      \"Epoch 793/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0261\\n\",\n      \"Epoch 794/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0294\\n\",\n      \"Epoch 795/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0250\\n\",\n      \"Epoch 796/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0292\\n\",\n      \"Epoch 797/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0286\\n\",\n      \"Epoch 798/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0271\\n\",\n      \"Epoch 799/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0307\\n\",\n      \"Epoch 800/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0298\\n\",\n      \"Epoch 801/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0371\\n\",\n      \"Epoch 802/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 803/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0274\\n\",\n      \"Epoch 804/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0266\\n\",\n      \"Epoch 805/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0260\\n\",\n      \"Epoch 806/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0254\\n\",\n      \"Epoch 807/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0252\\n\",\n      \"Epoch 809/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0280\\n\",\n      \"Epoch 810/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0249\\n\",\n      \"Epoch 811/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0255\\n\",\n      \"Epoch 812/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 813/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0310\\n\",\n      \"Epoch 814/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 815/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0246\\n\",\n      \"Epoch 816/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0263\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0328\\n\",\n      \"Epoch 818/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 819/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0250\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 821/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0252\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0256\\n\",\n      \"Epoch 823/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0299\\n\",\n      \"Epoch 824/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0312\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0243\\n\",\n      \"Epoch 826/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0263\\n\",\n      \"Epoch 827/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0233\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0246\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0262\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0259\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0238\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0221\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0240\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0248\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0253\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0340\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0229\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0294\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0286\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0268\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0283\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0271\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0247\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0235\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0300\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0246\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0244\\n\",\n      \"Epoch 849/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0219\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0258\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0244\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0257\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0220\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0221\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0256\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0211\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0227\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0252\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0224\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0214\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0204\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.0200\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0169\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0161\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0176\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0218\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0161\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0203\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0384\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0292\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0234\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0522\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0851\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0541\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0380\\n\",\n      \"Epoch 983/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0328\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0276\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0227\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0235\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0287\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0170\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0166\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0175\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0149\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0152\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0153\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0142\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0199\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0231\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0199\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0188\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0155\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0172\\n\",\n      \"Finished lambda = 0.0\\n\",\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.1055\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4858\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4067\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3608\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3565\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3595\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3211\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3000\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.2910\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2648\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2734\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2646\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2929\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2762\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3013\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2616\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2628\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2574\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2740\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2536\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2622\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2747\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2742\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2539\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2712\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2506\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2506\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2504\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2647\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2773\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2587\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2579\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2446\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2647\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2664\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2432\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2508\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2304\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2398\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2355\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2703\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2665\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2429\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2581\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2825\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2437\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2321\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2325\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2283\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2255\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2451\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2366\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2477\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2280\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2741\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2435\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2698\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2489\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2588\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2569\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2475\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2257\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2267\\n\",\n      \"Epoch 64/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2697\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2643\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2571\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2815\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2878\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2394\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2338\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2546\\n\",\n      \"Epoch 72/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2465\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2550\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2502\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2468\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2304\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2368\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2341\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2314\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2368\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2401\\n\",\n      \"Epoch 82/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2478\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2346\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2324\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2536\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2255\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2297\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2306\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2300\\n\",\n      \"Epoch 90/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2262\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2189\\n\",\n      \"Epoch 92/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2184\\n\",\n      \"Epoch 93/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2201\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2176\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2427\\n\",\n      \"Epoch 96/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2451\\n\",\n      \"Epoch 97/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2428\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2501\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2412\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2254\\n\",\n      \"Epoch 101/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2411\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2359\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2533\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2353\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2218\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2232\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2330\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2145\\n\",\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2194\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2264\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2220\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2372\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2271\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2141\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2125\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2254\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2180\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2147\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2193\\n\",\n      \"Epoch 120/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2160\\n\",\n      \"Epoch 121/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2220\\n\",\n      \"Epoch 122/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2230\\n\",\n      \"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2187\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2099\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2094\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2328\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2216\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2138\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2163\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2168\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2217\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2144\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2035\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2080\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2124\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2100\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2188\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2184\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2081\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2205\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2095\\n\",\n      \"Epoch 142/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2068\\n\",\n      \"Epoch 143/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2077\\n\",\n      \"Epoch 144/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2204\\n\",\n      \"Epoch 145/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2201\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2146\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2133\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2092\\n\",\n      \"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2115\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2234\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2182\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2191\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2164\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2110\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2173\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2131\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2189\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2196\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2014\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2044\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2024\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2071\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2171\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2202\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2135\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2072\\n\",\n      \"Epoch 167/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2135\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2180\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2160\\n\",\n      \"Epoch 170/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2288\\n\",\n      \"Epoch 171/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2118\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2039\\n\",\n      \"Epoch 173/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2185\\n\",\n      \"Epoch 174/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.2110\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2122\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2098\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2084\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1989\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2045\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2013\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2033\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2129\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2110\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2040\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2081\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2046\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1934\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1943\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2082\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2047\\n\",\n      \"Epoch 199/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2199\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2055\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1974\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1992\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1944\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2274\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1976\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1989\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2099\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2115\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1963\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2017\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2062\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2089\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2148\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2068\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2078\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2014\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2152\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.1886\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1925\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1953\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2075\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2373\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2167\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2053\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1968\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2008\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.2016\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1996\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1887\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2110\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2040\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1890\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1960\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2038\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1948\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1931\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1913\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1912\\n\",\n      \"Epoch 266/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1895\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1990\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1895\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1909\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1946\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1935\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.1983\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1969\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1901\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1988\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1857\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1860\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1946\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1907\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2126\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2023\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1985\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1901\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1820\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1869\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1866\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1950\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1952\\n\",\n      \"Epoch 297/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1884\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2042\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1900\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1985\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2013\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2040\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2127\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1954\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1994\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1881\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1973\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1940\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1895\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1879\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1832\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1879\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1920\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1844\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1927\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1871\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1866\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2143\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1956\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1846\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1823\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1892\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2019\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1810\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1885\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1797\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1900\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1975\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1947\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1871\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1923\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1922\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1962\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2092\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2044\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1936\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1886\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1911\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1960\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1932\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1838\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1832\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1799\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1793\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1950\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1947\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1916\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1930\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1804\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1765\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1839\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1919\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1982\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1934\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1957\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1822\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1815\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1859\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1802\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1887\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1839\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2091\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1962\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1910\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1972\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1994\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1840\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1756\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1775\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1890\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1755\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1778\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1861\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1799\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1866\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1800\\n\",\n      \"Epoch 377/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1793\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1850\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1849\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1833\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1820\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1913\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2015\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1958\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1810\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1816\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1793\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1775\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1880\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1937\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1957\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1833\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1794\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1742\\n\",\n      \"Epoch 395/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1827\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1841\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1877\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1852\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1804\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1793\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1811\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1814\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1797\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1781\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1703\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1769\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1819\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1797\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1801\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1771\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1905\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1850\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1878\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1764\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1702\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1813\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1852\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1756\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1730\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1734\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1806\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1750\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1810\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1855\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1778\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1800\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1719\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1764\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1678\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1733\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1739\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1725\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1822\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1725\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1819\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1952\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1776\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1788\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1701\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1785\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1721\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1750\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1800\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1697\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1742\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1905\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1883\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1722\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1719\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1731\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1819\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1721\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1752\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1742\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1744\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1711\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1748\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1788\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1799\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1711\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1777\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1691\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1777\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1802\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1750\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1755\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1792\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1744\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1654\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1720\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1833\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1737\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1689\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1731\\n\",\n      \"Epoch 475/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1811\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1756\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1820\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1972\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1826\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1786\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1727\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1761\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1784\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1865\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1801\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1695\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1741\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1731\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1732\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1808\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1713\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1770\\n\",\n      \"Epoch 493/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1698\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1808\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1732\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1892\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1761\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1763\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1737\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1731\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1714\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1789\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1904\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1732\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1770\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1708\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1765\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1627\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1732\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1714\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1680\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1712\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1709\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1726\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1670\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1681\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1617\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1680\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1839\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1735\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1882\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1784\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1759\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1657\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1669\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1650\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1754\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1749\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1827\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1671\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1650\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1651\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1686\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1726\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1681\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1750\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1724\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1626\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1664\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1730\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1869\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1679\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1635\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1683\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1704\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1692\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1742\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.1669\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1689\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1717\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1732\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1760\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1676\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1701\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1608\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1627\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1692\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1792\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1907\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1717\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1712\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1690\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1726\\n\",\n      \"Epoch 573/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1668\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1646\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1633\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1623\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1674\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1770\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1626\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1734\\n\",\n      \"Epoch 581/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1641\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1642\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1660\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1715\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1646\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1651\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1687\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1636\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1671\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1664\\n\",\n      \"Epoch 591/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1848\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1959\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1758\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1660\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1688\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1687\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1634\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1754\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1679\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1763\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1678\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1666\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1664\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1667\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1640\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1624\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1660\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1686\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1678\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.1643\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1705\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1785\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1632\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1720\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1634\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1655\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1685\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1638\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1630\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1606\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1633\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1651\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1616\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1572\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1576\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1616\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1724\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1782\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1818\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1664\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1669\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1659\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1644\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1588\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1608\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1689\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1672\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1631\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1641\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1597\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1691\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1637\\n\",\n      \"Epoch 652/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1590\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1591\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1654\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1628\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1575\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1627\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1637\\n\",\n      \"Epoch 659/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1640\\n\",\n      \"Epoch 660/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1630\\n\",\n      \"Epoch 661/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1638\\n\",\n      \"Epoch 662/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1606\\n\",\n      \"Epoch 663/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1622\\n\",\n      \"Epoch 664/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.1693\\n\",\n      \"Epoch 673/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1591\\n\",\n      \"Epoch 674/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1650\\n\",\n      \"Epoch 675/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1610\\n\",\n      \"Epoch 676/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1566\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1623\\n\",\n      \"Epoch 678/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1564\\n\",\n      \"Epoch 679/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1675\\n\",\n      \"Epoch 680/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1742\\n\",\n      \"Epoch 681/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1790\\n\",\n      \"Epoch 682/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1779\\n\",\n      \"Epoch 683/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1753\\n\",\n      \"Epoch 684/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1692\\n\",\n      \"Epoch 685/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1788\\n\",\n      \"Epoch 686/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1874\\n\",\n      \"Epoch 687/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1708\\n\",\n      \"Epoch 688/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1683\\n\",\n      \"Epoch 689/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1634\\n\",\n      \"Epoch 690/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1617\\n\",\n      \"Epoch 691/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1640\\n\",\n      \"Epoch 692/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1608\\n\",\n      \"Epoch 693/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1657\\n\",\n      \"Epoch 694/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1631\\n\",\n      \"Epoch 695/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1630\\n\",\n      \"Epoch 696/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1552\\n\",\n      \"Epoch 697/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1622\\n\",\n      \"Epoch 698/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1611\\n\",\n      \"Epoch 699/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1574\\n\",\n      \"Epoch 700/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1555\\n\",\n      \"Epoch 701/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1579\\n\",\n      \"Epoch 702/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1627\\n\",\n      \"Epoch 703/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1560\\n\",\n      \"Epoch 704/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1624\\n\",\n      \"Epoch 705/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1583\\n\",\n      \"Epoch 706/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1563\\n\",\n      \"Epoch 707/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1612\\n\",\n      \"Epoch 708/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1604\\n\",\n      \"Epoch 709/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1750\\n\",\n      \"Epoch 710/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1753\\n\",\n      \"Epoch 711/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1708\\n\",\n      \"Epoch 712/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1668\\n\",\n      \"Epoch 713/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1671\\n\",\n      \"Epoch 714/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1543\\n\",\n      \"Epoch 715/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1748\\n\",\n      \"Epoch 716/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1604\\n\",\n      \"Epoch 717/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.1570\\n\",\n      \"Epoch 754/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1626\\n\",\n      \"Epoch 755/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1578\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1551\\n\",\n      \"Epoch 757/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1586\\n\",\n      \"Epoch 758/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1532\\n\",\n      \"Epoch 759/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1573\\n\",\n      \"Epoch 760/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1603\\n\",\n      \"Epoch 761/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1582\\n\",\n      \"Epoch 762/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.1536\\n\",\n      \"Epoch 780/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1526\\n\",\n      \"Epoch 781/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1564\\n\",\n      \"Epoch 782/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1557\\n\",\n      \"Epoch 783/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1491\\n\",\n      \"Epoch 784/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1597\\n\",\n      \"Epoch 785/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1565\\n\",\n      \"Epoch 786/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1683\\n\",\n      \"Epoch 787/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1617\\n\",\n      \"Epoch 788/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.1757\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1684\\n\",\n      \"Epoch 826/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1590\\n\",\n      \"Epoch 827/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1590\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1637\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1608\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1513\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1630\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1555\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.1579\\n\",\n      \"Epoch 869/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1508\\n\",\n      \"Epoch 870/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1573\\n\",\n      \"Epoch 871/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1512\\n\",\n      \"Epoch 872/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1483\\n\",\n      \"Epoch 873/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1507\\n\",\n      \"Epoch 874/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1528\\n\",\n      \"Epoch 875/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1563\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1574\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.1552\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1557\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1586\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1580\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1534\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1542\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1603\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1542\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1605\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1594\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1528\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1592\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1519\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1682\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1645\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1666\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1746\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1785\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1790\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1830\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1629\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1628\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1514\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1558\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1518\\n\",\n      \"Epoch 911/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1519\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1464\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1500\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1474\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1558\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1530\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1539\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1493\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1500\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1471\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1483\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1545\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1526\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1526\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1487\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1503\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1488\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1499\\n\",\n      \"Epoch 929/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1495\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1526\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1513\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1522\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1556\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1561\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1499\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1436\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1455\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1599\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1516\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1553\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1578\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1452\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1505\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1507\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1521\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1551\\n\",\n      \"Epoch 947/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1554\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1675\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1505\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1504\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1501\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1580\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1586\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1537\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1565\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1492\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1532\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1590\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1550\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1483\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1471\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1516\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1588\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1493\\n\",\n      \"Epoch 965/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1506\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1461\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1539\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1624\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1565\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1548\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1566\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1498\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1529\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1591\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1545\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1460\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1581\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1575\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1494\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1558\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1530\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1445\\n\",\n      \"Epoch 983/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1529\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1450\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1494\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1509\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1414\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1559\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1550\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1503\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1496\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1508\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1506\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1482\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1551\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1615\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1736\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1754\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1714\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1538\\n\",\n      \"Finished lambda = 0.001\\n\",\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.4887\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7947\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6159\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5300\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4991\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4763\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.4761\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4651\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4366\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4063\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4032\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4115\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4421\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4607\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4457\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.4180\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3981\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3570\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3768\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3689\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3560\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3717\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3560\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3582\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4461\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4211\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4046\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3618\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3376\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3653\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3520\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3440\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3363\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3511\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3597\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3456\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3446\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3259\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3378\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3222\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3507\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3586\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3529\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3379\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3303\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3230\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3333\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3398\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3231\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3151\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3105\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3219\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3330\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3114\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3593\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3489\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3277\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3034\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3298\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3624\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3687\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3334\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3205\\n\",\n      \"Epoch 64/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3533\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3558\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3334\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3456\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3420\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3175\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3176\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3266\\n\",\n      \"Epoch 72/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2945\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3038\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3079\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3156\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3161\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3109\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3183\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2994\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3003\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3084\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3209\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2936\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2986\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3233\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3035\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3108\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3007\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3035\\n\",\n      \"Epoch 90/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2822\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3095\\n\",\n      \"Epoch 92/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2903\\n\",\n      \"Epoch 93/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2827\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2945\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3167\\n\",\n      \"Epoch 96/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2846\\n\",\n      \"Epoch 97/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2896\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3049\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3257\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2916\\n\",\n      \"Epoch 101/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3189\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2933\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3247\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2951\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3002\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2824\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3384\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2741\\n\",\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2800\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2805\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2771\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2853\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2788\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2871\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2790\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2960\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2773\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.2526\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2695\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2641\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2602\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2919\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2728\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2527\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2540\\n\",\n      \"Epoch 261/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2679\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2653\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2528\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2496\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2681\\n\",\n      \"Epoch 266/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2748\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2691\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2595\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2501\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2638\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2488\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2590\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2693\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2487\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2879\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2550\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2592\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2575\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2643\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2568\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2578\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2498\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2571\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2585\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2579\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2528\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2594\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2729\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2664\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2643\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2490\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2434\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2504\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2578\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2623\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2620\\n\",\n      \"Epoch 297/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.2600\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2774\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2600\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2554\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2507\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2488\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2486\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2906\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2616\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2615\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2564\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2498\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2488\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2740\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2479\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2435\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2432\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2625\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.2578\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2636\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2584\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2753\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2667\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2601\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2483\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2725\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2638\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.2456\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2712\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2578\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2887\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2854\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2674\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2514\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2513\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2746\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2747\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2498\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2489\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2508\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2364\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2547\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2383\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2511\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2466\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2412\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2559\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2437\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2488\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2475\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2464\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2670\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2800\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2617\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2426\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2533\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2661\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2635\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2408\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2482\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2569\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2386\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2495\\n\",\n      \"Epoch 395/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2407\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2408\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2595\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2593\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2553\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2609\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2553\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2450\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2569\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.2373\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2435\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2616\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2525\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2561\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2586\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2545\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2499\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2672\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.2458\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2448\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2400\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2629\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2405\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2611\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2648\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2939\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2452\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2321\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2363\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2472\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2394\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2387\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2373\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2383\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2410\\n\",\n      \"Epoch 457/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2328\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2508\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2407\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2385\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2616\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2420\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2382\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2448\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2504\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2577\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2492\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2464\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2335\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2444\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2558\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2628\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2500\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2561\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2550\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2445\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2627\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2382\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2497\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2403\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2397\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2462\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2380\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2496\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2341\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2354\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2446\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2531\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2325\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2684\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2498\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2579\\n\",\n      \"Epoch 493/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2474\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2443\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2402\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2610\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2524\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2309\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2316\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2554\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2426\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2469\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2586\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2434\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2349\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2417\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2423\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2325\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2506\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2414\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2281\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2615\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2466\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2527\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2626\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2465\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.2327\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2332\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.2452\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2321\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2464\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2609\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2537\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2444\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2454\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2386\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2503\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2617\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2843\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2692\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2460\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2337\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2344\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2491\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2367\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2357\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2612\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2328\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2255\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2572\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2541\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2671\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2553\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2462\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2463\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2651\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2532\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2443\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2399\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2355\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2454\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2438\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2343\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2389\\n\",\n      \"Epoch 555/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2409\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2248\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2315\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2424\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2582\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2552\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2392\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2518\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2296\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2423\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2346\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2342\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2471\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2330\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2397\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2283\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2418\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2382\\n\",\n      \"Epoch 573/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2442\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2335\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2367\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2277\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2395\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2603\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2301\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2313\\n\",\n      \"Epoch 581/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2450\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2500\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2311\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2429\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2329\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2245\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2313\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2322\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2269\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2333\\n\",\n      \"Epoch 591/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2522\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2364\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2329\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2228\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2820\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2668\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2413\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2448\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2390\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2366\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2245\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2445\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2743\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2642\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2378\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2433\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2302\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2481\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2356\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2379\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2629\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2389\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2414\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2402\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2316\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2385\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2397\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2352\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2253\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2218\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2407\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2301\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2476\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2301\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2331\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2387\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2347\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2320\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2314\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2271\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2385\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2352\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2366\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2325\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2425\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2644\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2497\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2471\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2270\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2334\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.2302\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2384\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2365\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2362\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.2304\\n\",\n      \"Epoch 806/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2248\\n\",\n      \"Epoch 807/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2460\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2547\\n\",\n      \"Epoch 809/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2359\\n\",\n      \"Epoch 810/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2245\\n\",\n      \"Epoch 811/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2224\\n\",\n      \"Epoch 812/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2189\\n\",\n      \"Epoch 813/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2203\\n\",\n      \"Epoch 814/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2263\\n\",\n      \"Epoch 815/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2176\\n\",\n      \"Epoch 816/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2202\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2299\\n\",\n      \"Epoch 818/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2367\\n\",\n      \"Epoch 819/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2642\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2803\\n\",\n      \"Epoch 821/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2474\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2318\\n\",\n      \"Epoch 823/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2275\\n\",\n      \"Epoch 824/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2407\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2327\\n\",\n      \"Epoch 826/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2186\\n\",\n      \"Epoch 827/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2222\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2474\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2372\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2284\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2329\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2307\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2382\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2240\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2350\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2152\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2193\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2178\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2286\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2240\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2376\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2407\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2402\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2171\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2288\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2238\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2356\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2237\\n\",\n      \"Epoch 849/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2212\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2196\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2211\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2321\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2166\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2278\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2295\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2205\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2324\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2211\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2291\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2217\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2264\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2207\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2172\\n\",\n      \"Epoch 864/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2645\\n\",\n      \"Epoch 865/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2141\\n\",\n      \"Epoch 866/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2284\\n\",\n      \"Epoch 867/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2350\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2147\\n\",\n      \"Epoch 869/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2257\\n\",\n      \"Epoch 870/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2455\\n\",\n      \"Epoch 871/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2240\\n\",\n      \"Epoch 872/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2168\\n\",\n      \"Epoch 873/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2118\\n\",\n      \"Epoch 874/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2201\\n\",\n      \"Epoch 875/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2169\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2210\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2245\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2229\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2236\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2220\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2483\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2266\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2498\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2208\\n\",\n      \"Epoch 885/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2339\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2193\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2172\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2284\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2224\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2220\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2212\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2136\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2337\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2264\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2330\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2417\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2455\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2388\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2360\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2188\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2176\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2218\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2210\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2187\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2268\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2371\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2417\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2359\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2163\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2201\\n\",\n      \"Epoch 911/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2338\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2328\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2200\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2241\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2216\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2247\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2269\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2097\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2288\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2311\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2256\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2331\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2274\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2231\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2187\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2305\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2289\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2198\\n\",\n      \"Epoch 929/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2242\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2250\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2304\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2380\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2341\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2247\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2184\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2141\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2289\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2357\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2224\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2304\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2195\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2152\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2170\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2233\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2180\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2208\\n\",\n      \"Epoch 947/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2318\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2295\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2165\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2121\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2133\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2369\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2243\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2267\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2148\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2183\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2261\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2436\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2156\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2192\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2169\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2213\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2270\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2190\\n\",\n      \"Epoch 965/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2220\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2323\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2295\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2384\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2391\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2178\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2133\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2157\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2138\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2120\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2169\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2236\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2259\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2185\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2188\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2210\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2097\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2217\\n\",\n      \"Epoch 983/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2284\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2144\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2173\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2212\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2123\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2157\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2375\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2285\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2383\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2293\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2309\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2335\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2322\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2391\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2348\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2433\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2373\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2322\\n\",\n      \"Finished lambda = 0.01\\n\",\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 3.0747\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.3029\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9929\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.8433\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7880\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7536\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7371\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7107\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6678\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6252\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6112\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6137\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6009\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6011\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5670\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6132\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5749\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5784\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5563\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5225\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5378\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5401\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5363\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5324\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5579\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6280\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5199\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4948\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4888\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5123\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5159\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5261\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5131\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5373\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5253\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5117\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.4719\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4354\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4829\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4672\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4401\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4549\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4391\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4545\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4341\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3837\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3719\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3816\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3767\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3858\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3643\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3615\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3757\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3786\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3619\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3649\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3631\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3582\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3640\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3485\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3717\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3803\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3586\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3544\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3660\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3993\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4098\\n\",\n      \"Epoch 167/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4308\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3903\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3753\\n\",\n      \"Epoch 170/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3902\\n\",\n      \"Epoch 171/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3694\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.3446\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3566\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3631\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3648\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3652\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3450\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3442\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3720\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3434\\n\",\n      \"Epoch 199/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3497\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3566\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3765\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3436\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3385\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3548\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4030\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3728\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3580\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3369\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3468\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3472\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3458\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3313\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3407\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3521\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3412\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3704\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3462\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3512\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3279\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3197\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3371\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3258\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3119\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3160\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3222\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3377\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3183\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3420\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3303\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3217\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3257\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3154\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3299\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3420\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3200\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3294\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3504\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3978\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3650\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3331\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3313\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3188\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3308\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3506\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3503\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3282\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3192\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3179\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3344\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3136\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3339\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3193\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3120\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3234\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3254\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3262\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3400\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3170\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3292\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3187\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3203\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3096\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3317\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3404\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3233\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3101\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3277\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3357\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3094\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3168\\n\",\n      \"Epoch 395/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3326\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3229\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3487\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3284\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3146\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3178\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3111\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3090\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3129\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3163\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3125\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3159\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3132\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3145\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3349\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3165\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3428\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3275\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3068\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3188\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3195\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3431\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3344\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3110\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3205\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3035\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3506\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3413\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3203\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3189\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3263\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3154\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3148\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3160\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3168\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3069\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3055\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3063\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3159\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3270\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3191\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3133\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3239\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3118\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3383\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3302\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3083\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3117\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3085\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3289\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3238\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3106\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3087\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3072\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3166\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3134\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3405\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3157\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3059\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3127\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3047\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3265\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3154\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.3047\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3221\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2998\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3077\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3022\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3124\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3084\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3180\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3195\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.2960\\n\",\n      \"Epoch 717/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3067\\n\",\n      \"Epoch 718/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3218\\n\",\n      \"Epoch 719/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3385\\n\",\n      \"Epoch 720/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3335\\n\",\n      \"Epoch 721/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3047\\n\",\n      \"Epoch 722/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2957\\n\",\n      \"Epoch 723/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3044\\n\",\n      \"Epoch 724/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2923\\n\",\n      \"Epoch 725/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.3022\\n\",\n      \"Epoch 734/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3208\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3123\\n\",\n      \"Epoch 736/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3176\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3273\\n\",\n      \"Epoch 738/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3049\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3050\\n\",\n      \"Epoch 740/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3130\\n\",\n      \"Epoch 741/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3245\\n\",\n      \"Epoch 742/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3185\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3020\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2999\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.2981\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2876\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2885\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2931\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3015\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2924\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.3098\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3227\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3095\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3019\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3168\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3623\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3255\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2925\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3025\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2911\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2834\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2863\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2999\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3020\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2855\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3085\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2975\\n\",\n      \"Epoch 911/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2906\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2871\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2801\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3079\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2924\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2985\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2970\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2881\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3222\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3140\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3104\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3201\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2898\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3058\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2919\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2899\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3058\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2935\\n\",\n      \"Epoch 929/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3047\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3171\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3025\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2939\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2956\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3315\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2986\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2862\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2846\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3029\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2871\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2850\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2796\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2750\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2966\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3175\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3066\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2863\\n\",\n      \"Epoch 947/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2902\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2894\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2828\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2810\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2752\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3170\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2976\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3142\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.2829\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2975\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2854\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3012\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2825\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2819\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2868\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2812\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3016\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2868\\n\",\n      \"Epoch 965/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2820\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2972\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2907\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3021\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3137\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3155\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2873\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2852\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2840\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2773\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2972\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2983\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2940\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2763\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2868\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2935\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2774\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2858\\n\",\n      \"Epoch 983/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2777\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2900\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2970\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2934\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2745\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2856\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2904\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2982\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2944\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3181\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2999\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2952\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3374\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3398\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3361\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3069\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3320\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3030\\n\",\n      \"Finished lambda = 0.05\\n\",\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 4.3818\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.6833\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.3267\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.0731\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9907\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9480\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.9363\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8857\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8156\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7781\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7658\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7665\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7486\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7418\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7066\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7251\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7118\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7096\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6862\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6676\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6589\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6815\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6695\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6469\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6573\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7559\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6332\\n\",\n      \"Epoch 28/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6171\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6046\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6245\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6138\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6378\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6188\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6609\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6587\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6282\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5878\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5705\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5768\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5739\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5892\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5575\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5815\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5930\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5921\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5793\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5428\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5718\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5572\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5398\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5474\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5298\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5358\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5206\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5577\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5117\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5207\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5135\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5387\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5452\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5715\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5423\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5137\\n\",\n      \"Epoch 64/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5286\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5152\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5087\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5046\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5054\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5075\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4994\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4990\\n\",\n      \"Epoch 72/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4936\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4913\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5040\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4984\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4991\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5025\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5214\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5492\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5199\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5427\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.4486\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4340\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4457\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4411\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4718\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4376\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4273\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4512\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4458\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.4031\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4683\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4416\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4051\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4056\\n\",\n      \"Epoch 266/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4168\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4283\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4139\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3850\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.4071\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3998\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3769\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3799\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3867\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3866\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4087\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3943\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3908\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3862\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3756\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3861\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3891\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3667\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3766\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3952\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4127\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3820\\n\",\n      \"Epoch 297/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.4095\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4046\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3951\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3824\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3739\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3923\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3759\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3641\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3680\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3700\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4051\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3900\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3944\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3973\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4070\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3750\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3673\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3872\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3865\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3781\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3888\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3748\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3696\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3682\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3537\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3577\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3564\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3552\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3467\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3546\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3859\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3999\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4060\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3923\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3745\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3792\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3577\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3494\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3511\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3464\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3608\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3712\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3667\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3665\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3555\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3747\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3676\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3976\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3967\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3440\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3676\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4114\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4012\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3884\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3700\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3609\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3839\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3651\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3499\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3552\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3492\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3707\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3708\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3668\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3713\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3493\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3422\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3490\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3367\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3526\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3671\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3623\\n\",\n      \"Epoch 493/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3560\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3794\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3596\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3588\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3719\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3493\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3342\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3468\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3426\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3447\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3572\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3438\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3417\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3476\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3536\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3602\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3600\\n\",\n      \"Epoch 519/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3615\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3602\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3425\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3611\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3701\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3372\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3461\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3518\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3366\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3418\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3717\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3589\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3710\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3415\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3336\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3588\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3556\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3378\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3470\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3501\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3704\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3771\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3524\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3451\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3323\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3272\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3307\\n\",\n      \"Epoch 591/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3571\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3329\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3372\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3517\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3426\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3557\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3698\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3592\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3457\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3234\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3666\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3905\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3792\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3499\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3506\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3339\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3266\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3235\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3377\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3526\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3644\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3665\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3643\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3580\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3650\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3649\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3430\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3526\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3598\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3512\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3445\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3445\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3431\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3294\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3431\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3590\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.3243\\n\",\n      \"Epoch 814/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3294\\n\",\n      \"Epoch 815/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3447\\n\",\n      \"Epoch 816/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3470\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3561\\n\",\n      \"Epoch 818/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3352\\n\",\n      \"Epoch 819/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3211\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3581\\n\",\n      \"Epoch 821/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3856\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3392\\n\",\n      \"Epoch 823/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3413\\n\",\n      \"Epoch 824/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3403\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3508\\n\",\n      \"Epoch 826/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3330\\n\",\n      \"Epoch 827/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3209\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3372\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3663\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4271\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3414\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3454\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3373\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3413\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3151\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3261\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3255\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3372\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3206\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3671\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3364\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3484\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3382\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3249\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3173\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3168\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3376\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3374\\n\",\n      \"Epoch 849/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3102\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3311\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3324\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3421\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3538\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3361\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3314\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3266\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3325\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3596\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3703\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3410\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3583\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3173\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3128\\n\",\n      \"Epoch 864/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3595\\n\",\n      \"Epoch 865/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3746\\n\",\n      \"Epoch 866/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3448\\n\",\n      \"Epoch 867/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3324\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3221\\n\",\n      \"Epoch 869/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3259\\n\",\n      \"Epoch 870/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3518\\n\",\n      \"Epoch 871/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3502\\n\",\n      \"Epoch 872/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3308\\n\",\n      \"Epoch 873/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3252\\n\",\n      \"Epoch 874/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3369\\n\",\n      \"Epoch 875/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3288\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3326\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3496\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3591\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3391\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3203\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3483\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3583\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4199\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3403\\n\",\n      \"Epoch 885/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3476\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3393\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3329\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3401\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3352\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3756\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3226\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3287\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3324\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3442\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3877\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3498\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3239\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3398\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3520\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3407\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3733\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3786\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3553\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3283\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3257\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3443\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3396\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3518\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3818\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3517\\n\",\n      \"Epoch 911/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3209\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3239\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3153\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3464\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3383\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3459\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3375\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3303\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3524\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3599\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3388\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3469\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3260\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3414\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3271\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3185\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3421\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3281\\n\",\n      \"Epoch 929/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3429\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3786\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4084\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3646\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3359\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3700\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3342\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3338\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3377\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3529\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3584\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3416\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3160\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3160\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3338\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3416\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3511\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3243\\n\",\n      \"Epoch 947/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3321\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3206\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3159\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3080\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3044\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3275\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3315\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3513\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3112\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3282\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3195\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3248\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3153\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3210\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3371\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3212\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3306\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3148\\n\",\n      \"Epoch 965/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3131\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3259\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3215\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3254\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3791\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3720\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3353\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3135\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3205\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3144\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3372\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3273\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3326\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3202\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3281\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3304\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3239\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3219\\n\",\n      \"Epoch 983/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3082\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3298\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3461\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3448\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3458\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3124\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3262\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3221\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3159\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3454\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3152\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3329\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3494\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3437\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3686\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3193\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3471\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3561\\n\",\n      \"Finished lambda = 0.1\\n\",\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 7.3305\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 2.0539\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.7673\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.4216\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.2743\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.2351\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 1.1670\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.0987\\n\",\n      \"Epoch 9/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.0284\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.0016\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.9683\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9504\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9524\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9500\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9075\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.8961\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8946\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8974\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8728\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.8463\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8204\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8321\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8348\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7978\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.8064\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9342\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8211\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7755\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7600\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7868\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7830\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7856\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7800\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7882\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7801\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7287\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7261\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7039\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7075\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7193\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7282\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6996\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7192\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7187\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7053\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6948\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6840\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7291\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6932\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6735\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6735\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6519\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6518\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6390\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6783\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6402\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6405\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6299\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6480\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6389\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6844\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6454\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6270\\n\",\n      \"Epoch 64/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6366\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6236\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6371\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6222\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6146\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6082\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6147\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6117\\n\",\n      \"Epoch 72/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6084\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6030\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6092\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6094\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6126\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6040\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6133\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6300\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6068\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6239\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6064\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5895\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5818\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5913\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5868\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6109\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5921\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5855\\n\",\n      \"Epoch 90/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5764\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5730\\n\",\n      \"Epoch 92/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5754\\n\",\n      \"Epoch 93/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5625\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5611\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5659\\n\",\n      \"Epoch 96/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5567\\n\",\n      \"Epoch 97/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5676\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5687\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5921\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5978\\n\",\n      \"Epoch 101/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5910\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5811\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5670\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5651\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6104\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5725\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5625\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5698\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5614\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5551\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5539\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5501\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5429\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5430\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5422\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5442\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5777\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5710\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5637\\n\",\n      \"Epoch 120/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5547\\n\",\n      \"Epoch 121/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5517\\n\",\n      \"Epoch 122/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5536\\n\",\n      \"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5413\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5441\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5283\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5392\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5473\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5317\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5325\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5271\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5492\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5412\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5121\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5440\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5316\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5315\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5231\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5441\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5339\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5477\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5526\\n\",\n      \"Epoch 142/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5221\\n\",\n      \"Epoch 143/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5245\\n\",\n      \"Epoch 144/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5446\\n\",\n      \"Epoch 145/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5251\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5246\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5687\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5433\\n\",\n      \"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5319\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5268\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5142\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5240\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5221\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5151\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5228\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5165\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5224\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5168\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5120\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5204\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5412\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5509\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5090\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5220\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5446\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5481\\n\",\n      \"Epoch 167/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5393\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5321\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5145\\n\",\n      \"Epoch 170/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5157\\n\",\n      \"Epoch 171/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5067\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5075\\n\",\n      \"Epoch 173/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5133\\n\",\n      \"Epoch 174/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5121\\n\",\n      \"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4920\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5140\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5133\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5101\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5034\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5260\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5079\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4972\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4897\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4984\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5047\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5136\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4977\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4979\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4941\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4839\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4993\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5013\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4866\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4877\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4781\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4782\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4984\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4815\\n\",\n      \"Epoch 199/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4885\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4851\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5004\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4760\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4897\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4938\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5428\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4986\\n\",\n      \"Epoch 207/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5041\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5060\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5147\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4868\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4933\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4781\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4768\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4778\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4822\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4962\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4872\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4994\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4827\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4782\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4634\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4772\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4859\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4898\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4762\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5080\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5122\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4841\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4845\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5012\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5028\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5129\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4736\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4731\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4769\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4821\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4804\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5055\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5058\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4825\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4596\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4687\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4724\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4737\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4775\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4740\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4572\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4732\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4754\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4668\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5030\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4835\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4646\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4846\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4731\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4620\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4765\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4625\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4723\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4722\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4673\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5219\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4987\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4643\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4634\\n\",\n      \"Epoch 266/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4906\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4997\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4965\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4502\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4541\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4623\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4632\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4831\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4564\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4582\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4576\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4575\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4556\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4813\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4732\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4504\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4533\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4590\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4457\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4667\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4691\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4781\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4670\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4534\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4645\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4711\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4496\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4550\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4619\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4962\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4634\\n\",\n      \"Epoch 297/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4523\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4710\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4771\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4646\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4555\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4486\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4648\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4790\\n\",\n      \"Epoch 305/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4483\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4747\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4580\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4517\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4528\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4420\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4358\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4391\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4479\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4500\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4424\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4400\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4350\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4383\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4381\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4483\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4567\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4630\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4733\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5019\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4804\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4697\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4579\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4670\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4432\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4404\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4418\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4393\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4917\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4656\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4601\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4738\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4712\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4765\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4381\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4280\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4316\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.4287\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4270\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4279\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4243\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4278\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4407\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4658\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4492\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4713\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4324\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4444\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4370\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4281\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4370\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4236\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4326\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4509\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4547\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4444\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4543\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4893\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4283\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4384\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4349\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4236\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4330\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4365\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4341\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4451\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4282\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4236\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4346\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4385\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4458\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4615\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4185\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4298\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4391\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4252\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4946\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4597\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4478\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4298\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4357\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4203\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4379\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4361\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4287\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4158\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4257\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4339\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4222\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4364\\n\",\n      \"Epoch 395/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4726\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4845\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4799\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4498\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4580\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4503\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4217\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4205\\n\",\n      \"Epoch 403/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4301\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4372\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4209\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4182\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4315\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4433\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4187\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4314\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4556\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4186\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4261\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4419\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4578\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4202\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4398\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4282\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4175\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4094\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4332\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4391\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4282\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4122\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4146\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4176\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4376\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4367\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4254\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4266\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4066\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4151\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4197\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4438\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4443\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4279\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4354\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4298\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4068\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4072\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4171\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4339\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 5ms/step - loss: 0.4308\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4424\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4486\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4689\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4488\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4233\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4066\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4106\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4080\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4176\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4377\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4549\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.4735\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4743\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4284\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4034\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4337\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4107\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4605\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4719\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4435\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4305\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4702\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4382\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4635\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4194\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4035\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4070\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3948\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4074\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.4184\\n\",\n      \"Epoch 501/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4050\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4194\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4447\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4713\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4511\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4484\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4303\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4030\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4085\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4034\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4171\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4094\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4106\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4130\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4106\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4075\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4169\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4110\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4151\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4168\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4337\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4078\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4504\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4166\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4297\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4195\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3891\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3932\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4283\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4208\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4168\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4060\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3915\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4155\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4210\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3900\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4409\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4066\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3981\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4260\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4895\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4213\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4211\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3997\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4019\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4425\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4326\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4539\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4324\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4510\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4201\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4035\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3899\\n\",\n      \"Epoch 599/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3899\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4048\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3811\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3963\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3960\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4078\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4062\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3806\\n\",\n      \"Epoch 697/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3900\\n\",\n      \"Epoch 698/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4123\\n\",\n      \"Epoch 699/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3941\\n\",\n      \"Epoch 700/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3848\\n\",\n      \"Epoch 701/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3979\\n\",\n      \"Epoch 702/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3843\\n\",\n      \"Epoch 703/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3733\\n\",\n      \"Epoch 704/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.4462\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4072\\n\",\n      \"Epoch 911/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3860\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3820\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3682\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3963\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3815\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3777\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3747\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3570\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3813\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4254\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3919\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3914\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3805\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3982\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3714\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3732\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3935\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3674\\n\",\n      \"Epoch 929/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3576\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4063\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4157\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4325\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4214\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4030\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3826\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3694\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3678\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3851\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3942\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3825\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3634\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3627\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3759\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3871\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3658\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3667\\n\",\n      \"Epoch 947/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3887\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3801\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3629\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3577\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3543\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3914\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3981\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3952\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3657\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3856\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4042\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3691\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3640\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3759\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3745\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3580\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3831\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3613\\n\",\n      \"Epoch 965/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3675\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3850\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3864\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4015\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4358\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4257\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4130\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3814\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3711\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3753\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3828\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3680\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3746\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3636\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3878\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3988\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3750\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3679\\n\",\n      \"Epoch 983/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3571\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3819\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3704\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3774\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3835\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3590\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3715\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3605\\n\",\n      \"Epoch 991/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3784\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3695\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3768\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3748\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3912\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3966\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4233\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3610\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3804\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3814\\n\",\n      \"Finished lambda = 0.2\\n\",\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 9.8240\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 2.2941\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 2.0224\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 1.6617\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.5529\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.5145\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.4504\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.4126\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 1.3823\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.3487\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.2859\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.2361\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 1.2227\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.1711\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.1076\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.0964\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.1096\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 1.0697\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.0372\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.0143\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9934\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.9875\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9718\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9638\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9312\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 1.0507\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.9802\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9319\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8973\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9171\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.9031\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.9235\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8815\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8816\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8773\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.8623\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8511\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8529\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8421\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8842\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.8582\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8142\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.7464\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7317\\n\",\n      \"Epoch 72/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7238\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7240\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7393\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7151\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7296\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7194\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7079\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.7030\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.6948\\n\",\n      \"Epoch 90/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6955\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6906\\n\",\n      \"Epoch 92/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6777\\n\",\n      \"Epoch 93/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6840\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6716\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6764\\n\",\n      \"Epoch 96/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6624\\n\",\n      \"Epoch 97/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6602\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6746\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6932\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7013\\n\",\n      \"Epoch 101/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6920\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6594\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6660\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6576\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/step - loss: 0.6560\\n\",\n      \"Epoch 142/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6105\\n\",\n      \"Epoch 143/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6082\\n\",\n      \"Epoch 144/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6236\\n\",\n      \"Epoch 145/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6155\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6151\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6563\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6250\\n\",\n      \"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6206\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.6015\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5850\\n\",\n      \"Epoch 170/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5979\\n\",\n      \"Epoch 171/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5919\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5865\\n\",\n      \"Epoch 173/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5903\\n\",\n      \"Epoch 174/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5936\\n\",\n      \"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5742\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5849\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5925\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5820\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5750\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5998\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5778\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5776\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5683\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5760\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5767\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5964\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5766\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5787\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5706\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5642\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5839\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5801\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5664\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5593\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5601\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5562\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5705\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5592\\n\",\n      \"Epoch 199/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5657\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5633\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5811\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5524\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.5593\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5552\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5513\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5621\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5765\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5582\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5613\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5630\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5490\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5398\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5513\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5616\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5562\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5407\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5777\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5613\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5426\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5774\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6289\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6246\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6114\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5505\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5442\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5481\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5558\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5640\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5762\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5807\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5607\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5305\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5363\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5389\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5349\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5480\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5423\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5265\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5461\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5600\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5393\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5742\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5605\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5376\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5612\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5493\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5328\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5379\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5341\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5378\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5596\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5316\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5894\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5886\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5514\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5341\\n\",\n      \"Epoch 266/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5542\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5488\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5537\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5158\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5175\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5335\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5297\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5438\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5211\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5224\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5301\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5300\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5242\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5581\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5602\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5229\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5211\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5244\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5133\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5264\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5398\\n\",\n      \"Epoch 287/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5664\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5609\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5300\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5308\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5363\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5152\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5233\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5243\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5526\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5256\\n\",\n      \"Epoch 297/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5166\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5176\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5311\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5199\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5168\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5179\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5326\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5514\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5159\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5327\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5149\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5062\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5167\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5128\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4996\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5101\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5197\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5244\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5149\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5031\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5038\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5014\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5003\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5168\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5150\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5330\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5399\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5837\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5624\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5438\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5508\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5268\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5066\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5103\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5066\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5149\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5705\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5294\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5344\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5519\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5316\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5338\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5176\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4924\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4977\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4918\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4912\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4919\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4882\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4927\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5046\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5200\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5069\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5078\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5019\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5059\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4981\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4918\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4945\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4885\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4901\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5085\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5091\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5016\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5009\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5345\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5000\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4916\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4995\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4880\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4875\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5087\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4973\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4983\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4931\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4857\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4981\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5115\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5029\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5152\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4865\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4977\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5074\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4781\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5396\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5069\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5307\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4899\\n\",\n      \"Epoch 385/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4973\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4799\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4963\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5063\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4968\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4818\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4857\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4882\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4835\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4820\\n\",\n      \"Epoch 395/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5112\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5247\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5552\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4918\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4983\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5034\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4922\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4887\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4894\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5069\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4901\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4851\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4993\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5236\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4846\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4871\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5102\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4781\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4878\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5123\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5582\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4744\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5019\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4824\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4802\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4689\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4928\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4989\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4960\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4718\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4728\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4741\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5050\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5020\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4834\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4839\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4712\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4728\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4744\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4895\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4844\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4811\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4876\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4867\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4849\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4659\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4843\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4955\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4792\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4975\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5037\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5413\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5125\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5104\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4718\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4688\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4700\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4748\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4865\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4990\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5044\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5010\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4604\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4743\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4600\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4704\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4734\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4611\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4615\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4810\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4620\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5179\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4669\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4898\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5280\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4852\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4716\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4931\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5223\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5125\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4927\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4665\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4745\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4668\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5035\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5107\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5189\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4660\\n\",\n      \"Epoch 483/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5081\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4698\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4998\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4739\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4748\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4635\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4543\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4666\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4733\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4962\\n\",\n      \"Epoch 493/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4731\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4574\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4664\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4773\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5681\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5085\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4731\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4930\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4552\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4649\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4852\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5127\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4980\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5039\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5173\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4811\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4696\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4579\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4757\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4565\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4569\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4735\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4653\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4670\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4640\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4570\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4549\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4687\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5037\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4801\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4854\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.4533\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4593\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4736\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4417\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4931\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4620\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4472\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4599\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5128\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4718\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4651\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4963\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4984\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4598\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4789\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4522\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4681\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4607\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4750\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4620\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4565\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4528\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4882\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4818\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4633\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4524\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4792\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4767\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4426\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4503\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4448\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4689\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4630\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4600\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4599\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4619\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4586\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4650\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4613\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4779\\n\",\n      \"Epoch 573/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4621\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4679\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4824\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4483\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4471\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5203\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4738\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4563\\n\",\n      \"Epoch 581/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4932\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4847\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4794\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4588\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4756\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4991\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4629\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4511\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4323\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4349\\n\",\n      \"Epoch 591/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4466\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4427\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4522\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4548\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5090\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4749\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4448\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4417\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4393\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4579\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4340\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4422\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4395\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4469\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4555\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4957\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.4595\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4698\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4383\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.4673\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4526\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4782\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4487\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4537\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4697\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4668\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4403\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4591\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5116\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4663\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4548\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4472\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4549\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4462\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4301\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4519\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4422\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4559\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4503\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4516\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4514\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4550\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4590\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4976\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4465\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4611\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4314\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4370\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4393\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4922\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4625\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5074\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5327\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4769\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5086\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4901\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4299\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4488\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4439\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4454\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4439\\n\",\n      \"Epoch 652/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4303\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4432\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4530\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4660\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4512\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4454\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.4151\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4369\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4382\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4563\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4236\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4180\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4094\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4167\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4342\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4484\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4351\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4410\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4283\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4515\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4804\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4197\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4352\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4425\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4208\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4370\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4538\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4194\\n\",\n      \"Epoch 849/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4411\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4484\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4477\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4351\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4319\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4269\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4171\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4179\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4200\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4226\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4801\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4219\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4458\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4490\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4100\\n\",\n      \"Epoch 864/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4665\\n\",\n      \"Epoch 865/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4699\\n\",\n      \"Epoch 866/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4448\\n\",\n      \"Epoch 867/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4305\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4167\\n\",\n      \"Epoch 869/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4149\\n\",\n      \"Epoch 870/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4555\\n\",\n      \"Epoch 871/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4461\\n\",\n      \"Epoch 872/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4055\\n\",\n      \"Epoch 873/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4015\\n\",\n      \"Epoch 874/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4146\\n\",\n      \"Epoch 875/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4179\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4315\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4150\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4233\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4197\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4269\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4417\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4555\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4783\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4646\\n\",\n      \"Epoch 885/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4510\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4065\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4240\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4344\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4485\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4250\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4140\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4319\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4683\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4260\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4523\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4118\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4270\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4296\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4432\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4243\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4281\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4253\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4413\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4084\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4166\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4271\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4333\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4262\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4665\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4322\\n\",\n      \"Epoch 911/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4380\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4384\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4129\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4369\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4126\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3965\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4115\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4059\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4157\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4617\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4539\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4337\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4489\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4879\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4501\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4430\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4677\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4154\\n\",\n      \"Epoch 929/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4102\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4601\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5159\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5052\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4658\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4520\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4250\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4249\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4062\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4344\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4334\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4134\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4028\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4031\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4098\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4279\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4228\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4177\\n\",\n      \"Epoch 947/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4441\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4412\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4108\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4060\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4017\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4060\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4185\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4438\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4277\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4673\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5126\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4279\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4095\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4276\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4277\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4032\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4115\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4028\\n\",\n      \"Epoch 965/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4090\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4175\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4125\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4216\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4576\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4428\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4425\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4371\\n\",\n      \"Epoch 973/1000\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4121\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4125\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4048\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4070\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4101\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4041\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4241\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4539\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4386\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4308\\n\",\n      \"Epoch 983/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4063\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4202\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4127\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4018\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4308\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3997\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4064\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3948\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4166\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4044\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4087\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4489\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4242\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4068\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4396\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4108\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4226\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4581\\n\",\n      \"Finished lambda = 0.3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tf.random.set_seed(1234)\\n\",\n    \"lambdas = [0.0, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3]\\n\",\n    \"models=[None] * len(lambdas)\\n\",\n    \"for i in range(len(lambdas)):\\n\",\n    \"    lambda_ = lambdas[i]\\n\",\n    \"    models[i] =  Sequential(\\n\",\n    \"        [\\n\",\n    \"            Dense(120, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(lambda_)),\\n\",\n    \"            Dense(40, activation = 'relu', kernel_regularizer=tf.keras.regularizers.l2(lambda_)),\\n\",\n    \"            Dense(classes, activation = 'linear')\\n\",\n    \"        ]\\n\",\n    \"    )\\n\",\n    \"    models[i].compile(\\n\",\n    \"        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"        optimizer=tf.keras.optimizers.Adam(0.01),\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"    models[i].fit(\\n\",\n    \"        X_train,y_train,\\n\",\n    \"        epochs=1000\\n\",\n    \"    )\\n\",\n    \"    print(f\\\"Finished lambda = {lambda_}\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"8a16670741a84fbf8cefa5dd47c3b8b1\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_iterate(lambdas, models, X_train, y_train, X_cv, y_cv)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"As regularization is increased, the performance of the model on the training and cross-validation data sets converge. For this data set and model, lambda > 0.01 seems to be a reasonable choice.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"<a name=\\\"7.1\\\"></a>\\n\",\n    \"### 7.1 Test\\n\",\n    \"Let's try our optimized models on the test set and compare them to 'ideal' performance. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"170d36fa0a9f4160b28ef7d6c55981f1\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt_compare(X_test,y_test, classes, model_predict_s, model_predict_r, centers)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Our test set is small and seems to have a number of outliers so classification error is high. However, the performance of our optimized models is comparable to ideal performance.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Congratulations! \\n\",\n    \"You have become familiar with important tools to apply when evaluating your machine learning models. Namely:  \\n\",\n    \"* splitting data into trained and untrained sets allows you to differentiate between underfitting and overfitting\\n\",\n    \"* creating three data sets, Training, Cross-Validation and Test allows you to\\n\",\n    \"    * train your parameters $W,B$ with the training set\\n\",\n    \"    * tune model parameters such as complexity, regularization and number of examples with the cross-validation set\\n\",\n    \"    * evaluate your 'real world' performance using the test set.\\n\",\n    \"* comparing training vs cross-validation performance provides insight into a model's propensity towards overfitting (high variance) or underfitting (high bias)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/C2W3A1/assigment_utils.py",
    "content": "\"\"\"\nassignment_utils.py\ncontains routines used by C2_W3 Assignments \n\"\"\"\nimport copy\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib.patches import FancyArrowPatch\nfrom matplotlib.colors import ListedColormap, LinearSegmentedColormap\nfrom matplotlib.widgets import Button, CheckButtons\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.preprocessing import StandardScaler, PolynomialFeatures\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import make_blobs\n\nfrom ipywidgets import Output\nnp.set_printoptions(precision=2)\n\ndlc = dict(dlblue = '#0096ff', dlorange = '#FF9300', dldarkred='#C00000', dlmagenta='#FF40FF', dlpurple='#7030A0', dldarkblue =  '#0D5BDC')\ndlblue = '#0096ff'; dlorange = '#FF9300'; dldarkred='#C00000'; dlmagenta='#FF40FF'; dlpurple='#7030A0'; dldarkblue =  '#0D5BDC'\ndlcolors = [dlblue, dlorange, dldarkred, dlmagenta, dlpurple]\nplt.style.use('./deeplearning.mplstyle')\n\n# --- Assignment ----------------------------------------\ndef gen_data(m, seed=1, scale=0.7):\n    \"\"\" generate a data set based on a x^2 with added noise \"\"\"\n    c = 0\n    x_train = np.linspace(0,49,m)\n    np.random.seed(seed)\n    y_ideal = x_train**2 + c\n    y_train = y_ideal + scale * y_ideal*(np.random.sample((m,))-0.5)\n    x_ideal = x_train #for redraw when new data included in X\n    return x_train, y_train, x_ideal, y_ideal\n\ndef gen_blobs():\n    classes = 6\n    m = 800\n    std = 0.4\n    centers = np.array([[-1, 0], [1, 0], [0, 1], [0, -1],  [-2,1],[-2,-1]])\n    X, y = make_blobs(n_samples=m, centers=centers, cluster_std=std, random_state=2, n_features=2)\n    return (X, y, centers, classes, std)\n\nclass lin_model:\n    def __init__(self, degree, regularization = False, lambda_=0):\n        if regularization:\n            self.linear_model = Ridge(alpha=lambda_)\n        else:\n            self.linear_model = LinearRegression()\n        self.poly = PolynomialFeatures(degree, include_bias=False)\n        self.scaler = StandardScaler()\n        \n    def fit(self, X_train,y_train):\n        ''' just fits the data. mapping and scaling are not repeated '''\n        X_train_mapped = self.poly.fit_transform(X_train.reshape(-1,1))\n        X_train_mapped_scaled = self.scaler.fit_transform(X_train_mapped)\n        self.linear_model.fit(X_train_mapped_scaled, y_train )\n\n    def predict(self, X):\n        X_mapped = self.poly.transform(X.reshape(-1,1))\n        X_mapped_scaled = self.scaler.transform(X_mapped)\n        yhat = self.linear_model.predict(X_mapped_scaled)\n        return(yhat)\n    \n    def mse(self, y, yhat):\n        err = mean_squared_error(y,yhat)/2   #sklean doesn't have div by 2\n        return (err)\n     \ndef plt_train_test(X_train, y_train, X_test, y_test, x, y_pred, x_ideal, y_ideal, degree):\n    fig, ax = plt.subplots(1,1, figsize=(4,4))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\n    ax.set_title(\"Poor Performance on Test Data\",fontsize = 12)\n    ax.set_xlabel(\"x\")\n    ax.set_ylabel(\"y\")\n\n    ax.scatter(X_train, y_train, color = \"red\",           label=\"train\")\n    ax.scatter(X_test, y_test,       color = dlc[\"dlblue\"], label=\"test\")\n    ax.set_xlim(ax.get_xlim())\n    ax.set_ylim(ax.get_ylim())\n    ax.plot(x, y_pred,  lw=0.5, label=f\"predicted, degree={degree}\")\n    ax.plot(x_ideal, y_ideal, \"--\", color = \"orangered\", label=\"y_ideal\", lw=1)\n    ax.legend(loc='upper left')\n    plt.tight_layout()\n    plt.show()\n\ndef plt_optimal_degree(X_train, y_train, X_cv, y_cv, x, y_pred, x_ideal, y_ideal, err_train, err_cv, optimal_degree, max_degree):\n    fig, ax = plt.subplots(1,2,figsize=(8,4))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\n    ax[0].set_title(\"predictions vs data\",fontsize = 12)\n    ax[0].set_xlabel(\"x\")\n    ax[0].set_ylabel(\"y\")\n\n    ax[0].plot(x_ideal, y_ideal, \"--\", color = \"orangered\", label=\"y_ideal\", lw=1)\n    ax[0].scatter(X_train, y_train, color = \"red\",           label=\"train\")\n    ax[0].scatter(X_cv, y_cv,       color = dlc[\"dlorange\"], label=\"cv\")\n    ax[0].set_xlim(ax[0].get_xlim())\n    ax[0].set_ylim(ax[0].get_ylim())\n    for i in range(0,max_degree):\n        ax[0].plot(x, y_pred[:,i],  lw=0.5, label=f\"{i+1}\")\n    ax[0].legend(loc='upper left')\n\n    ax[1].set_title(\"error vs degree\",fontsize = 12)\n    cpts = list(range(1, max_degree+1))\n    ax[1].plot(cpts, err_train[0:], marker='o',label=\"train error\", lw=2,  color = dlc[\"dlblue\"])\n    ax[1].plot(cpts, err_cv[0:],    marker='o',label=\"cv error\",  lw=2, color = dlc[\"dlorange\"])\n    ax[1].set_ylim(*ax[1].get_ylim())\n    ax[1].axvline(optimal_degree, lw=1, color = dlc[\"dlmagenta\"])\n    ax[1].annotate(\"optimal degree\", xy=(optimal_degree,80000),xycoords='data',\n                xytext=(0.3, 0.8), textcoords='axes fraction', fontsize=10,\n                   arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\", \n                                   color=dlc['dldarkred'], lw=1))\n    ax[1].set_xlabel(\"degree\")\n    ax[1].set_ylabel(\"error\")\n    ax[1].legend()\n    fig.suptitle(\"Find Optimal Degree\",fontsize = 12)\n    plt.tight_layout()\n\n    plt.show()\n    \ndef plt_tune_regularization(X_train, y_train, X_cv, y_cv, x, y_pred, err_train, err_cv, optimal_reg_idx, lambda_range):\n    fig, ax = plt.subplots(1,2,figsize=(8,4))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\n    ax[0].set_title(\"predictions vs data\",fontsize = 12)\n    ax[0].set_xlabel(\"x\")\n    ax[0].set_ylabel(\"y\")\n\n    ax[0].scatter(X_train, y_train, color = \"red\",           label=\"train\")\n    ax[0].scatter(X_cv, y_cv,       color = dlc[\"dlorange\"], label=\"cv\")\n    ax[0].set_xlim(ax[0].get_xlim())\n    ax[0].set_ylim(ax[0].get_ylim())\n#   ax[0].plot(x, y_pred[:,:],  lw=0.5, label=[f\"$\\lambda =${i}\" for i in lambda_range])\n    for i in (0,3,7,9):\n        ax[0].plot(x, y_pred[:,i],  lw=0.5, label=f\"$\\lambda =${lambda_range[i]}\")\n    ax[0].legend()\n\n    ax[1].set_title(\"error vs regularization\",fontsize = 12)\n    ax[1].plot(lambda_range, err_train[:], label=\"train error\", color = dlc[\"dlblue\"])\n    ax[1].plot(lambda_range, err_cv[:],    label=\"cv error\",    color = dlc[\"dlorange\"])\n    ax[1].set_xscale('log')\n    ax[1].set_ylim(*ax[1].get_ylim())\n    opt_x = lambda_range[optimal_reg_idx]\n    ax[1].vlines(opt_x, *ax[1].get_ylim(), color = \"black\", lw=1)\n    ax[1].annotate(\"optimal lambda\", (opt_x,150000), xytext=(-80,10), textcoords=\"offset points\",\n                  arrowprops={'arrowstyle':'simple'})\n    ax[1].set_xlabel(\"regularization (lambda)\")\n    ax[1].set_ylabel(\"error\")\n    fig.suptitle(\"Tuning Regularization\",fontsize = 12)\n    ax[1].text(0.05,0.44,\"High\\nVariance\",fontsize=12, ha='left',transform=ax[1].transAxes,color = dlc[\"dlblue\"])\n    ax[1].text(0.95,0.44,\"High\\nBias\",    fontsize=12, ha='right',transform=ax[1].transAxes,color = dlc[\"dlblue\"])\n    ax[1].legend(loc='upper left')\n    plt.tight_layout()\n    plt.show()\n\ndef tune_m():\n    \"\"\" tune the number of examples to reduce overfitting \"\"\"\n    m = 50\n    m_range = np.array(m*np.arange(1,16))\n    num_steps = m_range.shape[0]\n    degree = 16\n    err_train = np.zeros(num_steps)     \n    err_cv = np.zeros(num_steps)        \n    y_pred = np.zeros((100,num_steps))     \n    \n    for i in range(num_steps):\n        X, y, y_ideal, x_ideal = gen_data(m_range[i],5,0.7)\n        x = np.linspace(0,int(X.max()),100)  \n        X_train, X_, y_train, y_ = train_test_split(X,y,test_size=0.40, random_state=1)\n        X_cv, X_test, y_cv, y_test = train_test_split(X_,y_,test_size=0.50, random_state=1)\n\n        lmodel = lin_model(degree)  # no regularization\n        lmodel.fit(X_train, y_train)\n        yhat = lmodel.predict(X_train)\n        err_train[i] = lmodel.mse(y_train, yhat)\n        yhat = lmodel.predict(X_cv)\n        err_cv[i] = lmodel.mse(y_cv, yhat)\n        y_pred[:,i] = lmodel.predict(x)\n    return(X_train, y_train, X_cv, y_cv, x, y_pred, err_train, err_cv, m_range,degree)\n\ndef plt_tune_m(X_train, y_train, X_cv, y_cv, x, y_pred, err_train, err_cv, m_range, degree):\n    \n    fig, ax = plt.subplots(1,2,figsize=(8,4))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\n    ax[0].set_title(\"predictions vs data\",fontsize = 12)\n    ax[0].set_xlabel(\"x\")\n    ax[0].set_ylabel(\"y\")\n\n    ax[0].scatter(X_train, y_train, color = \"red\",           s=3, label=\"train\", alpha=0.4)\n    ax[0].scatter(X_cv, y_cv,       color = dlc[\"dlorange\"], s=3, label=\"cv\",    alpha=0.4)\n    ax[0].set_xlim(ax[0].get_xlim())\n    ax[0].set_ylim(ax[0].get_ylim())\n    for i in range(0,len(m_range),3):\n        ax[0].plot(x, y_pred[:,i],  lw=1, label=f\"$m =${m_range[i]}\")\n    ax[0].legend(loc='upper left')\n    ax[0].text(0.05,0.5,f\"degree = {degree}\", fontsize=10, ha='left',transform=ax[0].transAxes,color = dlc[\"dlblue\"])\n\n    ax[1].set_title(\"error vs number of examples\",fontsize = 12)\n    ax[1].plot(m_range, err_train[:], label=\"train error\", color = dlc[\"dlblue\"])\n    ax[1].plot(m_range, err_cv[:],    label=\"cv error\",    color = dlc[\"dlorange\"])\n    ax[1].set_xlabel(\"Number of Examples (m)\")\n    ax[1].set_ylabel(\"error\")\n    fig.suptitle(\"Tuning number of examples\",fontsize = 12)\n    ax[1].text(0.05,0.5,\"High\\nVariance\",        fontsize=12, ha='left',transform=ax[1].transAxes,color = dlc[\"dlblue\"])\n    ax[1].text(0.95,0.5,\"Good \\nGeneralization\", fontsize=12, ha='right',transform=ax[1].transAxes,color = dlc[\"dlblue\"])\n    ax[1].legend()\n    plt.tight_layout()\n    plt.show()  \n    \ndkcolors = plt.cm.Paired((1,3,7,9,5,11))\nltcolors = plt.cm.Paired((0,2,6,8,4,10))\ndkcolors_map = mpl.colors.ListedColormap(dkcolors)\nltcolors_map = mpl.colors.ListedColormap(ltcolors)\n\ndef plt_mc_data(ax, X, y, classes,  class_labels=None, map=plt.cm.Paired, legend=False,size=50, m='o'):\n    for i in range(classes):\n        idx = np.where(y == i)\n        col = len(idx[0])*[i]\n        label = class_labels[i] if class_labels else \"c{}\".format(i)\n        ax.scatter(X[idx, 0], X[idx, 1],  marker=m,\n                    c=col, vmin=0, vmax=map.N, cmap=map,\n                    s=size, label=label)\n    if legend: ax.legend()\n    ax.axis('equal')\n\n\n#Plot a multi-class categorical decision boundary\n# This version handles a non-vector prediction (adds a for-loop over points)\ndef plot_cat_decision_boundary(ax, X,predict , class_labels=None, legend=False, vector=True, color='g', lw = 1):\n\n    # create a mesh to points to plot\n    pad = 0.5\n    x_min, x_max = X[:, 0].min() - pad, X[:, 0].max() + pad\n    y_min, y_max = X[:, 1].min() - pad, X[:, 1].max() + pad\n    h = max(x_max-x_min, y_max-y_min)/200\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n                         np.arange(y_min, y_max, h))\n    points = np.c_[xx.ravel(), yy.ravel()]\n    #print(\"points\", points.shape)\n    #make predictions for each point in mesh\n    if vector:\n        Z = predict(points)\n    else:\n        Z = np.zeros((len(points),))\n        for i in range(len(points)):\n            Z[i] = predict(points[i].reshape(1,2))\n    Z = Z.reshape(xx.shape)\n\n    #contour plot highlights boundaries between values - classes in this case\n    ax.contour(xx, yy, Z, colors=color, linewidths=lw) \n    ax.axis('tight')\n\ndef recat(pt, origins):\n    \"\"\" categorize a point based on distance from origin of clusters \"\"\"\n    nclusters = len(origins)\n    min_dist = 10000\n    y_new = None\n    for j in range(nclusters):\n        temp = origins[j] - pt.reshape(2,)\n        #print(temp.shape,origins[j].shape)\n        dist = np.sqrt(np.dot(temp.T, temp))\n        if dist < min_dist:\n            y_new = j\n            min_dist = dist\n    return(y_new)\n\ndef plt_train_eq_dist(X_train,y_train,classes, X_cv,   y_cv, centers, std):\n    css = np.unique(y_train)\n    fig,ax = plt.subplots(1,2,figsize=(8,4))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n    plt_mc_data(ax[0], X_train,y_train,classes, map=dkcolors_map, legend=True, size=50)\n    plt_mc_data(ax[0], X_cv,   y_cv,   classes, map=ltcolors_map, legend=True, m=\"<\")\n    ax[0].set_title(\"Training, CV Data\")\n    for c in css:\n        circ = plt.Circle(centers[c], 2*std, color=dkcolors_map(c), clip_on=False, fill=False, lw=0.5)\n        ax[0].add_patch(circ)\n\n\n    #make a model for plotting routines to call\n    cat_predict = lambda pt: recat(pt.reshape(1,2), centers)\n    plot_cat_decision_boundary(ax[1], X_train, cat_predict,  vector=False, color = dlc[\"dlmagenta\"], lw=0.75)\n    ax[1].set_title(\"ideal performance\", fontsize=14)\n\n    #add the original data to the decison boundary\n    plt_mc_data(ax[1], X_train,y_train, classes, map=dkcolors_map, legend=True, size=50)\n    ax[1].set_xlabel('x0') ; ax[1].set_ylabel(\"x1\");\n    plt.show()\n    \n    \ndef plt_nn(model_predict,X_train,y_train, classes, X_cv, y_cv, suptitle=\"\"):\n    #plot the decison boundary.\n    fig,ax = plt.subplots(1,2, figsize=(8,4))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n    plot_cat_decision_boundary(ax[0], X_train, model_predict,  vector=True)\n    ax[0].set_title(\"training data\", fontsize=14)\n\n    #add the original data to the decison boundary\n    plt_mc_data(ax[0], X_train,y_train, classes, map=dkcolors_map, legend=True, size=75)\n    ax[0].set_xlabel('x0') ; ax[0].set_ylabel(\"x1\");\n\n    plot_cat_decision_boundary(ax[1], X_train, model_predict,  vector=True)\n    ax[1].set_title(\"cross-validation data\", fontsize=14)\n    plt_mc_data(ax[1], X_cv,y_cv, classes, \n                map=ltcolors_map, legend=True, size=100, m='<')\n    ax[1].set_xlabel('x0') ; ax[1].set_ylabel(\"x1\"); \n    fig.suptitle(suptitle,fontsize = 12)\n    plt.show()\n\n\ndef eval_cat_err(y, yhat):\n    \"\"\" \n    Calculate the categorization error\n    Args:\n      y    : (ndarray  Shape (m,) or (m,1))  target value of each example\n      yhat : (ndarray  Shape (m,) or (m,1))  predicted value of each example\n    Returns:|\n      err: (scalar)             \n    \"\"\"\n    m = len(y)\n    incorrect = 0\n    for i in range(m):\n        if yhat[i] != y[i]:\n            incorrect += 1\n    err = incorrect/m\n    return(err)\n\ndef plot_iterate(lambdas, models, X_train, y_train, X_cv, y_cv):\n    err_train = np.zeros(len(lambdas))\n    err_cv = np.zeros(len(lambdas))\n    for i in range(len(models)):\n        err_train[i] = eval_cat_err(y_train,np.argmax( models[i](X_train), axis=1))\n        err_cv[i] = eval_cat_err(y_cv, np.argmax( models[i](X_cv), axis=1))\n\n    fig, ax = plt.subplots(1,1,figsize=(6,4))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n    ax.set_title(\"error vs regularization\",fontsize = 12)\n    ax.plot(lambdas, err_train, marker='o', label=\"train error\", color = dlc[\"dlblue\"])\n    ax.plot(lambdas, err_cv,    marker='o', label=\"cv error\",    color = dlc[\"dlorange\"])\n    ax.set_xscale('log')\n    ax.set_ylim(*ax.get_ylim())\n    ax.set_xlabel(\"Regularization (lambda)\",fontsize = 14)\n    ax.set_ylabel(\"Error\",fontsize = 14)\n    ax.legend()\n    fig.suptitle(\"Tuning Regularization\",fontsize = 14)\n    ax.text(0.05,0.14,\"Training Error\\nlower than CV\",fontsize=12, ha='left',transform=ax.transAxes,color = dlc[\"dlblue\"])\n    ax.text(0.95,0.14,\"Similar\\nTraining, CV\",    fontsize=12, ha='right',transform=ax.transAxes,color = dlc[\"dlblue\"])\n    plt.show()\n \n# not used but will calculate the erro assuming an equal distance\ndef err_all_equal(X_train,X_cv,X_test, y_train,y_cv,y_test, centers):\n    X_all = np.concatenate((X_train,X_cv,X_test), axis=0)\n    y_all = np.concatenate((y_train,y_cv,y_test), axis=0)\n    m = len(X_all)\n    y_eq  = np.zeros(m)\n    for i in range(m):\n        y_eq[i] = recat(X_all[i], centers)\n    err_all = eval_cat_err(y_all, y_eq)\n    return(err_all)\n\ndef plt_compare(X,y, classes, simple, regularized, centers):\n    plt.close(\"all\")\n    fig,ax = plt.subplots(1,3, figsize=(8,3))\n    fig.canvas.toolbar_visible = False\n    fig.canvas.header_visible = False\n    fig.canvas.footer_visible = False\n\n  #plt simple   \n    plot_cat_decision_boundary(ax[0], X, simple,  vector=True)\n    ax[0].set_title(\"Simple Model\", fontsize=14)\n    plt_mc_data(ax[0], X,y, classes, map=dkcolors_map, legend=True, size=75)\n    ax[0].set_xlabel('x0') ; ax[0].set_ylabel(\"x1\");\n\n  #plt regularized   \n    plot_cat_decision_boundary(ax[1], X, regularized,  vector=True)\n    ax[1].set_title(\"Regularized Model\", fontsize=14)\n    plt_mc_data(ax[1], X,y, classes, map=dkcolors_map, legend=True, size=75)\n    ax[1].set_xlabel('x0') ; ax[0].set_ylabel(\"x1\");\n\n  #plt ideal\n    cat_predict = lambda pt: recat(pt.reshape(1,2), centers)\n    plot_cat_decision_boundary(ax[2], X, cat_predict,  vector=False)\n    ax[2].set_title(\"Ideal Model\", fontsize=14)\n    plt_mc_data(ax[2], X,y, classes, map=dkcolors_map, legend=True, size=75)\n    ax[2].set_xlabel('x0') ; ax[0].set_ylabel(\"x1\");\n\n    err_s = eval_cat_err(y, simple(X))\n    err_r = eval_cat_err(y, regularized(X))\n    ax[0].text(-2.75,3,f\"err_test={err_s:0.2f}\", fontsize=12)\n    ax[1].text(-2.75,3,f\"err_test={err_r:0.2f}\", fontsize=12)\n    m = len(X)\n    y_eq  = np.zeros(m)\n    for i in range(m):\n        y_eq[i] = recat(X[i], centers)\n    err_eq = eval_cat_err(y, y_eq)\n    ax[2].text(-2.75,3,f\"err_test={err_eq:0.2f}\", fontsize=12)\n    plt.show()\n\n# --- End Assignment ----------------------------------------\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/C2W3A1/deeplearning.mplstyle",
    "content": "# see https://matplotlib.org/stable/tutorials/introductory/customizing.html\nlines.linewidth: 4\nlines.solid_capstyle: butt\n\nlegend.fancybox: true\n\n# Verdana\" for non-math text,\n# Cambria Math\n\n#Blue (Crayon-Aqua) 0096FF\n#Dark Red C00000\n#Orange (Apple Orange) FF9300\n#Black 000000\n#Magenta FF40FF\n#Purple 7030A0\n\naxes.prop_cycle: cycler('color', ['0096FF', 'FF9300', 'FF40FF', '7030A0', 'C00000'])\n#axes.facecolor: f0f0f0 # grey\naxes.facecolor: ffffff  # white\naxes.labelsize: large\naxes.axisbelow: true\naxes.grid: False\naxes.edgecolor: f0f0f0\naxes.linewidth: 3.0\naxes.titlesize: x-large\n\npatch.edgecolor: f0f0f0\npatch.linewidth: 0.5\n\nsvg.fonttype: path\n\ngrid.linestyle: -\ngrid.linewidth: 1.0\ngrid.color: cbcbcb\n\nxtick.major.size: 0\nxtick.minor.size: 0\nytick.major.size: 0\nytick.minor.size: 0\n\nsavefig.edgecolor: f0f0f0\nsavefig.facecolor: f0f0f0\n\n#figure.subplot.left: 0.08\n#figure.subplot.right: 0.95\n#figure.subplot.bottom: 0.07\n\n#figure.facecolor: f0f0f0  # grey\nfigure.facecolor: ffffff  # white\n\n## ***************************************************************************\n## * FONT                                                                    *\n## ***************************************************************************\n## The font properties used by `text.Text`.\n## See https://matplotlib.org/api/font_manager_api.html for more information\n## on font properties.  The 6 font properties used for font matching are\n## given below with their default values.\n##\n## The font.family property can take either a concrete font name (not supported\n## when rendering text with usetex), or one of the following five generic\n## values:\n##     - 'serif' (e.g., Times),\n##     - 'sans-serif' (e.g., Helvetica),\n##     - 'cursive' (e.g., Zapf-Chancery),\n##     - 'fantasy' (e.g., Western), and\n##     - 'monospace' (e.g., Courier).\n## Each of these values has a corresponding default list of font names\n## (font.serif, etc.); the first available font in the list is used.  Note that\n## for font.serif, font.sans-serif, and font.monospace, the first element of\n## the list (a DejaVu font) will always be used because DejaVu is shipped with\n## Matplotlib and is thus guaranteed to be available; the other entries are\n## left as examples of other possible values.\n##\n## The font.style property has three values: normal (or roman), italic\n## or oblique.  The oblique style will be used for italic, if it is not\n## present.\n##\n## The font.variant property has two values: normal or small-caps.  For\n## TrueType fonts, which are scalable fonts, small-caps is equivalent\n## to using a font size of 'smaller', or about 83%% of the current font\n## size.\n##\n## The font.weight property has effectively 13 values: normal, bold,\n## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as\n## 400, and bold is 700.  bolder and lighter are relative values with\n## respect to the current weight.\n##\n## The font.stretch property has 11 values: ultra-condensed,\n## extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This\n## property is not currently implemented.\n##\n## The font.size property is the default font size for text, given in points.\n## 10 pt is the standard value.\n##\n## Note that font.size controls default text sizes.  To configure\n## special text sizes tick labels, axes, labels, title, etc., see the rc\n## settings for axes and ticks.  Special text sizes can be defined\n## relative to font.size, using the following values: xx-small, x-small,\n## small, medium, large, x-large, xx-large, larger, or smaller\n\n\nfont.family:  sans-serif\nfont.style:   normal\nfont.variant: normal\nfont.weight:  normal\nfont.stretch: normal\nfont.size:    8.0\n\nfont.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\nfont.sans-serif: Verdana, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\nfont.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive\nfont.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy\nfont.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n\n\n## ***************************************************************************\n## * TEXT                                                                    *\n## ***************************************************************************\n## The text properties used by `text.Text`.\n## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\n## for more information on text properties\n#text.color: black\n\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/C2W3A1/public_tests_a1.py",
    "content": "import tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.activations import relu,linear\nfrom tensorflow.keras.losses import SparseCategoricalCrossentropy\nfrom tensorflow.keras.optimizers import Adam\n\nimport numpy as np\n\ndef test_eval_mse(target):\n    y_hat = np.array([2.4, 4.2])\n    y_tmp = np.array([2.3, 4.1])\n    result = target(y_hat, y_tmp)\n    \n    assert np.isclose(result, 0.005, atol=1e-6), f\"Wrong value. Expected 0.005, got {result}\"\n    \n    y_hat = np.array([3.] * 10)\n    y_tmp = np.array([3.] * 10)\n    result = target(y_hat, y_tmp)\n    assert np.isclose(result, 0.), f\"Wrong value. Expected 0.0 when y_hat == t_tmp, but got {result}\"\n    \n    y_hat = np.array([3.])\n    y_tmp = np.array([0.])\n    result = target(y_hat, y_tmp)\n    assert np.isclose(result, 4.5), f\"Wrong value. Expected 4.5, but got {result}. Remember the square termn\"\n    \n    y_hat = np.array([3.] * 5)\n    y_tmp = np.array([2.] * 5)\n    result = target(y_hat, y_tmp)\n    assert np.isclose(result, 0.5), f\"Wrong value. Expected 0.5, but got {result}. Remember to divide by (2*m)\"\n    \n    print(\"\\033[92m All tests passed.\")\n    \ndef test_eval_cat_err(target):\n    y_hat = np.array([1, 0, 1, 1, 1, 0])\n    y_tmp = np.array([0, 1, 0, 0, 0, 1])\n    result = target(y_hat, y_tmp)\n    assert not np.isclose(result, 6.), f\"Wrong value. Expected 1, but got {result}. Did you divided by m?\"\n    \n    y_hat = np.array([1, 2, 0])\n    y_tmp = np.array([1, 2, 3])\n    result = target(y_hat, y_tmp)\n    assert np.isclose(result, 1./3., atol=1e-6), f\"Wrong value. Expected 0.333, but got {result}\"\n    \n    y_hat = np.array([1, 0, 1, 1, 1, 0])\n    y_tmp = np.array([1, 1, 1, 0, 0, 0])\n    result = target(y_hat, y_tmp)\n    assert np.isclose(result, 3./6., atol=1e-6), f\"Wrong value. Expected 0.5, but got {result}\"\n    \n    y_hat = np.array([[1], [2], [0], [3]])\n    y_tmp = np.array([[1], [2], [1], [3]])\n    res_tmp =  target(y_hat, y_tmp)\n    assert type(res_tmp) != np.ndarray, f\"The output must be an scalar but got {type(res_tmp)}\"\n    \n    print(\"\\033[92m All tests passed.\")\n    \ndef model_test(target, classes, input_size):\n    target.build(input_shape=(None,input_size))\n    expected_lr = 0.01\n    \n    assert len(target.layers) == 3, \\\n        f\"Wrong number of layers. Expected 3 but got {len(target.layers)}\"\n    assert target.input.shape.as_list() == [None, input_size], \\\n        f\"Wrong input shape. Expected [None,  {input_size}] but got {target.input.shape.as_list()}\"\n    i = 0\n    expected = [[Dense, [None, 120], relu],\n                [Dense, [None, 40], relu],\n                [Dense, [None, classes], linear]]\n\n    for layer in target.layers:\n        assert type(layer) == expected[i][0], \\\n            f\"Wrong type in layer {i}. Expected {expected[i][0]} but got {type(layer)}\"\n        assert layer.output.shape.as_list() == expected[i][1], \\\n            f\"Wrong number of units in layer {i}. Expected {expected[i][1]} but got {layer.output.shape.as_list()}\"\n        assert layer.activation == expected[i][2], \\\n            f\"Wrong activation in layer {i}. Expected {expected[i][2]} but got {layer.activation}\"\n        assert layer.kernel_regularizer == None, \"You must not specify any regularizer for any layer\"\n        i = i + 1\n        \n    assert type(target.loss)==SparseCategoricalCrossentropy, f\"Wrong loss function. Expected {SparseCategoricalCrossentropy}, but got {target.loss}\"\n    assert type(target.optimizer)==Adam, f\"Wrong loss function. Expected {Adam}, but got {target.optimizer}\"\n    lr = target.optimizer.learning_rate.numpy()\n    assert np.isclose(lr, expected_lr, atol=1e-8), f\"Wrong learning rate. Expected {expected_lr}, but got {lr}\"\n    assert target.loss.get_config()['from_logits'], f\"Set from_logits=True in loss function\"\n\n    print(\"\\033[92mAll tests passed!\")\n    \ndef model_s_test(target, classes, input_size):\n    target.build(input_shape=(None,input_size))\n    expected_lr = 0.01\n    \n    assert len(target.layers) == 2, \\\n        f\"Wrong number of layers. Expected 3 but got {len(target.layers)}\"\n    assert target.input.shape.as_list() == [None, input_size], \\\n        f\"Wrong input shape. Expected [None,  {input_size}] but got {target.input.shape.as_list()}\"\n    i = 0\n    expected = [[Dense, [None, 6], relu],\n                [Dense, [None, classes], linear]]\n\n    for layer in target.layers:\n        assert type(layer) == expected[i][0], \\\n            f\"Wrong type in layer {i}. Expected {expected[i][0]} but got {type(layer)}\"\n        assert layer.output.shape.as_list() == expected[i][1], \\\n            f\"Wrong number of units in layer {i}. Expected {expected[i][1]} but got {layer.output.shape.as_list()}\"\n        assert layer.activation == expected[i][2], \\\n            f\"Wrong activation in layer {i}. Expected {expected[i][2]} but got {layer.activation}\"\n        assert layer.kernel_regularizer == None, \"You must not specify any regularizer any layer\"\n        i = i + 1\n        \n    assert type(target.loss)==SparseCategoricalCrossentropy, f\"Wrong loss function. Expected {SparseCategoricalCrossentropy}, but got {target.loss}\"\n    assert type(target.optimizer)==Adam, f\"Wrong loss function. Expected {Adam}, but got {target.optimizer}\"\n    lr = target.optimizer.learning_rate.numpy()\n    assert np.isclose(lr, expected_lr, atol=1e-8), f\"Wrong learning rate. Expected {expected_lr}, but got {lr}\"\n    assert target.loss.get_config()['from_logits'], f\"Set from_logits=True in loss function\"\n\n    print(\"\\033[92mAll tests passed!\")\n    \ndef model_r_test(target, classes, input_size):\n    target.build(input_shape=(None,input_size))\n    expected_lr = 0.01\n    print(\"ddd\")\n    assert len(target.layers) == 3, \\\n        f\"Wrong number of layers. Expected 3 but got {len(target.layers)}\"\n    assert target.input.shape.as_list() == [None, input_size], \\\n        f\"Wrong input shape. Expected [None,  {input_size}] but got {target.input.shape.as_list()}\"\n    i = 0\n    expected = [[Dense, [None, 120], relu, (tf.keras.regularizers.l2, 0.1)],\n                [Dense, [None, 40], relu, (tf.keras.regularizers.l2, 0.1)],\n                [Dense, [None, classes], linear, None]]\n\n    for layer in target.layers:\n        assert type(layer) == expected[i][0], \\\n            f\"Wrong type in layer {i}. Expected {expected[i][0]} but got {type(layer)}\"\n        assert layer.output.shape.as_list() == expected[i][1], \\\n            f\"Wrong number of units in layer {i}. Expected {expected[i][1]} but got {layer.output.shape.as_list()}\"\n        assert layer.activation == expected[i][2], \\\n            f\"Wrong activation in layer {i}. Expected {expected[i][2]} but got {layer.activation}\"\n        if not (expected[i][3] == None):\n            assert type(layer.kernel_regularizer) == expected[i][3][0], f\"Wrong regularizer. Expected L2 regularizer but got {type(layer.kernel_regularizer)}\"\n            assert np.isclose(layer.kernel_regularizer.l2,  expected[i][3][1]), f\"Wrong regularization factor. Expected {expected[i][3][1]}, but got {layer.kernel_regularizer.l2}\"\n        else:\n            assert layer.kernel_regularizer == None, \"You must not specify any regularizer for the 3th layer\"\n        i = i + 1\n        \n    assert type(target.loss)==SparseCategoricalCrossentropy, f\"Wrong loss function. Expected {SparseCategoricalCrossentropy}, but got {target.loss}\"\n    assert type(target.optimizer)==Adam, f\"Wrong loss function. Expected {Adam}, but got {target.optimizer}\"\n    lr = target.optimizer.learning_rate.numpy()\n    assert np.isclose(lr, expected_lr, atol=1e-8), f\"Wrong learning rate. Expected {expected_lr}, but got {lr}\"\n    assert target.loss.get_config()['from_logits'], f\"Set from_logits=True in loss function\"\n\n    print(\"\\033[92mAll tests passed!\")\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/C2W3A1/utils.py",
    "content": "import numpy as np\nfrom sklearn import datasets\n\n\ndef load_data():\n    iris = datasets.load_iris()\n    X = iris.data[:, :2]  # we only take the first two features.\n    y = iris.target\n\n    X = X[y != 2] # only two classes\n    y = y[y != 2]\n    return X, y"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/Practice-Quiz-Advice-for-applying-machine-learning/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/0258a26a9120b0dcc56b591705455f2cd7e264d9/C2%20-%20Advanced%20Learning%20Algorithms/week3/Practice-Quiz-Advice-for-applying-machine-learning/ss1.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/Readme.md",
    "content": "### C2 - Week 3 Solutions \n\n- [Practice quiz : Advice for Applying Machine Learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/614fe817ac9b5fba6718512ba8c8a36b856a1cab/C2%20-%20Advanced%20Learning%20Algorithms/week3/Practice-Quiz-Advice-for-applying-machine-learning)    \n- [Practice quiz : Bias and Variance](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-bias-and-variance)\n- [Practice quiz : Machine Learning Development Process](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-machine-learning-development-process)\n- [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/C2W3A1)\n  - [Advice for Applied Machine Learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/C2W3A1/C2_W3_Assignment.ipynb)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/practice-quiz-bias-and-variance/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d04ab60f2334c9d2915b031a358a629c8403dd0/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-bias-and-variance/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d04ab60f2334c9d2915b031a358a629c8403dd0/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-bias-and-variance/ss2.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d04ab60f2334c9d2915b031a358a629c8403dd0/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-bias-and-variance/ss3.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d04ab60f2334c9d2915b031a358a629c8403dd0/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-bias-and-variance/ss4.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week3/practice-quiz-machine-learning-development-process/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-machine-learning-development-process/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-machine-learning-development-process/ss2.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7a8ce331775aa5c6ad3e9784744650fc77958b89/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-machine-learning-development-process/ss3.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/C2W4A1/.ipynb_checkpoints/C2_W4_Decision_Tree_with_Markdown-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Practice Lab: Decision Trees\\n\",\n    \"\\n\",\n    \"In this exercise, you will implement a decision tree from scratch and apply it to the task of classifying whether a mushroom is edible or poisonous.\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 -  Problem Statement](#2)\\n\",\n    \"- [ 3 - Dataset](#3)\\n\",\n    \"  - [ 3.1 One hot encoded dataset](#3.1)\\n\",\n    \"- [ 4 - Decision Tree Refresher](#4)\\n\",\n    \"  - [ 4.1  Calculate entropy](#4.1)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 4.2  Split dataset](#4.2)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"  - [ 4.3  Calculate information gain](#4.3)\\n\",\n    \"    - [ Exercise 3](#ex03)\\n\",\n    \"  - [ 4.4  Get best split](#4.4)\\n\",\n    \"    - [ Exercise 4](#ex04)\\n\",\n    \"- [ 5 - Building the tree](#5)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://www.numpy.org) is the fundamental package for working with matrices in Python.\\n\",\n    \"- [matplotlib](https://matplotlib.org) is a famous library to plot graphs in Python.\\n\",\n    \"- ``utils.py`` contains helper functions for this assignment. You do not need to modify code in this file.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from public_tests import *\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 -  Problem Statement\\n\",\n    \"\\n\",\n    \"Suppose you are starting a company that grows and sells wild mushrooms. \\n\",\n    \"- Since not all mushrooms are edible, you'd like to be able to tell whether a given mushroom is edible or poisonous based on it's physical attributes\\n\",\n    \"- You have some existing data that you can use for this task. \\n\",\n    \"\\n\",\n    \"Can you use the data to help you identify which mushrooms can be sold safely? \\n\",\n    \"\\n\",\n    \"Note: The dataset used is for illustrative purposes only. It is not meant to be a guide on identifying edible mushrooms.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. The dataset you have collected is as follows:\\n\",\n    \"\\n\",\n    \"| Cap Color | Stalk Shape | Solitary | Edible |\\n\",\n    \"|:---------:|:-----------:|:--------:|:------:|\\n\",\n    \"|   Brown   |   Tapering  |    Yes   |    1   |\\n\",\n    \"|   Brown   |  Enlarging  |    Yes   |    1   |\\n\",\n    \"|   Brown   |  Enlarging  |    No    |    0   |\\n\",\n    \"|   Brown   |  Enlarging  |    No    |    0   |\\n\",\n    \"|   Brown   |   Tapering  |    Yes   |    1   |\\n\",\n    \"|    Red    |   Tapering  |    Yes   |    0   |\\n\",\n    \"|    Red    |  Enlarging  |    No    |    0   |\\n\",\n    \"|   Brown   |  Enlarging  |    Yes   |    1   |\\n\",\n    \"|    Red    |   Tapering  |    No    |    1   |\\n\",\n    \"|   Brown   |  Enlarging  |    No    |    0   |\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"-  You have 10 examples of mushrooms. For each example, you have\\n\",\n    \"    - Three features\\n\",\n    \"        - Cap Color (`Brown` or `Red`),\\n\",\n    \"        - Stalk Shape (`Tapering` or `Enlarging`), and\\n\",\n    \"        - Solitary (`Yes` or `No`)\\n\",\n    \"    - Label\\n\",\n    \"        - Edible (`1` indicating yes or `0` indicating poisonous)\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.1\\\"></a>\\n\",\n    \"### 3.1 One hot encoded dataset\\n\",\n    \"For ease of implementation, we have one-hot encoded the features (turned them into 0 or 1 valued features)\\n\",\n    \"\\n\",\n    \"| Brown Cap | Tapering Stalk Shape | Solitary | Edible |\\n\",\n    \"|:---------:|:--------------------:|:--------:|:------:|\\n\",\n    \"|     1     |           1          |     1    |    1   |\\n\",\n    \"|     1     |           0          |     1    |    1   |\\n\",\n    \"|     1     |           0          |     0    |    0   |\\n\",\n    \"|     1     |           0          |     0    |    0   |\\n\",\n    \"|     1     |           1          |     1    |    1   |\\n\",\n    \"|     0     |           1          |     1    |    0   |\\n\",\n    \"|     0     |           0          |     0    |    0   |\\n\",\n    \"|     1     |           0          |     1    |    1   |\\n\",\n    \"|     0     |           1          |     0    |    1   |\\n\",\n    \"|     1     |           0          |     0    |    0   |\\n\",\n    \"\\n\",\n    \"Therefore,\\n\",\n    \"- `X_train` contains three features for each example \\n\",\n    \"    - Brown Color (A value of `1` indicates \\\"Brown\\\" cap color and `0` indicates \\\"Red\\\" cap color)\\n\",\n    \"    - Tapering Shape (A value of `1` indicates \\\"Tapering Stalk Shape\\\" and `0` indicates \\\"Enlarging\\\" stalk shape)\\n\",\n    \"    - Solitary  (A value of `1` indicates \\\"Yes\\\" and `0` indicates \\\"No\\\")\\n\",\n    \"\\n\",\n    \"- `y_train` is whether the mushroom is edible \\n\",\n    \"    - `y = 1` indicates edible\\n\",\n    \"    - `y = 0` indicates poisonous\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[1,1,1],[1,0,1],[1,0,0],[1,0,0],[1,1,1],[0,1,1],[0,0,0],[1,0,1],[0,1,0],[1,0,0]])\\n\",\n    \"y_train = np.array([1,1,0,0,1,0,0,1,1,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to just print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints the first few elements of `X_train` and the type of the variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"First few elements of X_train:\\\\n\\\", X_train[:5])\\n\",\n    \"print(\\\"Type of X_train:\\\",type(X_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's do the same for `y_train`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"First few elements of y_train:\\\", y_train[:5])\\n\",\n    \"print(\\\"Type of y_train:\\\",type(y_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions.\\n\",\n    \"\\n\",\n    \"Please print the shape of `X_train` and `y_train` and see how many training examples you have in your dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print ('The shape of X_train is:', X_train.shape)\\n\",\n    \"print ('The shape of y_train is: ', y_train.shape)\\n\",\n    \"print ('Number of training examples (m):', len(X_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Decision Tree Refresher\\n\",\n    \"\\n\",\n    \"In this practice lab, you will build a decision tree based on the dataset provided.\\n\",\n    \"\\n\",\n    \"- Recall that the steps for building a decision tree are as follows:\\n\",\n    \"    - Start with all examples at the root node\\n\",\n    \"    - Calculate information gain for splitting on all possible features, and pick the one with the highest information gain\\n\",\n    \"    - Split dataset according to the selected feature, and create left and right branches of the tree\\n\",\n    \"    - Keep repeating splitting process until stopping criteria is met\\n\",\n    \"  \\n\",\n    \"  \\n\",\n    \"- In this lab, you'll implement the following functions, which will let you split a node into left and right branches using the feature with the highest information gain\\n\",\n    \"    - Calculate the entropy at a node \\n\",\n    \"    - Split the dataset at a node into left and right branches based on a given feature\\n\",\n    \"    - Calculate the information gain from splitting on a given feature\\n\",\n    \"    - Choose the feature that maximizes information gain\\n\",\n    \"    \\n\",\n    \"- We'll then use the helper functions you've implemented to build a decision tree by repeating the splitting process until the stopping criteria is met \\n\",\n    \"    - For this lab, the stopping criteria we've chosen is setting a maximum depth of 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.1\\\"></a>\\n\",\n    \"### 4.1  Calculate entropy\\n\",\n    \"\\n\",\n    \"First, you'll write a helper function called `compute_entropy` that computes the entropy (measure of impurity) at a node. \\n\",\n    \"- The function takes in a numpy array (`y`) that indicates whether the examples in that node are edible (`1`) or poisonous(`0`) \\n\",\n    \"\\n\",\n    \"Complete the `compute_entropy()` function below to:\\n\",\n    \"* Compute $p_1$, which is the fraction of examples that are edible (i.e. have value = `1` in `y`)\\n\",\n    \"* The entropy is then calculated as \\n\",\n    \"\\n\",\n    \"$$H(p_1) = -p_1 \\\\text{log}_2(p_1) - (1- p_1) \\\\text{log}_2(1- p_1)$$\\n\",\n    \"* Note \\n\",\n    \"    * The log is calculated with base $2$\\n\",\n    \"    * For implementation purposes, $0\\\\text{log}_2(0) = 0$. That is, if `p_1 = 0` or `p_1 = 1`, set the entropy to `0`\\n\",\n    \"    * Make sure to check that the data at a node is not empty (i.e. `len(y) != 0`). Return `0` if it is\\n\",\n    \"    \\n\",\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Please complete the `compute_entropy()` function using the previous instructions.\\n\",\n    \"    \\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED FUNCTION: compute_entropy\\n\",\n    \"\\n\",\n    \"def compute_entropy(y):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the entropy for \\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"       y (ndarray): Numpy array indicating whether each example at a node is\\n\",\n    \"           edible (`1`) or poisonous (`0`)\\n\",\n    \"       \\n\",\n    \"    Returns:\\n\",\n    \"        entropy (float): Entropy at that node\\n\",\n    \"        \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    entropy = 0.\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"           \\n\",\n    \"    ### END CODE HERE ###        \\n\",\n    \"    \\n\",\n    \"    return entropy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * To calculate `p1`\\n\",\n    \"       * You can get the subset of examples in `y` that have the value `1` as `y[y == 1]`\\n\",\n    \"       * You can use `len(y)` to get the number of examples in `y`\\n\",\n    \"   * To calculate `entropy`\\n\",\n    \"       * <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.log2.html\\\">np.log2</a> let's you calculate the logarithm to base 2 for a numpy array\\n\",\n    \"       * If the value of `p1` is 0 or 1, make sure to set the entropy to `0` \\n\",\n    \"     \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_entropy(y):\\n\",\n    \"        \\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        entropy = 0.\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        if len(y) != 0:\\n\",\n    \"            # Your code here to calculate the fraction of edible examples (i.e with value = 1 in y)\\n\",\n    \"            p1 =\\n\",\n    \"\\n\",\n    \"            # For p1 = 0 and 1, set the entropy to 0 (to handle 0log0)\\n\",\n    \"            if p1 != 0 and p1 != 1:\\n\",\n    \"                # Your code here to calculate the entropy using the formula provided above\\n\",\n    \"                entropy = \\n\",\n    \"            else:\\n\",\n    \"                entropy = 0. \\n\",\n    \"        ### END CODE HERE ###        \\n\",\n    \"\\n\",\n    \"        return entropy\\n\",\n    \"    ```\\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `p1` and `entropy`.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate p1</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can compute p1 as <code>p1 = len(y[y == 1]) / len(y) </code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate entropy</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can compute entropy as <code>entropy = -p1 * np.log2(p1) - (1 - p1) * np.log2(1 - p1)</code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can check if your implementation was correct by running the following test code:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Compute entropy at the root node (i.e. with all examples)\\n\",\n    \"# Since we have 5 edible and 5 non-edible mushrooms, the entropy should be 1\\\"\\n\",\n    \"\\n\",\n    \"print(\\\"Entropy at root node: \\\", compute_entropy(y_train)) \\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"compute_entropy_test(compute_entropy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Entropy at root node:<b> 1.0 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.2\\\"></a>\\n\",\n    \"### 4.2  Split dataset\\n\",\n    \"\\n\",\n    \"Next, you'll write a helper function called `split_dataset` that takes in the data at a node and a feature to split on and splits it into left and right branches. Later in the lab, you'll implement code to calculate how good the split is.\\n\",\n    \"\\n\",\n    \"- The function takes in the training data, the list of indices of data points at that node, along with the feature to split on. \\n\",\n    \"- It splits the data and returns the subset of indices at the left and the right branch.\\n\",\n    \"- For example, say we're starting at the root node (so `node_indices = [0,1,2,3,4,5,6,7,8,9]`), and we chose to split on feature `0`, which is whether or not the example has a brown cap.\\n\",\n    \"    - The output of the function is then, `left_indices = [0,1,2,3,4,7,9]` and `right_indices = [5,6,8]`\\n\",\n    \"    \\n\",\n    \"| Index | Brown Cap | Tapering Stalk Shape | Solitary | Edible |\\n\",\n    \"|:-----:|:---------:|:--------------------:|:--------:|:------:|\\n\",\n    \"|   0   |     1     |           1          |     1    |    1   |\\n\",\n    \"|   1   |     1     |           0          |     1    |    1   |\\n\",\n    \"|   2   |     1     |           0          |     0    |    0   |\\n\",\n    \"|   3   |     1     |           0          |     0    |    0   |\\n\",\n    \"|   4   |     1     |           1          |     1    |    1   |\\n\",\n    \"|   5   |     0     |           1          |     1    |    0   |\\n\",\n    \"|   6   |     0     |           0          |     0    |    0   |\\n\",\n    \"|   7   |     1     |           0          |     1    |    1   |\\n\",\n    \"|   8   |     0     |           1          |     0    |    1   |\\n\",\n    \"|   9   |     1     |           0          |     0    |    0   |\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Please complete the `split_dataset()` function shown below\\n\",\n    \"\\n\",\n    \"- For each index in `node_indices`\\n\",\n    \"    - If the value of `X` at that index for that feature is `1`, add the index to `left_indices`\\n\",\n    \"    - If the value of `X` at that index for that feature is `0`, add the index to `right_indices`\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: split_dataset\\n\",\n    \"\\n\",\n    \"def split_dataset(X, node_indices, feature):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Splits the data at the given node into\\n\",\n    \"    left and right branches\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):             Data matrix of shape(n_samples, n_features)\\n\",\n    \"        node_indices (list):  List containing the active indices. I.e, the samples being considered at this step.\\n\",\n    \"        feature (int):           Index of feature to split on\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"        left_indices (list): Indices with feature value == 1\\n\",\n    \"        right_indices (list): Indices with feature value == 0\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    left_indices = []\\n\",\n    \"    right_indices = []\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"           \\n\",\n    \"    ### END CODE HERE ###\\n\",\n    \"        \\n\",\n    \"    return left_indices, right_indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def split_dataset(X, node_indices, feature):\\n\",\n    \"    \\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        left_indices = []\\n\",\n    \"        right_indices = []\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        # Go through the indices of examples at that node\\n\",\n    \"        for i in node_indices:   \\n\",\n    \"            if # Your code here to check if the value of X at that index for the feature is 1\\n\",\n    \"                left_indices.append(i)\\n\",\n    \"            else:\\n\",\n    \"                right_indices.append(i)\\n\",\n    \"        ### END CODE HERE ###\\n\",\n    \"        \\n\",\n    \"    return left_indices, right_indices\\n\",\n    \"    ```\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    The condition is <code> if X[i][feature] == 1:</code>.\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's check your implementation using the code blocks below. Let's try splitting the dataset at the root node, which contains all examples at feature 0 (Brown Cap) as we'd discussed above\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"root_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\\n\",\n    \"\\n\",\n    \"# Feel free to play around with these variables\\n\",\n    \"# The dataset only has three features, so this value can be 0 (Brown Cap), 1 (Tapering Stalk Shape) or 2 (Solitary)\\n\",\n    \"feature = 0\\n\",\n    \"\\n\",\n    \"left_indices, right_indices = split_dataset(X_train, root_indices, feature)\\n\",\n    \"\\n\",\n    \"print(\\\"Left indices: \\\", left_indices)\\n\",\n    \"print(\\\"Right indices: \\\", right_indices)\\n\",\n    \"\\n\",\n    \"# UNIT TESTS    \\n\",\n    \"split_dataset_test(split_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"```\\n\",\n    \"Left indices:  [0, 1, 2, 3, 4, 7, 9]\\n\",\n    \"Right indices:  [5, 6, 8]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.3\\\"></a>\\n\",\n    \"### 4.3  Calculate information gain\\n\",\n    \"\\n\",\n    \"Next, you'll write a function called `information_gain` that takes in the training data, the indices at a node and a feature to split on and returns the information gain from the split.\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex03\\\"></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"\\n\",\n    \"Please complete the `compute_information_gain()` function shown below to compute\\n\",\n    \"\\n\",\n    \"$$\\\\text{Information Gain} = H(p_1^\\\\text{node})- (w^{\\\\text{left}}H(p_1^\\\\text{left}) + w^{\\\\text{right}}H(p_1^\\\\text{right}))$$\\n\",\n    \"\\n\",\n    \"where \\n\",\n    \"- $H(p_1^\\\\text{node})$ is entropy at the node \\n\",\n    \"- $H(p_1^\\\\text{left})$ and $H(p_1^\\\\text{right})$ are the entropies at the left and the right branches resulting from the split\\n\",\n    \"- $w^{\\\\text{left}}$ and $w^{\\\\text{right}}$ are the proportion of examples at the left and right branch, respectively\\n\",\n    \"\\n\",\n    \"Note:\\n\",\n    \"- You can use the `compute_entropy()` function that you implemented above to calculate the entropy\\n\",\n    \"- We've provided some starter code that uses the `split_dataset()` function you implemented above to split the dataset \\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED FUNCTION: compute_information_gain\\n\",\n    \"\\n\",\n    \"def compute_information_gain(X, y, node_indices, feature):\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Compute the information of splitting the node on a given feature\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):            Data matrix of shape(n_samples, n_features)\\n\",\n    \"        y (array like):         list or ndarray with n_samples containing the target variable\\n\",\n    \"        node_indices (ndarray): List containing the active indices. I.e, the samples being considered in this step.\\n\",\n    \"   \\n\",\n    \"    Returns:\\n\",\n    \"        cost (float):        Cost computed\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"    \\n\",\n    \"    # Split dataset\\n\",\n    \"    left_indices, right_indices = split_dataset(X, node_indices, feature)\\n\",\n    \"    \\n\",\n    \"    # Some useful variables\\n\",\n    \"    X_node, y_node = X[node_indices], y[node_indices]\\n\",\n    \"    X_left, y_left = X[left_indices], y[left_indices]\\n\",\n    \"    X_right, y_right = X[right_indices], y[right_indices]\\n\",\n    \"    \\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    information_gain = 0\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    \\n\",\n    \"    # Weights \\n\",\n    \"    \\n\",\n    \"    #Weighted entropy\\n\",\n    \"     \\n\",\n    \"    #Information gain                                                   \\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ###  \\n\",\n    \"    \\n\",\n    \"    return information_gain\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_information_gain(X, y, node_indices, feature):\\n\",\n    \"        # Split dataset\\n\",\n    \"        left_indices, right_indices = split_dataset(X, node_indices, feature)\\n\",\n    \"\\n\",\n    \"        # Some useful variables\\n\",\n    \"        X_node, y_node = X[node_indices], y[node_indices]\\n\",\n    \"        X_left, y_left = X[left_indices], y[left_indices]\\n\",\n    \"        X_right, y_right = X[right_indices], y[right_indices]\\n\",\n    \"\\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        information_gain = 0\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        # Your code here to compute the entropy at the node using compute_entropy()\\n\",\n    \"        node_entropy = \\n\",\n    \"        # Your code here to compute the entropy at the left branch\\n\",\n    \"        left_entropy = \\n\",\n    \"        # Your code here to compute the entropy at the right branch\\n\",\n    \"        right_entropy = \\n\",\n    \"\\n\",\n    \"        # Your code here to compute the proportion of examples at the left branch\\n\",\n    \"        w_left = \\n\",\n    \"        \\n\",\n    \"        # Your code here to compute the proportion of examples at the right branch\\n\",\n    \"        w_right = \\n\",\n    \"\\n\",\n    \"        # Your code here to compute weighted entropy from the split using \\n\",\n    \"        # w_left, w_right, left_entropy and right_entropy\\n\",\n    \"        weighted_entropy = \\n\",\n    \"\\n\",\n    \"        # Your code here to compute the information gain as the entropy at the node\\n\",\n    \"        # minus the weighted entropy\\n\",\n    \"        information_gain = \\n\",\n    \"        ### END CODE HERE ###  \\n\",\n    \"\\n\",\n    \"        return information_gain\\n\",\n    \"    ```\\n\",\n    \"    If you're still stuck, check out the hints below.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Hint to calculate the entropies</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    <code>node_entropy = compute_entropy(y_node)</code><br>\\n\",\n    \"    <code>left_entropy = compute_entropy(y_left)</code><br>\\n\",\n    \"    <code>right_entropy = compute_entropy(y_right)</code>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate w_left and w_right</b></font></summary>\\n\",\n    \"           <code>w_left = len(X_left) / len(X_node)</code><br>\\n\",\n    \"           <code>w_right = len(X_right) / len(X_node)</code>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate weighted_entropy</b></font></summary>\\n\",\n    \"           <code>weighted_entropy = w_left * left_entropy + w_right * right_entropy</code>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate information_gain</b></font></summary>\\n\",\n    \"           <code> information_gain = node_entropy - weighted_entropy</code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"</details>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can now check your implementation using the cell below and calculate what the information gain would be from splitting on each of the featues\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"info_gain0 = compute_information_gain(X_train, y_train, root_indices, feature=0)\\n\",\n    \"print(\\\"Information Gain from splitting the root on brown cap: \\\", info_gain0)\\n\",\n    \"    \\n\",\n    \"info_gain1 = compute_information_gain(X_train, y_train, root_indices, feature=1)\\n\",\n    \"print(\\\"Information Gain from splitting the root on tapering stalk shape: \\\", info_gain1)\\n\",\n    \"\\n\",\n    \"info_gain2 = compute_information_gain(X_train, y_train, root_indices, feature=2)\\n\",\n    \"print(\\\"Information Gain from splitting the root on solitary: \\\", info_gain2)\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"compute_information_gain_test(compute_information_gain)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"```\\n\",\n    \"Information Gain from splitting the root on brown cap:  0.034851554559677034\\n\",\n    \"Information Gain from splitting the root on tapering stalk shape:  0.12451124978365313\\n\",\n    \"Information Gain from splitting the root on solitary:  0.2780719051126377\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Splitting on \\\"Solitary\\\" (feature = 2) at the root node gives the maximum information gain. Therefore, it's the best feature to split on at the root node.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.4\\\"></a>\\n\",\n    \"### 4.4  Get best split\\n\",\n    \"Now let's write a function to get the best feature to split on by computing the information gain from each feature as we did above and returning the feature that gives the maximum information gain\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex04\\\"></a>\\n\",\n    \"### Exercise 4\\n\",\n    \"Please complete the `get_best_split()` function shown below.\\n\",\n    \"- The function takes in the training data, along with the indices of datapoint at that node\\n\",\n    \"- The output of the function is the feature that gives the maximum information gain \\n\",\n    \"    - You can use the `compute_information_gain()` function to iterate through the features and calculate the information for each feature\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C4\\n\",\n    \"# GRADED FUNCTION: get_best_split\\n\",\n    \"\\n\",\n    \"def get_best_split(X, y, node_indices):   \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Returns the optimal feature and threshold value\\n\",\n    \"    to split the node data \\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):            Data matrix of shape(n_samples, n_features)\\n\",\n    \"        y (array like):         list or ndarray with n_samples containing the target variable\\n\",\n    \"        node_indices (ndarray): List containing the active indices. I.e, the samples being considered in this step.\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"        best_feature (int):     The index of the best feature to split\\n\",\n    \"    \\\"\\\"\\\"    \\n\",\n    \"    \\n\",\n    \"    # Some useful variables\\n\",\n    \"    num_features = X.shape[1]\\n\",\n    \"    \\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    best_feature = -1\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"       \\n\",\n    \"    ### END CODE HERE ##    \\n\",\n    \"   \\n\",\n    \"    return best_feature\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * Here's how you can structure the overall implementation for this function\\n\",\n    \"    \\n\",\n    \"    ```python \\n\",\n    \"    def get_best_split(X, y, node_indices):   \\n\",\n    \"\\n\",\n    \"        # Some useful variables\\n\",\n    \"        num_features = X.shape[1]\\n\",\n    \"\\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        best_feature = -1\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        max_info_gain = 0\\n\",\n    \"\\n\",\n    \"        # Iterate through all features\\n\",\n    \"        for feature in range(num_features): \\n\",\n    \"            \\n\",\n    \"            # Your code here to compute the information gain from splitting on this feature\\n\",\n    \"            info_gain = \\n\",\n    \"            \\n\",\n    \"            # If the information gain is larger than the max seen so far\\n\",\n    \"            if info_gain > max_info_gain:  \\n\",\n    \"                # Your code here to set the max_info_gain and best_feature\\n\",\n    \"                max_info_gain = \\n\",\n    \"                best_feature = \\n\",\n    \"        ### END CODE HERE ##    \\n\",\n    \"   \\n\",\n    \"    return best_feature\\n\",\n    \"    ```\\n\",\n    \"    If you're still stuck, check out the hints below.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Hint to calculate info_gain</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    <code>info_gain = compute_information_gain(X, y, node_indices, feature)</code>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to update the max_info_gain and best_feature</b></font></summary>\\n\",\n    \"           <code>max_info_gain = info_gain</code><br>\\n\",\n    \"           <code>best_feature = feature</code>\\n\",\n    \"    </details>\\n\",\n    \"</details>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's check the implementation of your function using the cell below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"best_feature = get_best_split(X_train, y_train, root_indices)\\n\",\n    \"print(\\\"Best feature to split on: %d\\\" % best_feature)\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"get_best_split_test(get_best_split)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we saw above, the function returns that the best feature to split on at the root node is feature 2 (\\\"Solitary\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"5\\\"></a>\\n\",\n    \"## 5 - Building the tree\\n\",\n    \"\\n\",\n    \"In this section, we use the functions you implemented above to generate a decision tree by successively picking the best feature to split on until we reach the stopping criteria (maximum depth is 2).\\n\",\n    \"\\n\",\n    \"You do not need to implement anything for this part.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Not graded\\n\",\n    \"tree = []\\n\",\n    \"\\n\",\n    \"def build_tree_recursive(X, y, node_indices, branch_name, max_depth, current_depth):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Build a tree using the recursive algorithm that split the dataset into 2 subgroups at each node.\\n\",\n    \"    This function just prints the tree.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):            Data matrix of shape(n_samples, n_features)\\n\",\n    \"        y (array like):         list or ndarray with n_samples containing the target variable\\n\",\n    \"        node_indices (ndarray): List containing the active indices. I.e, the samples being considered in this step.\\n\",\n    \"        branch_name (string):   Name of the branch. ['Root', 'Left', 'Right']\\n\",\n    \"        max_depth (int):        Max depth of the resulting tree. \\n\",\n    \"        current_depth (int):    Current depth. Parameter used during recursive call.\\n\",\n    \"   \\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"\\n\",\n    \"    # Maximum depth reached - stop splitting\\n\",\n    \"    if current_depth == max_depth:\\n\",\n    \"        formatting = \\\" \\\"*current_depth + \\\"-\\\"*current_depth\\n\",\n    \"        print(formatting, \\\"%s leaf node with indices\\\" % branch_name, node_indices)\\n\",\n    \"        return\\n\",\n    \"   \\n\",\n    \"    # Otherwise, get best split and split the data\\n\",\n    \"    # Get the best feature and threshold at this node\\n\",\n    \"    best_feature = get_best_split(X, y, node_indices) \\n\",\n    \"    tree.append((current_depth, branch_name, best_feature, node_indices))\\n\",\n    \"    \\n\",\n    \"    formatting = \\\"-\\\"*current_depth\\n\",\n    \"    print(\\\"%s Depth %d, %s: Split on feature: %d\\\" % (formatting, current_depth, branch_name, best_feature))\\n\",\n    \"    \\n\",\n    \"    # Split the dataset at the best feature\\n\",\n    \"    left_indices, right_indices = split_dataset(X, node_indices, best_feature)\\n\",\n    \"    \\n\",\n    \"    # continue splitting the left and the right child. Increment current depth\\n\",\n    \"    build_tree_recursive(X, y, left_indices, \\\"Left\\\", max_depth, current_depth+1)\\n\",\n    \"    build_tree_recursive(X, y, right_indices, \\\"Right\\\", max_depth, current_depth+1)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"build_tree_recursive(X_train, y_train, root_indices, \\\"Root\\\", max_depth=2, current_depth=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/C2W4A1/C2_W4_Decision_Tree_with_Markdown.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Practice Lab: Decision Trees\\n\",\n    \"\\n\",\n    \"In this exercise, you will implement a decision tree from scratch and apply it to the task of classifying whether a mushroom is edible or poisonous.\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 -  Problem Statement](#2)\\n\",\n    \"- [ 3 - Dataset](#3)\\n\",\n    \"  - [ 3.1 One hot encoded dataset](#3.1)\\n\",\n    \"- [ 4 - Decision Tree Refresher](#4)\\n\",\n    \"  - [ 4.1  Calculate entropy](#4.1)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 4.2  Split dataset](#4.2)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"  - [ 4.3  Calculate information gain](#4.3)\\n\",\n    \"    - [ Exercise 3](#ex03)\\n\",\n    \"  - [ 4.4  Get best split](#4.4)\\n\",\n    \"    - [ Exercise 4](#ex04)\\n\",\n    \"- [ 5 - Building the tree](#5)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](https://www.numpy.org) is the fundamental package for working with matrices in Python.\\n\",\n    \"- [matplotlib](https://matplotlib.org) is a famous library to plot graphs in Python.\\n\",\n    \"- ``utils.py`` contains helper functions for this assignment. You do not need to modify code in this file.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from public_tests import *\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 -  Problem Statement\\n\",\n    \"\\n\",\n    \"Suppose you are starting a company that grows and sells wild mushrooms. \\n\",\n    \"- Since not all mushrooms are edible, you'd like to be able to tell whether a given mushroom is edible or poisonous based on it's physical attributes\\n\",\n    \"- You have some existing data that you can use for this task. \\n\",\n    \"\\n\",\n    \"Can you use the data to help you identify which mushrooms can be sold safely? \\n\",\n    \"\\n\",\n    \"Note: The dataset used is for illustrative purposes only. It is not meant to be a guide on identifying edible mushrooms.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. The dataset you have collected is as follows:\\n\",\n    \"\\n\",\n    \"| Cap Color | Stalk Shape | Solitary | Edible |\\n\",\n    \"|:---------:|:-----------:|:--------:|:------:|\\n\",\n    \"|   Brown   |   Tapering  |    Yes   |    1   |\\n\",\n    \"|   Brown   |  Enlarging  |    Yes   |    1   |\\n\",\n    \"|   Brown   |  Enlarging  |    No    |    0   |\\n\",\n    \"|   Brown   |  Enlarging  |    No    |    0   |\\n\",\n    \"|   Brown   |   Tapering  |    Yes   |    1   |\\n\",\n    \"|    Red    |   Tapering  |    Yes   |    0   |\\n\",\n    \"|    Red    |  Enlarging  |    No    |    0   |\\n\",\n    \"|   Brown   |  Enlarging  |    Yes   |    1   |\\n\",\n    \"|    Red    |   Tapering  |    No    |    1   |\\n\",\n    \"|   Brown   |  Enlarging  |    No    |    0   |\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"-  You have 10 examples of mushrooms. For each example, you have\\n\",\n    \"    - Three features\\n\",\n    \"        - Cap Color (`Brown` or `Red`),\\n\",\n    \"        - Stalk Shape (`Tapering` or `Enlarging`), and\\n\",\n    \"        - Solitary (`Yes` or `No`)\\n\",\n    \"    - Label\\n\",\n    \"        - Edible (`1` indicating yes or `0` indicating poisonous)\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.1\\\"></a>\\n\",\n    \"### 3.1 One hot encoded dataset\\n\",\n    \"For ease of implementation, we have one-hot encoded the features (turned them into 0 or 1 valued features)\\n\",\n    \"\\n\",\n    \"| Brown Cap | Tapering Stalk Shape | Solitary | Edible |\\n\",\n    \"|:---------:|:--------------------:|:--------:|:------:|\\n\",\n    \"|     1     |           1          |     1    |    1   |\\n\",\n    \"|     1     |           0          |     1    |    1   |\\n\",\n    \"|     1     |           0          |     0    |    0   |\\n\",\n    \"|     1     |           0          |     0    |    0   |\\n\",\n    \"|     1     |           1          |     1    |    1   |\\n\",\n    \"|     0     |           1          |     1    |    0   |\\n\",\n    \"|     0     |           0          |     0    |    0   |\\n\",\n    \"|     1     |           0          |     1    |    1   |\\n\",\n    \"|     0     |           1          |     0    |    1   |\\n\",\n    \"|     1     |           0          |     0    |    0   |\\n\",\n    \"\\n\",\n    \"Therefore,\\n\",\n    \"- `X_train` contains three features for each example \\n\",\n    \"    - Brown Color (A value of `1` indicates \\\"Brown\\\" cap color and `0` indicates \\\"Red\\\" cap color)\\n\",\n    \"    - Tapering Shape (A value of `1` indicates \\\"Tapering Stalk Shape\\\" and `0` indicates \\\"Enlarging\\\" stalk shape)\\n\",\n    \"    - Solitary  (A value of `1` indicates \\\"Yes\\\" and `0` indicates \\\"No\\\")\\n\",\n    \"\\n\",\n    \"- `y_train` is whether the mushroom is edible \\n\",\n    \"    - `y = 1` indicates edible\\n\",\n    \"    - `y = 0` indicates poisonous\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[1,1,1],[1,0,1],[1,0,0],[1,0,0],[1,1,1],[0,1,1],[0,0,0],[1,0,1],[0,1,0],[1,0,0]])\\n\",\n    \"y_train = np.array([1,1,0,0,1,0,0,1,1,0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to just print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints the first few elements of `X_train` and the type of the variable.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"First few elements of X_train:\\n\",\n      \" [[1 1 1]\\n\",\n      \" [1 0 1]\\n\",\n      \" [1 0 0]\\n\",\n      \" [1 0 0]\\n\",\n      \" [1 1 1]]\\n\",\n      \"Type of X_train: <class 'numpy.ndarray'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"First few elements of X_train:\\\\n\\\", X_train[:5])\\n\",\n    \"print(\\\"Type of X_train:\\\",type(X_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's do the same for `y_train`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"First few elements of y_train: [1 1 0 0 1]\\n\",\n      \"Type of y_train: <class 'numpy.ndarray'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"First few elements of y_train:\\\", y_train[:5])\\n\",\n    \"print(\\\"Type of y_train:\\\",type(y_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions.\\n\",\n    \"\\n\",\n    \"Please print the shape of `X_train` and `y_train` and see how many training examples you have in your dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of X_train is: (10, 3)\\n\",\n      \"The shape of y_train is:  (10,)\\n\",\n      \"Number of training examples (m): 10\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of X_train is:', X_train.shape)\\n\",\n    \"print ('The shape of y_train is: ', y_train.shape)\\n\",\n    \"print ('Number of training examples (m):', len(X_train))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Decision Tree Refresher\\n\",\n    \"\\n\",\n    \"In this practice lab, you will build a decision tree based on the dataset provided.\\n\",\n    \"\\n\",\n    \"- Recall that the steps for building a decision tree are as follows:\\n\",\n    \"    - Start with all examples at the root node\\n\",\n    \"    - Calculate information gain for splitting on all possible features, and pick the one with the highest information gain\\n\",\n    \"    - Split dataset according to the selected feature, and create left and right branches of the tree\\n\",\n    \"    - Keep repeating splitting process until stopping criteria is met\\n\",\n    \"  \\n\",\n    \"  \\n\",\n    \"- In this lab, you'll implement the following functions, which will let you split a node into left and right branches using the feature with the highest information gain\\n\",\n    \"    - Calculate the entropy at a node \\n\",\n    \"    - Split the dataset at a node into left and right branches based on a given feature\\n\",\n    \"    - Calculate the information gain from splitting on a given feature\\n\",\n    \"    - Choose the feature that maximizes information gain\\n\",\n    \"    \\n\",\n    \"- We'll then use the helper functions you've implemented to build a decision tree by repeating the splitting process until the stopping criteria is met \\n\",\n    \"    - For this lab, the stopping criteria we've chosen is setting a maximum depth of 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.1\\\"></a>\\n\",\n    \"### 4.1  Calculate entropy\\n\",\n    \"\\n\",\n    \"First, you'll write a helper function called `compute_entropy` that computes the entropy (measure of impurity) at a node. \\n\",\n    \"- The function takes in a numpy array (`y`) that indicates whether the examples in that node are edible (`1`) or poisonous(`0`) \\n\",\n    \"\\n\",\n    \"Complete the `compute_entropy()` function below to:\\n\",\n    \"* Compute $p_1$, which is the fraction of examples that are edible (i.e. have value = `1` in `y`)\\n\",\n    \"* The entropy is then calculated as \\n\",\n    \"\\n\",\n    \"$$H(p_1) = -p_1 \\\\text{log}_2(p_1) - (1- p_1) \\\\text{log}_2(1- p_1)$$\\n\",\n    \"* Note \\n\",\n    \"    * The log is calculated with base $2$\\n\",\n    \"    * For implementation purposes, $0\\\\text{log}_2(0) = 0$. That is, if `p_1 = 0` or `p_1 = 1`, set the entropy to `0`\\n\",\n    \"    * Make sure to check that the data at a node is not empty (i.e. `len(y) != 0`). Return `0` if it is\\n\",\n    \"    \\n\",\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Please complete the `compute_entropy()` function using the previous instructions.\\n\",\n    \"    \\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED FUNCTION: compute_entropy\\n\",\n    \"\\n\",\n    \"def compute_entropy(y):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the entropy for \\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"       y (ndarray): Numpy array indicating whether each example at a node is\\n\",\n    \"           edible (`1`) or poisonous (`0`)\\n\",\n    \"       \\n\",\n    \"    Returns:\\n\",\n    \"        entropy (float): Entropy at that node\\n\",\n    \"        \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    entropy = 0.\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    if len(y) != 0:\\n\",\n    \"        p1 = p1 = len(y[y == 1]) / len(y) \\n\",\n    \"     # For p1 = 0 and 1, set the entropy to 0 (to handle 0log0)\\n\",\n    \"        if p1 != 0 and p1 != 1:\\n\",\n    \"             entropy = -p1 * np.log2(p1) - (1 - p1) * np.log2(1 - p1)\\n\",\n    \"        else:\\n\",\n    \"             entropy = 0\\n\",\n    \"    ### END CODE HERE ###        \\n\",\n    \"    \\n\",\n    \"    return entropy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * To calculate `p1`\\n\",\n    \"       * You can get the subset of examples in `y` that have the value `1` as `y[y == 1]`\\n\",\n    \"       * You can use `len(y)` to get the number of examples in `y`\\n\",\n    \"   * To calculate `entropy`\\n\",\n    \"       * <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.log2.html\\\">np.log2</a> let's you calculate the logarithm to base 2 for a numpy array\\n\",\n    \"       * If the value of `p1` is 0 or 1, make sure to set the entropy to `0` \\n\",\n    \"     \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_entropy(y):\\n\",\n    \"        \\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        entropy = 0.\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        if len(y) != 0:\\n\",\n    \"            # Your code here to calculate the fraction of edible examples (i.e with value = 1 in y)\\n\",\n    \"            p1 =\\n\",\n    \"\\n\",\n    \"            # For p1 = 0 and 1, set the entropy to 0 (to handle 0log0)\\n\",\n    \"            if p1 != 0 and p1 != 1:\\n\",\n    \"                # Your code here to calculate the entropy using the formula provided above\\n\",\n    \"                entropy = \\n\",\n    \"            else:\\n\",\n    \"                entropy = 0. \\n\",\n    \"        ### END CODE HERE ###        \\n\",\n    \"\\n\",\n    \"        return entropy\\n\",\n    \"    ```\\n\",\n    \"    \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `p1` and `entropy`.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate p1</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can compute p1 as <code>p1 = len(y[y == 1]) / len(y) </code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate entropy</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can compute entropy as <code>entropy = -p1 * np.log2(p1) - (1 - p1) * np.log2(1 - p1)</code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can check if your implementation was correct by running the following test code:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Entropy at root node:  1.0\\n\",\n      \"\\u001b[92m All tests passed.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Compute entropy at the root node (i.e. with all examples)\\n\",\n    \"# Since we have 5 edible and 5 non-edible mushrooms, the entropy should be 1\\\"\\n\",\n    \"\\n\",\n    \"print(\\\"Entropy at root node: \\\", compute_entropy(y_train)) \\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"compute_entropy_test(compute_entropy)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Entropy at root node:<b> 1.0 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.2\\\"></a>\\n\",\n    \"### 4.2  Split dataset\\n\",\n    \"\\n\",\n    \"Next, you'll write a helper function called `split_dataset` that takes in the data at a node and a feature to split on and splits it into left and right branches. Later in the lab, you'll implement code to calculate how good the split is.\\n\",\n    \"\\n\",\n    \"- The function takes in the training data, the list of indices of data points at that node, along with the feature to split on. \\n\",\n    \"- It splits the data and returns the subset of indices at the left and the right branch.\\n\",\n    \"- For example, say we're starting at the root node (so `node_indices = [0,1,2,3,4,5,6,7,8,9]`), and we chose to split on feature `0`, which is whether or not the example has a brown cap.\\n\",\n    \"    - The output of the function is then, `left_indices = [0,1,2,3,4,7,9]` and `right_indices = [5,6,8]`\\n\",\n    \"    \\n\",\n    \"| Index | Brown Cap | Tapering Stalk Shape | Solitary | Edible |\\n\",\n    \"|:-----:|:---------:|:--------------------:|:--------:|:------:|\\n\",\n    \"|   0   |     1     |           1          |     1    |    1   |\\n\",\n    \"|   1   |     1     |           0          |     1    |    1   |\\n\",\n    \"|   2   |     1     |           0          |     0    |    0   |\\n\",\n    \"|   3   |     1     |           0          |     0    |    0   |\\n\",\n    \"|   4   |     1     |           1          |     1    |    1   |\\n\",\n    \"|   5   |     0     |           1          |     1    |    0   |\\n\",\n    \"|   6   |     0     |           0          |     0    |    0   |\\n\",\n    \"|   7   |     1     |           0          |     1    |    1   |\\n\",\n    \"|   8   |     0     |           1          |     0    |    1   |\\n\",\n    \"|   9   |     1     |           0          |     0    |    0   |\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Please complete the `split_dataset()` function shown below\\n\",\n    \"\\n\",\n    \"- For each index in `node_indices`\\n\",\n    \"    - If the value of `X` at that index for that feature is `1`, add the index to `left_indices`\\n\",\n    \"    - If the value of `X` at that index for that feature is `0`, add the index to `right_indices`\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: split_dataset\\n\",\n    \"\\n\",\n    \"def split_dataset(X, node_indices, feature):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Splits the data at the given node into\\n\",\n    \"    left and right branches\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):             Data matrix of shape(n_samples, n_features)\\n\",\n    \"        node_indices (list):  List containing the active indices. I.e, the samples being considered at this step.\\n\",\n    \"        feature (int):           Index of feature to split on\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"        left_indices (list): Indices with feature value == 1\\n\",\n    \"        right_indices (list): Indices with feature value == 0\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    left_indices = []\\n\",\n    \"    right_indices = []\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    for i in node_indices:   \\n\",\n    \"        if X[i][feature] == 1:\\n\",\n    \"            left_indices.append(i)\\n\",\n    \"        else:\\n\",\n    \"            right_indices.append(i)\\n\",\n    \"    ### END CODE HERE ###\\n\",\n    \"        \\n\",\n    \"    return left_indices, right_indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def split_dataset(X, node_indices, feature):\\n\",\n    \"    \\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        left_indices = []\\n\",\n    \"        right_indices = []\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        # Go through the indices of examples at that node\\n\",\n    \"        for i in node_indices:   \\n\",\n    \"            if # Your code here to check if the value of X at that index for the feature is 1\\n\",\n    \"                left_indices.append(i)\\n\",\n    \"            else:\\n\",\n    \"                right_indices.append(i)\\n\",\n    \"        ### END CODE HERE ###\\n\",\n    \"        \\n\",\n    \"    return left_indices, right_indices\\n\",\n    \"    ```\\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    The condition is <code> if X[i][feature] == 1:</code>.\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's check your implementation using the code blocks below. Let's try splitting the dataset at the root node, which contains all examples at feature 0 (Brown Cap) as we'd discussed above\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Left indices:  [0, 1, 2, 3, 4, 7, 9]\\n\",\n      \"Right indices:  [5, 6, 8]\\n\",\n      \"\\u001b[92m All tests passed.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"root_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\\n\",\n    \"\\n\",\n    \"# Feel free to play around with these variables\\n\",\n    \"# The dataset only has three features, so this value can be 0 (Brown Cap), 1 (Tapering Stalk Shape) or 2 (Solitary)\\n\",\n    \"feature = 0\\n\",\n    \"\\n\",\n    \"left_indices, right_indices = split_dataset(X_train, root_indices, feature)\\n\",\n    \"\\n\",\n    \"print(\\\"Left indices: \\\", left_indices)\\n\",\n    \"print(\\\"Right indices: \\\", right_indices)\\n\",\n    \"\\n\",\n    \"# UNIT TESTS    \\n\",\n    \"split_dataset_test(split_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"```\\n\",\n    \"Left indices:  [0, 1, 2, 3, 4, 7, 9]\\n\",\n    \"Right indices:  [5, 6, 8]\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.3\\\"></a>\\n\",\n    \"### 4.3  Calculate information gain\\n\",\n    \"\\n\",\n    \"Next, you'll write a function called `information_gain` that takes in the training data, the indices at a node and a feature to split on and returns the information gain from the split.\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex03\\\"></a>\\n\",\n    \"### Exercise 3\\n\",\n    \"\\n\",\n    \"Please complete the `compute_information_gain()` function shown below to compute\\n\",\n    \"\\n\",\n    \"$$\\\\text{Information Gain} = H(p_1^\\\\text{node})- (w^{\\\\text{left}}H(p_1^\\\\text{left}) + w^{\\\\text{right}}H(p_1^\\\\text{right}))$$\\n\",\n    \"\\n\",\n    \"where \\n\",\n    \"- $H(p_1^\\\\text{node})$ is entropy at the node \\n\",\n    \"- $H(p_1^\\\\text{left})$ and $H(p_1^\\\\text{right})$ are the entropies at the left and the right branches resulting from the split\\n\",\n    \"- $w^{\\\\text{left}}$ and $w^{\\\\text{right}}$ are the proportion of examples at the left and right branch, respectively\\n\",\n    \"\\n\",\n    \"Note:\\n\",\n    \"- You can use the `compute_entropy()` function that you implemented above to calculate the entropy\\n\",\n    \"- We've provided some starter code that uses the `split_dataset()` function you implemented above to split the dataset \\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C3\\n\",\n    \"# GRADED FUNCTION: compute_information_gain\\n\",\n    \"\\n\",\n    \"def compute_information_gain(X, y, node_indices, feature):\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Compute the information of splitting the node on a given feature\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):            Data matrix of shape(n_samples, n_features)\\n\",\n    \"        y (array like):         list or ndarray with n_samples containing the target variable\\n\",\n    \"        node_indices (ndarray): List containing the active indices. I.e, the samples being considered in this step.\\n\",\n    \"   \\n\",\n    \"    Returns:\\n\",\n    \"        cost (float):        Cost computed\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"    \\n\",\n    \"    # Split dataset\\n\",\n    \"    left_indices, right_indices = split_dataset(X, node_indices, feature)\\n\",\n    \"    \\n\",\n    \"    # Some useful variables\\n\",\n    \"    X_node, y_node = X[node_indices], y[node_indices]\\n\",\n    \"    X_left, y_left = X[left_indices], y[left_indices]\\n\",\n    \"    X_right, y_right = X[right_indices], y[right_indices]\\n\",\n    \"    \\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    information_gain = 0\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    node_entropy = compute_entropy(y_node)\\n\",\n    \"    left_entropy = compute_entropy(y_left)\\n\",\n    \"    right_entropy = compute_entropy(y_right)\\n\",\n    \"    \\n\",\n    \"    # Weights \\n\",\n    \"    w_left = len(X_left) / len(X_node)\\n\",\n    \"    w_right = len(X_right) / len(X_node)\\n\",\n    \"    \\n\",\n    \"    #Weighted entropy\\n\",\n    \"    weighted_entropy = w_left * left_entropy + w_right * right_entropy\\n\",\n    \"    \\n\",\n    \"    #Information gain \\n\",\n    \"    information_gain = node_entropy - weighted_entropy\\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ###  \\n\",\n    \"    \\n\",\n    \"    return information_gain\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_information_gain(X, y, node_indices, feature):\\n\",\n    \"        # Split dataset\\n\",\n    \"        left_indices, right_indices = split_dataset(X, node_indices, feature)\\n\",\n    \"\\n\",\n    \"        # Some useful variables\\n\",\n    \"        X_node, y_node = X[node_indices], y[node_indices]\\n\",\n    \"        X_left, y_left = X[left_indices], y[left_indices]\\n\",\n    \"        X_right, y_right = X[right_indices], y[right_indices]\\n\",\n    \"\\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        information_gain = 0\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        # Your code here to compute the entropy at the node using compute_entropy()\\n\",\n    \"        node_entropy = \\n\",\n    \"        # Your code here to compute the entropy at the left branch\\n\",\n    \"        left_entropy = \\n\",\n    \"        # Your code here to compute the entropy at the right branch\\n\",\n    \"        right_entropy = \\n\",\n    \"\\n\",\n    \"        # Your code here to compute the proportion of examples at the left branch\\n\",\n    \"        w_left = \\n\",\n    \"        \\n\",\n    \"        # Your code here to compute the proportion of examples at the right branch\\n\",\n    \"        w_right = \\n\",\n    \"\\n\",\n    \"        # Your code here to compute weighted entropy from the split using \\n\",\n    \"        # w_left, w_right, left_entropy and right_entropy\\n\",\n    \"        weighted_entropy = \\n\",\n    \"\\n\",\n    \"        # Your code here to compute the information gain as the entropy at the node\\n\",\n    \"        # minus the weighted entropy\\n\",\n    \"        information_gain = \\n\",\n    \"        ### END CODE HERE ###  \\n\",\n    \"\\n\",\n    \"        return information_gain\\n\",\n    \"    ```\\n\",\n    \"    If you're still stuck, check out the hints below.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Hint to calculate the entropies</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    <code>node_entropy = compute_entropy(y_node)</code><br>\\n\",\n    \"    <code>left_entropy = compute_entropy(y_left)</code><br>\\n\",\n    \"    <code>right_entropy = compute_entropy(y_right)</code>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate w_left and w_right</b></font></summary>\\n\",\n    \"           <code>w_left = len(X_left) / len(X_node)</code><br>\\n\",\n    \"           <code>w_right = len(X_right) / len(X_node)</code>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate weighted_entropy</b></font></summary>\\n\",\n    \"           <code>weighted_entropy = w_left * left_entropy + w_right * right_entropy</code>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate information_gain</b></font></summary>\\n\",\n    \"           <code> information_gain = node_entropy - weighted_entropy</code>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"</details>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can now check your implementation using the cell below and calculate what the information gain would be from splitting on each of the featues\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Information Gain from splitting the root on brown cap:  0.034851554559677034\\n\",\n      \"Information Gain from splitting the root on tapering stalk shape:  0.12451124978365313\\n\",\n      \"Information Gain from splitting the root on solitary:  0.2780719051126377\\n\",\n      \"\\u001b[92m All tests passed.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"info_gain0 = compute_information_gain(X_train, y_train, root_indices, feature=0)\\n\",\n    \"print(\\\"Information Gain from splitting the root on brown cap: \\\", info_gain0)\\n\",\n    \"    \\n\",\n    \"info_gain1 = compute_information_gain(X_train, y_train, root_indices, feature=1)\\n\",\n    \"print(\\\"Information Gain from splitting the root on tapering stalk shape: \\\", info_gain1)\\n\",\n    \"\\n\",\n    \"info_gain2 = compute_information_gain(X_train, y_train, root_indices, feature=2)\\n\",\n    \"print(\\\"Information Gain from splitting the root on solitary: \\\", info_gain2)\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"compute_information_gain_test(compute_information_gain)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"```\\n\",\n    \"Information Gain from splitting the root on brown cap:  0.034851554559677034\\n\",\n    \"Information Gain from splitting the root on tapering stalk shape:  0.12451124978365313\\n\",\n    \"Information Gain from splitting the root on solitary:  0.2780719051126377\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Splitting on \\\"Solitary\\\" (feature = 2) at the root node gives the maximum information gain. Therefore, it's the best feature to split on at the root node.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.4\\\"></a>\\n\",\n    \"### 4.4  Get best split\\n\",\n    \"Now let's write a function to get the best feature to split on by computing the information gain from each feature as we did above and returning the feature that gives the maximum information gain\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex04\\\"></a>\\n\",\n    \"### Exercise 4\\n\",\n    \"Please complete the `get_best_split()` function shown below.\\n\",\n    \"- The function takes in the training data, along with the indices of datapoint at that node\\n\",\n    \"- The output of the function is the feature that gives the maximum information gain \\n\",\n    \"    - You can use the `compute_information_gain()` function to iterate through the features and calculate the information for each feature\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C4\\n\",\n    \"# GRADED FUNCTION: get_best_split\\n\",\n    \"\\n\",\n    \"def get_best_split(X, y, node_indices):   \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Returns the optimal feature and threshold value\\n\",\n    \"    to split the node data \\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):            Data matrix of shape(n_samples, n_features)\\n\",\n    \"        y (array like):         list or ndarray with n_samples containing the target variable\\n\",\n    \"        node_indices (ndarray): List containing the active indices. I.e, the samples being considered in this step.\\n\",\n    \"\\n\",\n    \"    Returns:\\n\",\n    \"        best_feature (int):     The index of the best feature to split\\n\",\n    \"    \\\"\\\"\\\"    \\n\",\n    \"    \\n\",\n    \"    # Some useful variables\\n\",\n    \"    num_features = X.shape[1]\\n\",\n    \"    \\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    best_feature = -1\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    max_info_gain=0\\n\",\n    \"    for feature in range(num_features):\\n\",\n    \"        info_gain = compute_information_gain(X, y, node_indices, feature)\\n\",\n    \"        if info_gain > max_info_gain:\\n\",\n    \"            max_info_gain = info_gain\\n\",\n    \"            best_feature = feature\\n\",\n    \"                \\n\",\n    \"        \\n\",\n    \"    ### END CODE HERE ##    \\n\",\n    \"       \\n\",\n    \"    return best_feature\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"   * Here's how you can structure the overall implementation for this function\\n\",\n    \"    \\n\",\n    \"    ```python \\n\",\n    \"    def get_best_split(X, y, node_indices):   \\n\",\n    \"\\n\",\n    \"        # Some useful variables\\n\",\n    \"        num_features = X.shape[1]\\n\",\n    \"\\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        best_feature = -1\\n\",\n    \"\\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        max_info_gain = 0\\n\",\n    \"\\n\",\n    \"        # Iterate through all features\\n\",\n    \"        for feature in range(num_features): \\n\",\n    \"            \\n\",\n    \"            # Your code here to compute the information gain from splitting on this feature\\n\",\n    \"            info_gain = \\n\",\n    \"            \\n\",\n    \"            # If the information gain is larger than the max seen so far\\n\",\n    \"            if info_gain > max_info_gain:  \\n\",\n    \"                # Your code here to set the max_info_gain and best_feature\\n\",\n    \"                max_info_gain = \\n\",\n    \"                best_feature = \\n\",\n    \"        ### END CODE HERE ##    \\n\",\n    \"   \\n\",\n    \"    return best_feature\\n\",\n    \"    ```\\n\",\n    \"    If you're still stuck, check out the hints below.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Hint to calculate info_gain</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    <code>info_gain = compute_information_gain(X, y, node_indices, feature)</code>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to update the max_info_gain and best_feature</b></font></summary>\\n\",\n    \"           <code>max_info_gain = info_gain</code><br>\\n\",\n    \"           <code>best_feature = feature</code>\\n\",\n    \"    </details>\\n\",\n    \"</details>\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's check the implementation of your function using the cell below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best feature to split on: 2\\n\",\n      \"\\u001b[92m All tests passed.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_feature = get_best_split(X_train, y_train, root_indices)\\n\",\n    \"print(\\\"Best feature to split on: %d\\\" % best_feature)\\n\",\n    \"\\n\",\n    \"# UNIT TESTS\\n\",\n    \"get_best_split_test(get_best_split)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As we saw above, the function returns that the best feature to split on at the root node is feature 2 (\\\"Solitary\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"5\\\"></a>\\n\",\n    \"## 5 - Building the tree\\n\",\n    \"\\n\",\n    \"In this section, we use the functions you implemented above to generate a decision tree by successively picking the best feature to split on until we reach the stopping criteria (maximum depth is 2).\\n\",\n    \"\\n\",\n    \"You do not need to implement anything for this part.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Not graded\\n\",\n    \"tree = []\\n\",\n    \"\\n\",\n    \"def build_tree_recursive(X, y, node_indices, branch_name, max_depth, current_depth):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Build a tree using the recursive algorithm that split the dataset into 2 subgroups at each node.\\n\",\n    \"    This function just prints the tree.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):            Data matrix of shape(n_samples, n_features)\\n\",\n    \"        y (array like):         list or ndarray with n_samples containing the target variable\\n\",\n    \"        node_indices (ndarray): List containing the active indices. I.e, the samples being considered in this step.\\n\",\n    \"        branch_name (string):   Name of the branch. ['Root', 'Left', 'Right']\\n\",\n    \"        max_depth (int):        Max depth of the resulting tree. \\n\",\n    \"        current_depth (int):    Current depth. Parameter used during recursive call.\\n\",\n    \"   \\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"\\n\",\n    \"    # Maximum depth reached - stop splitting\\n\",\n    \"    if current_depth == max_depth:\\n\",\n    \"        formatting = \\\" \\\"*current_depth + \\\"-\\\"*current_depth\\n\",\n    \"        print(formatting, \\\"%s leaf node with indices\\\" % branch_name, node_indices)\\n\",\n    \"        return\\n\",\n    \"   \\n\",\n    \"    # Otherwise, get best split and split the data\\n\",\n    \"    # Get the best feature and threshold at this node\\n\",\n    \"    best_feature = get_best_split(X, y, node_indices) \\n\",\n    \"    tree.append((current_depth, branch_name, best_feature, node_indices))\\n\",\n    \"    \\n\",\n    \"    formatting = \\\"-\\\"*current_depth\\n\",\n    \"    print(\\\"%s Depth %d, %s: Split on feature: %d\\\" % (formatting, current_depth, branch_name, best_feature))\\n\",\n    \"    \\n\",\n    \"    # Split the dataset at the best feature\\n\",\n    \"    left_indices, right_indices = split_dataset(X, node_indices, best_feature)\\n\",\n    \"    \\n\",\n    \"    # continue splitting the left and the right child. Increment current depth\\n\",\n    \"    build_tree_recursive(X, y, left_indices, \\\"Left\\\", max_depth, current_depth+1)\\n\",\n    \"    build_tree_recursive(X, y, right_indices, \\\"Right\\\", max_depth, current_depth+1)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" Depth 0, Root: Split on feature: 2\\n\",\n      \"- Depth 1, Left: Split on feature: 0\\n\",\n      \"  -- Left leaf node with indices [0, 1, 4, 7]\\n\",\n      \"  -- Right leaf node with indices [5]\\n\",\n      \"- Depth 1, Right: Split on feature: 1\\n\",\n      \"  -- Left leaf node with indices [8]\\n\",\n      \"  -- Right leaf node with indices [2, 3, 6, 9]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"build_tree_recursive(X_train, y_train, root_indices, \\\"Root\\\", max_depth=2, current_depth=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/C2W4A1/public_tests.py",
    "content": "import numpy as np\n\ndef compute_entropy_test(target):\n    y = np.array([1] * 10)\n    result = target(y)\n    \n    assert result == 0, \"Entropy must be 0 with array of ones\"\n    \n    y = np.array([0] * 10)\n    result = target(y)\n    \n    assert result == 0, \"Entropy must be 0 with array of zeros\"\n    \n    y = np.array([0] * 12 + [1] * 12)\n    result = target(y)\n    \n    assert result == 1, \"Entropy must be 1 with same ammount of ones and zeros\"\n    \n    y = np.array([1, 0, 1, 0, 1, 1, 1, 0, 1])\n    assert np.isclose(target(y), 0.918295, atol=1e-6), \"Wrong value. Something between 0 and 1\"\n    assert np.isclose(target(-y + 1), target(y), atol=1e-6), \"Wrong value\"\n    \n    print(\"\\033[92m All tests passed.\")\n\ndef split_dataset_test(target):\n    X = np.array([[1, 0], \n         [1, 0], \n         [1, 1], \n         [0, 0], \n         [0, 1]])\n    X_t = np.array([[0, 1, 0, 1, 0]])\n    X = np.concatenate((X, X_t.T), axis=1)\n\n    left, right = target(X, list(range(5)), 2)\n    expected = {'left': np.array([1, 3]),\n                'right': np.array([0, 2, 4])}\n\n    assert type(left) == list, f\"Wrong type for left. Expected: list got: {type(left)}\"\n    assert type(right) == list, f\"Wrong type for right. Expected: list got: {type(right)}\"\n    \n    assert type(left[0]) == int, f\"Wrong type for elements in the left list. Expected: int got: {type(left[0])}\"\n    assert type(right[0]) == int, f\"Wrong type for elements in the right list. Expected: number got: {type(right[0])}\"\n    \n    assert len(left) == 2, f\"left must have 2 elements but got: {len(left)}\"\n    assert len(right) == 3, f\"right must have 3 elements but got: {len(right)}\"\n\n    assert np.allclose(right, expected['right']), f\"Wrong value for right. Expected: { expected['right']} \\ngot: {right}\"\n    assert np.allclose(left, expected['left']), f\"Wrong value for left. Expected: { expected['left']} \\ngot: {left}\"\n\n    X = np.array([[0, 1], \n         [1, 1], \n         [1, 1], \n         [0, 0], \n         [1, 0]])\n    X_t = np.array([[0, 1, 0, 1, 0]])\n    X = np.concatenate((X_t.T, X), axis=1)\n\n    left, right = target(X, list(range(5)), 0)\n    expected = {'left': np.array([1, 3]),\n                'right': np.array([0, 2, 4])}\n\n    assert np.allclose(right, expected['right']) and np.allclose(left, expected['left']), f\"Wrong value when target is at index 0.\"\n    \n    X = (np.random.rand(11, 3) > 0.5) * 1 # Just random binary numbers\n    X_t = np.array([[0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0]])\n    X = np.concatenate((X, X_t.T), axis=1)\n\n    left, right = target(X, [1, 2, 3, 6, 7, 9, 10], 3)\n    expected = {'left': np.array([1, 3, 6]),\n                'right': np.array([2, 7, 9, 10])}\n\n    assert np.allclose(right, expected['right']) and np.allclose(left, expected['left']), f\"Wrong value when target is at index 0. \\nExpected: {expected} \\ngot: \\{left:{left}, 'right': {right}\\}\"\n \n    \n    print(\"\\033[92m All tests passed.\")\n    \ndef compute_information_gain_test(target):\n    X = np.array([[1, 0], \n         [1, 0], \n         [1, 0], \n         [0, 0], \n         [0, 1]])\n    \n    y = np.array([[0, 0, 0, 0, 0]]).T\n    node_indexes = list(range(5))\n\n    result1 = target(X, y, node_indexes, 0)\n    result2 = target(X, y, node_indexes, 0)\n    \n    assert result1 == 0 and result2 == 0, f\"Information gain must be 0 when target variable is pure. Got {result1} and {result2}\"\n    \n    y = np.array([[0, 1, 0, 1, 0]]).T\n    node_indexes = list(range(5))\n    \n    result = target(X, y, node_indexes, 0)\n    assert np.isclose(result, 0.019973, atol=1e-6), f\"Wrong information gain. Expected {0.019973} got: {result}\"\n    \n    result = target(X, y, node_indexes, 1)\n    assert np.isclose(result, 0.170951, atol=1e-6), f\"Wrong information gain. Expected {0.170951} got: {result}\"\n\n    node_indexes = list(range(4))\n    result = target(X, y, node_indexes, 0)\n    assert np.isclose(result, 0.311278, atol=1e-6), f\"Wrong information gain. Expected {0.311278} got: {result}\"\n\n    result = target(X, y, node_indexes, 1)\n    assert np.isclose(result, 0, atol=1e-6), f\"Wrong information gain. Expected {0.0} got: {result}\"\n\n    print(\"\\033[92m All tests passed.\")\n    \ndef get_best_split_test(target):\n    X = np.array([[1, 0], \n         [1, 0], \n         [1, 0], \n         [0, 0], \n         [0, 1]])\n\n    y = np.array([[0, 0, 0, 0, 0]]).T\n    node_indexes = list(range(5))\n\n    result = target(X, y, node_indexes)\n    \n    assert result == -1, f\"When the target variable is pure, there is no best split to do. Expected -1, got {result}\"\n    \n    y = X[:,0]\n    result = target(X, y, node_indexes)\n    assert result == 0, f\"If the target is fully correlated with other feature, that feature must be the best split. Expected 0, got {result}\"\n    y = X[:,1]\n    result = target(X, y, node_indexes)\n    assert result == 1, f\"If the target is fully correlated with other feature, that feature must be the best split. Expected 1, got {result}\"\n\n    y = 1 - X[:,0]\n    result = target(X, y, node_indexes)\n    assert result == 0, f\"If the target is fully correlated with other feature, that feature must be the best split. Expected 0, got {result}\"\n\n    y = np.array([[0, 1, 0, 1, 0]]).T\n    result = target(X, y, node_indexes)\n    assert result == 1, f\"Wrong result. Expected 1, got {result}\"\n\n    y = np.array([[0, 1, 0, 1, 0]]).T    \n    node_indexes = [2, 3, 4]\n    result = target(X, y, node_indexes)\n    assert result == 0, f\"Wrong result. Expected 0, got {result}\"\n\n    n_samples = 100\n    X0 = np.array([[1] * n_samples])\n    X1 = np.array([[0] * n_samples])\n    X2 = (np.random.rand(1, 100) > 0.5) * 1\n    X3 = np.array([[1] * int(n_samples / 2) + [0] * int(n_samples / 2)])\n    \n    y = X2.T\n    node_indexes = list(range(20, 80))\n    X = np.array([X0, X1, X2, X3]).T.reshape(n_samples, 4)\n    result = target(X, y, node_indexes)\n    \n    assert result == 2, f\"Wrong result. Expected 2, got {result}\"\n    \n    y = X0.T\n    result = target(X, y, node_indexes)\n    assert result == -1, f\"When the target variable is pure, there is no best split to do. Expected -1, got {result}\"\n    print(\"\\033[92m All tests passed.\")"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/Readme.md",
    "content": "### C2 - Week 4 Solutions \n\n\n- [Practice quiz : Decision Trees](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-trees)\n- [Practice Quiz : Decision Trees Learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-tree-learning)\n- [Practice quiz : Decision Trees Ensembles](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-tree-ensembles)\n- [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/C2W4A1)\n  - [Decision Trees](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/C2W4A1/C2_W4_Decision_Tree_with_Markdown.ipynb)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/optional labs/.ipynb_checkpoints/C2_W4_Lab_01_Decision_Trees-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Ungraded Lab: Decision Trees\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this notebook you will visualize how a decision tree is splitted using information gain.\\n\",\n    \"\\n\",\n    \"We will revisit the dataset used in the video lectures. The dataset is:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you saw in the lectures, in a decision tree, we decide if a node will be split or not by looking at the **information gain** that split would give us. (Image of video IG)\\n\",\n    \"\\n\",\n    \"Where \\n\",\n    \"\\n\",\n    \"$$\\\\text{Information Gain} = H(p_1^\\\\text{node})- \\\\left(w^{\\\\text{left}}H\\\\left(p_1^\\\\text{left}\\\\right) + w^{\\\\text{right}}H\\\\left(p_1^\\\\text{right}\\\\right)\\\\right),$$\\n\",\n    \"\\n\",\n    \"and $H$ is the entropy, defined as\\n\",\n    \"\\n\",\n    \"$$H(p_1) = -p_1 \\\\text{log}_2(p_1) - (1- p_1) \\\\text{log}_2(1- p_1)$$\\n\",\n    \"\\n\",\n    \"Remember that log here is defined to be in base 2. Run the code block below to see by yourself how the entropy. $H(p)$ behaves while $p$ varies.\\n\",\n    \"\\n\",\n    \"Note that the H attains its higher value when $p = 0.5$. This means that the probability of event is $0.5$. And its minimum value is attained in $p = 0$ and $p = 1$, i.e., the probability of the event happening is totally predictable. Thus, the entropy shows the degree of predictability of an event.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import *\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"fff2d6bf7abb458085d7acf5b62d993c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib widget\\n\",\n    \"_ = plot_entropy()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"|                                                     |   Ear Shape | Face Shape | Whiskers |   Cat  |\\n\",\n    \"|:---------------------------------------------------:|:---------:|:-----------:|:---------:|:------:|\\n\",\n    \"| <img src=\\\"images/0.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |   Round     |  Present  |    1   |\\n\",\n    \"| <img src=\\\"images/1.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Floppy   |  Not Round  |  Present  |    1   |\\n\",\n    \"| <img src=\\\"images/2.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Floppy   |  Round      |  Absent   |    0   |\\n\",\n    \"| <img src=\\\"images/3.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |  Not Round  |  Present  |    0   |\\n\",\n    \"| <img src=\\\"images/4.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |   Round     |  Present  |    1   |\\n\",\n    \"| <img src=\\\"images/5.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |   Round     |  Absent   |    1   |\\n\",\n    \"| <img src=\\\"images/6.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Floppy   |  Not Round  |  Absent   |    0   |\\n\",\n    \"| <img src=\\\"images/7.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |  Round      |  Absent   |    1   |\\n\",\n    \"| <img src=\\\"images/8.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |    Floppy  |   Round     |  Absent   |    0   |\\n\",\n    \"| <img src=\\\"images/9.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Floppy   |  Round      |  Absent   |    0   |\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"We will use **one-hot encoding** to encode the categorical features. They will be as follows:\\n\",\n    \"\\n\",\n    \"- Ear Shape: Pointy = 1, Floppy = 0\\n\",\n    \"- Face Shape: Round = 1, Not Round = 0\\n\",\n    \"- Whiskers: Present = 1, Absent = 0\\n\",\n    \"\\n\",\n    \"Therefore, we have two sets:\\n\",\n    \"\\n\",\n    \"- `X_train`: for each example, contains 3 features:\\n\",\n    \"            - Ear Shape (1 if pointy, 0 otherwise)\\n\",\n    \"            - Face Shape (1 if round, 0 otherwise)\\n\",\n    \"            - Whiskers (1 if present, 0 otherwise)\\n\",\n    \"            \\n\",\n    \"- `y_train`: whether the animal is a cat\\n\",\n    \"            - 1 if the animal is a cat\\n\",\n    \"            - 0 otherwise\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[1, 1, 1],\\n\",\n    \"[0, 0, 1],\\n\",\n    \" [0, 1, 0],\\n\",\n    \" [1, 0, 1],\\n\",\n    \" [1, 1, 1],\\n\",\n    \" [1, 1, 0],\\n\",\n    \" [0, 0, 0],\\n\",\n    \" [1, 1, 0],\\n\",\n    \" [0, 1, 0],\\n\",\n    \" [0, 1, 0]])\\n\",\n    \"\\n\",\n    \"y_train = np.array([1, 1, 0, 0, 1, 1, 0, 1, 0, 0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 1, 1])\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"#For instance, the first example\\n\",\n    \"X_train[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This means that the first example has a pointy ear shape, round face shape and it has whiskers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"On each node, we compute the information gain for each feature, then split the node on the feature with the higher information gain, by comparing the entropy of the node with the weighted entropy in the two splitted nodes. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, the root node has every animal in our dataset. Remember that $p_1^{node}$ is the proportion of positive class (cats) in the root node. So\\n\",\n    \"\\n\",\n    \"$$p_1^{node} = \\\\frac{5}{10} = 0.5$$\\n\",\n    \"\\n\",\n    \"Now let's write a function to compute the entropy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def entropy(p):\\n\",\n    \"    if p == 0 or p == 1:\\n\",\n    \"        return 0\\n\",\n    \"    else:\\n\",\n    \"        return -p * np.log2(p) - (1- p)*np.log2(1 - p)\\n\",\n    \"    \\n\",\n    \"print(entropy(0.5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To illustrate, let's compute the information gain if we split the node for each of the features. To do this, let's write some functions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def split_indices(X, index_feature):\\n\",\n    \"    \\\"\\\"\\\"Given a dataset and a index feature, return two lists for the two split nodes, the left node has the animals that have \\n\",\n    \"    that feature = 1 and the right node those that have the feature = 0 \\n\",\n    \"    index feature = 0 => ear shape\\n\",\n    \"    index feature = 1 => face shape\\n\",\n    \"    index feature = 2 => whiskers\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    left_indices = []\\n\",\n    \"    right_indices = []\\n\",\n    \"    for i,x in enumerate(X):\\n\",\n    \"        if x[index_feature] == 1:\\n\",\n    \"            left_indices.append(i)\\n\",\n    \"        else:\\n\",\n    \"            right_indices.append(i)\\n\",\n    \"    return left_indices, right_indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, if we choose Ear Shape to split, then we must have in the left node (check the table above) the indices:\\n\",\n    \"\\n\",\n    \"$$0 \\\\quad 3 \\\\quad 4 \\\\quad 5 \\\\quad 7$$\\n\",\n    \"\\n\",\n    \"and the right indices, the remaining ones.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"([0, 3, 4, 5, 7], [1, 2, 6, 8, 9])\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"split_indices(X_train, 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we need another function to compute the weighted entropy in the splitted nodes. As you've seen in the video lecture, we must find:\\n\",\n    \"\\n\",\n    \"- $w^{\\\\text{left}}$ and $w^{\\\\text{right}}$, the proportion of animals in **each node**.\\n\",\n    \"- $p^{\\\\text{left}}$ and $p^{\\\\text{right}}$, the proportion of cats in **each split**.\\n\",\n    \"\\n\",\n    \"Note the difference between these two definitions!! To illustrate, if we split the root node on the feature of index 0 (Ear Shape), then in the left node, the one that has the animals 0, 3, 4, 5 and 7, we have:\\n\",\n    \"\\n\",\n    \"$$w^{\\\\text{left}}= \\\\frac{5}{10} = 0.5 \\\\text{ and } p^{\\\\text{left}} = \\\\frac{4}{5}$$\\n\",\n    \"$$w^{\\\\text{right}}= \\\\frac{5}{10} = 0.5 \\\\text{ and } p^{\\\\text{right}} = \\\\frac{1}{5}$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def weighted_entropy(X,y,left_indices,right_indices):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    This function takes the splitted dataset, the indices we chose to split and returns the weighted entropy.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    w_left = len(left_indices)/len(X)\\n\",\n    \"    w_right = len(right_indices)/len(X)\\n\",\n    \"    p_left = sum(y[left_indices])/len(left_indices)\\n\",\n    \"    p_right = sum(y[right_indices])/len(right_indices)\\n\",\n    \"    \\n\",\n    \"    weighted_entropy = w_left * entropy(p_left) + w_right * entropy(p_right)\\n\",\n    \"    return weighted_entropy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.7219280948873623\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"left_indices, right_indices = split_indices(X_train, 0)\\n\",\n    \"weighted_entropy(X_train, y_train, left_indices, right_indices)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, the weighted entropy in the 2 split nodes is 0.72. To compute the **Information Gain** we must subtract it from the entropy in the node we chose to split (in this case, the root node). \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def information_gain(X, y, left_indices, right_indices):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Here, X has the elements in the node and y is theirs respectives classes\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    p_node = sum(y)/len(y)\\n\",\n    \"    h_node = entropy(p_node)\\n\",\n    \"    w_entropy = weighted_entropy(X,y,left_indices,right_indices)\\n\",\n    \"    return h_node - w_entropy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.2780719051126377\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"information_gain(X_train, y_train, left_indices, right_indices)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's compute the information gain if we split the root node for each feature:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Feature: Ear Shape, information gain if we split the root node using this feature: 0.28\\n\",\n      \"Feature: Face Shape, information gain if we split the root node using this feature: 0.03\\n\",\n      \"Feature: Whiskers, information gain if we split the root node using this feature: 0.12\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i, feature_name in enumerate(['Ear Shape', 'Face Shape', 'Whiskers']):\\n\",\n    \"    left_indices, right_indices = split_indices(X_train, i)\\n\",\n    \"    i_gain = information_gain(X_train, y_train, left_indices, right_indices)\\n\",\n    \"    print(f\\\"Feature: {feature_name}, information gain if we split the root node using this feature: {i_gain:.2f}\\\")\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, the best feature to split is indeed the Ear Shape. Run the code below to see the split in action. You do not need to understand the following code block. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" Depth 0, Root: Split on feature: 0\\n\",\n      \" - Left leaf node with indices [0, 3, 4, 5, 7]\\n\",\n      \" - Right leaf node with indices [1, 2, 6, 8, 9]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4fa863daf0d844b8a6bd81c57b31b378\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"tree = []\\n\",\n    \"build_tree_recursive(X_train, y_train, [0,1,2,3,4,5,6,7,8,9], \\\"Root\\\", max_depth=1, current_depth=0, tree = tree)\\n\",\n    \"generate_tree_viz([0,1,2,3,4,5,6,7,8,9], y_train, tree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The process is **recursive**, which means we must perform these calculations for each node until we meet a stopping criteria:\\n\",\n    \"\\n\",\n    \"- If the tree depth after splitting exceeds a threshold\\n\",\n    \"- If the resulting node has only 1 class\\n\",\n    \"- If the information gain of splitting is below a threshold\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The final tree looks like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" Depth 0, Root: Split on feature: 0\\n\",\n      \"- Depth 1, Left: Split on feature: 1\\n\",\n      \"  -- Left leaf node with indices [0, 4, 5, 7]\\n\",\n      \"  -- Right leaf node with indices [3]\\n\",\n      \"- Depth 1, Right: Split on feature: 2\\n\",\n      \"  -- Left leaf node with indices [1]\\n\",\n      \"  -- Right leaf node with indices [2, 6, 8, 9]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e2f8b1bdc2eb4485a466273f0a965c78\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"tree = []\\n\",\n    \"build_tree_recursive(X_train, y_train, [0,1,2,3,4,5,6,7,8,9], \\\"Root\\\", max_depth=2, current_depth=0, tree = tree)\\n\",\n    \"generate_tree_viz([0,1,2,3,4,5,6,7,8,9], y_train, tree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Congratulations! You completed the notebook!\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"56d44d6a8424451b5ce45d1ae0b0b7865dc60710e7f74571dd51dd80d7829ee9\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/optional labs/.ipynb_checkpoints/C2_W4_Lab_02_Tree_Ensemble-checkpoint (2).ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Ungraded Lab - Trees Ensemble\\n\",\n    \"\\n\",\n    \"In this notebook, you will:\\n\",\n    \"\\n\",\n    \" - Use Pandas to perform one-hot encoding of a dataset\\n\",\n    \" - Use scikit-learn to implement a Decision Tree, Random Forest and XGBoost models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's import the libraries we will use.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"from xgboost import XGBClassifier\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"\\n\",\n    \"RANDOM_STATE = 55 ## We will pass it to every sklearn call so we ensure reproducibility\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1. Introduction\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Datatset\\n\",\n    \"- This dataset is obtained from Kaggle: [Heart Failure Prediction Dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction)\\n\",\n    \"\\n\",\n    \"#### Context\\n\",\n    \"- Cardiovascular disease (CVDs) is the number one cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of five CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs.\\n\",\n    \"- People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management.  \\n\",\n    \"- This dataset contains 11 features that can be used to predict possible heart disease.\\n\",\n    \"- Let's train a machine learning model to assist with diagnosing this disease.\\n\",\n    \"\\n\",\n    \"#### Attribute Information\\n\",\n    \"- Age: age of the patient [years]\\n\",\n    \"- Sex: sex of the patient [M: Male, F: Female]\\n\",\n    \"- ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]\\n\",\n    \"- RestingBP: resting blood pressure [mm Hg]\\n\",\n    \"- Cholesterol: serum cholesterol [mm/dl]\\n\",\n    \"- FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]\\n\",\n    \"- RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]\\n\",\n    \"- MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]\\n\",\n    \"- ExerciseAngina: exercise-induced angina [Y: Yes, N: No]\\n\",\n    \"- Oldpeak: oldpeak = ST [Numeric value measured in depression]\\n\",\n    \"- ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]\\n\",\n    \"- HeartDisease: output class [1: heart disease, 0: Normal]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's now load the dataset. As we can see above, the variables:\\n\",\n    \"\\n\",\n    \"- Sex\\n\",\n    \"- ChestPainType\\n\",\n    \"- RestingECG\\n\",\n    \"- ExerciseAngina\\n\",\n    \"- ST_Slope\\n\",\n    \"\\n\",\n    \"Are *categorical*, so we must one-hot encode them. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load the dataset using pandas\\n\",\n    \"df = pd.read_csv(\\\"heart.csv\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We must perform some data engineering before working with the models. There are 5 categorical features, so we will use Pandas to one-hot encode them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2. One-hot encoding using Pandas\\n\",\n    \"\\n\",\n    \"First we will remove the binary variables, because one-hot encoding them would do nothing to them. To achieve this we will just count how many different values there are in each categorical variable and consider only the variables with 3 or more values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cat_variables = ['Sex',\\n\",\n    \"'ChestPainType',\\n\",\n    \"'RestingECG',\\n\",\n    \"'ExerciseAngina',\\n\",\n    \"'ST_Slope'\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As a reminder, one-hot encoding aims to transform a categorical variable with `n` outputs into `n` binary variables.\\n\",\n    \"\\n\",\n    \"Pandas has a built-in method to one-hot encode variables, it is the function `pd.get_dummies`. There are several arguments to this function, but here we will use only a few. They are:\\n\",\n    \"\\n\",\n    \" - data: DataFrame to be used\\n\",\n    \" - prefix: A list with prefixes, so we know which value we are dealing with\\n\",\n    \" - columns: the list of columns that will be one-hot encoded. 'prefix' and 'columns' must have the same length.\\n\",\n    \" \\n\",\n    \"For more information, you can always type `help(pd.get_dummies)` to read the function's full documentation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# This will replace the columns with the one-hot encoded ones and keep the columns outside 'columns' argument as it is.\\n\",\n    \"df = pd.get_dummies(data = df,\\n\",\n    \"                         prefix = cat_variables,\\n\",\n    \"                         columns = cat_variables)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's choose the variables that will be the input features of the model.\\n\",\n    \"- The target is `HeartDisease`.\\n\",\n    \"- All other variables are features that can potentially be used to predict the target, `HeartDisease`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"features = [x for x in df.columns if x not in 'HeartDisease'] ## Removing our target variable\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We started with 11 features.  Let's see how many feature variables we have after one-hot encoding.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(len(features))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3. Splitting the Dataset\\n\",\n    \"\\n\",\n    \"In this section, we will split our dataset into train and test datasets. We will use the function `train_test_split` from Scikit-learn. Let's just check its arguments.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"help(train_test_split)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, X_val, y_train, y_val = train_test_split(df[features], df['HeartDisease'], train_size = 0.8, random_state = RANDOM_STATE)\\n\",\n    \"\\n\",\n    \"# We will keep the shuffle = True since our dataset has not any time dependency.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(f'train samples: {len(X_train)}\\\\validation samples: {len(X_val)}')\\n\",\n    \"print(f'target proportion: {sum(y_train)/len(y_train):.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4. Building the Models\\n\",\n    \"\\n\",\n    \"## 4.1 Decision Tree\\n\",\n    \"\\n\",\n    \"In this section, let's work with the Decision Tree we previously learned, but now using the [Scikit-learn implementation](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html). \\n\",\n    \"\\n\",\n    \"There are several hyperparameters in the Decision Tree object from Scikit-learn. We will use only some of them and also we will not perform feature selection nor hyperparameter tuning in this lab (but you are encouraged to do so and compare the results 😄 )\\n\",\n    \"\\n\",\n    \"The hyperparameters we will use and investigate here are:\\n\",\n    \"\\n\",\n    \" - min_samples_split: The minimum number of samples required to split an internal node. \\n\",\n    \"   - Choosing a higher min_samples_split can reduce the number of splits and may help to reduce overfitting.\\n\",\n    \" - max_depth: The maximum depth of the tree. \\n\",\n    \"   - Choosing a lower max_depth can reduce the number of splits and may help to reduce overfitting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700] ## If the number is an integer, then it is the actual quantity of samples,\\n\",\n    \"max_depth_list = [1,2, 3, 4, 8, 16, 32, 64, None] # None means that there is no depth limit.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for min_samples_split in min_samples_split_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = DecisionTreeClassifier(min_samples_split = min_samples_split,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('min_samples_split')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note how increasing the the number of `min_samples_split` reduces overfitting.\\n\",\n    \"- Increasing `min_samples_split` from 10 to 30, and from 30 to 50, even though it does not improve the validation accuracy, it brings the training accuracy closer to it, showing a reduction in overfitting.\\n\",\n    \"\\n\",\n    \"Let's do the same experiment with `max_depth`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for max_depth in max_depth_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = DecisionTreeClassifier(max_depth = max_depth,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('max_depth')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can see that in general, reducing `max_depth` can help to reduce overfitting.\\n\",\n    \"- Reducing `max_depth` from 8 to 4 increases validation accuracy closer to training accuracy, while significantly reducing training accuracy.\\n\",\n    \"- The validation accuracy reaches the highest at tree_depth=4. \\n\",\n    \"- When the `max_depth` is smaller than 3, both training and validation accuracy decreases.  The tree cannot make enough splits to distinguish positives from negatives (the model is underfitting the training set). \\n\",\n    \"- When the `max_depth` is too high ( >= 5), validation accuracy decreases while training accuracy increases, indicating that the model is overfitting to the training set.\\n\",\n    \"\\n\",\n    \"So we can choose the best values for these two hyper-parameters for our model to be:\\n\",\n    \"- `max_depth = 4`\\n\",\n    \"- `min_samples_split = 50` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"decision_tree_model = DecisionTreeClassifier(min_samples_split = 50,\\n\",\n    \"                                             max_depth = 3,\\n\",\n    \"                                             random_state = RANDOM_STATE).fit(X_train,y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(f\\\"Metrics train:\\\\n\\\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_train),y_train):.4f}\\\")\\n\",\n    \"print(f\\\"Metrics validation:\\\\n\\\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_val),y_val):.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"No sign of overfitting, even though the metrics are not that good.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.2 Random Forest\\n\",\n    \"\\n\",\n    \"Now let's try the Random Forest algorithm also, using the Scikit-learn implementation. \\n\",\n    \"- All of the hyperparameters found in the decision tree model will also exist in this algorithm, since a random forest is an ensemble of many Decision Trees.\\n\",\n    \"- One additional hyperparameter for Random Forest is called `n_estimators` which is the number of Decision Trees that make up the Random Forest. \\n\",\n    \"\\n\",\n    \"Remember that for a Random Forest, we randomly choose a subset of the features AND randomly choose a subset of the training examples to train each individual tree.\\n\",\n    \"- Following the lectures, if $n$ is the number of features, we will randomly select $\\\\sqrt{n}$ of these features to train each individual tree. \\n\",\n    \"- Note that you can modify this by setting the `max_features` parameter.\\n\",\n    \"\\n\",\n    \"You can also speed up your training jobs with another parameter, `n_jobs`. \\n\",\n    \"- Since the fitting of each tree is independent of each other, it is possible fit more than one tree in parallel. \\n\",\n    \"- So setting `n_jobs` higher will increase how many CPU cores it will use. Note that the numbers very close to the maximum cores of your CPU may impact on the overall performance of your PC and even lead to freezes. \\n\",\n    \"- Changing this parameter does not impact on the final result but can reduce the training time.\\n\",\n    \"\\n\",\n    \"We will run the same script again, but with another parameter, `n_estimators`, where we will choose between 10, 50, and 100. The default is 100.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700]  ## If the number is an integer, then it is the actual quantity of samples,\\n\",\n    \"                                             ## If it is a float, then it is the percentage of the dataset\\n\",\n    \"max_depth_list = [2, 4, 8, 16, 32, 64, None]\\n\",\n    \"n_estimators_list = [10,50,100,500]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for min_samples_split in min_samples_split_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = RandomForestClassifier(min_samples_split = min_samples_split,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('min_samples_split')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list) \\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that, even though the validation accuraty reaches is the same both at `min_samples_split = 2` and `min_samples_split = 10`, in the latter the difference in training and validation set reduces, showing less overfitting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for max_depth in max_depth_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = RandomForestClassifier(max_depth = max_depth,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('max_depth')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for n_estimators in n_estimators_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = RandomForestClassifier(n_estimators = n_estimators,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('n_estimators')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(n_estimators_list )),labels=n_estimators_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's then fit a random forest with the following parameters:\\n\",\n    \"\\n\",\n    \" - max_depth: 16\\n\",\n    \" - min_samples_split: 10\\n\",\n    \" - n_estimators: 100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"random_forest_model = RandomForestClassifier(n_estimators = 100,\\n\",\n    \"                                             max_depth = 16, \\n\",\n    \"                                             min_samples_split = 10).fit(X_train,y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(f\\\"Metrics train:\\\\n\\\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_train),y_train):.4f}\\\\nMetrics test:\\\\n\\\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_val),y_val):.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that we are searching for the best value one hyperparameter while leaving the other hyperparameters at their default values.\\n\",\n    \"- Ideally, we would want to check every combination of values for every hyperparameter that we are tuning.\\n\",\n    \"- If we have 3 hyperparameters, and each hyperparameter has 4 values to try out, we should have a total of 4 x 4 x 4 = 64 combinations to try.\\n\",\n    \"- When we only modify one hyperparameter while leaving the rest as their default value, we are trying 4 + 4 + 4 = 12 results. \\n\",\n    \"- To try out all combinations, we can use a sklearn implementation called GridSearchCV. GridSearchCV has a refit parameter that will automatically refit a model on the best combination so we will not need to program it explicitly. For more on GridSearchCV, please refer to its [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.3 XGBoost\\n\",\n    \"\\n\",\n    \"Next is the Gradient Boosting model, called XGBoost. The boosting methods train several trees, but instead of them being uncorrelated to each other, now the trees are fit one after the other in order to minimize the error. \\n\",\n    \"\\n\",\n    \"The model has the same parameters as a decision tree, plus the learning rate.\\n\",\n    \"- The learning rate is the size of the step on the Gradient Descent method that the XGBoost uses internally to minimize the error on each train step.\\n\",\n    \"\\n\",\n    \"One interesting thing about the XGBoost is that during fitting, it can take in an evaluation dataset of the form `(X_val,y_val)`.\\n\",\n    \"- On each iteration, it measures the cost (or evaluation metric) on the evaluation datasets.\\n\",\n    \"- Once the cost (or metric) stops decreasing for a number of rounds (called early_stopping_rounds), the training will stop. \\n\",\n    \"- More iterations lead to more estimators, and more estimators can result in overfitting.  \\n\",\n    \"- By stopping once the validation metric no longer improves, we can limit the number of estimators created, and reduce overfitting.\\n\",\n    \"\\n\",\n    \"First, let's define a subset of our training set (we should not use the test set here).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = int(len(X_train)*0.8) ## Let's use 80% to train and 20% to eval\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train_fit, X_train_eval, y_train_fit, y_train_eval = X_train[:n], X_train[n:], y_train[:n], y_train[n:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can then set a large number of estimators, because we can stop if the cost function stops decreasing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note some of the `.fit()` parameters:\\n\",\n    \"- `eval_set = [(X_train_eval,y_train_eval)]`:Here we must pass a list to the eval_set, because you can have several different tuples ov eval sets. \\n\",\n    \"- `early_stopping_rounds`: This parameter helps to stop the model training if its evaluation metric is no longer improving on the validation set. It's set to 10.\\n\",\n    \"  - The model keeps track of the round with the best performance (lowest evaluation metric).  For example, let's say round 16 has the lowest evaluation metric so far.\\n\",\n    \"  - Each successive round's evaluation metric is compared to the best metric.  If the model goes 10 rounds where none have a better metric than the best one, then the model stops training.\\n\",\n    \"  - The model is returned at its last state when training terminated, not its state during the best round.  For example, if the model stops at round 26, but the best round was 16, the model's training state at round 26 is returned, not round 16.\\n\",\n    \"  - Note that this is different from returning the model's \\\"best\\\" state (from when the evaluation metric was the lowest).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"xgb_model = XGBClassifier(n_estimators = 500, learning_rate = 0.1,verbosity = 1, random_state = RANDOM_STATE)\\n\",\n    \"xgb_model.fit(X_train_fit,y_train_fit, eval_set = [(X_train_eval,y_train_eval)], early_stopping_rounds = 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Even though we initialized the model to allow up to 500 estimators, the algorithm only fit 26 estimators (over 26 rounds of training).\\n\",\n    \"\\n\",\n    \"To see why, let's look for the round of training that had the best performance (lowest evaluation metric).  You can either view the validation log loss metrics that were output above, or view the model's `.best_iteration` attribute:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"xgb_model.best_iteration\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best round of training was round 16, with a log loss of 4.3948.  \\n\",\n    \"- For 10 rounds of training after that (from round 17 to 26), the log loss was higher than this.\\n\",\n    \"- Since we set `early_stopping_rounds` to 10, then by the 10th round where the log loss doesn't improve upon the best one, training stops.\\n\",\n    \"- You can try out different values of `early_stopping_rounds` to verify this.  If you set it to 20, for instance, the model stops training at round 36 (16 + 20).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(f\\\"Metrics train:\\\\n\\\\tAccuracy score: {accuracy_score(xgb_model.predict(X_train),y_train):.4f}\\\\nMetrics test:\\\\n\\\\tAccuracy score: {accuracy_score(xgb_model.predict(X_val),y_val):.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this example, both Random Forest and XGBoost had similar performance (test accuracy).  \\n\",\n    \"\\n\",\n    \"Congratulations, you have learned how to use Decision Tree, Random Forest from the scikit-learn library and XGBoost!\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"56d44d6a8424451b5ce45d1ae0b0b7865dc60710e7f74571dd51dd80d7829ee9\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/optional labs/.ipynb_checkpoints/C2_W4_Lab_02_Tree_Ensemble-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab - Trees Ensemble\\n\",\n    \"\\n\",\n    \"In this notebook, you will:\\n\",\n    \"\\n\",\n    \" - Use Pandas to perform one-hot encoding of a dataset\\n\",\n    \" - Use scikit-learn to implement a Decision Tree, Random Forest and XGBoost models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's import the libraries you will use.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"!pip install xgboost --quiet\\n\",\n    \"from xgboost import XGBClassifier\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"\\n\",\n    \"RANDOM_STATE = 55 ## You will pass it to every sklearn call so we ensure reproducibility\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1. Loading the Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"From [Kaggle](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction)\\n\",\n    \"\\n\",\n    \"Context\\n\",\n    \"Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.\\n\",\n    \"\\n\",\n    \"People with cardiovascular disease or who are at high cardiovascular risk need early detection and management wherein a machine learning model can be of great help.\\n\",\n    \"\\n\",\n    \"You will develop models to predict how likely a particular person is in developint cardiovascular disease, given all the information below.\\n\",\n    \"\\n\",\n    \"#### Attribute Information\\n\",\n    \"- Age: age of the patient [years]\\n\",\n    \"- Sex: sex of the patient [M: Male, F: Female]\\n\",\n    \"- ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]\\n\",\n    \"- RestingBP: resting blood pressure [mm Hg]\\n\",\n    \"- Cholesterol: serum cholesterol [mm/dl]\\n\",\n    \"- FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]\\n\",\n    \"- RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]\\n\",\n    \"- MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]\\n\",\n    \"- ExerciseAngina: exercise-induced angina [Y: Yes, N: No]\\n\",\n    \"- Oldpeak: oldpeak = ST [Numeric value measured in depression]\\n\",\n    \"- ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]\\n\",\n    \"- HeartDisease: output class [1: heart disease, 0: Normal]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's now load the dataset. As you can see above, the variables:\\n\",\n    \"\\n\",\n    \"- Sex\\n\",\n    \"- ChestPainType\\n\",\n    \"- RestingECG\\n\",\n    \"- ExerciseAngina\\n\",\n    \"- ST_Slope\\n\",\n    \"\\n\",\n    \"Are *categorical*, so you must one-hot encode them. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load the dataset using pandas\\n\",\n    \"df = pd.read_csv(\\\"heart.csv\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You must perform some data engineering before working with the models. There are 5 categorical features, so you will use Pandas to one-hot encode them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2. One-hot encoding using Pandas\\n\",\n    \"\\n\",\n    \"First you will remove the binary variables, because one-hot encoding them would do nothing to them. To achieve this you will just count how many different values there are in each categorical variable and consider only the variables with 3 or more values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cat_variables = ['Sex',\\n\",\n    \"'ChestPainType',\\n\",\n    \"'RestingECG',\\n\",\n    \"'ExerciseAngina',\\n\",\n    \"'ST_Slope'\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As a reminder, one-hot encoding aims to transform a categorical variable with `n` outputs into `n` binary variables.\\n\",\n    \"\\n\",\n    \"Pandas has a built-in method to one-hot encode variables, it is the function `pd.get_dummies`. There are several arguments to this function, but here you will use only a few. They are:\\n\",\n    \"\\n\",\n    \" - data: DataFrame to be used\\n\",\n    \" - prefix: A list with prefixes, so you know which value you are dealing with\\n\",\n    \" - columns: the list of columns that will be one-hot encoded. 'prefix' and 'columns' must have the same length.\\n\",\n    \" \\n\",\n    \"For more information, you can always type `help(pd.get_dummies)` to read the function's full documentation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# This will replace the columns with the one-hot encoded ones and keep the columns outside 'columns' argument as it is.\\n\",\n    \"df = pd.get_dummies(data = df,\\n\",\n    \"                         prefix = cat_variables,\\n\",\n    \"                         columns = cat_variables)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You now will define the final set of variables that will be used by the models you will build in this lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"var = [x for x in df.columns if x not in 'HeartDisease'] ## Removing our target variable\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note how the number of variables has changed. You started with 11 variables now you have:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(len(var))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3. Splitting the Dataset\\n\",\n    \"\\n\",\n    \"In this section, you will split our dataset into train and test datasets. You will use the function `train_test_split` from Scikit-learn. Let's just check its arguments.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"help(train_test_split)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, X_test, y_train, y_test = train_test_split(df[var], df['HeartDisease'], train_size = 0.8, random_state = RANDOM_STATE)\\n\",\n    \"\\n\",\n    \"# We will keep the shuffle = True since our dataset has not any time dependency.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(f'train samples: {len(X_train)}\\\\ntest samples: {len(X_test)}')\\n\",\n    \"print(f'target proportion: {sum(y_train)/len(y_train):.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4. Building the Models\\n\",\n    \"\\n\",\n    \"## 4.1 Decision Tree\\n\",\n    \"\\n\",\n    \"In this section, let's work with the Decision Tree you previously learned, but now using the [Scikit-learn implementation](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html). \\n\",\n    \"\\n\",\n    \"There are several hyperparameters in the Decision Tree object from Scikit-learn. You will use only some of them and also you will not perform feature selection nor hyperparameter tuning in this lab (but you are encouraged to do so and compare the results :-) )\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The hyperparameters you will use and investigate here is:\\n\",\n    \"\\n\",\n    \" - min_samples_split: The minimum number of samples required to split an internal node. This may prevent overfitting.\\n\",\n    \" - max_depth: The maximum depth of the tree. This may prevent overfitting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700] ## If the number is an integer, then it is the actual quantity of samples,\\n\",\n    \"max_depth_list = [1,2, 3, 4, 8, 16, 32, 64, None] # None means that there is no depth limit.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_test = []\\n\",\n    \"for min_samples_split in min_samples_split_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = DecisionTreeClassifier(min_samples_split = min_samples_split,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_test = accuracy_score(predictions_test,y_test)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_test.append(accuracy_test)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Test metrics')\\n\",\n    \"plt.xlabel('min_samples_split')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_test)\\n\",\n    \"plt.legend(['Train','Test'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note how increasing the the number of `min_samples_split` decreases the overfit. \\n\",\n    \"\\n\",\n    \"Let's do the same experiment with `max_depth`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_test = []\\n\",\n    \"for max_depth in max_depth_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = DecisionTreeClassifier(max_depth = max_depth,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_test = accuracy_score(predictions_test,y_test)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_test.append(accuracy_test)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Test metrics')\\n\",\n    \"plt.xlabel('max_depth')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_test)\\n\",\n    \"plt.legend(['Train','Test'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The test accuracy reaches the highest at tree_depth=3. When the allowed depth is smaller, the tree cannot make enough splits to distinguish positives from negatives (having the underfit problem), but when the allowed depth is too high ( >= 5), the tree becomes too specialized to the training set and thus losing accuracy to the test dataset (having the overfit problem). Our final tree model then will have:\\n\",\n    \"\\n\",\n    \"- `max_depth = 3`\\n\",\n    \"- `min_samples_split = 50` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"decision_tree_model = DecisionTreeClassifier(min_samples_split = 50,\\n\",\n    \"                                             max_depth = 3,\\n\",\n    \"                                             random_state = RANDOM_STATE).fit(X_train,y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(f\\\"Metrics train:\\\\n\\\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_train),y_train):.4f}\\\\nMetrics test:\\\\n\\\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_test),y_test):.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"No sign of overfit, even though the metrics are not that good.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.2 Random Forest\\n\",\n    \"\\n\",\n    \"Now let's try the Random Forest algorithm also, using the Scikit-learn implementation. Naturally, all of the above hyperparameters will exist in this algorithm, since it is just an ensemble of Decision Trees, but will have another hyperparameter that you will use, called `n_estimators` which is how many different Decision Trees will be fitted. \\n\",\n    \"\\n\",\n    \"Remember that for a Random Forest, you use a subset of the features AND a subset of the training set to train each tree, chosen randomly. In this case, you will use the number of features as you saw in the lecture, which is $\\\\sqrt{n}$ where $n$ is the number of features. However, this can be modified. For further information on the Random Forest hyperparameters, you can run `help(RandomForestClassifier)`.\\n\",\n    \"\\n\",\n    \"Another parameter that does not impact on the final result but can speed up the computation is called `n_jobs`. Since the fitting of each tree is independent of each other, it is possible to run parallel fits. So setting `n_jobs` higher will increase how many CPU cores it will use. Note that the numbers very close to the maximum cores of your CPU may impact on the overall performance of your PC and even lead to freezes. \\n\",\n    \"\\n\",\n    \"You will run the same script again, but with another parameter, `n_estimators`, where we will choose between 10, 50, and 100. The default is 100.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700]  ## If the number is an integer, then it is the actual quantity of samples,\\n\",\n    \"                                             ## If it is a float, then it is the percentage of the dataset\\n\",\n    \"max_depth_list = [2, 4, 8, 16, 32, 64, None]\\n\",\n    \"n_estimators_list = [10,50,100,500]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_test = []\\n\",\n    \"for min_samples_split in min_samples_split_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = RandomForestClassifier(min_samples_split = min_samples_split,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_test = accuracy_score(predictions_test,y_test)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_test.append(accuracy_test)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Test metrics')\\n\",\n    \"plt.xlabel('min_samples_split')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list) \\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_test)\\n\",\n    \"plt.legend(['Train','Test'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_test = []\\n\",\n    \"for max_depth in max_depth_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = RandomForestClassifier(max_depth = max_depth,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_test = accuracy_score(predictions_test,y_test)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_test.append(accuracy_test)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Test metrics')\\n\",\n    \"plt.xlabel('max_depth')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_test)\\n\",\n    \"plt.legend(['Train','Test'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_test = []\\n\",\n    \"for n_estimators in n_estimators_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = RandomForestClassifier(n_estimators = n_estimators,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_test = accuracy_score(predictions_test,y_test)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_test.append(accuracy_test)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Test metrics')\\n\",\n    \"plt.xlabel('n_estimators')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(n_estimators_list )),labels=n_estimators_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_test)\\n\",\n    \"plt.legend(['Train','Test'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's then fit a random forest with the following parameters:\\n\",\n    \"\\n\",\n    \" - max_depth: 8\\n\",\n    \" - min_samples_split: 10\\n\",\n    \" - n_estimators: 100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"random_forest_model = RandomForestClassifier(n_estimators = 100,\\n\",\n    \"                                             max_depth = 8, \\n\",\n    \"                                             min_samples_split = 10).fit(X_train,y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(f\\\"Metrics train:\\\\n\\\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_train),y_train):.4f}\\\\nMetrics test:\\\\n\\\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_test),y_test):.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You have demonstrated how to look for the best value hyperparameter-by-hyperparameter. However, you should not overlook that as we experiment with one hyperparameter we always have to fix the others at some default values. This makes us only able to tell how the hyperparameter value changes with respect to those defaults. In princple, if you have 4 values to try out in each of the 3 hyperparameters being tuned, you should have a total of 4 x 4 x 4 = 64 combinations, however, the way you are doing will only give us 4 + 4 + 4 = 12 results. To try out all combinations, you can use a sklearn implementation called GridSearchCV, moreover, it has a refit parameter that will automatically refit a model on the best combination so you will not need to program it explicitly. For more on GridSearchCV, please refer to its [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.3 XGBoost\\n\",\n    \"\\n\",\n    \"Now, the last model you will test in this lab is the Gradient Boosting model, called XGBoost. As you've seen in the lectures, the boosting methods train several trees, but instead of them being uncorrelated to each other, now the trees are fitted subsequently to minimize the error. \\n\",\n    \"\\n\",\n    \"The parameters that this model comprises is the same as the parameters for any decision tree, plus some others, such as the learning rate, which is the size of the step on the Gradient Descent method that the XGBoost uses internally to minimize the error on each train step.\\n\",\n    \"\\n\",\n    \"One interesting thing about the XGBoost is that it allows, during the fit, to pass a list evaluation datasets of the form `(X_val,y_val)`, where on each iteration, it measures the cost (or evaluation metric) on the evaluation datasets so that once the cost (or metric) stops to descrease for a number of rounds (called early_stopping_rounds), the training will stop. This is how we can automatically control how many estimators is enough, and how we can avoid overfitting due to too many estimators.\\n\",\n    \"\\n\",\n    \"First, let's define a subset of our training set (we should not use the test set here).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = int(len(X_train)*0.8) ## Let's use 80% to train and 20% to eval\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train_fit, X_train_eval, y_train_fit, y_train_eval = X_train[:n], X_train[n:], y_train[:n], y_train[n:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can then set a large number of estimators, because you can stop if the cost function stops decreasing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"xgb_model = XGBClassifier(n_estimators = 500, learning_rate = 0.1,verbosity = 1, random_state = RANDOM_STATE)\\n\",\n    \"xgb_model.fit(X_train_fit,y_train_fit, eval_set = [(X_train_eval,y_train_eval)], early_stopping_rounds = 50)\\n\",\n    \"# Here we must pass a list to the eval_set, because you can have several different tuples ov eval sets. The parameter \\n\",\n    \"# early_stopping_rounds is the number of iterations that it will wait to check if the cost function decreased or not.\\n\",\n    \"# If not, it will stop and get the iteration that returned the lowest metric on the eval set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, even though you passed 500 estimators to fit, the algorithm only fitted 66 because the log-loss used to metrify the training rounds started to increase. In fact, the number of estimators is even less than 66. If you take a closeer look to the metrics, you see that with 16 fitted trees, we achieved the minimum value of the log-loss, and in fact, this is the number of fitted trees in the final model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"xgb_model.best_iteration\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(f\\\"Metrics train:\\\\n\\\\tAccuracy score: {accuracy_score(xgb_model.predict(X_train),y_train):.4f}\\\\nMetrics test:\\\\n\\\\tAccuracy score: {accuracy_score(xgb_model.predict(X_test),y_test):.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can see that RandomForest achieved the best accuracy, but the results overall were close. And note that we got a very close test metric with XGBoost compared to RandomForest, and we didn't even performed any hyperparameter search! The advantage of XGBoost is that it is faster than the Random Forest and also it has more parameters, therefore you are able to fine-tune the model to achieve even better results.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Congratulations, you have learned how to use Decision Tree, Random Forest from the scikit-learn library and XGBoost!\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"56d44d6a8424451b5ce45d1ae0b0b7865dc60710e7f74571dd51dd80d7829ee9\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/optional labs/C2_W4_Lab_01_Decision_Trees.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Ungraded Lab: Decision Trees\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this notebook you will visualize how a decision tree is splitted using information gain.\\n\",\n    \"\\n\",\n    \"We will revisit the dataset used in the video lectures. The dataset is:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you saw in the lectures, in a decision tree, we decide if a node will be split or not by looking at the **information gain** that split would give us. (Image of video IG)\\n\",\n    \"\\n\",\n    \"Where \\n\",\n    \"\\n\",\n    \"$$\\\\text{Information Gain} = H(p_1^\\\\text{node})- \\\\left(w^{\\\\text{left}}H\\\\left(p_1^\\\\text{left}\\\\right) + w^{\\\\text{right}}H\\\\left(p_1^\\\\text{right}\\\\right)\\\\right),$$\\n\",\n    \"\\n\",\n    \"and $H$ is the entropy, defined as\\n\",\n    \"\\n\",\n    \"$$H(p_1) = -p_1 \\\\text{log}_2(p_1) - (1- p_1) \\\\text{log}_2(1- p_1)$$\\n\",\n    \"\\n\",\n    \"Remember that log here is defined to be in base 2. Run the code block below to see by yourself how the entropy. $H(p)$ behaves while $p$ varies.\\n\",\n    \"\\n\",\n    \"Note that the H attains its higher value when $p = 0.5$. This means that the probability of event is $0.5$. And its minimum value is attained in $p = 0$ and $p = 1$, i.e., the probability of the event happening is totally predictable. Thus, the entropy shows the degree of predictability of an event.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import *\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2bbd8a6861c2443da67561bc162942ee\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"image/png\": 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\",\n 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' width=640.0/>\\n\",\n       \"            </div>\\n\",\n       \"        \"\n      ],\n      \"text/plain\": [\n       \"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib widget\\n\",\n    \"_ = plot_entropy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"|                                                     |   Ear Shape | Face Shape | Whiskers |   Cat  |\\n\",\n    \"|:---------------------------------------------------:|:---------:|:-----------:|:---------:|:------:|\\n\",\n    \"| <img src=\\\"images/0.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |   Round     |  Present  |    1   |\\n\",\n    \"| <img src=\\\"images/1.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Floppy   |  Not Round  |  Present  |    1   |\\n\",\n    \"| <img src=\\\"images/2.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Floppy   |  Round      |  Absent   |    0   |\\n\",\n    \"| <img src=\\\"images/3.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |  Not Round  |  Present  |    0   |\\n\",\n    \"| <img src=\\\"images/4.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |   Round     |  Present  |    1   |\\n\",\n    \"| <img src=\\\"images/5.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |   Round     |  Absent   |    1   |\\n\",\n    \"| <img src=\\\"images/6.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Floppy   |  Not Round  |  Absent   |    0   |\\n\",\n    \"| <img src=\\\"images/7.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Pointy   |  Round      |  Absent   |    1   |\\n\",\n    \"| <img src=\\\"images/8.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |    Floppy  |   Round     |  Absent   |    0   |\\n\",\n    \"| <img src=\\\"images/9.png\\\" alt=\\\"drawing\\\" width=\\\"50\\\"/> |   Floppy   |  Round      |  Absent   |    0   |\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"We will use **one-hot encoding** to encode the categorical features. They will be as follows:\\n\",\n    \"\\n\",\n    \"- Ear Shape: Pointy = 1, Floppy = 0\\n\",\n    \"- Face Shape: Round = 1, Not Round = 0\\n\",\n    \"- Whiskers: Present = 1, Absent = 0\\n\",\n    \"\\n\",\n    \"Therefore, we have two sets:\\n\",\n    \"\\n\",\n    \"- `X_train`: for each example, contains 3 features:\\n\",\n    \"            - Ear Shape (1 if pointy, 0 otherwise)\\n\",\n    \"            - Face Shape (1 if round, 0 otherwise)\\n\",\n    \"            - Whiskers (1 if present, 0 otherwise)\\n\",\n    \"            \\n\",\n    \"- `y_train`: whether the animal is a cat\\n\",\n    \"            - 1 if the animal is a cat\\n\",\n    \"            - 0 otherwise\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train = np.array([[1, 1, 1],\\n\",\n    \"[0, 0, 1],\\n\",\n    \" [0, 1, 0],\\n\",\n    \" [1, 0, 1],\\n\",\n    \" [1, 1, 1],\\n\",\n    \" [1, 1, 0],\\n\",\n    \" [0, 0, 0],\\n\",\n    \" [1, 1, 0],\\n\",\n    \" [0, 1, 0],\\n\",\n    \" [0, 1, 0]])\\n\",\n    \"\\n\",\n    \"y_train = np.array([1, 1, 0, 0, 1, 1, 0, 1, 0, 0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([1, 1, 1])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"#For instance, the first example\\n\",\n    \"X_train[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This means that the first example has a pointy ear shape, round face shape and it has whiskers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"On each node, we compute the information gain for each feature, then split the node on the feature with the higher information gain, by comparing the entropy of the node with the weighted entropy in the two splitted nodes. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, the root node has every animal in our dataset. Remember that $p_1^{node}$ is the proportion of positive class (cats) in the root node. So\\n\",\n    \"\\n\",\n    \"$$p_1^{node} = \\\\frac{5}{10} = 0.5$$\\n\",\n    \"\\n\",\n    \"Now let's write a function to compute the entropy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def entropy(p):\\n\",\n    \"    if p == 0 or p == 1:\\n\",\n    \"        return 0\\n\",\n    \"    else:\\n\",\n    \"        return -p * np.log2(p) - (1 - p) * np.log2(1 - p)\\n\",\n    \"    \\n\",\n    \"print(entropy(0.5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To illustrate, let's compute the information gain if we split the node for each of the features. To do this, let's write some functions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def split_indices(X, index_feature):\\n\",\n    \"    \\\"\\\"\\\"Given a dataset and a index feature, return two lists for the two split nodes, the left node has the animals that have \\n\",\n    \"    that feature = 1 and the right node those that have the feature = 0 \\n\",\n    \"    index feature = 0 => ear shape\\n\",\n    \"    index feature = 1 => face shape\\n\",\n    \"    index feature = 2 => whiskers\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    left_indices = []\\n\",\n    \"    right_indices = []\\n\",\n    \"    for i, x in enumerate(X):\\n\",\n    \"        if x[index_feature] == 1:\\n\",\n    \"            left_indices.append(i)\\n\",\n    \"        else:\\n\",\n    \"            right_indices.append(i)\\n\",\n    \"    return left_indices, right_indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, if we choose Ear Shape to split, then we must have in the left node (check the table above) the indices:\\n\",\n    \"\\n\",\n    \"$$0 \\\\quad 3 \\\\quad 4 \\\\quad 5 \\\\quad 7$$\\n\",\n    \"\\n\",\n    \"and the right indices, the remaining ones.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"([0, 3, 4, 5, 7], [1, 2, 6, 8, 9])\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"split_indices(X_train, 0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we need another function to compute the weighted entropy in the splitted nodes. As you've seen in the video lecture, we must find:\\n\",\n    \"\\n\",\n    \"- $w^{\\\\text{left}}$ and $w^{\\\\text{right}}$, the proportion of animals in **each node**.\\n\",\n    \"- $p^{\\\\text{left}}$ and $p^{\\\\text{right}}$, the proportion of cats in **each split**.\\n\",\n    \"\\n\",\n    \"Note the difference between these two definitions!! To illustrate, if we split the root node on the feature of index 0 (Ear Shape), then in the left node, the one that has the animals 0, 3, 4, 5 and 7, we have:\\n\",\n    \"\\n\",\n    \"$$w^{\\\\text{left}}= \\\\frac{5}{10} = 0.5 \\\\text{ and } p^{\\\\text{left}} = \\\\frac{4}{5}$$\\n\",\n    \"$$w^{\\\\text{right}}= \\\\frac{5}{10} = 0.5 \\\\text{ and } p^{\\\\text{right}} = \\\\frac{1}{5}$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def weighted_entropy(X,y,left_indices,right_indices):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    This function takes the splitted dataset, the indices we chose to split and returns the weighted entropy.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    w_left = len(left_indices)/len(X)\\n\",\n    \"    w_right = len(right_indices)/len(X)\\n\",\n    \"    p_left = sum(y[left_indices])/len(left_indices)\\n\",\n    \"    p_right = sum(y[right_indices])/len(right_indices)\\n\",\n    \"    \\n\",\n    \"    weighted_entropy = w_left * entropy(p_left) + w_right * entropy(p_right)\\n\",\n    \"    return weighted_entropy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.7219280948873623\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"left_indices, right_indices = split_indices(X_train, 0)\\n\",\n    \"weighted_entropy(X_train, y_train, left_indices, right_indices)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, the weighted entropy in the 2 split nodes is 0.72. To compute the **Information Gain** we must subtract it from the entropy in the node we chose to split (in this case, the root node). \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def information_gain(X, y, left_indices, right_indices):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Here, X has the elements in the node and y is theirs respectives classes\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    p_node = sum(y)/len(y)\\n\",\n    \"    h_node = entropy(p_node)\\n\",\n    \"    w_entropy = weighted_entropy(X,y,left_indices,right_indices)\\n\",\n    \"    return h_node - w_entropy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.2780719051126377\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"information_gain(X_train, y_train, left_indices, right_indices)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's compute the information gain if we split the root node for each feature:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Feature: Ear Shape, information gain if we split the root node using this feature: 0.28\\n\",\n      \"Feature: Face Shape, information gain if we split the root node using this feature: 0.03\\n\",\n      \"Feature: Whiskers, information gain if we split the root node using this feature: 0.12\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for i, feature_name in enumerate(['Ear Shape', 'Face Shape', 'Whiskers']):\\n\",\n    \"    left_indices, right_indices = split_indices(X_train, i)\\n\",\n    \"    i_gain = information_gain(X_train, y_train, left_indices, right_indices)\\n\",\n    \"    print(f\\\"Feature: {feature_name}, information gain if we split the root node using this feature: {i_gain:.2f}\\\")\\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"So, the best feature to split is indeed the Ear Shape. Run the code below to see the split in action. You do not need to understand the following code block. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" Depth 0, Root: Split on feature: 0\\n\",\n      \" - Left leaf node with indices [0, 3, 4, 5, 7]\\n\",\n      \" - Right leaf node with indices [1, 2, 6, 8, 9]\\n\"\n     ]\n    },\n    {\n     \"ename\": \"FileNotFoundError\",\n     \"evalue\": \"[WinError 2] \\\"dot\\\" not found in path.\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mFileNotFoundError\\u001b[0m                         Traceback (most recent call last)\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\pydot.py:1923\\u001b[0m, in \\u001b[0;36mDot.create\\u001b[1;34m(self, prog, format, encoding)\\u001b[0m\\n\\u001b[0;32m   1922\\u001b[0m \\u001b[39mtry\\u001b[39;00m:\\n\\u001b[1;32m-> 1923\\u001b[0m     stdout_data, stderr_data, process \\u001b[39m=\\u001b[39m call_graphviz(\\n\\u001b[0;32m   1924\\u001b[0m         program\\u001b[39m=\\u001b[39;49mprog,\\n\\u001b[0;32m   1925\\u001b[0m         arguments\\u001b[39m=\\u001b[39;49marguments,\\n\\u001b[0;32m   1926\\u001b[0m         working_dir\\u001b[39m=\\u001b[39;49mtmp_dir,\\n\\u001b[0;32m   1927\\u001b[0m     )\\n\\u001b[0;32m   1928\\u001b[0m \\u001b[39mexcept\\u001b[39;00m \\u001b[39mOSError\\u001b[39;00m \\u001b[39mas\\u001b[39;00m e:\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\pydot.py:132\\u001b[0m, in \\u001b[0;36mcall_graphviz\\u001b[1;34m(program, arguments, working_dir, **kwargs)\\u001b[0m\\n\\u001b[0;32m    130\\u001b[0m program_with_args \\u001b[39m=\\u001b[39m [program, ] \\u001b[39m+\\u001b[39m arguments\\n\\u001b[1;32m--> 132\\u001b[0m process \\u001b[39m=\\u001b[39m subprocess\\u001b[39m.\\u001b[39mPopen(\\n\\u001b[0;32m    133\\u001b[0m     program_with_args,\\n\\u001b[0;32m    134\\u001b[0m     env\\u001b[39m=\\u001b[39menv,\\n\\u001b[0;32m    135\\u001b[0m     cwd\\u001b[39m=\\u001b[39mworking_dir,\\n\\u001b[0;32m    136\\u001b[0m     shell\\u001b[39m=\\u001b[39m\\u001b[39mFalse\\u001b[39;00m,\\n\\u001b[0;32m    137\\u001b[0m     stderr\\u001b[39m=\\u001b[39msubprocess\\u001b[39m.\\u001b[39mPIPE,\\n\\u001b[0;32m    138\\u001b[0m     stdout\\u001b[39m=\\u001b[39msubprocess\\u001b[39m.\\u001b[39mPIPE,\\n\\u001b[0;32m    139\\u001b[0m     \\u001b[39m*\\u001b[39m\\u001b[39m*\\u001b[39mkwargs\\n\\u001b[0;32m    140\\u001b[0m )\\n\\u001b[0;32m    141\\u001b[0m stdout_data, stderr_data \\u001b[39m=\\u001b[39m process\\u001b[39m.\\u001b[39mcommunicate()\\n\",\n      \"File \\u001b[1;32m~\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\subprocess.py:971\\u001b[0m, in \\u001b[0;36mPopen.__init__\\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize)\\u001b[0m\\n\\u001b[0;32m    968\\u001b[0m             \\u001b[39mself\\u001b[39m\\u001b[39m.\\u001b[39mstderr \\u001b[39m=\\u001b[39m io\\u001b[39m.\\u001b[39mTextIOWrapper(\\u001b[39mself\\u001b[39m\\u001b[39m.\\u001b[39mstderr,\\n\\u001b[0;32m    969\\u001b[0m                     encoding\\u001b[39m=\\u001b[39mencoding, errors\\u001b[39m=\\u001b[39merrors)\\n\\u001b[1;32m--> 971\\u001b[0m     \\u001b[39mself\\u001b[39;49m\\u001b[39m.\\u001b[39;49m_execute_child(args, executable, preexec_fn, close_fds,\\n\\u001b[0;32m    972\\u001b[0m                         pass_fds, cwd, env,\\n\\u001b[0;32m    973\\u001b[0m                         startupinfo, creationflags, shell,\\n\\u001b[0;32m    974\\u001b[0m                         p2cread, p2cwrite,\\n\\u001b[0;32m    975\\u001b[0m                         c2pread, c2pwrite,\\n\\u001b[0;32m    976\\u001b[0m                         errread, errwrite,\\n\\u001b[0;32m    977\\u001b[0m                         restore_signals,\\n\\u001b[0;32m    978\\u001b[0m                         gid, gids, uid, umask,\\n\\u001b[0;32m    979\\u001b[0m                         start_new_session)\\n\\u001b[0;32m    980\\u001b[0m \\u001b[39mexcept\\u001b[39;00m:\\n\\u001b[0;32m    981\\u001b[0m     \\u001b[39m# Cleanup if the child failed starting.\\u001b[39;00m\\n\",\n      \"File \\u001b[1;32m~\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\subprocess.py:1440\\u001b[0m, in \\u001b[0;36mPopen._execute_child\\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_gid, unused_gids, unused_uid, unused_umask, unused_start_new_session)\\u001b[0m\\n\\u001b[0;32m   1439\\u001b[0m \\u001b[39mtry\\u001b[39;00m:\\n\\u001b[1;32m-> 1440\\u001b[0m     hp, ht, pid, tid \\u001b[39m=\\u001b[39m _winapi\\u001b[39m.\\u001b[39;49mCreateProcess(executable, args,\\n\\u001b[0;32m   1441\\u001b[0m                              \\u001b[39m# no special security\\u001b[39;49;00m\\n\\u001b[0;32m   1442\\u001b[0m                              \\u001b[39mNone\\u001b[39;49;00m, \\u001b[39mNone\\u001b[39;49;00m,\\n\\u001b[0;32m   1443\\u001b[0m                              \\u001b[39mint\\u001b[39;49m(\\u001b[39mnot\\u001b[39;49;00m close_fds),\\n\\u001b[0;32m   1444\\u001b[0m                              creationflags,\\n\\u001b[0;32m   1445\\u001b[0m                              env,\\n\\u001b[0;32m   1446\\u001b[0m                              cwd,\\n\\u001b[0;32m   1447\\u001b[0m                              startupinfo)\\n\\u001b[0;32m   1448\\u001b[0m \\u001b[39mfinally\\u001b[39;00m:\\n\\u001b[0;32m   1449\\u001b[0m     \\u001b[39m# Child is launched. Close the parent's copy of those pipe\\u001b[39;00m\\n\\u001b[0;32m   1450\\u001b[0m     \\u001b[39m# handles that only the child should have open.  You need\\u001b[39;00m\\n\\u001b[1;32m   (...)\\u001b[0m\\n\\u001b[0;32m   1453\\u001b[0m     \\u001b[39m# pipe will not close when the child process exits and the\\u001b[39;00m\\n\\u001b[0;32m   1454\\u001b[0m     \\u001b[39m# ReadFile will hang.\\u001b[39;00m\\n\",\n      \"\\u001b[1;31mFileNotFoundError\\u001b[0m: [WinError 2] The system cannot find the file specified\",\n      \"\\nDuring handling of the above exception, another exception occurred:\\n\",\n      \"\\u001b[1;31mFileNotFoundError\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\C2 - Advanced Learning Algorithms\\\\week4\\\\optional labs\\\\C2_W4_Lab_01_Decision_Trees.ipynb Cell 26\\u001b[0m line \\u001b[0;36m4\\n\\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/TA/machine-learning-specialization-coursera/C2%20-%20Advanced%20Learning%20Algorithms/week4/optional%20labs/C2_W4_Lab_01_Decision_Trees.ipynb#X36sZmlsZQ%3D%3D?line=0'>1</a>\\u001b[0m tree \\u001b[39m=\\u001b[39m []\\n\\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/TA/machine-learning-specialization-coursera/C2%20-%20Advanced%20Learning%20Algorithms/week4/optional%20labs/C2_W4_Lab_01_Decision_Trees.ipynb#X36sZmlsZQ%3D%3D?line=1'>2</a>\\u001b[0m build_tree_recursive(X_train, y_train, [\\u001b[39m0\\u001b[39m,\\u001b[39m1\\u001b[39m,\\u001b[39m2\\u001b[39m,\\u001b[39m3\\u001b[39m,\\u001b[39m4\\u001b[39m,\\u001b[39m5\\u001b[39m,\\u001b[39m6\\u001b[39m,\\u001b[39m7\\u001b[39m,\\u001b[39m8\\u001b[39m,\\u001b[39m9\\u001b[39m], \\u001b[39m\\\"\\u001b[39m\\u001b[39mRoot\\u001b[39m\\u001b[39m\\\"\\u001b[39m, max_depth\\u001b[39m=\\u001b[39m\\u001b[39m1\\u001b[39m, current_depth\\u001b[39m=\\u001b[39m\\u001b[39m0\\u001b[39m, tree \\u001b[39m=\\u001b[39m tree)\\n\\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/TA/machine-learning-specialization-coursera/C2%20-%20Advanced%20Learning%20Algorithms/week4/optional%20labs/C2_W4_Lab_01_Decision_Trees.ipynb#X36sZmlsZQ%3D%3D?line=3'>4</a>\\u001b[0m generate_tree_viz([\\u001b[39m0\\u001b[39;49m,\\u001b[39m1\\u001b[39;49m,\\u001b[39m2\\u001b[39;49m,\\u001b[39m3\\u001b[39;49m,\\u001b[39m4\\u001b[39;49m,\\u001b[39m5\\u001b[39;49m,\\u001b[39m6\\u001b[39;49m,\\u001b[39m7\\u001b[39;49m,\\u001b[39m8\\u001b[39;49m,\\u001b[39m9\\u001b[39;49m], y_train, tree)\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\C2 - Advanced Learning Algorithms\\\\week4\\\\optional labs\\\\utils.py:185\\u001b[0m, in \\u001b[0;36mgenerate_tree_viz\\u001b[1;34m(root_indices, y, tree)\\u001b[0m\\n\\u001b[0;32m    181\\u001b[0m     root \\u001b[39m+\\u001b[39m\\u001b[39m=\\u001b[39m \\u001b[39m1\\u001b[39m\\n\\u001b[0;32m    184\\u001b[0m node_names \\u001b[39m=\\u001b[39m decision_names \\u001b[39m+\\u001b[39m leaf_names\\n\\u001b[1;32m--> 185\\u001b[0m pos \\u001b[39m=\\u001b[39m graphviz_layout(G, prog\\u001b[39m=\\u001b[39;49m\\u001b[39m\\\"\\u001b[39;49m\\u001b[39mdot\\u001b[39;49m\\u001b[39m\\\"\\u001b[39;49m)\\n\\u001b[0;32m    187\\u001b[0m fig\\u001b[39m=\\u001b[39mplt\\u001b[39m.\\u001b[39mfigure(figsize\\u001b[39m=\\u001b[39m(\\u001b[39m14\\u001b[39m, \\u001b[39m10\\u001b[39m))\\n\\u001b[0;32m    188\\u001b[0m ax\\u001b[39m=\\u001b[39mplt\\u001b[39m.\\u001b[39msubplot(\\u001b[39m111\\u001b[39m)\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\networkx\\\\drawing\\\\nx_pydot.py:359\\u001b[0m, in \\u001b[0;36mgraphviz_layout\\u001b[1;34m(G, prog, root)\\u001b[0m\\n\\u001b[0;32m    351\\u001b[0m msg \\u001b[39m=\\u001b[39m (\\n\\u001b[0;32m    352\\u001b[0m     \\u001b[39m\\\"\\u001b[39m\\u001b[39mnx.nx_pydot.graphviz_layout depends on the pydot package, which has \\u001b[39m\\u001b[39m\\\"\\u001b[39m\\n\\u001b[0;32m    353\\u001b[0m     \\u001b[39m\\\"\\u001b[39m\\u001b[39mknown issues and is not actively maintained. Consider using \\u001b[39m\\u001b[39m\\\"\\u001b[39m\\n\\u001b[0;32m    354\\u001b[0m     \\u001b[39m\\\"\\u001b[39m\\u001b[39mnx.nx_agraph.graphviz_layout instead.\\u001b[39m\\u001b[39m\\\\n\\u001b[39;00m\\u001b[39m\\\\n\\u001b[39;00m\\u001b[39m\\\"\\u001b[39m\\n\\u001b[0;32m    355\\u001b[0m     \\u001b[39m\\\"\\u001b[39m\\u001b[39mSee https://github.com/networkx/networkx/issues/5723\\u001b[39m\\u001b[39m\\\"\\u001b[39m\\n\\u001b[0;32m    356\\u001b[0m )\\n\\u001b[0;32m    357\\u001b[0m warnings\\u001b[39m.\\u001b[39mwarn(msg, \\u001b[39mDeprecationWarning\\u001b[39;00m, stacklevel\\u001b[39m=\\u001b[39m\\u001b[39m2\\u001b[39m)\\n\\u001b[1;32m--> 359\\u001b[0m \\u001b[39mreturn\\u001b[39;00m pydot_layout(G\\u001b[39m=\\u001b[39;49mG, prog\\u001b[39m=\\u001b[39;49mprog, root\\u001b[39m=\\u001b[39;49mroot)\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\networkx\\\\drawing\\\\nx_pydot.py:414\\u001b[0m, in \\u001b[0;36mpydot_layout\\u001b[1;34m(G, prog, root)\\u001b[0m\\n\\u001b[0;32m    410\\u001b[0m     P\\u001b[39m.\\u001b[39mset(\\u001b[39m\\\"\\u001b[39m\\u001b[39mroot\\u001b[39m\\u001b[39m\\\"\\u001b[39m, \\u001b[39mstr\\u001b[39m(root))\\n\\u001b[0;32m    412\\u001b[0m \\u001b[39m# List of low-level bytes comprising a string in the dot language converted\\u001b[39;00m\\n\\u001b[0;32m    413\\u001b[0m \\u001b[39m# from the passed graph with the passed external GraphViz command.\\u001b[39;00m\\n\\u001b[1;32m--> 414\\u001b[0m D_bytes \\u001b[39m=\\u001b[39m P\\u001b[39m.\\u001b[39;49mcreate_dot(prog\\u001b[39m=\\u001b[39;49mprog)\\n\\u001b[0;32m    416\\u001b[0m \\u001b[39m# Unique string decoded from these bytes with the preferred locale encoding\\u001b[39;00m\\n\\u001b[0;32m    417\\u001b[0m D \\u001b[39m=\\u001b[39m \\u001b[39mstr\\u001b[39m(D_bytes, encoding\\u001b[39m=\\u001b[39mgetpreferredencoding())\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\pydot.py:1733\\u001b[0m, in \\u001b[0;36mDot.__init__.<locals>.new_method\\u001b[1;34m(f, prog, encoding)\\u001b[0m\\n\\u001b[0;32m   1729\\u001b[0m \\u001b[39mdef\\u001b[39;00m \\u001b[39mnew_method\\u001b[39m(\\n\\u001b[0;32m   1730\\u001b[0m         f\\u001b[39m=\\u001b[39mfrmt, prog\\u001b[39m=\\u001b[39m\\u001b[39mself\\u001b[39m\\u001b[39m.\\u001b[39mprog,\\n\\u001b[0;32m   1731\\u001b[0m         encoding\\u001b[39m=\\u001b[39m\\u001b[39mNone\\u001b[39;00m):\\n\\u001b[0;32m   1732\\u001b[0m \\u001b[39m    \\u001b[39m\\u001b[39m\\\"\\\"\\\"Refer to docstring of method `create`.\\\"\\\"\\\"\\u001b[39;00m\\n\\u001b[1;32m-> 1733\\u001b[0m     \\u001b[39mreturn\\u001b[39;00m \\u001b[39mself\\u001b[39;49m\\u001b[39m.\\u001b[39;49mcreate(\\n\\u001b[0;32m   1734\\u001b[0m         \\u001b[39mformat\\u001b[39;49m\\u001b[39m=\\u001b[39;49mf, prog\\u001b[39m=\\u001b[39;49mprog, encoding\\u001b[39m=\\u001b[39;49mencoding)\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\pydot.py:1933\\u001b[0m, in \\u001b[0;36mDot.create\\u001b[1;34m(self, prog, format, encoding)\\u001b[0m\\n\\u001b[0;32m   1930\\u001b[0m     args \\u001b[39m=\\u001b[39m \\u001b[39mlist\\u001b[39m(e\\u001b[39m.\\u001b[39margs)\\n\\u001b[0;32m   1931\\u001b[0m     args[\\u001b[39m1\\u001b[39m] \\u001b[39m=\\u001b[39m \\u001b[39m'\\u001b[39m\\u001b[39m\\\"\\u001b[39m\\u001b[39m{prog}\\u001b[39;00m\\u001b[39m\\\"\\u001b[39m\\u001b[39m not found in path.\\u001b[39m\\u001b[39m'\\u001b[39m\\u001b[39m.\\u001b[39mformat(\\n\\u001b[0;32m   1932\\u001b[0m         prog\\u001b[39m=\\u001b[39mprog)\\n\\u001b[1;32m-> 1933\\u001b[0m     \\u001b[39mraise\\u001b[39;00m \\u001b[39mOSError\\u001b[39;00m(\\u001b[39m*\\u001b[39margs)\\n\\u001b[0;32m   1934\\u001b[0m \\u001b[39melse\\u001b[39;00m:\\n\\u001b[0;32m   1935\\u001b[0m     \\u001b[39mraise\\u001b[39;00m\\n\",\n      \"\\u001b[1;31mFileNotFoundError\\u001b[0m: [WinError 2] \\\"dot\\\" not found in path.\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tree = []\\n\",\n    \"build_tree_recursive(X_train, y_train, [0,1,2,3,4,5,6,7,8,9], \\\"Root\\\", max_depth=1, current_depth=0, tree = tree)\\n\",\n    \"\\n\",\n    \"generate_tree_viz([0,1,2,3,4,5,6,7,8,9], y_train, tree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The process is **recursive**, which means we must perform these calculations for each node until we meet a stopping criteria:\\n\",\n    \"\\n\",\n    \"- If the tree depth after splitting exceeds a threshold\\n\",\n    \"- If the resulting node has only 1 class\\n\",\n    \"- If the information gain of splitting is below a threshold\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The final tree looks like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \" Depth 0, Root: Split on feature: 0\\n\",\n      \"- Depth 1, Left: Split on feature: 1\\n\",\n      \"  -- Left leaf node with indices [0, 4, 5, 7]\\n\",\n      \"  -- Right leaf node with indices [3]\\n\",\n      \"- Depth 1, Right: Split on feature: 2\\n\",\n      \"  -- Left leaf node with indices [1]\\n\",\n      \"  -- Right leaf node with indices [2, 6, 8, 9]\\n\"\n     ]\n    },\n    {\n     \"ename\": \"FileNotFoundError\",\n     \"evalue\": \"[WinError 2] \\\"dot\\\" not found in path.\",\n     \"output_type\": \"error\",\n     \"traceback\": [\n      \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n      \"\\u001b[1;31mFileNotFoundError\\u001b[0m                         Traceback (most recent call last)\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\pydot.py:1923\\u001b[0m, in \\u001b[0;36mDot.create\\u001b[1;34m(self, prog, format, encoding)\\u001b[0m\\n\\u001b[0;32m   1922\\u001b[0m \\u001b[39mtry\\u001b[39;00m:\\n\\u001b[1;32m-> 1923\\u001b[0m     stdout_data, stderr_data, process \\u001b[39m=\\u001b[39m call_graphviz(\\n\\u001b[0;32m   1924\\u001b[0m         program\\u001b[39m=\\u001b[39;49mprog,\\n\\u001b[0;32m   1925\\u001b[0m         arguments\\u001b[39m=\\u001b[39;49marguments,\\n\\u001b[0;32m   1926\\u001b[0m         working_dir\\u001b[39m=\\u001b[39;49mtmp_dir,\\n\\u001b[0;32m   1927\\u001b[0m     )\\n\\u001b[0;32m   1928\\u001b[0m \\u001b[39mexcept\\u001b[39;00m \\u001b[39mOSError\\u001b[39;00m \\u001b[39mas\\u001b[39;00m e:\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\pydot.py:132\\u001b[0m, in \\u001b[0;36mcall_graphviz\\u001b[1;34m(program, arguments, working_dir, **kwargs)\\u001b[0m\\n\\u001b[0;32m    130\\u001b[0m program_with_args \\u001b[39m=\\u001b[39m [program, ] \\u001b[39m+\\u001b[39m arguments\\n\\u001b[1;32m--> 132\\u001b[0m process \\u001b[39m=\\u001b[39m subprocess\\u001b[39m.\\u001b[39mPopen(\\n\\u001b[0;32m    133\\u001b[0m     program_with_args,\\n\\u001b[0;32m    134\\u001b[0m     env\\u001b[39m=\\u001b[39menv,\\n\\u001b[0;32m    135\\u001b[0m     cwd\\u001b[39m=\\u001b[39mworking_dir,\\n\\u001b[0;32m    136\\u001b[0m     shell\\u001b[39m=\\u001b[39m\\u001b[39mFalse\\u001b[39;00m,\\n\\u001b[0;32m    137\\u001b[0m     stderr\\u001b[39m=\\u001b[39msubprocess\\u001b[39m.\\u001b[39mPIPE,\\n\\u001b[0;32m    138\\u001b[0m     stdout\\u001b[39m=\\u001b[39msubprocess\\u001b[39m.\\u001b[39mPIPE,\\n\\u001b[0;32m    139\\u001b[0m     \\u001b[39m*\\u001b[39m\\u001b[39m*\\u001b[39mkwargs\\n\\u001b[0;32m    140\\u001b[0m )\\n\\u001b[0;32m    141\\u001b[0m stdout_data, stderr_data \\u001b[39m=\\u001b[39m process\\u001b[39m.\\u001b[39mcommunicate()\\n\",\n      \"File \\u001b[1;32m~\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\subprocess.py:971\\u001b[0m, in \\u001b[0;36mPopen.__init__\\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask, pipesize)\\u001b[0m\\n\\u001b[0;32m    968\\u001b[0m             \\u001b[39mself\\u001b[39m\\u001b[39m.\\u001b[39mstderr \\u001b[39m=\\u001b[39m io\\u001b[39m.\\u001b[39mTextIOWrapper(\\u001b[39mself\\u001b[39m\\u001b[39m.\\u001b[39mstderr,\\n\\u001b[0;32m    969\\u001b[0m                     encoding\\u001b[39m=\\u001b[39mencoding, errors\\u001b[39m=\\u001b[39merrors)\\n\\u001b[1;32m--> 971\\u001b[0m     \\u001b[39mself\\u001b[39;49m\\u001b[39m.\\u001b[39;49m_execute_child(args, executable, preexec_fn, close_fds,\\n\\u001b[0;32m    972\\u001b[0m                         pass_fds, cwd, env,\\n\\u001b[0;32m    973\\u001b[0m                         startupinfo, creationflags, shell,\\n\\u001b[0;32m    974\\u001b[0m                         p2cread, p2cwrite,\\n\\u001b[0;32m    975\\u001b[0m                         c2pread, c2pwrite,\\n\\u001b[0;32m    976\\u001b[0m                         errread, errwrite,\\n\\u001b[0;32m    977\\u001b[0m                         restore_signals,\\n\\u001b[0;32m    978\\u001b[0m                         gid, gids, uid, umask,\\n\\u001b[0;32m    979\\u001b[0m                         start_new_session)\\n\\u001b[0;32m    980\\u001b[0m \\u001b[39mexcept\\u001b[39;00m:\\n\\u001b[0;32m    981\\u001b[0m     \\u001b[39m# Cleanup if the child failed starting.\\u001b[39;00m\\n\",\n      \"File \\u001b[1;32m~\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python310\\\\lib\\\\subprocess.py:1440\\u001b[0m, in \\u001b[0;36mPopen._execute_child\\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_gid, unused_gids, unused_uid, unused_umask, unused_start_new_session)\\u001b[0m\\n\\u001b[0;32m   1439\\u001b[0m \\u001b[39mtry\\u001b[39;00m:\\n\\u001b[1;32m-> 1440\\u001b[0m     hp, ht, pid, tid \\u001b[39m=\\u001b[39m _winapi\\u001b[39m.\\u001b[39;49mCreateProcess(executable, args,\\n\\u001b[0;32m   1441\\u001b[0m                              \\u001b[39m# no special security\\u001b[39;49;00m\\n\\u001b[0;32m   1442\\u001b[0m                              \\u001b[39mNone\\u001b[39;49;00m, \\u001b[39mNone\\u001b[39;49;00m,\\n\\u001b[0;32m   1443\\u001b[0m                              \\u001b[39mint\\u001b[39;49m(\\u001b[39mnot\\u001b[39;49;00m close_fds),\\n\\u001b[0;32m   1444\\u001b[0m                              creationflags,\\n\\u001b[0;32m   1445\\u001b[0m                              env,\\n\\u001b[0;32m   1446\\u001b[0m                              cwd,\\n\\u001b[0;32m   1447\\u001b[0m                              startupinfo)\\n\\u001b[0;32m   1448\\u001b[0m \\u001b[39mfinally\\u001b[39;00m:\\n\\u001b[0;32m   1449\\u001b[0m     \\u001b[39m# Child is launched. Close the parent's copy of those pipe\\u001b[39;00m\\n\\u001b[0;32m   1450\\u001b[0m     \\u001b[39m# handles that only the child should have open.  You need\\u001b[39;00m\\n\\u001b[1;32m   (...)\\u001b[0m\\n\\u001b[0;32m   1453\\u001b[0m     \\u001b[39m# pipe will not close when the child process exits and the\\u001b[39;00m\\n\\u001b[0;32m   1454\\u001b[0m     \\u001b[39m# ReadFile will hang.\\u001b[39;00m\\n\",\n      \"\\u001b[1;31mFileNotFoundError\\u001b[0m: [WinError 2] The system cannot find the file specified\",\n      \"\\nDuring handling of the above exception, another exception occurred:\\n\",\n      \"\\u001b[1;31mFileNotFoundError\\u001b[0m                         Traceback (most recent call last)\",\n      \"\\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\C2 - Advanced Learning Algorithms\\\\week4\\\\optional labs\\\\C2_W4_Lab_01_Decision_Trees.ipynb Cell 29\\u001b[0m line \\u001b[0;36m3\\n\\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/TA/machine-learning-specialization-coursera/C2%20-%20Advanced%20Learning%20Algorithms/week4/optional%20labs/C2_W4_Lab_01_Decision_Trees.ipynb#X42sZmlsZQ%3D%3D?line=0'>1</a>\\u001b[0m tree \\u001b[39m=\\u001b[39m []\\n\\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/TA/machine-learning-specialization-coursera/C2%20-%20Advanced%20Learning%20Algorithms/week4/optional%20labs/C2_W4_Lab_01_Decision_Trees.ipynb#X42sZmlsZQ%3D%3D?line=1'>2</a>\\u001b[0m build_tree_recursive(X_train, y_train, [\\u001b[39m0\\u001b[39m,\\u001b[39m1\\u001b[39m,\\u001b[39m2\\u001b[39m,\\u001b[39m3\\u001b[39m,\\u001b[39m4\\u001b[39m,\\u001b[39m5\\u001b[39m,\\u001b[39m6\\u001b[39m,\\u001b[39m7\\u001b[39m,\\u001b[39m8\\u001b[39m,\\u001b[39m9\\u001b[39m], \\u001b[39m\\\"\\u001b[39m\\u001b[39mRoot\\u001b[39m\\u001b[39m\\\"\\u001b[39m, max_depth\\u001b[39m=\\u001b[39m\\u001b[39m2\\u001b[39m, current_depth\\u001b[39m=\\u001b[39m\\u001b[39m0\\u001b[39m, tree \\u001b[39m=\\u001b[39m tree)\\n\\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/TA/machine-learning-specialization-coursera/C2%20-%20Advanced%20Learning%20Algorithms/week4/optional%20labs/C2_W4_Lab_01_Decision_Trees.ipynb#X42sZmlsZQ%3D%3D?line=2'>3</a>\\u001b[0m generate_tree_viz([\\u001b[39m0\\u001b[39;49m,\\u001b[39m1\\u001b[39;49m,\\u001b[39m2\\u001b[39;49m,\\u001b[39m3\\u001b[39;49m,\\u001b[39m4\\u001b[39;49m,\\u001b[39m5\\u001b[39;49m,\\u001b[39m6\\u001b[39;49m,\\u001b[39m7\\u001b[39;49m,\\u001b[39m8\\u001b[39;49m,\\u001b[39m9\\u001b[39;49m], y_train, tree)\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\C2 - Advanced Learning Algorithms\\\\week4\\\\optional labs\\\\utils.py:185\\u001b[0m, in \\u001b[0;36mgenerate_tree_viz\\u001b[1;34m(root_indices, y, tree)\\u001b[0m\\n\\u001b[0;32m    181\\u001b[0m     root \\u001b[39m+\\u001b[39m\\u001b[39m=\\u001b[39m \\u001b[39m1\\u001b[39m\\n\\u001b[0;32m    184\\u001b[0m node_names \\u001b[39m=\\u001b[39m decision_names \\u001b[39m+\\u001b[39m leaf_names\\n\\u001b[1;32m--> 185\\u001b[0m pos \\u001b[39m=\\u001b[39m graphviz_layout(G, prog\\u001b[39m=\\u001b[39;49m\\u001b[39m\\\"\\u001b[39;49m\\u001b[39mdot\\u001b[39;49m\\u001b[39m\\\"\\u001b[39;49m)\\n\\u001b[0;32m    187\\u001b[0m fig\\u001b[39m=\\u001b[39mplt\\u001b[39m.\\u001b[39mfigure(figsize\\u001b[39m=\\u001b[39m(\\u001b[39m14\\u001b[39m, \\u001b[39m10\\u001b[39m))\\n\\u001b[0;32m    188\\u001b[0m ax\\u001b[39m=\\u001b[39mplt\\u001b[39m.\\u001b[39msubplot(\\u001b[39m111\\u001b[39m)\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\networkx\\\\drawing\\\\nx_pydot.py:359\\u001b[0m, in \\u001b[0;36mgraphviz_layout\\u001b[1;34m(G, prog, root)\\u001b[0m\\n\\u001b[0;32m    351\\u001b[0m msg \\u001b[39m=\\u001b[39m (\\n\\u001b[0;32m    352\\u001b[0m     \\u001b[39m\\\"\\u001b[39m\\u001b[39mnx.nx_pydot.graphviz_layout depends on the pydot package, which has \\u001b[39m\\u001b[39m\\\"\\u001b[39m\\n\\u001b[0;32m    353\\u001b[0m     \\u001b[39m\\\"\\u001b[39m\\u001b[39mknown issues and is not actively maintained. Consider using \\u001b[39m\\u001b[39m\\\"\\u001b[39m\\n\\u001b[0;32m    354\\u001b[0m     \\u001b[39m\\\"\\u001b[39m\\u001b[39mnx.nx_agraph.graphviz_layout instead.\\u001b[39m\\u001b[39m\\\\n\\u001b[39;00m\\u001b[39m\\\\n\\u001b[39;00m\\u001b[39m\\\"\\u001b[39m\\n\\u001b[0;32m    355\\u001b[0m     \\u001b[39m\\\"\\u001b[39m\\u001b[39mSee https://github.com/networkx/networkx/issues/5723\\u001b[39m\\u001b[39m\\\"\\u001b[39m\\n\\u001b[0;32m    356\\u001b[0m )\\n\\u001b[0;32m    357\\u001b[0m warnings\\u001b[39m.\\u001b[39mwarn(msg, \\u001b[39mDeprecationWarning\\u001b[39;00m, stacklevel\\u001b[39m=\\u001b[39m\\u001b[39m2\\u001b[39m)\\n\\u001b[1;32m--> 359\\u001b[0m \\u001b[39mreturn\\u001b[39;00m pydot_layout(G\\u001b[39m=\\u001b[39;49mG, prog\\u001b[39m=\\u001b[39;49mprog, root\\u001b[39m=\\u001b[39;49mroot)\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\networkx\\\\drawing\\\\nx_pydot.py:414\\u001b[0m, in \\u001b[0;36mpydot_layout\\u001b[1;34m(G, prog, root)\\u001b[0m\\n\\u001b[0;32m    410\\u001b[0m     P\\u001b[39m.\\u001b[39mset(\\u001b[39m\\\"\\u001b[39m\\u001b[39mroot\\u001b[39m\\u001b[39m\\\"\\u001b[39m, \\u001b[39mstr\\u001b[39m(root))\\n\\u001b[0;32m    412\\u001b[0m \\u001b[39m# List of low-level bytes comprising a string in the dot language converted\\u001b[39;00m\\n\\u001b[0;32m    413\\u001b[0m \\u001b[39m# from the passed graph with the passed external GraphViz command.\\u001b[39;00m\\n\\u001b[1;32m--> 414\\u001b[0m D_bytes \\u001b[39m=\\u001b[39m P\\u001b[39m.\\u001b[39;49mcreate_dot(prog\\u001b[39m=\\u001b[39;49mprog)\\n\\u001b[0;32m    416\\u001b[0m \\u001b[39m# Unique string decoded from these bytes with the preferred locale encoding\\u001b[39;00m\\n\\u001b[0;32m    417\\u001b[0m D \\u001b[39m=\\u001b[39m \\u001b[39mstr\\u001b[39m(D_bytes, encoding\\u001b[39m=\\u001b[39mgetpreferredencoding())\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\pydot.py:1733\\u001b[0m, in \\u001b[0;36mDot.__init__.<locals>.new_method\\u001b[1;34m(f, prog, encoding)\\u001b[0m\\n\\u001b[0;32m   1729\\u001b[0m \\u001b[39mdef\\u001b[39;00m \\u001b[39mnew_method\\u001b[39m(\\n\\u001b[0;32m   1730\\u001b[0m         f\\u001b[39m=\\u001b[39mfrmt, prog\\u001b[39m=\\u001b[39m\\u001b[39mself\\u001b[39m\\u001b[39m.\\u001b[39mprog,\\n\\u001b[0;32m   1731\\u001b[0m         encoding\\u001b[39m=\\u001b[39m\\u001b[39mNone\\u001b[39;00m):\\n\\u001b[0;32m   1732\\u001b[0m \\u001b[39m    \\u001b[39m\\u001b[39m\\\"\\\"\\\"Refer to docstring of method `create`.\\\"\\\"\\\"\\u001b[39;00m\\n\\u001b[1;32m-> 1733\\u001b[0m     \\u001b[39mreturn\\u001b[39;00m \\u001b[39mself\\u001b[39;49m\\u001b[39m.\\u001b[39;49mcreate(\\n\\u001b[0;32m   1734\\u001b[0m         \\u001b[39mformat\\u001b[39;49m\\u001b[39m=\\u001b[39;49mf, prog\\u001b[39m=\\u001b[39;49mprog, encoding\\u001b[39m=\\u001b[39;49mencoding)\\n\",\n      \"File \\u001b[1;32md:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\pydot.py:1933\\u001b[0m, in \\u001b[0;36mDot.create\\u001b[1;34m(self, prog, format, encoding)\\u001b[0m\\n\\u001b[0;32m   1930\\u001b[0m     args \\u001b[39m=\\u001b[39m \\u001b[39mlist\\u001b[39m(e\\u001b[39m.\\u001b[39margs)\\n\\u001b[0;32m   1931\\u001b[0m     args[\\u001b[39m1\\u001b[39m] \\u001b[39m=\\u001b[39m \\u001b[39m'\\u001b[39m\\u001b[39m\\\"\\u001b[39m\\u001b[39m{prog}\\u001b[39;00m\\u001b[39m\\\"\\u001b[39m\\u001b[39m not found in path.\\u001b[39m\\u001b[39m'\\u001b[39m\\u001b[39m.\\u001b[39mformat(\\n\\u001b[0;32m   1932\\u001b[0m         prog\\u001b[39m=\\u001b[39mprog)\\n\\u001b[1;32m-> 1933\\u001b[0m     \\u001b[39mraise\\u001b[39;00m \\u001b[39mOSError\\u001b[39;00m(\\u001b[39m*\\u001b[39margs)\\n\\u001b[0;32m   1934\\u001b[0m \\u001b[39melse\\u001b[39;00m:\\n\\u001b[0;32m   1935\\u001b[0m     \\u001b[39mraise\\u001b[39;00m\\n\",\n      \"\\u001b[1;31mFileNotFoundError\\u001b[0m: [WinError 2] \\\"dot\\\" not found in path.\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tree = []\\n\",\n    \"build_tree_recursive(X_train, y_train, [0,1,2,3,4,5,6,7,8,9], \\\"Root\\\", max_depth=2, current_depth=0, tree = tree)\\n\",\n    \"generate_tree_viz([0,1,2,3,4,5,6,7,8,9], y_train, tree)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Congratulations! You completed the notebook!\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.10\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"56d44d6a8424451b5ce45d1ae0b0b7865dc60710e7f74571dd51dd80d7829ee9\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/optional labs/C2_W4_Lab_02_Tree_Ensemble.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Ungraded Lab - Trees Ensemble\\n\",\n    \"\\n\",\n    \"In this notebook, you will:\\n\",\n    \"\\n\",\n    \" - Use Pandas to perform one-hot encoding of a dataset\\n\",\n    \" - Use scikit-learn to implement a Decision Tree, Random Forest and XGBoost models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's import the libraries we will use.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"from xgboost import XGBClassifier\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\\n\",\n    \"\\n\",\n    \"RANDOM_STATE = 55 ## We will pass it to every sklearn call so we ensure reproducibility\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1. Introduction\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Datatset\\n\",\n    \"- This dataset is obtained from Kaggle: [Heart Failure Prediction Dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction)\\n\",\n    \"\\n\",\n    \"#### Context\\n\",\n    \"- Cardiovascular disease (CVDs) is the number one cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of five CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs.\\n\",\n    \"- People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management.  \\n\",\n    \"- This dataset contains 11 features that can be used to predict possible heart disease.\\n\",\n    \"- Let's train a machine learning model to assist with diagnosing this disease.\\n\",\n    \"\\n\",\n    \"#### Attribute Information\\n\",\n    \"- Age: age of the patient [years]\\n\",\n    \"- Sex: sex of the patient [M: Male, F: Female]\\n\",\n    \"- ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]\\n\",\n    \"- RestingBP: resting blood pressure [mm Hg]\\n\",\n    \"- Cholesterol: serum cholesterol [mm/dl]\\n\",\n    \"- FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]\\n\",\n    \"- RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]\\n\",\n    \"- MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]\\n\",\n    \"- ExerciseAngina: exercise-induced angina [Y: Yes, N: No]\\n\",\n    \"- Oldpeak: oldpeak = ST [Numeric value measured in depression]\\n\",\n    \"- ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]\\n\",\n    \"- HeartDisease: output class [1: heart disease, 0: Normal]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's now load the dataset. As we can see above, the variables:\\n\",\n    \"\\n\",\n    \"- Sex\\n\",\n    \"- ChestPainType\\n\",\n    \"- RestingECG\\n\",\n    \"- ExerciseAngina\\n\",\n    \"- ST_Slope\\n\",\n    \"\\n\",\n    \"Are *categorical*, so we must one-hot encode them. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load the dataset using pandas\\n\",\n    \"df = pd.read_csv(\\\"heart.csv\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>Sex</th>\\n\",\n       \"      <th>ChestPainType</th>\\n\",\n       \"      <th>RestingBP</th>\\n\",\n       \"      <th>Cholesterol</th>\\n\",\n       \"      <th>FastingBS</th>\\n\",\n       \"      <th>RestingECG</th>\\n\",\n       \"      <th>MaxHR</th>\\n\",\n       \"      <th>ExerciseAngina</th>\\n\",\n       \"      <th>Oldpeak</th>\\n\",\n       \"      <th>ST_Slope</th>\\n\",\n       \"      <th>HeartDisease</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>ATA</td>\\n\",\n       \"      <td>140</td>\\n\",\n       \"      <td>289</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"      <td>172</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>Up</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>49</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>NAP</td>\\n\",\n       \"      <td>160</td>\\n\",\n       \"      <td>180</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"      <td>156</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>Flat</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>37</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>ATA</td>\\n\",\n       \"      <td>130</td>\\n\",\n       \"      <td>283</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>ST</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>Up</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>48</td>\\n\",\n       \"      <td>F</td>\\n\",\n       \"      <td>ASY</td>\\n\",\n       \"      <td>138</td>\\n\",\n       \"      <td>214</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"      <td>108</td>\\n\",\n       \"      <td>Y</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>Flat</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>54</td>\\n\",\n       \"      <td>M</td>\\n\",\n       \"      <td>NAP</td>\\n\",\n       \"      <td>150</td>\\n\",\n       \"      <td>195</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>Normal</td>\\n\",\n       \"      <td>122</td>\\n\",\n       \"      <td>N</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>Up</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\\\\n\",\n       \"0   40   M           ATA        140          289          0     Normal    172   \\n\",\n       \"1   49   F           NAP        160          180          0     Normal    156   \\n\",\n       \"2   37   M           ATA        130          283          0         ST     98   \\n\",\n       \"3   48   F           ASY        138          214          0     Normal    108   \\n\",\n       \"4   54   M           NAP        150          195          0     Normal    122   \\n\",\n       \"\\n\",\n       \"  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \\n\",\n       \"0              N      0.0       Up             0  \\n\",\n       \"1              N      1.0     Flat             1  \\n\",\n       \"2              N      0.0       Up             0  \\n\",\n       \"3              Y      1.5     Flat             1  \\n\",\n       \"4              N      0.0       Up             0  \"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We must perform some data engineering before working with the models. There are 5 categorical features, so we will use Pandas to one-hot encode them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2. One-hot encoding using Pandas\\n\",\n    \"\\n\",\n    \"First we will remove the binary variables, because one-hot encoding them would do nothing to them. To achieve this we will just count how many different values there are in each categorical variable and consider only the variables with 3 or more values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cat_variables = ['Sex',\\n\",\n    \"'ChestPainType',\\n\",\n    \"'RestingECG',\\n\",\n    \"'ExerciseAngina',\\n\",\n    \"'ST_Slope'\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As a reminder, one-hot encoding aims to transform a categorical variable with `n` outputs into `n` binary variables.\\n\",\n    \"\\n\",\n    \"Pandas has a built-in method to one-hot encode variables, it is the function `pd.get_dummies`. There are several arguments to this function, but here we will use only a few. They are:\\n\",\n    \"\\n\",\n    \" - data: DataFrame to be used\\n\",\n    \" - prefix: A list with prefixes, so we know which value we are dealing with\\n\",\n    \" - columns: the list of columns that will be one-hot encoded. 'prefix' and 'columns' must have the same length.\\n\",\n    \" \\n\",\n    \"For more information, you can always type `help(pd.get_dummies)` to read the function's full documentation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# This will replace the columns with the one-hot encoded ones and keep the columns outside 'columns' argument as it is.\\n\",\n    \"df = pd.get_dummies(data = df, prefix = cat_variables, columns = cat_variables)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Age</th>\\n\",\n       \"      <th>RestingBP</th>\\n\",\n       \"      <th>Cholesterol</th>\\n\",\n       \"      <th>FastingBS</th>\\n\",\n       \"      <th>MaxHR</th>\\n\",\n       \"      <th>Oldpeak</th>\\n\",\n       \"      <th>HeartDisease</th>\\n\",\n       \"      <th>Sex_F</th>\\n\",\n       \"      <th>Sex_M</th>\\n\",\n       \"      <th>ChestPainType_ASY</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>ChestPainType_NAP</th>\\n\",\n       \"      <th>ChestPainType_TA</th>\\n\",\n       \"      <th>RestingECG_LVH</th>\\n\",\n       \"      <th>RestingECG_Normal</th>\\n\",\n       \"      <th>RestingECG_ST</th>\\n\",\n       \"      <th>ExerciseAngina_N</th>\\n\",\n       \"      <th>ExerciseAngina_Y</th>\\n\",\n       \"      <th>ST_Slope_Down</th>\\n\",\n       \"      <th>ST_Slope_Flat</th>\\n\",\n       \"      <th>ST_Slope_Up</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>40</td>\\n\",\n       \"      <td>140</td>\\n\",\n       \"      <td>289</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>172</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>49</td>\\n\",\n       \"      <td>160</td>\\n\",\n       \"      <td>180</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>156</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>37</td>\\n\",\n       \"      <td>130</td>\\n\",\n       \"      <td>283</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>98</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>48</td>\\n\",\n       \"      <td>138</td>\\n\",\n       \"      <td>214</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>108</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>54</td>\\n\",\n       \"      <td>150</td>\\n\",\n       \"      <td>195</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>122</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>True</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 21 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  HeartDisease  \\\\\\n\",\n       \"0   40        140          289          0    172      0.0             0   \\n\",\n       \"1   49        160          180          0    156      1.0             1   \\n\",\n       \"2   37        130          283          0     98      0.0             0   \\n\",\n       \"3   48        138          214          0    108      1.5             1   \\n\",\n       \"4   54        150          195          0    122      0.0             0   \\n\",\n       \"\\n\",\n       \"   Sex_F  Sex_M  ChestPainType_ASY  ...  ChestPainType_NAP  ChestPainType_TA  \\\\\\n\",\n       \"0  False   True              False  ...              False             False   \\n\",\n       \"1   True  False              False  ...               True             False   \\n\",\n       \"2  False   True              False  ...              False             False   \\n\",\n       \"3   True  False               True  ...              False             False   \\n\",\n       \"4  False   True              False  ...               True             False   \\n\",\n       \"\\n\",\n       \"   RestingECG_LVH  RestingECG_Normal  RestingECG_ST  ExerciseAngina_N  \\\\\\n\",\n       \"0           False               True          False              True   \\n\",\n       \"1           False               True          False              True   \\n\",\n       \"2           False              False           True              True   \\n\",\n       \"3           False               True          False             False   \\n\",\n       \"4           False               True          False              True   \\n\",\n       \"\\n\",\n       \"   ExerciseAngina_Y  ST_Slope_Down  ST_Slope_Flat  ST_Slope_Up  \\n\",\n       \"0             False          False          False         True  \\n\",\n       \"1             False          False           True        False  \\n\",\n       \"2             False          False          False         True  \\n\",\n       \"3              True          False           True        False  \\n\",\n       \"4             False          False          False         True  \\n\",\n       \"\\n\",\n       \"[5 rows x 21 columns]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's choose the variables that will be the input features of the model.\\n\",\n    \"- The target is `HeartDisease`.\\n\",\n    \"- All other variables are features that can potentially be used to predict the target, `HeartDisease`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"features = [x for x in df.columns if x not in 'HeartDisease'] ## Removing our target variable\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We started with 11 features.  Let's see how many feature variables we have after one-hot encoding.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"20\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(len(features))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3. Splitting the Dataset\\n\",\n    \"\\n\",\n    \"In this section, we will split our dataset into train and test datasets. We will use the function `train_test_split` from Scikit-learn. Let's just check its arguments.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Help on function train_test_split in module sklearn.model_selection._split:\\n\",\n      \"\\n\",\n      \"train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)\\n\",\n      \"    Split arrays or matrices into random train and test subsets.\\n\",\n      \"    \\n\",\n      \"    Quick utility that wraps input validation,\\n\",\n      \"    ``next(ShuffleSplit().split(X, y))``, and application to input data\\n\",\n      \"    into a single call for splitting (and optionally subsampling) data into a\\n\",\n      \"    one-liner.\\n\",\n      \"    \\n\",\n      \"    Read more in the :ref:`User Guide <cross_validation>`.\\n\",\n      \"    \\n\",\n      \"    Parameters\\n\",\n      \"    ----------\\n\",\n      \"    *arrays : sequence of indexables with same length / shape[0]\\n\",\n      \"        Allowed inputs are lists, numpy arrays, scipy-sparse\\n\",\n      \"        matrices or pandas dataframes.\\n\",\n      \"    \\n\",\n      \"    test_size : float or int, default=None\\n\",\n      \"        If float, should be between 0.0 and 1.0 and represent the proportion\\n\",\n      \"        of the dataset to include in the test split. If int, represents the\\n\",\n      \"        absolute number of test samples. If None, the value is set to the\\n\",\n      \"        complement of the train size. If ``train_size`` is also None, it will\\n\",\n      \"        be set to 0.25.\\n\",\n      \"    \\n\",\n      \"    train_size : float or int, default=None\\n\",\n      \"        If float, should be between 0.0 and 1.0 and represent the\\n\",\n      \"        proportion of the dataset to include in the train split. If\\n\",\n      \"        int, represents the absolute number of train samples. If None,\\n\",\n      \"        the value is automatically set to the complement of the test size.\\n\",\n      \"    \\n\",\n      \"    random_state : int, RandomState instance or None, default=None\\n\",\n      \"        Controls the shuffling applied to the data before applying the split.\\n\",\n      \"        Pass an int for reproducible output across multiple function calls.\\n\",\n      \"        See :term:`Glossary <random_state>`.\\n\",\n      \"    \\n\",\n      \"    shuffle : bool, default=True\\n\",\n      \"        Whether or not to shuffle the data before splitting. If shuffle=False\\n\",\n      \"        then stratify must be None.\\n\",\n      \"    \\n\",\n      \"    stratify : array-like, default=None\\n\",\n      \"        If not None, data is split in a stratified fashion, using this as\\n\",\n      \"        the class labels.\\n\",\n      \"        Read more in the :ref:`User Guide <stratification>`.\\n\",\n      \"    \\n\",\n      \"    Returns\\n\",\n      \"    -------\\n\",\n      \"    splitting : list, length=2 * len(arrays)\\n\",\n      \"        List containing train-test split of inputs.\\n\",\n      \"    \\n\",\n      \"        .. versionadded:: 0.16\\n\",\n      \"            If the input is sparse, the output will be a\\n\",\n      \"            ``scipy.sparse.csr_matrix``. Else, output type is the same as the\\n\",\n      \"            input type.\\n\",\n      \"    \\n\",\n      \"    Examples\\n\",\n      \"    --------\\n\",\n      \"    >>> import numpy as np\\n\",\n      \"    >>> from sklearn.model_selection import train_test_split\\n\",\n      \"    >>> X, y = np.arange(10).reshape((5, 2)), range(5)\\n\",\n      \"    >>> X\\n\",\n      \"    array([[0, 1],\\n\",\n      \"           [2, 3],\\n\",\n      \"           [4, 5],\\n\",\n      \"           [6, 7],\\n\",\n      \"           [8, 9]])\\n\",\n      \"    >>> list(y)\\n\",\n      \"    [0, 1, 2, 3, 4]\\n\",\n      \"    \\n\",\n      \"    >>> X_train, X_test, y_train, y_test = train_test_split(\\n\",\n      \"    ...     X, y, test_size=0.33, random_state=42)\\n\",\n      \"    ...\\n\",\n      \"    >>> X_train\\n\",\n      \"    array([[4, 5],\\n\",\n      \"           [0, 1],\\n\",\n      \"           [6, 7]])\\n\",\n      \"    >>> y_train\\n\",\n      \"    [2, 0, 3]\\n\",\n      \"    >>> X_test\\n\",\n      \"    array([[2, 3],\\n\",\n      \"           [8, 9]])\\n\",\n      \"    >>> y_test\\n\",\n      \"    [1, 4]\\n\",\n      \"    \\n\",\n      \"    >>> train_test_split(y, shuffle=False)\\n\",\n      \"    [[0, 1, 2], [3, 4]]\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"help(train_test_split)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train, X_val, y_train, y_val = train_test_split(df[features], df['HeartDisease'], train_size = 0.8, random_state = RANDOM_STATE)\\n\",\n    \"\\n\",\n    \"# We will keep the shuffle = True since our dataset has not any time dependency.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"train samples: 734 validation samples: 184\\n\",\n      \"target proportion: 0.5518\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'train samples: {len(X_train)} validation samples: {len(X_val)}')\\n\",\n    \"print(f'target proportion: {sum(y_train)/len(y_train):.4f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4. Building the Models\\n\",\n    \"\\n\",\n    \"## 4.1 Decision Tree\\n\",\n    \"\\n\",\n    \"In this section, let's work with the Decision Tree we previously learned, but now using the [Scikit-learn implementation](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html). \\n\",\n    \"\\n\",\n    \"There are several hyperparameters in the Decision Tree object from Scikit-learn. We will use only some of them and also we will not perform feature selection nor hyperparameter tuning in this lab (but you are encouraged to do so and compare the results 😄 )\\n\",\n    \"\\n\",\n    \"The hyperparameters we will use and investigate here are:\\n\",\n    \"\\n\",\n    \" - min_samples_split: The minimum number of samples required to split an internal node. \\n\",\n    \"   - Choosing a higher min_samples_split can reduce the number of splits and may help to reduce overfitting.\\n\",\n    \" - max_depth: The maximum depth of the tree. \\n\",\n    \"   - Choosing a lower max_depth can reduce the number of splits and may help to reduce overfitting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700] ## If the number is an integer, then it is the actual quantity of samples,\\n\",\n    \"max_depth_list = [1,2, 3, 4, 8, 16, 32, 64, None] # None means that there is no depth limit.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x20368a29480>\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for min_samples_split in min_samples_split_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = DecisionTreeClassifier(min_samples_split = min_samples_split,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('min_samples_split')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note how increasing the the number of `min_samples_split` reduces overfitting.\\n\",\n    \"- Increasing `min_samples_split` from 10 to 30, and from 30 to 50, even though it does not improve the validation accuracy, it brings the training accuracy closer to it, showing a reduction in overfitting.\\n\",\n    \"\\n\",\n    \"Let's do the same experiment with `max_depth`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x2036ac65810>\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for max_depth in max_depth_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = DecisionTreeClassifier(max_depth = max_depth,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('max_depth')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can see that in general, reducing `max_depth` can help to reduce overfitting.\\n\",\n    \"- Reducing `max_depth` from 8 to 4 increases validation accuracy closer to training accuracy, while significantly reducing training accuracy.\\n\",\n    \"- The validation accuracy reaches the highest at tree_depth=4. \\n\",\n    \"- When the `max_depth` is smaller than 3, both training and validation accuracy decreases.  The tree cannot make enough splits to distinguish positives from negatives (the model is underfitting the training set). \\n\",\n    \"- When the `max_depth` is too high ( >= 5), validation accuracy decreases while training accuracy increases, indicating that the model is overfitting to the training set.\\n\",\n    \"\\n\",\n    \"So we can choose the best values for these two hyper-parameters for our model to be:\\n\",\n    \"- `max_depth = 4`\\n\",\n    \"- `min_samples_split = 50` \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"decision_tree_model = DecisionTreeClassifier(min_samples_split = 50,\\n\",\n    \"                                             max_depth = 3,\\n\",\n    \"                                             random_state = RANDOM_STATE).fit(X_train,y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Metrics train:\\n\",\n      \"\\tAccuracy score: 0.8583\\n\",\n      \"Metrics validation:\\n\",\n      \"\\tAccuracy score: 0.8641\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Metrics train:\\\\n\\\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_train),y_train):.4f}\\\")\\n\",\n    \"print(f\\\"Metrics validation:\\\\n\\\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_val),y_val):.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"No sign of overfitting, even though the metrics are not that good.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.2 Random Forest\\n\",\n    \"\\n\",\n    \"Now let's try the Random Forest algorithm also, using the Scikit-learn implementation. \\n\",\n    \"- All of the hyperparameters found in the decision tree model will also exist in this algorithm, since a random forest is an ensemble of many Decision Trees.\\n\",\n    \"- One additional hyperparameter for Random Forest is called `n_estimators` which is the number of Decision Trees that make up the Random Forest. \\n\",\n    \"\\n\",\n    \"Remember that for a Random Forest, we randomly choose a subset of the features AND randomly choose a subset of the training examples to train each individual tree.\\n\",\n    \"- Following the lectures, if $n$ is the number of features, we will randomly select $\\\\sqrt{n}$ of these features to train each individual tree. \\n\",\n    \"- Note that you can modify this by setting the `max_features` parameter.\\n\",\n    \"\\n\",\n    \"You can also speed up your training jobs with another parameter, `n_jobs`. \\n\",\n    \"- Since the fitting of each tree is independent of each other, it is possible fit more than one tree in parallel. \\n\",\n    \"- So setting `n_jobs` higher will increase how many CPU cores it will use. Note that the numbers very close to the maximum cores of your CPU may impact on the overall performance of your PC and even lead to freezes. \\n\",\n    \"- Changing this parameter does not impact on the final result but can reduce the training time.\\n\",\n    \"\\n\",\n    \"We will run the same script again, but with another parameter, `n_estimators`, where we will choose between 10, 50, and 100. The default is 100.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700]  ## If the number is an integer, then it is the actual quantity of samples,\\n\",\n    \"                                             ## If it is a float, then it is the percentage of the dataset\\n\",\n    \"max_depth_list = [2, 4, 8, 16, 32, 64, None]\\n\",\n    \"n_estimators_list = [10,50,100,500]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x2036ace76a0>\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for min_samples_split in min_samples_split_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = RandomForestClassifier(min_samples_split = min_samples_split,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('min_samples_split')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list) \\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Notice that, even though the validation accuraty reaches is the same both at `min_samples_split = 2` and `min_samples_split = 10`, in the latter the difference in training and validation set reduces, showing less overfitting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x20368b5bca0>\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for max_depth in max_depth_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = RandomForestClassifier(max_depth = max_depth,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('max_depth')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.legend.Legend at 0x20368beb430>\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"accuracy_list_train = []\\n\",\n    \"accuracy_list_val = []\\n\",\n    \"for n_estimators in n_estimators_list:\\n\",\n    \"    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\\n\",\n    \"    model = RandomForestClassifier(n_estimators = n_estimators,\\n\",\n    \"                                   random_state = RANDOM_STATE).fit(X_train,y_train) \\n\",\n    \"    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\\n\",\n    \"    predictions_val = model.predict(X_val) ## The predicted values for the test dataset\\n\",\n    \"    accuracy_train = accuracy_score(predictions_train,y_train)\\n\",\n    \"    accuracy_val = accuracy_score(predictions_val,y_val)\\n\",\n    \"    accuracy_list_train.append(accuracy_train)\\n\",\n    \"    accuracy_list_val.append(accuracy_val)\\n\",\n    \"\\n\",\n    \"plt.title('Train x Validation metrics')\\n\",\n    \"plt.xlabel('n_estimators')\\n\",\n    \"plt.ylabel('accuracy')\\n\",\n    \"plt.xticks(ticks = range(len(n_estimators_list )),labels=n_estimators_list)\\n\",\n    \"plt.plot(accuracy_list_train)\\n\",\n    \"plt.plot(accuracy_list_val)\\n\",\n    \"plt.legend(['Train','Validation'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's then fit a random forest with the following parameters:\\n\",\n    \"\\n\",\n    \" - max_depth: 16\\n\",\n    \" - min_samples_split: 10\\n\",\n    \" - n_estimators: 100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"random_forest_model = RandomForestClassifier(n_estimators = 100,\\n\",\n    \"                                             max_depth = 16, \\n\",\n    \"                                             min_samples_split = 10).fit(X_train,y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Metrics train:\\n\",\n      \"\\tAccuracy score: 0.9278\\n\",\n      \"Metrics test:\\n\",\n      \"\\tAccuracy score: 0.8859\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Metrics train:\\\\n\\\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_train),y_train):.4f}\\\\nMetrics test:\\\\n\\\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_val),y_val):.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that we are searching for the best value one hyperparameter while leaving the other hyperparameters at their default values.\\n\",\n    \"- Ideally, we would want to check every combination of values for every hyperparameter that we are tuning.\\n\",\n    \"- If we have 3 hyperparameters, and each hyperparameter has 4 values to try out, we should have a total of 4 x 4 x 4 = 64 combinations to try.\\n\",\n    \"- When we only modify one hyperparameter while leaving the rest as their default value, we are trying 4 + 4 + 4 = 12 results. \\n\",\n    \"- To try out all combinations, we can use a sklearn implementation called GridSearchCV. GridSearchCV has a refit parameter that will automatically refit a model on the best combination so we will not need to program it explicitly. For more on GridSearchCV, please refer to its [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.3 XGBoost\\n\",\n    \"\\n\",\n    \"Next is the Gradient Boosting model, called XGBoost. The boosting methods train several trees, but instead of them being uncorrelated to each other, now the trees are fit one after the other in order to minimize the error. \\n\",\n    \"\\n\",\n    \"The model has the same parameters as a decision tree, plus the learning rate.\\n\",\n    \"- The learning rate is the size of the step on the Gradient Descent method that the XGBoost uses internally to minimize the error on each train step.\\n\",\n    \"\\n\",\n    \"One interesting thing about the XGBoost is that during fitting, it can take in an evaluation dataset of the form `(X_val,y_val)`.\\n\",\n    \"- On each iteration, it measures the cost (or evaluation metric) on the evaluation datasets.\\n\",\n    \"- Once the cost (or metric) stops decreasing for a number of rounds (called early_stopping_rounds), the training will stop. \\n\",\n    \"- More iterations lead to more estimators, and more estimators can result in overfitting.  \\n\",\n    \"- By stopping once the validation metric no longer improves, we can limit the number of estimators created, and reduce overfitting.\\n\",\n    \"\\n\",\n    \"First, let's define a subset of our training set (we should not use the test set here).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"n = int(len(X_train)*0.8) ## Let's use 80% to train and 20% to eval\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train_fit, X_train_eval, y_train_fit, y_train_eval = X_train[:n], X_train[n:], y_train[:n], y_train[n:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can then set a large number of estimators, because we can stop if the cost function stops decreasing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note some of the `.fit()` parameters:\\n\",\n    \"- `eval_set = [(X_train_eval,y_train_eval)]`:Here we must pass a list to the eval_set, because you can have several different tuples ov eval sets. \\n\",\n    \"- `early_stopping_rounds`: This parameter helps to stop the model training if its evaluation metric is no longer improving on the validation set. It's set to 10.\\n\",\n    \"  - The model keeps track of the round with the best performance (lowest evaluation metric).  For example, let's say round 16 has the lowest evaluation metric so far.\\n\",\n    \"  - Each successive round's evaluation metric is compared to the best metric.  If the model goes 10 rounds where none have a better metric than the best one, then the model stops training.\\n\",\n    \"  - The model is returned at its last state when training terminated, not its state during the best round.  For example, if the model stops at round 26, but the best round was 16, the model's training state at round 26 is returned, not round 16.\\n\",\n    \"  - Note that this is different from returning the model's \\\"best\\\" state (from when the evaluation metric was the lowest).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0]\\tvalidation_0-logloss:0.63302\\n\",\n      \"[1]\\tvalidation_0-logloss:0.59554\\n\",\n      \"[2]\\tvalidation_0-logloss:0.56614\\n\",\n      \"[3]\\tvalidation_0-logloss:0.54187\\n\",\n      \"[4]\\tvalidation_0-logloss:0.52349\\n\",\n      \"[5]\\tvalidation_0-logloss:0.50611\\n\",\n      \"[6]\\tvalidation_0-logloss:0.49373\\n\",\n      \"[7]\\tvalidation_0-logloss:0.48366\\n\",\n      \"[8]\\tvalidation_0-logloss:0.47323\\n\",\n      \"[9]\\tvalidation_0-logloss:0.46538\\n\",\n      \"[10]\\tvalidation_0-logloss:0.46000\\n\",\n      \"[11]\\tvalidation_0-logloss:0.45621\\n\",\n      \"[12]\\tvalidation_0-logloss:0.45483\\n\",\n      \"[13]\\tvalidation_0-logloss:0.44975\\n\",\n      \"[14]\\tvalidation_0-logloss:0.44495\\n\",\n      \"[15]\\tvalidation_0-logloss:0.44073\\n\",\n      \"[16]\\tvalidation_0-logloss:0.44078\\n\",\n      \"[17]\\tvalidation_0-logloss:0.43936\\n\",\n      \"[18]\\tvalidation_0-logloss:0.44206\\n\",\n      \"[19]\\tvalidation_0-logloss:0.44536\\n\",\n      \"[20]\\tvalidation_0-logloss:0.44322\\n\",\n      \"[21]\\tvalidation_0-logloss:0.44310\\n\",\n      \"[22]\\tvalidation_0-logloss:0.44419\\n\",\n      \"[23]\\tvalidation_0-logloss:0.44797\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"d:\\\\TA\\\\machine-learning-specialization-coursera\\\\.venv\\\\lib\\\\site-packages\\\\xgboost\\\\sklearn.py:885: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\\n\",\n      \"  warnings.warn(\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[24]\\tvalidation_0-logloss:0.44843\\n\",\n      \"[25]\\tvalidation_0-logloss:0.45337\\n\",\n      \"[26]\\tvalidation_0-logloss:0.45206\\n\",\n      \"[27]\\tvalidation_0-logloss:0.45435\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \\\"▸\\\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \\\"▾\\\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \\\"\\\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \\\"\\\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \\\"\\\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\\\"sk-container-id-1\\\" class=\\\"sk-top-container\\\"><div class=\\\"sk-text-repr-fallback\\\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\\n\",\n       \"              colsample_bylevel=None, colsample_bynode=None,\\n\",\n       \"              colsample_bytree=None, device=None, early_stopping_rounds=None,\\n\",\n       \"              enable_categorical=False, eval_metric=None, feature_types=None,\\n\",\n       \"              gamma=None, grow_policy=None, importance_type=None,\\n\",\n       \"              interaction_constraints=None, learning_rate=0.1, max_bin=None,\\n\",\n       \"              max_cat_threshold=None, max_cat_to_onehot=None,\\n\",\n       \"              max_delta_step=None, max_depth=None, max_leaves=None,\\n\",\n       \"              min_child_weight=None, missing=nan, monotone_constraints=None,\\n\",\n       \"              multi_strategy=None, n_estimators=500, n_jobs=None,\\n\",\n       \"              num_parallel_tree=None, random_state=55, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\\\"sk-container\\\" hidden><div class=\\\"sk-item\\\"><div class=\\\"sk-estimator sk-toggleable\\\"><input class=\\\"sk-toggleable__control sk-hidden--visually\\\" id=\\\"sk-estimator-id-1\\\" type=\\\"checkbox\\\" checked><label for=\\\"sk-estimator-id-1\\\" class=\\\"sk-toggleable__label sk-toggleable__label-arrow\\\">XGBClassifier</label><div class=\\\"sk-toggleable__content\\\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\\n\",\n       \"              colsample_bylevel=None, colsample_bynode=None,\\n\",\n       \"              colsample_bytree=None, device=None, early_stopping_rounds=None,\\n\",\n       \"              enable_categorical=False, eval_metric=None, feature_types=None,\\n\",\n       \"              gamma=None, grow_policy=None, importance_type=None,\\n\",\n       \"              interaction_constraints=None, learning_rate=0.1, max_bin=None,\\n\",\n       \"              max_cat_threshold=None, max_cat_to_onehot=None,\\n\",\n       \"              max_delta_step=None, max_depth=None, max_leaves=None,\\n\",\n       \"              min_child_weight=None, missing=nan, monotone_constraints=None,\\n\",\n       \"              multi_strategy=None, n_estimators=500, n_jobs=None,\\n\",\n       \"              num_parallel_tree=None, random_state=55, ...)</pre></div></div></div></div></div>\"\n      ],\n      \"text/plain\": [\n       \"XGBClassifier(base_score=None, booster=None, callbacks=None,\\n\",\n       \"              colsample_bylevel=None, colsample_bynode=None,\\n\",\n       \"              colsample_bytree=None, device=None, early_stopping_rounds=None,\\n\",\n       \"              enable_categorical=False, eval_metric=None, feature_types=None,\\n\",\n       \"              gamma=None, grow_policy=None, importance_type=None,\\n\",\n       \"              interaction_constraints=None, learning_rate=0.1, max_bin=None,\\n\",\n       \"              max_cat_threshold=None, max_cat_to_onehot=None,\\n\",\n       \"              max_delta_step=None, max_depth=None, max_leaves=None,\\n\",\n       \"              min_child_weight=None, missing=nan, monotone_constraints=None,\\n\",\n       \"              multi_strategy=None, n_estimators=500, n_jobs=None,\\n\",\n       \"              num_parallel_tree=None, random_state=55, ...)\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"xgb_model = XGBClassifier(n_estimators = 500, learning_rate = 0.1,verbosity = 1, random_state = RANDOM_STATE)\\n\",\n    \"xgb_model.fit(X_train_fit,y_train_fit, eval_set = [(X_train_eval,y_train_eval)], early_stopping_rounds = 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Even though we initialized the model to allow up to 500 estimators, the algorithm only fit 26 estimators (over 26 rounds of training).\\n\",\n    \"\\n\",\n    \"To see why, let's look for the round of training that had the best performance (lowest evaluation metric).  You can either view the validation log loss metrics that were output above, or view the model's `.best_iteration` attribute:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"17\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"xgb_model.best_iteration\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best round of training was round 16, with a log loss of 4.3948.  \\n\",\n    \"- For 10 rounds of training after that (from round 17 to 26), the log loss was higher than this.\\n\",\n    \"- Since we set `early_stopping_rounds` to 10, then by the 10th round where the log loss doesn't improve upon the best one, training stops.\\n\",\n    \"- You can try out different values of `early_stopping_rounds` to verify this.  If you set it to 20, for instance, the model stops training at round 36 (16 + 20).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Metrics train:\\n\",\n      \"\\tAccuracy score: 0.9319\\n\",\n      \"Metrics test:\\n\",\n      \"\\tAccuracy score: 0.8533\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Metrics train:\\\\n\\\\tAccuracy score: {accuracy_score(xgb_model.predict(X_train),y_train):.4f}\\\\nMetrics test:\\\\n\\\\tAccuracy score: {accuracy_score(xgb_model.predict(X_val),y_val):.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In this example, both Random Forest and XGBoost had similar performance (test accuracy).  \\n\",\n    \"\\n\",\n    \"Congratulations, you have learned how to use Decision Tree, Random Forest from the scikit-learn library and XGBoost!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cb064499\",\n   \"metadata\": {},\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.10\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"56d44d6a8424451b5ce45d1ae0b0b7865dc60710e7f74571dd51dd80d7829ee9\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/optional labs/deeplearning (2).mplstyle",
    "content": "# see https://matplotlib.org/stable/tutorials/introductory/customizing.html\nlines.linewidth: 4\nlines.solid_capstyle: butt\n\nlegend.fancybox: true\n\n# Verdana\" for non-math text,\n# Cambria Math\n\n#Blue (Crayon-Aqua) 0096FF\n#Dark Red C00000\n#Orange (Apple Orange) FF9300\n#Black 000000\n#Magenta FF40FF\n#Purple 7030A0\n\naxes.prop_cycle: cycler('color', ['0096FF', 'FF9300', 'FF40FF', '7030A0', 'C00000'])\n#axes.facecolor: f0f0f0 # grey\naxes.facecolor: ffffff  # white\naxes.labelsize: large\naxes.axisbelow: true\naxes.grid: False\naxes.edgecolor: f0f0f0\naxes.linewidth: 3.0\naxes.titlesize: x-large\n\npatch.edgecolor: f0f0f0\npatch.linewidth: 0.5\n\nsvg.fonttype: path\n\ngrid.linestyle: -\ngrid.linewidth: 1.0\ngrid.color: cbcbcb\n\nxtick.major.size: 0\nxtick.minor.size: 0\nytick.major.size: 0\nytick.minor.size: 0\n\nsavefig.edgecolor: f0f0f0\nsavefig.facecolor: f0f0f0\n\n#figure.subplot.left: 0.08\n#figure.subplot.right: 0.95\n#figure.subplot.bottom: 0.07\n\n#figure.facecolor: f0f0f0  # grey\nfigure.facecolor: ffffff  # white\n\n## ***************************************************************************\n## * FONT                                                                    *\n## ***************************************************************************\n## The font properties used by `text.Text`.\n## See https://matplotlib.org/api/font_manager_api.html for more information\n## on font properties.  The 6 font properties used for font matching are\n## given below with their default values.\n##\n## The font.family property can take either a concrete font name (not supported\n## when rendering text with usetex), or one of the following five generic\n## values:\n##     - 'serif' (e.g., Times),\n##     - 'sans-serif' (e.g., Helvetica),\n##     - 'cursive' (e.g., Zapf-Chancery),\n##     - 'fantasy' (e.g., Western), and\n##     - 'monospace' (e.g., Courier).\n## Each of these values has a corresponding default list of font names\n## (font.serif, etc.); the first available font in the list is used.  Note that\n## for font.serif, font.sans-serif, and font.monospace, the first element of\n## the list (a DejaVu font) will always be used because DejaVu is shipped with\n## Matplotlib and is thus guaranteed to be available; the other entries are\n## left as examples of other possible values.\n##\n## The font.style property has three values: normal (or roman), italic\n## or oblique.  The oblique style will be used for italic, if it is not\n## present.\n##\n## The font.variant property has two values: normal or small-caps.  For\n## TrueType fonts, which are scalable fonts, small-caps is equivalent\n## to using a font size of 'smaller', or about 83%% of the current font\n## size.\n##\n## The font.weight property has effectively 13 values: normal, bold,\n## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as\n## 400, and bold is 700.  bolder and lighter are relative values with\n## respect to the current weight.\n##\n## The font.stretch property has 11 values: ultra-condensed,\n## extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This\n## property is not currently implemented.\n##\n## The font.size property is the default font size for text, given in points.\n## 10 pt is the standard value.\n##\n## Note that font.size controls default text sizes.  To configure\n## special text sizes tick labels, axes, labels, title, etc., see the rc\n## settings for axes and ticks.  Special text sizes can be defined\n## relative to font.size, using the following values: xx-small, x-small,\n## small, medium, large, x-large, xx-large, larger, or smaller\n\n\nfont.family:  sans-serif\nfont.style:   normal\nfont.variant: normal\nfont.weight:  normal\nfont.stretch: normal\nfont.size:    8.0\n\nfont.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\nfont.sans-serif: Verdana, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\nfont.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive\nfont.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy\nfont.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n\n\n## ***************************************************************************\n## * TEXT                                                                    *\n## ***************************************************************************\n## The text properties used by `text.Text`.\n## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\n## for more information on text properties\n#text.color: black\n\n"
  },
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    "path": "C2 - Advanced Learning Algorithms/week4/optional labs/deeplearning.mplstyle",
    "content": "# see https://matplotlib.org/stable/tutorials/introductory/customizing.html\nlines.linewidth: 4\nlines.solid_capstyle: butt\n\nlegend.fancybox: true\n\n# Verdana\" for non-math text,\n# Cambria Math\n\n#Blue (Crayon-Aqua) 0096FF\n#Dark Red C00000\n#Orange (Apple Orange) FF9300\n#Black 000000\n#Magenta FF40FF\n#Purple 7030A0\n\naxes.prop_cycle: cycler('color', ['0096FF', 'FF9300', 'FF40FF', '7030A0', 'C00000'])\n#axes.facecolor: f0f0f0 # grey\naxes.facecolor: ffffff  # white\naxes.labelsize: large\naxes.axisbelow: true\naxes.grid: False\naxes.edgecolor: f0f0f0\naxes.linewidth: 3.0\naxes.titlesize: x-large\n\npatch.edgecolor: f0f0f0\npatch.linewidth: 0.5\n\nsvg.fonttype: path\n\ngrid.linestyle: -\ngrid.linewidth: 1.0\ngrid.color: cbcbcb\n\nxtick.major.size: 0\nxtick.minor.size: 0\nytick.major.size: 0\nytick.minor.size: 0\n\nsavefig.edgecolor: f0f0f0\nsavefig.facecolor: f0f0f0\n\n#figure.subplot.left: 0.08\n#figure.subplot.right: 0.95\n#figure.subplot.bottom: 0.07\n\n#figure.facecolor: f0f0f0  # grey\nfigure.facecolor: ffffff  # white\n\n## ***************************************************************************\n## * FONT                                                                    *\n## ***************************************************************************\n## The font properties used by `text.Text`.\n## See https://matplotlib.org/api/font_manager_api.html for more information\n## on font properties.  The 6 font properties used for font matching are\n## given below with their default values.\n##\n## The font.family property can take either a concrete font name (not supported\n## when rendering text with usetex), or one of the following five generic\n## values:\n##     - 'serif' (e.g., Times),\n##     - 'sans-serif' (e.g., Helvetica),\n##     - 'cursive' (e.g., Zapf-Chancery),\n##     - 'fantasy' (e.g., Western), and\n##     - 'monospace' (e.g., Courier).\n## Each of these values has a corresponding default list of font names\n## (font.serif, etc.); the first available font in the list is used.  Note that\n## for font.serif, font.sans-serif, and font.monospace, the first element of\n## the list (a DejaVu font) will always be used because DejaVu is shipped with\n## Matplotlib and is thus guaranteed to be available; the other entries are\n## left as examples of other possible values.\n##\n## The font.style property has three values: normal (or roman), italic\n## or oblique.  The oblique style will be used for italic, if it is not\n## present.\n##\n## The font.variant property has two values: normal or small-caps.  For\n## TrueType fonts, which are scalable fonts, small-caps is equivalent\n## to using a font size of 'smaller', or about 83%% of the current font\n## size.\n##\n## The font.weight property has effectively 13 values: normal, bold,\n## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as\n## 400, and bold is 700.  bolder and lighter are relative values with\n## respect to the current weight.\n##\n## The font.stretch property has 11 values: ultra-condensed,\n## extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This\n## property is not currently implemented.\n##\n## The font.size property is the default font size for text, given in points.\n## 10 pt is the standard value.\n##\n## Note that font.size controls default text sizes.  To configure\n## special text sizes tick labels, axes, labels, title, etc., see the rc\n## settings for axes and ticks.  Special text sizes can be defined\n## relative to font.size, using the following values: xx-small, x-small,\n## small, medium, large, x-large, xx-large, larger, or smaller\n\n\nfont.family:  sans-serif\nfont.style:   normal\nfont.variant: normal\nfont.weight:  normal\nfont.stretch: normal\nfont.size:    8.0\n\nfont.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\nfont.sans-serif: Verdana, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\nfont.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive\nfont.fantasy:    Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy\nfont.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n\n\n## ***************************************************************************\n## * TEXT                                                                    *\n## ***************************************************************************\n## The text properties used by `text.Text`.\n## See https://matplotlib.org/api/artist_api.html#module-matplotlib.text\n## for more information on text properties\n#text.color: black\n\n"
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"Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope,HeartDisease\n40,M,ATA,140,289,0,Normal,172,N,0,Up,0\n49,F,NAP,160,180,0,Normal,156,N,1,Flat,1\n37,M,ATA,130,283,0,ST,98,N,0,Up,0\n48,F,ASY,138,214,0,Normal,108,Y,1.5,Flat,1\n54,M,NAP,150,195,0,Normal,122,N,0,Up,0\n39,M,NAP,120,339,0,Normal,170,N,0,Up,0\n45,F,ATA,130,237,0,Normal,170,N,0,Up,0\n54,M,ATA,110,208,0,Normal,142,N,0,Up,0\n37,M,ASY,140,207,0,Normal,130,Y,1.5,Flat,1\n48,F,ATA,120,284,0,Normal,120,N,0,Up,0\n37,F,NAP,130,211,0,Normal,142,N,0,Up,0\n58,M,ATA,136,164,0,ST,99,Y,2,Flat,1\n39,M,ATA,120,204,0,Normal,145,N,0,Up,0\n49,M,ASY,140,234,0,Normal,140,Y,1,Flat,1\n42,F,NAP,115,211,0,ST,137,N,0,Up,0\n54,F,ATA,120,273,0,Normal,150,N,1.5,Flat,0\n38,M,ASY,110,196,0,Normal,166,N,0,Flat,1\n43,F,ATA,120,201,0,Normal,165,N,0,Up,0\n60,M,ASY,100,248,0,Normal,125,N,1,Flat,1\n36,M,ATA,120,267,0,Normal,160,N,3,Flat,1\n43,F,TA,100,223,0,Normal,142,N,0,Up,0\n44,M,ATA,120,184,0,Normal,142,N,1,Flat,0\n49,F,ATA,124,201,0,Normal,164,N,0,Up,0\n44,M,ATA,150,288,0,Normal,150,Y,3,Flat,1\n40,M,NAP,130,215,0,Normal,138,N,0,Up,0\n36,M,NAP,130,209,0,Normal,178,N,0,Up,0\n53,M,ASY,124,260,0,ST,112,Y,3,Flat,0\n52,M,ATA,120,284,0,Normal,118,N,0,Up,0\n53,F,ATA,113,468,0,Normal,127,N,0,Up,0\n51,M,ATA,125,188,0,Normal,145,N,0,Up,0\n53,M,NAP,145,518,0,Normal,130,N,0,Flat,1\n56,M,NAP,130,167,0,Normal,114,N,0,Up,0\n54,M,ASY,125,224,0,Normal,122,N,2,Flat,1\n41,M,ASY,130,172,0,ST,130,N,2,Flat,1\n43,F,ATA,150,186,0,Normal,154,N,0,Up,0\n32,M,ATA,125,254,0,Normal,155,N,0,Up,0\n65,M,ASY,140,306,1,Normal,87,Y,1.5,Flat,1\n41,F,ATA,110,250,0,ST,142,N,0,Up,0\n48,F,ATA,120,177,1,ST,148,N,0,Up,0\n48,F,ASY,150,227,0,Normal,130,Y,1,Flat,0\n54,F,ATA,150,230,0,Normal,130,N,0,Up,0\n54,F,NAP,130,294,0,ST,100,Y,0,Flat,1\n35,M,ATA,150,264,0,Normal,168,N,0,Up,0\n52,M,NAP,140,259,0,ST,170,N,0,Up,0\n43,M,ASY,120,175,0,Normal,120,Y,1,Flat,1\n59,M,NAP,130,318,0,Normal,120,Y,1,Flat,0\n37,M,ASY,120,223,0,Normal,168,N,0,Up,0\n50,M,ATA,140,216,0,Normal,170,N,0,Up,0\n36,M,NAP,112,340,0,Normal,184,N,1,Flat,0\n41,M,ASY,110,289,0,Normal,170,N,0,Flat,1\n50,M,ASY,130,233,0,Normal,121,Y,2,Flat,1\n47,F,ASY,120,205,0,Normal,98,Y,2,Flat,1\n45,M,ATA,140,224,1,Normal,122,N,0,Up,0\n41,F,ATA,130,245,0,Normal,150,N,0,Up,0\n52,F,ASY,130,180,0,Normal,140,Y,1.5,Flat,0\n51,F,ATA,160,194,0,Normal,170,N,0,Up,0\n31,M,ASY,120,270,0,Normal,153,Y,1.5,Flat,1\n58,M,NAP,130,213,0,ST,140,N,0,Flat,1\n54,M,ASY,150,365,0,ST,134,N,1,Up,0\n52,M,ASY,112,342,0,ST,96,Y,1,Flat,1\n49,M,ATA,100,253,0,Normal,174,N,0,Up,0\n43,F,NAP,150,254,0,Normal,175,N,0,Up,0\n45,M,ASY,140,224,0,Normal,144,N,0,Up,0\n46,M,ASY,120,277,0,Normal,125,Y,1,Flat,1\n50,F,ATA,110,202,0,Normal,145,N,0,Up,0\n37,F,ATA,120,260,0,Normal,130,N,0,Up,0\n45,F,ASY,132,297,0,Normal,144,N,0,Up,0\n32,M,ATA,110,225,0,Normal,184,N,0,Up,0\n52,M,ASY,160,246,0,ST,82,Y,4,Flat,1\n44,M,ASY,150,412,0,Normal,170,N,0,Up,0\n57,M,ATA,140,265,0,ST,145,Y,1,Flat,1\n44,M,ATA,130,215,0,Normal,135,N,0,Up,0\n52,M,ASY,120,182,0,Normal,150,N,0,Flat,1\n44,F,ASY,120,218,0,ST,115,N,0,Up,0\n55,M,ASY,140,268,0,Normal,128,Y,1.5,Flat,1\n46,M,NAP,150,163,0,Normal,116,N,0,Up,0\n32,M,ASY,118,529,0,Normal,130,N,0,Flat,1\n35,F,ASY,140,167,0,Normal,150,N,0,Up,0\n52,M,ATA,140,100,0,Normal,138,Y,0,Up,0\n49,M,ASY,130,206,0,Normal,170,N,0,Flat,1\n55,M,NAP,110,277,0,Normal,160,N,0,Up,0\n54,M,ATA,120,238,0,Normal,154,N,0,Up,0\n63,M,ASY,150,223,0,Normal,115,N,0,Flat,1\n52,M,ATA,160,196,0,Normal,165,N,0,Up,0\n56,M,ASY,150,213,1,Normal,125,Y,1,Flat,1\n66,M,ASY,140,139,0,Normal,94,Y,1,Flat,1\n65,M,ASY,170,263,1,Normal,112,Y,2,Flat,1\n53,F,ATA,140,216,0,Normal,142,Y,2,Flat,0\n43,M,TA,120,291,0,ST,155,N,0,Flat,1\n55,M,ASY,140,229,0,Normal,110,Y,0.5,Flat,0\n49,F,ATA,110,208,0,Normal,160,N,0,Up,0\n39,M,ASY,130,307,0,Normal,140,N,0,Up,0\n52,F,ATA,120,210,0,Normal,148,N,0,Up,0\n48,M,ASY,160,329,0,Normal,92,Y,1.5,Flat,1\n39,F,NAP,110,182,0,ST,180,N,0,Up,0\n58,M,ASY,130,263,0,Normal,140,Y,2,Flat,1\n43,M,ATA,142,207,0,Normal,138,N,0,Up,0\n39,M,NAP,160,147,1,Normal,160,N,0,Up,0\n56,M,ASY,120,85,0,Normal,140,N,0,Up,0\n41,M,ATA,125,269,0,Normal,144,N,0,Up,0\n65,M,ASY,130,275,0,ST,115,Y,1,Flat,1\n51,M,ASY,130,179,0,Normal,100,N,0,Up,0\n40,F,ASY,150,392,0,Normal,130,N,2,Flat,1\n40,M,ASY,120,466,1,Normal,152,Y,1,Flat,1\n46,M,ASY,118,186,0,Normal,124,N,0,Flat,1\n57,M,ATA,140,260,1,Normal,140,N,0,Up,0\n48,F,ASY,120,254,0,ST,110,N,0,Up,0\n34,M,ATA,150,214,0,ST,168,N,0,Up,0\n50,M,ASY,140,129,0,Normal,135,N,0,Up,0\n39,M,ATA,190,241,0,Normal,106,N,0,Up,0\n59,F,ATA,130,188,0,Normal,124,N,1,Flat,0\n57,M,ASY,150,255,0,Normal,92,Y,3,Flat,1\n47,M,ASY,140,276,1,Normal,125,Y,0,Up,0\n38,M,ATA,140,297,0,Normal,150,N,0,Up,0\n49,F,NAP,130,207,0,ST,135,N,0,Up,0\n33,F,ASY,100,246,0,Normal,150,Y,1,Flat,1\n38,M,ASY,120,282,0,Normal,170,N,0,Flat,1\n59,F,ASY,130,338,1,ST,130,Y,1.5,Flat,1\n35,F,TA,120,160,0,ST,185,N,0,Up,0\n34,M,TA,140,156,0,Normal,180,N,0,Flat,1\n47,F,NAP,135,248,1,Normal,170,N,0,Flat,1\n52,F,NAP,125,272,0,Normal,139,N,0,Up,0\n46,M,ASY,110,240,0,ST,140,N,0,Up,0\n58,F,ATA,180,393,0,Normal,110,Y,1,Flat,1\n58,M,ATA,130,230,0,Normal,150,N,0,Up,0\n54,M,ATA,120,246,0,Normal,110,N,0,Up,0\n34,F,ATA,130,161,0,Normal,190,N,0,Up,0\n48,F,ASY,108,163,0,Normal,175,N,2,Up,0\n54,F,ATA,120,230,1,Normal,140,N,0,Up,0\n42,M,NAP,120,228,0,Normal,152,Y,1.5,Flat,0\n38,M,NAP,145,292,0,Normal,130,N,0,Up,0\n46,M,ASY,110,202,0,Normal,150,Y,0,Flat,1\n56,M,ASY,170,388,0,ST,122,Y,2,Flat,1\n56,M,ASY,150,230,0,ST,124,Y,1.5,Flat,1\n61,F,ASY,130,294,0,ST,120,Y,1,Flat,0\n49,M,NAP,115,265,0,Normal,175,N,0,Flat,1\n43,F,ATA,120,215,0,ST,175,N,0,Up,0\n39,M,ATA,120,241,0,ST,146,N,2,Up,0\n54,M,ASY,140,166,0,Normal,118,Y,0,Flat,1\n43,M,ASY,150,247,0,Normal,130,Y,2,Flat,1\n52,M,ASY,160,331,0,Normal,94,Y,2.5,Flat,1\n50,M,ASY,140,341,0,ST,125,Y,2.5,Flat,1\n47,M,ASY,160,291,0,ST,158,Y,3,Flat,1\n53,M,ASY,140,243,0,Normal,155,N,0,Up,0\n56,F,ATA,120,279,0,Normal,150,N,1,Flat,1\n39,M,ASY,110,273,0,Normal,132,N,0,Up,0\n42,M,ATA,120,198,0,Normal,155,N,0,Up,0\n43,F,ATA,120,249,0,ST,176,N,0,Up,0\n50,M,ATA,120,168,0,Normal,160,N,0,Up,0\n54,M,ASY,130,603,1,Normal,125,Y,1,Flat,1\n39,M,ATA,130,215,0,Normal,120,N,0,Up,0\n48,M,ATA,100,159,0,Normal,100,N,0,Up,0\n40,M,ATA,130,275,0,Normal,150,N,0,Up,0\n55,M,ASY,120,270,0,Normal,140,N,0,Up,0\n41,M,ATA,120,291,0,ST,160,N,0,Up,0\n56,M,ASY,155,342,1,Normal,150,Y,3,Flat,1\n38,M,ASY,110,190,0,Normal,150,Y,1,Flat,1\n49,M,ASY,140,185,0,Normal,130,N,0,Up,0\n44,M,ASY,130,290,0,Normal,100,Y,2,Flat,1\n54,M,ATA,160,195,0,ST,130,N,1,Up,0\n59,M,ASY,140,264,1,LVH,119,Y,0,Flat,1\n49,M,ASY,128,212,0,Normal,96,Y,0,Flat,1\n47,M,ATA,160,263,0,Normal,174,N,0,Up,0\n42,M,ATA,120,196,0,Normal,150,N,0,Up,0\n52,F,ATA,140,225,0,Normal,140,N,0,Up,0\n46,M,TA,140,272,1,Normal,175,N,2,Flat,1\n50,M,ASY,140,231,0,ST,140,Y,5,Flat,1\n48,M,ATA,140,238,0,Normal,118,N,0,Up,0\n58,M,ASY,135,222,0,Normal,100,N,0,Up,0\n58,M,NAP,140,179,0,Normal,160,N,0,Up,0\n29,M,ATA,120,243,0,Normal,160,N,0,Up,0\n40,M,NAP,140,235,0,Normal,188,N,0,Up,0\n53,M,ATA,140,320,0,Normal,162,N,0,Up,0\n49,M,NAP,140,187,0,Normal,172,N,0,Up,0\n52,M,ASY,140,266,0,Normal,134,Y,2,Flat,1\n43,M,ASY,140,288,0,Normal,135,Y,2,Flat,1\n54,M,ASY,140,216,0,Normal,105,N,1.5,Flat,1\n59,M,ATA,140,287,0,Normal,150,N,0,Up,0\n37,M,NAP,130,194,0,Normal,150,N,0,Up,0\n46,F,ASY,130,238,0,Normal,90,N,0,Up,0\n52,M,ASY,130,225,0,Normal,120,Y,2,Flat,1\n51,M,ATA,130,224,0,Normal,150,N,0,Up,0\n52,M,ASY,140,404,0,Normal,124,Y,2,Flat,1\n46,M,ASY,110,238,0,ST,140,Y,1,Flat,0\n54,F,ATA,160,312,0,Normal,130,N,0,Up,0\n58,M,NAP,160,211,1,ST,92,N,0,Flat,1\n58,M,ATA,130,251,0,Normal,110,N,0,Up,0\n41,M,ASY,120,237,1,Normal,138,Y,1,Flat,1\n50,F,ASY,120,328,0,Normal,110,Y,1,Flat,0\n53,M,ASY,180,285,0,ST,120,Y,1.5,Flat,1\n46,M,ASY,180,280,0,ST,120,N,0,Up,0\n50,M,ATA,170,209,0,ST,116,N,0,Up,0\n48,M,ATA,130,245,0,Normal,160,N,0,Up,0\n45,M,NAP,135,192,0,Normal,110,N,0,Up,0\n41,F,ATA,125,184,0,Normal,180,N,0,Up,0\n62,F,TA,160,193,0,Normal,116,N,0,Up,0\n49,M,ASY,120,297,0,Normal,132,N,1,Flat,0\n42,M,ATA,150,268,0,Normal,136,N,0,Up,0\n53,M,ASY,120,246,0,Normal,116,Y,0,Flat,1\n57,F,TA,130,308,0,Normal,98,N,1,Flat,0\n47,M,TA,110,249,0,Normal,150,N,0,Up,0\n46,M,NAP,120,230,0,Normal,150,N,0,Up,0\n42,M,NAP,160,147,0,Normal,146,N,0,Up,0\n31,F,ATA,100,219,0,ST,150,N,0,Up,0\n56,M,ATA,130,184,0,Normal,100,N,0,Up,0\n50,M,ASY,150,215,0,Normal,140,Y,0,Up,0\n35,M,ATA,120,308,0,LVH,180,N,0,Up,0\n35,M,ATA,110,257,0,Normal,140,N,0,Flat,1\n28,M,ATA,130,132,0,LVH,185,N,0,Up,0\n54,M,ASY,125,216,0,Normal,140,N,0,Flat,1\n48,M,ASY,106,263,1,Normal,110,N,0,Flat,1\n50,F,NAP,140,288,0,Normal,140,Y,0,Flat,1\n56,M,NAP,130,276,0,Normal,128,Y,1,Up,0\n56,F,NAP,130,219,0,ST,164,N,0,Up,0\n47,M,ASY,150,226,0,Normal,98,Y,1.5,Flat,1\n30,F,TA,170,237,0,ST,170,N,0,Up,0\n39,M,ASY,110,280,0,Normal,150,N,0,Flat,1\n54,M,NAP,120,217,0,Normal,137,N,0,Up,0\n55,M,ATA,140,196,0,Normal,150,N,0,Up,0\n29,M,ATA,140,263,0,Normal,170,N,0,Up,0\n46,M,ASY,130,222,0,Normal,112,N,0,Flat,1\n51,F,ASY,160,303,0,Normal,150,Y,1,Flat,1\n48,F,NAP,120,195,0,Normal,125,N,0,Up,0\n33,M,NAP,120,298,0,Normal,185,N,0,Up,0\n55,M,ATA,120,256,1,Normal,137,N,0,Up,0\n50,M,ASY,145,264,0,Normal,150,N,0,Flat,1\n53,M,NAP,120,195,0,Normal,140,N,0,Up,0\n38,M,ASY,92,117,0,Normal,134,Y,2.5,Flat,1\n41,M,ATA,120,295,0,Normal,170,N,0,Up,0\n37,F,ASY,130,173,0,ST,184,N,0,Up,0\n37,M,ASY,130,315,0,Normal,158,N,0,Up,0\n40,M,NAP,130,281,0,Normal,167,N,0,Up,0\n38,F,ATA,120,275,0,Normal,129,N,0,Up,0\n41,M,ASY,112,250,0,Normal,142,N,0,Up,0\n54,F,ATA,140,309,0,ST,140,N,0,Up,0\n39,M,ATA,120,200,0,Normal,160,Y,1,Flat,0\n41,M,ASY,120,336,0,Normal,118,Y,3,Flat,1\n55,M,TA,140,295,0,Normal,136,N,0,Flat,1\n48,M,ASY,160,355,0,Normal,99,Y,2,Flat,1\n48,M,ASY,160,193,0,Normal,102,Y,3,Flat,1\n55,M,ATA,145,326,0,Normal,155,N,0,Up,0\n54,M,ASY,200,198,0,Normal,142,Y,2,Flat,1\n55,M,ATA,160,292,1,Normal,143,Y,2,Flat,1\n43,F,ATA,120,266,0,Normal,118,N,0,Up,0\n48,M,ASY,160,268,0,Normal,103,Y,1,Flat,1\n54,M,TA,120,171,0,Normal,137,N,2,Up,0\n54,M,NAP,120,237,0,Normal,150,Y,1.5,Flat,1\n48,M,ASY,122,275,1,ST,150,Y,2,Down,1\n45,M,ASY,130,219,0,ST,130,Y,1,Flat,1\n49,M,ASY,130,341,0,Normal,120,Y,1,Flat,1\n44,M,ASY,135,491,0,Normal,135,N,0,Flat,1\n48,M,ASY,120,260,0,Normal,115,N,2,Flat,1\n61,M,ASY,125,292,0,ST,115,Y,0,Up,0\n62,M,ATA,140,271,0,Normal,152,N,1,Up,0\n55,M,ASY,145,248,0,Normal,96,Y,2,Flat,1\n53,F,NAP,120,274,0,Normal,130,N,0,Up,0\n55,F,ATA,130,394,0,LVH,150,N,0,Up,0\n36,M,NAP,150,160,0,Normal,172,N,0,Up,0\n51,F,NAP,150,200,0,Normal,120,N,0.5,Up,0\n55,F,ATA,122,320,0,Normal,155,N,0,Up,0\n46,M,ATA,140,275,0,Normal,165,Y,0,Up,0\n54,F,ATA,120,221,0,Normal,138,N,1,Up,0\n46,M,ASY,120,231,0,Normal,115,Y,0,Flat,1\n59,M,ASY,130,126,0,Normal,125,N,0,Flat,1\n47,M,NAP,140,193,0,Normal,145,Y,1,Flat,1\n54,M,ATA,160,305,0,Normal,175,N,0,Up,0\n52,M,ASY,130,298,0,Normal,110,Y,1,Flat,1\n34,M,ATA,98,220,0,Normal,150,N,0,Up,0\n54,M,ASY,130,242,0,Normal,91,Y,1,Flat,1\n47,F,NAP,130,235,0,Normal,145,N,2,Flat,0\n45,M,ASY,120,225,0,Normal,140,N,0,Up,0\n32,F,ATA,105,198,0,Normal,165,N,0,Up,0\n55,M,ASY,140,201,0,Normal,130,Y,3,Flat,1\n55,M,NAP,120,220,0,LVH,134,N,0,Up,0\n45,F,ATA,180,295,0,Normal,180,N,0,Up,0\n59,M,NAP,180,213,0,Normal,100,N,0,Up,0\n51,M,NAP,135,160,0,Normal,150,N,2,Flat,1\n52,M,ASY,170,223,0,Normal,126,Y,1.5,Flat,1\n57,F,ASY,180,347,0,ST,126,Y,0.8,Flat,0\n54,F,ATA,130,253,0,ST,155,N,0,Up,0\n60,M,NAP,120,246,0,LVH,135,N,0,Up,0\n49,M,ASY,150,222,0,Normal,122,N,2,Flat,1\n51,F,NAP,130,220,0,Normal,160,Y,2,Up,0\n55,F,ATA,110,344,0,ST,160,N,0,Up,0\n42,M,ASY,140,358,0,Normal,170,N,0,Up,0\n51,F,NAP,110,190,0,Normal,120,N,0,Up,0\n59,M,ASY,140,169,0,Normal,140,N,0,Up,0\n53,M,ATA,120,181,0,Normal,132,N,0,Up,0\n48,F,ATA,133,308,0,ST,156,N,2,Up,0\n36,M,ATA,120,166,0,Normal,180,N,0,Up,0\n48,M,NAP,110,211,0,Normal,138,N,0,Up,0\n47,F,ATA,140,257,0,Normal,135,N,1,Up,0\n53,M,ASY,130,182,0,Normal,148,N,0,Up,0\n65,M,ASY,115,0,0,Normal,93,Y,0,Flat,1\n32,M,TA,95,0,1,Normal,127,N,0.7,Up,1\n61,M,ASY,105,0,1,Normal,110,Y,1.5,Up,1\n50,M,ASY,145,0,1,Normal,139,Y,0.7,Flat,1\n57,M,ASY,110,0,1,ST,131,Y,1.4,Up,1\n51,M,ASY,110,0,1,Normal,92,N,0,Flat,1\n47,M,ASY,110,0,1,ST,149,N,2.1,Up,1\n60,M,ASY,160,0,1,Normal,149,N,0.4,Flat,1\n55,M,ATA,140,0,0,ST,150,N,0.2,Up,0\n53,M,ASY,125,0,1,Normal,120,N,1.5,Up,1\n62,F,ASY,120,0,1,ST,123,Y,1.7,Down,1\n51,M,ASY,95,0,1,Normal,126,N,2.2,Flat,1\n51,F,ASY,120,0,1,Normal,127,Y,1.5,Up,1\n55,M,ASY,115,0,1,Normal,155,N,0.1,Flat,1\n53,M,ATA,130,0,0,ST,120,N,0.7,Down,0\n58,M,ASY,115,0,1,Normal,138,N,0.5,Up,1\n57,M,ASY,95,0,1,Normal,182,N,0.7,Down,1\n65,M,ASY,155,0,0,Normal,154,N,1,Up,0\n60,M,ASY,125,0,1,Normal,110,N,0.1,Up,1\n41,M,ASY,125,0,1,Normal,176,N,1.6,Up,1\n34,M,ASY,115,0,1,Normal,154,N,0.2,Up,1\n53,M,ASY,80,0,0,Normal,141,Y,2,Down,0\n74,M,ATA,145,0,1,ST,123,N,1.3,Up,1\n57,M,NAP,105,0,1,Normal,148,N,0.3,Flat,1\n56,M,ASY,140,0,1,Normal,121,Y,1.8,Up,1\n61,M,ASY,130,0,1,Normal,77,N,2.5,Flat,1\n68,M,ASY,145,0,1,Normal,136,N,1.8,Up,1\n59,M,NAP,125,0,1,Normal,175,N,2.6,Flat,1\n63,M,ASY,100,0,1,Normal,109,N,-0.9,Flat,1\n38,F,ASY,105,0,1,Normal,166,N,2.8,Up,1\n62,M,ASY,115,0,1,Normal,128,Y,2.5,Down,1\n46,M,ASY,100,0,1,ST,133,N,-2.6,Flat,1\n42,M,ASY,105,0,1,Normal,128,Y,-1.5,Down,1\n45,M,NAP,110,0,0,Normal,138,N,-0.1,Up,0\n59,M,ASY,125,0,1,Normal,119,Y,0.9,Up,1\n52,M,ASY,95,0,1,Normal,82,Y,0.8,Flat,1\n60,M,ASY,130,0,1,ST,130,Y,1.1,Down,1\n60,M,NAP,115,0,1,Normal,143,N,2.4,Up,1\n56,M,ASY,115,0,1,ST,82,N,-1,Up,1\n38,M,NAP,100,0,0,Normal,179,N,-1.1,Up,0\n40,M,ASY,95,0,1,ST,144,N,0,Up,1\n51,M,ASY,130,0,1,Normal,170,N,-0.7,Up,1\n62,M,TA,120,0,1,LVH,134,N,-0.8,Flat,1\n72,M,NAP,160,0,0,LVH,114,N,1.6,Flat,0\n63,M,ASY,150,0,1,ST,154,N,3.7,Up,1\n63,M,ASY,140,0,1,LVH,149,N,2,Up,1\n64,F,ASY,95,0,1,Normal,145,N,1.1,Down,1\n43,M,ASY,100,0,1,Normal,122,N,1.5,Down,1\n64,M,ASY,110,0,1,Normal,114,Y,1.3,Down,1\n61,M,ASY,110,0,1,Normal,113,N,1.4,Flat,1\n52,M,ASY,130,0,1,Normal,120,N,0,Flat,1\n51,M,ASY,120,0,1,Normal,104,N,0,Flat,1\n69,M,ASY,135,0,0,Normal,130,N,0,Flat,1\n59,M,ASY,120,0,0,Normal,115,N,0,Flat,1\n48,M,ASY,115,0,1,Normal,128,N,0,Flat,1\n69,M,ASY,137,0,0,ST,104,Y,1.6,Flat,1\n36,M,ASY,110,0,1,Normal,125,Y,1,Flat,1\n53,M,ASY,120,0,1,Normal,120,N,0,Flat,1\n43,M,ASY,140,0,0,ST,140,Y,0.5,Up,1\n56,M,ASY,120,0,0,ST,100,Y,-1,Down,1\n58,M,ASY,130,0,0,ST,100,Y,1,Flat,1\n55,M,ASY,120,0,0,ST,92,N,0.3,Up,1\n67,M,TA,145,0,0,LVH,125,N,0,Flat,1\n46,M,ASY,115,0,0,Normal,113,Y,1.5,Flat,1\n53,M,ATA,120,0,0,Normal,95,N,0,Flat,1\n38,M,NAP,115,0,0,Normal,128,Y,0,Flat,1\n53,M,NAP,105,0,0,Normal,115,N,0,Flat,1\n62,M,NAP,160,0,0,Normal,72,Y,0,Flat,1\n47,M,ASY,160,0,0,Normal,124,Y,0,Flat,1\n56,M,NAP,155,0,0,ST,99,N,0,Flat,1\n56,M,ASY,120,0,0,ST,148,N,0,Flat,1\n56,M,NAP,120,0,0,Normal,97,N,0,Flat,0\n64,F,ASY,200,0,0,Normal,140,Y,1,Flat,1\n61,M,ASY,150,0,0,Normal,117,Y,2,Flat,1\n68,M,ASY,135,0,0,ST,120,Y,0,Up,1\n57,M,ASY,140,0,0,Normal,120,Y,2,Flat,1\n63,M,ASY,150,0,0,Normal,86,Y,2,Flat,1\n60,M,ASY,135,0,0,Normal,63,Y,0.5,Up,1\n66,M,ASY,150,0,0,Normal,108,Y,2,Flat,1\n63,M,ASY,185,0,0,Normal,98,Y,0,Up,1\n59,M,ASY,135,0,0,Normal,115,Y,1,Flat,1\n61,M,ASY,125,0,0,Normal,105,Y,0,Down,1\n73,F,NAP,160,0,0,ST,121,N,0,Up,1\n47,M,NAP,155,0,0,Normal,118,Y,1,Flat,1\n65,M,ASY,160,0,1,ST,122,N,1.2,Flat,1\n70,M,ASY,140,0,1,Normal,157,Y,2,Flat,1\n50,M,ASY,120,0,0,ST,156,Y,0,Up,1\n60,M,ASY,160,0,0,ST,99,Y,0.5,Flat,1\n50,M,ASY,115,0,0,Normal,120,Y,0.5,Flat,1\n43,M,ASY,115,0,0,Normal,145,Y,2,Flat,1\n38,F,ASY,110,0,0,Normal,156,N,0,Flat,1\n54,M,ASY,120,0,0,Normal,155,N,0,Flat,1\n61,M,ASY,150,0,0,Normal,105,Y,0,Flat,1\n42,M,ASY,145,0,0,Normal,99,Y,0,Flat,1\n53,M,ASY,130,0,0,LVH,135,Y,1,Flat,1\n55,M,ASY,140,0,0,Normal,83,N,0,Flat,1\n61,M,ASY,160,0,1,ST,145,N,1,Flat,1\n51,M,ASY,140,0,0,Normal,60,N,0,Flat,1\n70,M,ASY,115,0,0,ST,92,Y,0,Flat,1\n61,M,ASY,130,0,0,LVH,115,N,0,Flat,1\n38,M,ASY,150,0,1,Normal,120,Y,0.7,Flat,1\n57,M,ASY,160,0,1,Normal,98,Y,2,Flat,1\n38,M,ASY,135,0,1,Normal,150,N,0,Flat,1\n62,F,TA,140,0,1,Normal,143,N,0,Flat,1\n58,M,ASY,170,0,1,ST,105,Y,0,Flat,1\n52,M,ASY,165,0,1,Normal,122,Y,1,Up,1\n61,M,NAP,200,0,1,ST,70,N,0,Flat,1\n50,F,ASY,160,0,1,Normal,110,N,0,Flat,1\n51,M,ASY,130,0,1,ST,163,N,0,Flat,1\n65,M,ASY,145,0,1,ST,67,N,0.7,Flat,1\n52,M,ASY,135,0,1,Normal,128,Y,2,Flat,1\n47,M,NAP,110,0,1,Normal,120,Y,0,Flat,1\n35,M,ASY,120,0,1,Normal,130,Y,1.2,Flat,1\n57,M,ASY,140,0,1,Normal,100,Y,0,Flat,1\n62,M,ASY,115,0,1,Normal,72,Y,-0.5,Flat,1\n59,M,ASY,110,0,1,Normal,94,N,0,Flat,1\n53,M,NAP,160,0,1,LVH,122,Y,0,Flat,1\n62,M,ASY,150,0,1,ST,78,N,2,Flat,1\n54,M,ASY,180,0,1,Normal,150,N,1.5,Flat,1\n56,M,ASY,125,0,1,Normal,103,Y,1,Flat,1\n56,M,NAP,125,0,1,Normal,98,N,-2,Flat,1\n54,M,ASY,130,0,1,Normal,110,Y,3,Flat,1\n66,F,ASY,155,0,1,Normal,90,N,0,Flat,1\n63,M,ASY,140,260,0,ST,112,Y,3,Flat,1\n44,M,ASY,130,209,0,ST,127,N,0,Up,0\n60,M,ASY,132,218,0,ST,140,Y,1.5,Down,1\n55,M,ASY,142,228,0,ST,149,Y,2.5,Up,1\n66,M,NAP,110,213,1,LVH,99,Y,1.3,Flat,0\n66,M,NAP,120,0,0,ST,120,N,-0.5,Up,0\n65,M,ASY,150,236,1,ST,105,Y,0,Flat,1\n60,M,NAP,180,0,0,ST,140,Y,1.5,Flat,0\n60,M,NAP,120,0,1,Normal,141,Y,2,Up,1\n60,M,ATA,160,267,1,ST,157,N,0.5,Flat,1\n56,M,ATA,126,166,0,ST,140,N,0,Up,0\n59,M,ASY,140,0,0,ST,117,Y,1,Flat,1\n62,M,ASY,110,0,0,Normal,120,Y,0.5,Flat,1\n63,M,NAP,133,0,0,LVH,120,Y,1,Flat,1\n57,M,ASY,128,0,1,ST,148,Y,1,Flat,1\n62,M,ASY,120,220,0,ST,86,N,0,Up,0\n63,M,ASY,170,177,0,Normal,84,Y,2.5,Down,1\n46,M,ASY,110,236,0,Normal,125,Y,2,Flat,1\n63,M,ASY,126,0,0,ST,120,N,1.5,Down,0\n60,M,ASY,152,0,0,ST,118,Y,0,Up,0\n58,M,ASY,116,0,0,Normal,124,N,1,Up,1\n64,M,ASY,120,0,1,ST,106,N,2,Flat,1\n63,M,NAP,130,0,0,ST,111,Y,0,Flat,1\n74,M,NAP,138,0,0,Normal,116,N,0.2,Up,0\n52,M,NAP,128,0,0,ST,180,N,3,Up,1\n69,M,ASY,130,0,1,ST,129,N,1,Flat,1\n51,M,ASY,128,0,1,ST,125,Y,1.2,Flat,1\n60,M,ASY,130,186,1,ST,140,Y,0.5,Flat,1\n56,M,ASY,120,100,0,Normal,120,Y,1.5,Flat,1\n55,M,NAP,136,228,0,ST,124,Y,1.6,Flat,1\n54,M,ASY,130,0,0,ST,117,Y,1.4,Flat,1\n77,M,ASY,124,171,0,ST,110,Y,2,Up,1\n63,M,ASY,160,230,1,Normal,105,Y,1,Flat,1\n55,M,NAP,0,0,0,Normal,155,N,1.5,Flat,1\n52,M,NAP,122,0,0,Normal,110,Y,2,Down,1\n64,M,ASY,144,0,0,ST,122,Y,1,Flat,1\n60,M,ASY,140,281,0,ST,118,Y,1.5,Flat,1\n60,M,ASY,120,0,0,Normal,133,Y,2,Up,0\n58,M,ASY,136,203,1,Normal,123,Y,1.2,Flat,1\n59,M,ASY,154,0,0,ST,131,Y,1.5,Up,0\n61,M,NAP,120,0,0,Normal,80,Y,0,Flat,1\n40,M,ASY,125,0,1,Normal,165,N,0,Flat,1\n61,M,ASY,134,0,1,ST,86,N,1.5,Flat,1\n41,M,ASY,104,0,0,ST,111,N,0,Up,0\n57,M,ASY,139,277,1,ST,118,Y,1.9,Flat,1\n63,M,ASY,136,0,0,Normal,84,Y,0,Flat,1\n59,M,ASY,122,233,0,Normal,117,Y,1.3,Down,1\n51,M,ASY,128,0,0,Normal,107,N,0,Up,0\n59,M,NAP,131,0,0,Normal,128,Y,2,Down,1\n42,M,NAP,134,240,0,Normal,160,N,0,Up,0\n55,M,NAP,120,0,0,ST,125,Y,2.5,Flat,1\n63,F,ATA,132,0,0,Normal,130,N,0.1,Up,0\n62,M,ASY,152,153,0,ST,97,Y,1.6,Up,1\n56,M,ATA,124,224,1,Normal,161,N,2,Flat,0\n53,M,ASY,126,0,0,Normal,106,N,0,Flat,1\n68,M,ASY,138,0,0,Normal,130,Y,3,Flat,1\n53,M,ASY,154,0,1,ST,140,Y,1.5,Flat,1\n60,M,NAP,141,316,1,ST,122,Y,1.7,Flat,1\n62,M,ATA,131,0,0,Normal,130,N,0.1,Up,0\n59,M,ASY,178,0,1,LVH,120,Y,0,Flat,1\n51,M,ASY,132,218,1,LVH,139,N,0.1,Up,0\n61,M,ASY,110,0,1,Normal,108,Y,2,Down,1\n57,M,ASY,130,311,1,ST,148,Y,2,Flat,1\n56,M,NAP,170,0,0,LVH,123,Y,2.5,Flat,1\n58,M,ATA,126,0,1,Normal,110,Y,2,Flat,1\n69,M,NAP,140,0,1,ST,118,N,2.5,Down,1\n67,M,TA,142,270,1,Normal,125,N,2.5,Up,1\n58,M,ASY,120,0,0,LVH,106,Y,1.5,Down,1\n65,M,ASY,134,0,0,Normal,112,Y,1.1,Flat,1\n63,M,ATA,139,217,1,ST,128,Y,1.2,Flat,1\n55,M,ATA,110,214,1,ST,180,N,0.4,Up,0\n57,M,ASY,140,214,0,ST,144,Y,2,Flat,1\n65,M,TA,140,252,0,Normal,135,N,0.3,Up,0\n54,M,ASY,136,220,0,Normal,140,Y,3,Flat,1\n72,M,NAP,120,214,0,Normal,102,Y,1,Flat,1\n75,M,ASY,170,203,1,ST,108,N,0,Flat,1\n49,M,TA,130,0,0,ST,145,N,3,Flat,1\n51,M,NAP,137,339,0,Normal,127,Y,1.7,Flat,1\n60,M,ASY,142,216,0,Normal,110,Y,2.5,Flat,1\n64,F,ASY,142,276,0,Normal,140,Y,1,Flat,1\n58,M,ASY,132,458,1,Normal,69,N,1,Down,0\n61,M,ASY,146,241,0,Normal,148,Y,3,Down,1\n67,M,ASY,160,384,1,ST,130,Y,0,Flat,1\n62,M,ASY,135,297,0,Normal,130,Y,1,Flat,1\n65,M,ASY,136,248,0,Normal,140,Y,4,Down,1\n63,M,ASY,130,308,0,Normal,138,Y,2,Flat,1\n69,M,ASY,140,208,0,ST,140,Y,2,Flat,1\n51,M,ASY,132,227,1,ST,138,N,0.2,Up,0\n62,M,ASY,158,210,1,Normal,112,Y,3,Down,1\n55,M,NAP,136,245,1,ST,131,Y,1.2,Flat,1\n75,M,ASY,136,225,0,Normal,112,Y,3,Flat,1\n40,M,NAP,106,240,0,Normal,80,Y,0,Up,0\n67,M,ASY,120,0,1,Normal,150,N,1.5,Down,1\n58,M,ASY,110,198,0,Normal,110,N,0,Flat,1\n60,M,ASY,136,195,0,Normal,126,N,0.3,Up,0\n63,M,ASY,160,267,1,ST,88,Y,2,Flat,1\n35,M,NAP,123,161,0,ST,153,N,-0.1,Up,0\n62,M,TA,112,258,0,ST,150,Y,1.3,Flat,1\n43,M,ASY,122,0,0,Normal,120,N,0.5,Up,1\n63,M,NAP,130,0,1,ST,160,N,3,Flat,0\n68,M,NAP,150,195,1,Normal,132,N,0,Flat,1\n65,M,ASY,150,235,0,Normal,120,Y,1.5,Flat,1\n48,M,NAP,102,0,1,ST,110,Y,1,Down,1\n63,M,ASY,96,305,0,ST,121,Y,1,Up,1\n64,M,ASY,130,223,0,ST,128,N,0.5,Flat,0\n61,M,ASY,120,282,0,ST,135,Y,4,Down,1\n50,M,ASY,144,349,0,LVH,120,Y,1,Up,1\n59,M,ASY,124,160,0,Normal,117,Y,1,Flat,1\n55,M,ASY,150,160,0,ST,150,N,0,Up,0\n45,M,NAP,130,236,0,Normal,144,N,0.1,Up,0\n65,M,ASY,144,312,0,LVH,113,Y,1.7,Flat,1\n61,M,ATA,139,283,0,Normal,135,N,0.3,Up,0\n49,M,NAP,131,142,0,Normal,127,Y,1.5,Flat,1\n72,M,ASY,143,211,0,Normal,109,Y,1.4,Flat,1\n50,M,ASY,133,218,0,Normal,128,Y,1.1,Flat,1\n64,M,ASY,143,306,1,ST,115,Y,1.8,Flat,1\n55,M,ASY,116,186,1,ST,102,N,0,Flat,1\n63,M,ASY,110,252,0,ST,140,Y,2,Flat,1\n59,M,ASY,125,222,0,Normal,135,Y,2.5,Down,1\n56,M,ASY,130,0,0,LVH,122,Y,1,Flat,1\n62,M,NAP,133,0,1,ST,119,Y,1.2,Flat,1\n74,M,ASY,150,258,1,ST,130,Y,4,Down,1\n54,M,ASY,130,202,1,Normal,112,Y,2,Flat,1\n57,M,ASY,110,197,0,LVH,100,N,0,Up,0\n62,M,NAP,138,204,0,ST,122,Y,1.2,Flat,1\n76,M,NAP,104,113,0,LVH,120,N,3.5,Down,1\n54,F,ASY,138,274,0,Normal,105,Y,1.5,Flat,1\n70,M,ASY,170,192,0,ST,129,Y,3,Down,1\n61,F,ATA,140,298,1,Normal,120,Y,0,Up,0\n48,M,ASY,132,272,0,ST,139,N,0.2,Up,0\n48,M,NAP,132,220,1,ST,162,N,0,Flat,1\n61,M,TA,142,200,1,ST,100,N,1.5,Down,1\n66,M,ASY,112,261,0,Normal,140,N,1.5,Up,1\n68,M,TA,139,181,1,ST,135,N,0.2,Up,0\n55,M,ASY,172,260,0,Normal,73,N,2,Flat,1\n62,M,NAP,120,220,0,LVH,86,N,0,Up,0\n71,M,NAP,144,221,0,Normal,108,Y,1.8,Flat,1\n74,M,TA,145,216,1,Normal,116,Y,1.8,Flat,1\n53,M,NAP,155,175,1,ST,160,N,0.3,Up,0\n58,M,NAP,150,219,0,ST,118,Y,0,Flat,1\n75,M,ASY,160,310,1,Normal,112,Y,2,Down,0\n56,M,NAP,137,208,1,ST,122,Y,1.8,Flat,1\n58,M,NAP,137,232,0,ST,124,Y,1.4,Flat,1\n64,M,ASY,134,273,0,Normal,102,Y,4,Down,1\n54,M,NAP,133,203,0,ST,137,N,0.2,Up,0\n54,M,ATA,132,182,0,ST,141,N,0.1,Up,0\n59,M,ASY,140,274,0,Normal,154,Y,2,Flat,0\n55,M,ASY,135,204,1,ST,126,Y,1.1,Flat,1\n57,M,ASY,144,270,1,ST,160,Y,2,Flat,1\n61,M,ASY,141,292,0,ST,115,Y,1.7,Flat,1\n41,M,ASY,150,171,0,Normal,128,Y,1.5,Flat,0\n71,M,ASY,130,221,0,ST,115,Y,0,Flat,1\n38,M,ASY,110,289,0,Normal,105,Y,1.5,Down,1\n55,M,ASY,158,217,0,Normal,110,Y,2.5,Flat,1\n56,M,ASY,128,223,0,ST,119,Y,2,Down,1\n69,M,ASY,140,110,1,Normal,109,Y,1.5,Flat,1\n64,M,ASY,150,193,0,ST,135,Y,0.5,Flat,1\n72,M,ASY,160,123,1,LVH,130,N,1.5,Flat,1\n69,M,ASY,142,210,1,ST,112,Y,1.5,Flat,1\n56,M,ASY,137,282,1,Normal,126,Y,1.2,Flat,1\n62,M,ASY,139,170,0,ST,120,Y,3,Flat,1\n67,M,ASY,146,369,0,Normal,110,Y,1.9,Flat,1\n57,M,ASY,156,173,0,LVH,119,Y,3,Down,1\n69,M,ASY,145,289,1,ST,110,Y,1.8,Flat,1\n51,M,ASY,131,152,1,LVH,130,Y,1,Flat,1\n48,M,ASY,140,208,0,Normal,159,Y,1.5,Up,1\n69,M,ASY,122,216,1,LVH,84,Y,0,Flat,1\n69,M,NAP,142,271,0,LVH,126,N,0.3,Up,0\n64,M,ASY,141,244,1,ST,116,Y,1.5,Flat,1\n57,M,ATA,180,285,1,ST,120,N,0.8,Flat,1\n53,M,ASY,124,243,0,Normal,122,Y,2,Flat,1\n37,M,NAP,118,240,0,LVH,165,N,1,Flat,0\n67,M,ASY,140,219,0,ST,122,Y,2,Flat,1\n74,M,NAP,140,237,1,Normal,94,N,0,Flat,1\n63,M,ATA,136,165,0,ST,133,N,0.2,Up,0\n58,M,ASY,100,213,0,ST,110,N,0,Up,0\n61,M,ASY,190,287,1,LVH,150,Y,2,Down,1\n64,M,ASY,130,258,1,LVH,130,N,0,Flat,1\n58,M,ASY,160,256,1,LVH,113,Y,1,Up,1\n60,M,ASY,130,186,1,LVH,140,Y,0.5,Flat,1\n57,M,ASY,122,264,0,LVH,100,N,0,Flat,1\n55,M,NAP,133,185,0,ST,136,N,0.2,Up,0\n55,M,ASY,120,226,0,LVH,127,Y,1.7,Down,1\n56,M,ASY,130,203,1,Normal,98,N,1.5,Flat,1\n57,M,ASY,130,207,0,ST,96,Y,1,Flat,0\n61,M,NAP,140,284,0,Normal,123,Y,1.3,Flat,1\n61,M,NAP,120,337,0,Normal,98,Y,0,Flat,1\n74,M,ASY,155,310,0,Normal,112,Y,1.5,Down,1\n68,M,NAP,134,254,1,Normal,151,Y,0,Up,0\n51,F,ASY,114,258,1,LVH,96,N,1,Up,0\n62,M,ASY,160,254,1,ST,108,Y,3,Flat,1\n53,M,ASY,144,300,1,ST,128,Y,1.5,Flat,1\n62,M,ASY,158,170,0,ST,138,Y,0,Flat,1\n46,M,ASY,134,310,0,Normal,126,N,0,Flat,1\n54,F,ASY,127,333,1,ST,154,N,0,Flat,1\n62,M,TA,135,139,0,ST,137,N,0.2,Up,0\n55,M,ASY,122,223,1,ST,100,N,0,Flat,1\n58,M,ASY,140,385,1,LVH,135,N,0.3,Up,0\n62,M,ATA,120,254,0,LVH,93,Y,0,Flat,1\n70,M,ASY,130,322,0,LVH,109,N,2.4,Flat,1\n67,F,NAP,115,564,0,LVH,160,N,1.6,Flat,0\n57,M,ATA,124,261,0,Normal,141,N,0.3,Up,1\n64,M,ASY,128,263,0,Normal,105,Y,0.2,Flat,0\n74,F,ATA,120,269,0,LVH,121,Y,0.2,Up,0\n65,M,ASY,120,177,0,Normal,140,N,0.4,Up,0\n56,M,NAP,130,256,1,LVH,142,Y,0.6,Flat,1\n59,M,ASY,110,239,0,LVH,142,Y,1.2,Flat,1\n60,M,ASY,140,293,0,LVH,170,N,1.2,Flat,1\n63,F,ASY,150,407,0,LVH,154,N,4,Flat,1\n59,M,ASY,135,234,0,Normal,161,N,0.5,Flat,0\n53,M,ASY,142,226,0,LVH,111,Y,0,Up,0\n44,M,NAP,140,235,0,LVH,180,N,0,Up,0\n61,M,TA,134,234,0,Normal,145,N,2.6,Flat,1\n57,F,ASY,128,303,0,LVH,159,N,0,Up,0\n71,F,ASY,112,149,0,Normal,125,N,1.6,Flat,0\n46,M,ASY,140,311,0,Normal,120,Y,1.8,Flat,1\n53,M,ASY,140,203,1,LVH,155,Y,3.1,Down,1\n64,M,TA,110,211,0,LVH,144,Y,1.8,Flat,0\n40,M,TA,140,199,0,Normal,178,Y,1.4,Up,0\n67,M,ASY,120,229,0,LVH,129,Y,2.6,Flat,1\n48,M,ATA,130,245,0,LVH,180,N,0.2,Flat,0\n43,M,ASY,115,303,0,Normal,181,N,1.2,Flat,0\n47,M,ASY,112,204,0,Normal,143,N,0.1,Up,0\n54,F,ATA,132,288,1,LVH,159,Y,0,Up,0\n48,F,NAP,130,275,0,Normal,139,N,0.2,Up,0\n46,F,ASY,138,243,0,LVH,152,Y,0,Flat,0\n51,F,NAP,120,295,0,LVH,157,N,0.6,Up,0\n58,M,NAP,112,230,0,LVH,165,N,2.5,Flat,1\n71,F,NAP,110,265,1,LVH,130,N,0,Up,0\n57,M,NAP,128,229,0,LVH,150,N,0.4,Flat,1\n66,M,ASY,160,228,0,LVH,138,N,2.3,Up,0\n37,F,NAP,120,215,0,Normal,170,N,0,Up,0\n59,M,ASY,170,326,0,LVH,140,Y,3.4,Down,1\n50,M,ASY,144,200,0,LVH,126,Y,0.9,Flat,1\n48,M,ASY,130,256,1,LVH,150,Y,0,Up,1\n61,M,ASY,140,207,0,LVH,138,Y,1.9,Up,1\n59,M,TA,160,273,0,LVH,125,N,0,Up,1\n42,M,NAP,130,180,0,Normal,150,N,0,Up,0\n48,M,ASY,122,222,0,LVH,186,N,0,Up,0\n40,M,ASY,152,223,0,Normal,181,N,0,Up,1\n62,F,ASY,124,209,0,Normal,163,N,0,Up,0\n44,M,NAP,130,233,0,Normal,179,Y,0.4,Up,0\n46,M,ATA,101,197,1,Normal,156,N,0,Up,0\n59,M,NAP,126,218,1,Normal,134,N,2.2,Flat,1\n58,M,NAP,140,211,1,LVH,165,N,0,Up,0\n49,M,NAP,118,149,0,LVH,126,N,0.8,Up,1\n44,M,ASY,110,197,0,LVH,177,N,0,Up,1\n66,M,ATA,160,246,0,Normal,120,Y,0,Flat,1\n65,F,ASY,150,225,0,LVH,114,N,1,Flat,1\n42,M,ASY,136,315,0,Normal,125,Y,1.8,Flat,1\n52,M,ATA,128,205,1,Normal,184,N,0,Up,0\n65,F,NAP,140,417,1,LVH,157,N,0.8,Up,0\n63,F,ATA,140,195,0,Normal,179,N,0,Up,0\n45,F,ATA,130,234,0,LVH,175,N,0.6,Flat,0\n41,F,ATA,105,198,0,Normal,168,N,0,Up,0\n61,M,ASY,138,166,0,LVH,125,Y,3.6,Flat,1\n60,F,NAP,120,178,1,Normal,96,N,0,Up,0\n59,F,ASY,174,249,0,Normal,143,Y,0,Flat,1\n62,M,ATA,120,281,0,LVH,103,N,1.4,Flat,1\n57,M,NAP,150,126,1,Normal,173,N,0.2,Up,0\n51,F,ASY,130,305,0,Normal,142,Y,1.2,Flat,1\n44,M,NAP,120,226,0,Normal,169,N,0,Up,0\n60,F,TA,150,240,0,Normal,171,N,0.9,Up,0\n63,M,TA,145,233,1,LVH,150,N,2.3,Down,0\n57,M,ASY,150,276,0,LVH,112,Y,0.6,Flat,1\n51,M,ASY,140,261,0,LVH,186,Y,0,Up,0\n58,F,ATA,136,319,1,LVH,152,N,0,Up,1\n44,F,NAP,118,242,0,Normal,149,N,0.3,Flat,0\n47,M,NAP,108,243,0,Normal,152,N,0,Up,1\n61,M,ASY,120,260,0,Normal,140,Y,3.6,Flat,1\n57,F,ASY,120,354,0,Normal,163,Y,0.6,Up,0\n70,M,ATA,156,245,0,LVH,143,N,0,Up,0\n76,F,NAP,140,197,0,ST,116,N,1.1,Flat,0\n67,F,ASY,106,223,0,Normal,142,N,0.3,Up,0\n45,M,ASY,142,309,0,LVH,147,Y,0,Flat,1\n45,M,ASY,104,208,0,LVH,148,Y,3,Flat,0\n39,F,NAP,94,199,0,Normal,179,N,0,Up,0\n42,F,NAP,120,209,0,Normal,173,N,0,Flat,0\n56,M,ATA,120,236,0,Normal,178,N,0.8,Up,0\n58,M,ASY,146,218,0,Normal,105,N,2,Flat,1\n35,M,ASY,120,198,0,Normal,130,Y,1.6,Flat,1\n58,M,ASY,150,270,0,LVH,111,Y,0.8,Up,1\n41,M,NAP,130,214,0,LVH,168,N,2,Flat,0\n57,M,ASY,110,201,0,Normal,126,Y,1.5,Flat,0\n42,M,TA,148,244,0,LVH,178,N,0.8,Up,0\n62,M,ATA,128,208,1,LVH,140,N,0,Up,0\n59,M,TA,178,270,0,LVH,145,N,4.2,Down,0\n41,F,ATA,126,306,0,Normal,163,N,0,Up,0\n50,M,ASY,150,243,0,LVH,128,N,2.6,Flat,1\n59,M,ATA,140,221,0,Normal,164,Y,0,Up,0\n61,F,ASY,130,330,0,LVH,169,N,0,Up,1\n54,M,ASY,124,266,0,LVH,109,Y,2.2,Flat,1\n54,M,ASY,110,206,0,LVH,108,Y,0,Flat,1\n52,M,ASY,125,212,0,Normal,168,N,1,Up,1\n47,M,ASY,110,275,0,LVH,118,Y,1,Flat,1\n66,M,ASY,120,302,0,LVH,151,N,0.4,Flat,0\n58,M,ASY,100,234,0,Normal,156,N,0.1,Up,1\n64,F,NAP,140,313,0,Normal,133,N,0.2,Up,0\n50,F,ATA,120,244,0,Normal,162,N,1.1,Up,0\n44,F,NAP,108,141,0,Normal,175,N,0.6,Flat,0\n67,M,ASY,120,237,0,Normal,71,N,1,Flat,1\n49,F,ASY,130,269,0,Normal,163,N,0,Up,0\n57,M,ASY,165,289,1,LVH,124,N,1,Flat,1\n63,M,ASY,130,254,0,LVH,147,N,1.4,Flat,1\n48,M,ASY,124,274,0,LVH,166,N,0.5,Flat,1\n51,M,NAP,100,222,0,Normal,143,Y,1.2,Flat,0\n60,F,ASY,150,258,0,LVH,157,N,2.6,Flat,1\n59,M,ASY,140,177,0,Normal,162,Y,0,Up,1\n45,F,ATA,112,160,0,Normal,138,N,0,Flat,0\n55,F,ASY,180,327,0,ST,117,Y,3.4,Flat,1\n41,M,ATA,110,235,0,Normal,153,N,0,Up,0\n60,F,ASY,158,305,0,LVH,161,N,0,Up,1\n54,F,NAP,135,304,1,Normal,170,N,0,Up,0\n42,M,ATA,120,295,0,Normal,162,N,0,Up,0\n49,F,ATA,134,271,0,Normal,162,N,0,Flat,0\n46,M,ASY,120,249,0,LVH,144,N,0.8,Up,1\n56,F,ASY,200,288,1,LVH,133,Y,4,Down,1\n66,F,TA,150,226,0,Normal,114,N,2.6,Down,0\n56,M,ASY,130,283,1,LVH,103,Y,1.6,Down,1\n49,M,NAP,120,188,0,Normal,139,N,2,Flat,1\n54,M,ASY,122,286,0,LVH,116,Y,3.2,Flat,1\n57,M,ASY,152,274,0,Normal,88,Y,1.2,Flat,1\n65,F,NAP,160,360,0,LVH,151,N,0.8,Up,0\n54,M,NAP,125,273,0,LVH,152,N,0.5,Down,0\n54,F,NAP,160,201,0,Normal,163,N,0,Up,0\n62,M,ASY,120,267,0,Normal,99,Y,1.8,Flat,1\n52,F,NAP,136,196,0,LVH,169,N,0.1,Flat,0\n52,M,ATA,134,201,0,Normal,158,N,0.8,Up,0\n60,M,ASY,117,230,1,Normal,160,Y,1.4,Up,1\n63,F,ASY,108,269,0,Normal,169,Y,1.8,Flat,1\n66,M,ASY,112,212,0,LVH,132,Y,0.1,Up,1\n42,M,ASY,140,226,0,Normal,178,N,0,Up,0\n64,M,ASY,120,246,0,LVH,96,Y,2.2,Down,1\n54,M,NAP,150,232,0,LVH,165,N,1.6,Up,0\n46,F,NAP,142,177,0,LVH,160,Y,1.4,Down,0\n67,F,NAP,152,277,0,Normal,172,N,0,Up,0\n56,M,ASY,125,249,1,LVH,144,Y,1.2,Flat,1\n34,F,ATA,118,210,0,Normal,192,N,0.7,Up,0\n57,M,ASY,132,207,0,Normal,168,Y,0,Up,0\n64,M,ASY,145,212,0,LVH,132,N,2,Flat,1\n59,M,ASY,138,271,0,LVH,182,N,0,Up,0\n50,M,NAP,140,233,0,Normal,163,N,0.6,Flat,1\n51,M,TA,125,213,0,LVH,125,Y,1.4,Up,0\n54,M,ATA,192,283,0,LVH,195,N,0,Up,1\n53,M,ASY,123,282,0,Normal,95,Y,2,Flat,1\n52,M,ASY,112,230,0,Normal,160,N,0,Up,1\n40,M,ASY,110,167,0,LVH,114,Y,2,Flat,1\n58,M,NAP,132,224,0,LVH,173,N,3.2,Up,1\n41,F,NAP,112,268,0,LVH,172,Y,0,Up,0\n41,M,NAP,112,250,0,Normal,179,N,0,Up,0\n50,F,NAP,120,219,0,Normal,158,N,1.6,Flat,0\n54,F,NAP,108,267,0,LVH,167,N,0,Up,0\n64,F,ASY,130,303,0,Normal,122,N,2,Flat,0\n51,F,NAP,130,256,0,LVH,149,N,0.5,Up,0\n46,F,ATA,105,204,0,Normal,172,N,0,Up,0\n55,M,ASY,140,217,0,Normal,111,Y,5.6,Down,1\n45,M,ATA,128,308,0,LVH,170,N,0,Up,0\n56,M,TA,120,193,0,LVH,162,N,1.9,Flat,0\n66,F,ASY,178,228,1,Normal,165,Y,1,Flat,1\n38,M,TA,120,231,0,Normal,182,Y,3.8,Flat,1\n62,F,ASY,150,244,0,Normal,154,Y,1.4,Flat,1\n55,M,ATA,130,262,0,Normal,155,N,0,Up,0\n58,M,ASY,128,259,0,LVH,130,Y,3,Flat,1\n43,M,ASY,110,211,0,Normal,161,N,0,Up,0\n64,F,ASY,180,325,0,Normal,154,Y,0,Up,0\n50,F,ASY,110,254,0,LVH,159,N,0,Up,0\n53,M,NAP,130,197,1,LVH,152,N,1.2,Down,0\n45,F,ASY,138,236,0,LVH,152,Y,0.2,Flat,0\n65,M,TA,138,282,1,LVH,174,N,1.4,Flat,1\n69,M,TA,160,234,1,LVH,131,N,0.1,Flat,0\n69,M,NAP,140,254,0,LVH,146,N,2,Flat,1\n67,M,ASY,100,299,0,LVH,125,Y,0.9,Flat,1\n68,F,NAP,120,211,0,LVH,115,N,1.5,Flat,0\n34,M,TA,118,182,0,LVH,174,N,0,Up,0\n62,F,ASY,138,294,1,Normal,106,N,1.9,Flat,1\n51,M,ASY,140,298,0,Normal,122,Y,4.2,Flat,1\n46,M,NAP,150,231,0,Normal,147,N,3.6,Flat,1\n67,M,ASY,125,254,1,Normal,163,N,0.2,Flat,1\n50,M,NAP,129,196,0,Normal,163,N,0,Up,0\n42,M,NAP,120,240,1,Normal,194,N,0.8,Down,0\n56,F,ASY,134,409,0,LVH,150,Y,1.9,Flat,1\n41,M,ASY,110,172,0,LVH,158,N,0,Up,1\n42,F,ASY,102,265,0,LVH,122,N,0.6,Flat,0\n53,M,NAP,130,246,1,LVH,173,N,0,Up,0\n43,M,NAP,130,315,0,Normal,162,N,1.9,Up,0\n56,M,ASY,132,184,0,LVH,105,Y,2.1,Flat,1\n52,M,ASY,108,233,1,Normal,147,N,0.1,Up,0\n62,F,ASY,140,394,0,LVH,157,N,1.2,Flat,0\n70,M,NAP,160,269,0,Normal,112,Y,2.9,Flat,1\n54,M,ASY,140,239,0,Normal,160,N,1.2,Up,0\n70,M,ASY,145,174,0,Normal,125,Y,2.6,Down,1\n54,M,ATA,108,309,0,Normal,156,N,0,Up,0\n35,M,ASY,126,282,0,LVH,156,Y,0,Up,1\n48,M,NAP,124,255,1,Normal,175,N,0,Up,0\n55,F,ATA,135,250,0,LVH,161,N,1.4,Flat,0\n58,F,ASY,100,248,0,LVH,122,N,1,Flat,0\n54,F,NAP,110,214,0,Normal,158,N,1.6,Flat,0\n69,F,TA,140,239,0,Normal,151,N,1.8,Up,0\n77,M,ASY,125,304,0,LVH,162,Y,0,Up,1\n68,M,NAP,118,277,0,Normal,151,N,1,Up,0\n58,M,ASY,125,300,0,LVH,171,N,0,Up,1\n60,M,ASY,125,258,0,LVH,141,Y,2.8,Flat,1\n51,M,ASY,140,299,0,Normal,173,Y,1.6,Up,1\n55,M,ASY,160,289,0,LVH,145,Y,0.8,Flat,1\n52,M,TA,152,298,1,Normal,178,N,1.2,Flat,0\n60,F,NAP,102,318,0,Normal,160,N,0,Up,0\n58,M,NAP,105,240,0,LVH,154,Y,0.6,Flat,0\n64,M,NAP,125,309,0,Normal,131,Y,1.8,Flat,1\n37,M,NAP,130,250,0,Normal,187,N,3.5,Down,0\n59,M,TA,170,288,0,LVH,159,N,0.2,Flat,1\n51,M,NAP,125,245,1,LVH,166,N,2.4,Flat,0\n43,F,NAP,122,213,0,Normal,165,N,0.2,Flat,0\n58,M,ASY,128,216,0,LVH,131,Y,2.2,Flat,1\n29,M,ATA,130,204,0,LVH,202,N,0,Up,0\n41,F,ATA,130,204,0,LVH,172,N,1.4,Up,0\n63,F,NAP,135,252,0,LVH,172,N,0,Up,0\n51,M,NAP,94,227,0,Normal,154,Y,0,Up,0\n54,M,NAP,120,258,0,LVH,147,N,0.4,Flat,0\n44,M,ATA,120,220,0,Normal,170,N,0,Up,0\n54,M,ASY,110,239,0,Normal,126,Y,2.8,Flat,1\n65,M,ASY,135,254,0,LVH,127,N,2.8,Flat,1\n57,M,NAP,150,168,0,Normal,174,N,1.6,Up,0\n63,M,ASY,130,330,1,LVH,132,Y,1.8,Up,1\n35,F,ASY,138,183,0,Normal,182,N,1.4,Up,0\n41,M,ATA,135,203,0,Normal,132,N,0,Flat,0\n62,F,NAP,130,263,0,Normal,97,N,1.2,Flat,1\n43,F,ASY,132,341,1,LVH,136,Y,3,Flat,1\n58,F,TA,150,283,1,LVH,162,N,1,Up,0\n52,M,TA,118,186,0,LVH,190,N,0,Flat,0\n61,F,ASY,145,307,0,LVH,146,Y,1,Flat,1\n39,M,ASY,118,219,0,Normal,140,N,1.2,Flat,1\n45,M,ASY,115,260,0,LVH,185,N,0,Up,0\n52,M,ASY,128,255,0,Normal,161,Y,0,Up,1\n62,M,NAP,130,231,0,Normal,146,N,1.8,Flat,0\n62,F,ASY,160,164,0,LVH,145,N,6.2,Down,1\n53,F,ASY,138,234,0,LVH,160,N,0,Up,0\n43,M,ASY,120,177,0,LVH,120,Y,2.5,Flat,1\n47,M,NAP,138,257,0,LVH,156,N,0,Up,0\n52,M,ATA,120,325,0,Normal,172,N,0.2,Up,0\n68,M,NAP,180,274,1,LVH,150,Y,1.6,Flat,1\n39,M,NAP,140,321,0,LVH,182,N,0,Up,0\n53,F,ASY,130,264,0,LVH,143,N,0.4,Flat,0\n62,F,ASY,140,268,0,LVH,160,N,3.6,Down,1\n51,F,NAP,140,308,0,LVH,142,N,1.5,Up,0\n60,M,ASY,130,253,0,Normal,144,Y,1.4,Up,1\n65,M,ASY,110,248,0,LVH,158,N,0.6,Up,1\n65,F,NAP,155,269,0,Normal,148,N,0.8,Up,0\n60,M,NAP,140,185,0,LVH,155,N,3,Flat,1\n60,M,ASY,145,282,0,LVH,142,Y,2.8,Flat,1\n54,M,ASY,120,188,0,Normal,113,N,1.4,Flat,1\n44,M,ATA,130,219,0,LVH,188,N,0,Up,0\n44,M,ASY,112,290,0,LVH,153,N,0,Up,1\n51,M,NAP,110,175,0,Normal,123,N,0.6,Up,0\n59,M,NAP,150,212,1,Normal,157,N,1.6,Up,0\n71,F,ATA,160,302,0,Normal,162,N,0.4,Up,0\n61,M,NAP,150,243,1,Normal,137,Y,1,Flat,0\n55,M,ASY,132,353,0,Normal,132,Y,1.2,Flat,1\n64,M,NAP,140,335,0,Normal,158,N,0,Up,1\n43,M,ASY,150,247,0,Normal,171,N,1.5,Up,0\n58,F,NAP,120,340,0,Normal,172,N,0,Up,0\n60,M,ASY,130,206,0,LVH,132,Y,2.4,Flat,1\n58,M,ATA,120,284,0,LVH,160,N,1.8,Flat,1\n49,M,ATA,130,266,0,Normal,171,N,0.6,Up,0\n48,M,ATA,110,229,0,Normal,168,N,1,Down,1\n52,M,NAP,172,199,1,Normal,162,N,0.5,Up,0\n44,M,ATA,120,263,0,Normal,173,N,0,Up,0\n56,F,ATA,140,294,0,LVH,153,N,1.3,Flat,0\n57,M,ASY,140,192,0,Normal,148,N,0.4,Flat,0\n67,M,ASY,160,286,0,LVH,108,Y,1.5,Flat,1\n53,F,NAP,128,216,0,LVH,115,N,0,Up,0\n52,M,NAP,138,223,0,Normal,169,N,0,Up,0\n43,M,ASY,132,247,1,LVH,143,Y,0.1,Flat,1\n52,M,ASY,128,204,1,Normal,156,Y,1,Flat,1\n59,M,TA,134,204,0,Normal,162,N,0.8,Up,1\n64,M,TA,170,227,0,LVH,155,N,0.6,Flat,0\n66,F,NAP,146,278,0,LVH,152,N,0,Flat,0\n39,F,NAP,138,220,0,Normal,152,N,0,Flat,0\n57,M,ATA,154,232,0,LVH,164,N,0,Up,1\n58,F,ASY,130,197,0,Normal,131,N,0.6,Flat,0\n57,M,ASY,110,335,0,Normal,143,Y,3,Flat,1\n47,M,NAP,130,253,0,Normal,179,N,0,Up,0\n55,F,ASY,128,205,0,ST,130,Y,2,Flat,1\n35,M,ATA,122,192,0,Normal,174,N,0,Up,0\n61,M,ASY,148,203,0,Normal,161,N,0,Up,1\n58,M,ASY,114,318,0,ST,140,N,4.4,Down,1\n58,F,ASY,170,225,1,LVH,146,Y,2.8,Flat,1\n58,M,ATA,125,220,0,Normal,144,N,0.4,Flat,0\n56,M,ATA,130,221,0,LVH,163,N,0,Up,0\n56,M,ATA,120,240,0,Normal,169,N,0,Down,0\n67,M,NAP,152,212,0,LVH,150,N,0.8,Flat,1\n55,F,ATA,132,342,0,Normal,166,N,1.2,Up,0\n44,M,ASY,120,169,0,Normal,144,Y,2.8,Down,1\n63,M,ASY,140,187,0,LVH,144,Y,4,Up,1\n63,F,ASY,124,197,0,Normal,136,Y,0,Flat,1\n41,M,ATA,120,157,0,Normal,182,N,0,Up,0\n59,M,ASY,164,176,1,LVH,90,N,1,Flat,1\n57,F,ASY,140,241,0,Normal,123,Y,0.2,Flat,1\n45,M,TA,110,264,0,Normal,132,N,1.2,Flat,1\n68,M,ASY,144,193,1,Normal,141,N,3.4,Flat,1\n57,M,ASY,130,131,0,Normal,115,Y,1.2,Flat,1\n57,F,ATA,130,236,0,LVH,174,N,0,Flat,1\n38,M,NAP,138,175,0,Normal,173,N,0,Up,0\n"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/optional labs/utils.py",
    "content": "from PIL import Image\nimport networkx as nx\nimport matplotlib.pyplot as plt\nfrom networkx.drawing.nx_pydot import graphviz_layout\nimport numpy as np\nfrom matplotlib.widgets import Slider, Button\nplt.style.use('./deeplearning.mplstyle')\n\ndef compute_entropy(y):\n\n    entropy = 0\n    \n    if len(y) == 0:\n        return 0\n    entropy = sum(y[y==1])/len(y)\n    if entropy == 0 or entropy == 1:\n        return 0\n    else:\n        return -entropy*np.log2(entropy) - (1-entropy)*np.log2(1-entropy)\n     \n\ndef split_dataset(X, node_indices, feature):\n\n    left_indices = []\n    right_indices = []\n\n    for i in node_indices:\n        if X[i][feature] == 1:\n            left_indices.append(i)\n        else:\n            right_indices.append(i)\n        \n    return left_indices, right_indices   \n    \n    \n\ndef compute_information_gain(X, y, node_indices, feature):\n    \n    left_indices, right_indices = split_dataset(X, node_indices, feature)\n    \n    X_node, y_node = X[node_indices], y[node_indices]\n    X_left, y_left = X[left_indices], y[left_indices]\n    X_right, y_right = X[right_indices], y[right_indices]\n    \n    information_gain = 0\n    \n    node_entropy = compute_entropy(y_node)\n    left_entropy = compute_entropy(y_left)\n    right_entropy = compute_entropy(y_right)\n    w_left = len(X_left) / len(X_node)\n    w_right = len(X_right) / len(X_node)\n    weighted_entropy = w_left * left_entropy + w_right * right_entropy\n    information_gain = node_entropy - weighted_entropy\n    \n    return information_gain\n\ndef get_best_split(X, y, node_indices):   \n    num_features = X.shape[1]\n    \n    best_feature = -1\n\n    max_info_gain = 0\n    for feature in range(num_features):\n        info_gain = compute_information_gain(X, y, node_indices, feature)\n        if info_gain > max_info_gain:\n            max_info_gain = info_gain\n            best_feature = feature\n        \n   \n    return best_feature\n\n\ndef build_tree_recursive(X, y, node_indices, branch_name, max_depth, current_depth, tree):\n\n    if current_depth == max_depth:\n        formatting = \" \"*current_depth + \"-\"*current_depth\n        print(formatting, \"%s leaf node with indices\" % branch_name, node_indices)\n        return\n   \n\n    best_feature = get_best_split(X, y, node_indices) \n    \n    formatting = \"-\"*current_depth\n    print(\"%s Depth %d, %s: Split on feature: %d\" % (formatting, current_depth, branch_name, best_feature))\n    \n\n    left_indices, right_indices = split_dataset(X, node_indices, best_feature)\n    tree.append((left_indices, right_indices, best_feature))\n    \n    build_tree_recursive(X, y, left_indices, \"Left\", max_depth, current_depth+1, tree)\n    build_tree_recursive(X, y, right_indices, \"Right\", max_depth, current_depth+1, tree)\n    return tree\n\ndef generate_node_image(node_indices):\n    image_paths = [\"images/%d.png\" % idx for idx in node_indices]\n    images = [Image.open(x) for x in image_paths]\n    widths, heights = zip(*(i.size for i in images))\n\n    total_width = sum(widths)\n    max_height = max(heights)\n\n    new_im = Image.new('RGB', (total_width, max_height))\n\n    x_offset = 0\n    for im in images:\n        new_im.paste(im, (x_offset,0))\n        x_offset += im.size[0]\n    \n    new_im = new_im.resize((int(total_width*len(node_indices)/10), int(max_height*len(node_indices)/10)))\n    \n    return new_im\n\n\ndef generate_split_viz(node_indices, left_indices, right_indices, feature):\n    \n    G=nx.DiGraph()\n    \n    indices_list = [node_indices, left_indices, right_indices]\n    for idx, indices in enumerate(indices_list):\n        G.add_node(idx,image= generate_node_image(indices))\n\n    G.add_edge(0,1)\n    G.add_edge(0,2)\n\n    pos = graphviz_layout(G, prog=\"dot\")\n\n    fig=plt.figure()\n    ax=plt.subplot(111)\n    ax.set_aspect('equal')\n    nx.draw_networkx_edges(G,pos,ax=ax, arrows=True, arrowsize=40)\n    \n    trans=ax.transData.transform\n    trans2=fig.transFigure.inverted().transform\n\n    feature_name = [\"Ear Shape\", \"Face Shape\", \"Whiskers\"][feature]\n    ax_name = [\"Splitting on %s\" % feature_name , \"Left: %s = 1\" % feature_name, \"Right: %s = 0\" % feature_name]\n    for idx, n in enumerate(G):\n        xx,yy=trans(pos[n]) # figure coordinates\n        xa,ya=trans2((xx,yy)) # axes coordinates\n        piesize = len(indices_list[idx])/9\n        p2=piesize/2.0\n        a = plt.axes([xa-p2,ya-p2, piesize, piesize])\n        a.set_aspect('equal')\n        a.imshow(G.nodes[n]['image'])\n        a.axis('off')\n        a.set_title(ax_name[idx])\n    ax.axis('off')\n    plt.show()\n    \n    \ndef generate_tree_viz(root_indices, y, tree):\n    \n    G=nx.DiGraph()\n    \n    \n    G.add_node(0,image= generate_node_image(root_indices))\n    idx = 1\n    root = 0\n    \n    num_images = [len(root_indices)]\n    \n    feature_name = [\"Ear Shape\", \"Face Shape\", \"Whiskers\"]\n    y_name = [\"Non Cat\",\"Cat\"]\n    \n    decision_names = []\n    leaf_names = []\n    \n    for i, level in enumerate(tree):\n        indices_list = level[:2]\n        for indices in indices_list:\n            G.add_node(idx,image= generate_node_image(indices))\n            G.add_edge(root, idx)\n            \n            # For visualization\n            num_images.append(len(indices))\n            idx += 1\n            if i > 0:\n                leaf_names.append(\"Leaf node: %s\" % y_name[max(y[indices])])\n            \n        decision_names.append(\"Split on: %s\" % feature_name[level[2]])\n        root += 1\n    \n    \n    node_names = decision_names + leaf_names\n    pos = graphviz_layout(G, prog=\"dot\")\n\n    fig=plt.figure(figsize=(14, 10))\n    ax=plt.subplot(111)\n    ax.set_aspect('equal')\n    nx.draw_networkx_edges(G,pos,ax=ax, arrows=True, arrowsize=40)\n    \n    trans=ax.transData.transform\n    trans2=fig.transFigure.inverted().transform\n\n    for idx, n in enumerate(G):\n        xx,yy=trans(pos[n]) # figure coordinates\n        xa,ya=trans2((xx,yy)) # axes coordinates\n        piesize = num_images[idx]/25\n        p2=piesize/2.0\n        a = plt.axes([xa-p2,ya-p2, piesize, piesize])\n        a.set_aspect('equal')\n        a.imshow(G.nodes[n]['image'])\n        a.axis('off')\n        try:\n            a.set_title(node_names[idx], y=-0.8, fontsize=13, loc=\"left\")\n        except:\n            pass\n    ax.axis('off')\n    plt.show()\n\ndef plot_entropy():\n    def entropy(p):\n        if p == 0 or p == 1:\n            return 0\n        else:\n            return -p * np.log2(p) - (1- p)*np.log2(1 - p)\n    p_array = np.linspace(0,1,201)\n    h_array = [entropy(p) for p in p_array]\n    fig, ax = plt.subplots()\n    plt.subplots_adjust(left=0.25, bottom=0.25)\n    ax.set_title('p x H(p)')\n    ax.set_xlabel('p')\n    ax.set_ylabel('H(p)')\n    axfreq = plt.axes([0.25, 0.1, 0.65, 0.03])\n    h_plot = ax.plot(p_array,h_array)\n    scatter = ax.scatter(0,0,color = 'red', zorder = 100, s = 70)\n    slider = Slider(axfreq, 'p', 0, 1, valinit = 0, valstep = 0.05)\n\n    def update(val):\n        x = val\n        y = entropy(x)\n        scatter.set_offsets((x,y))\n\n    slider.on_changed(update)\n    return slider\n    #plt.plot()"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/practice-quiz-decision-tree-learning/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-tree-learning/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-tree-learning/ss2.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-tree-learning/ss3.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/practice-quiz-decision-trees/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-trees/ss1.png)"
  },
  {
    "path": "C2 - Advanced Learning Algorithms/week4/practice-quiz-tree-ensembles/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/9d6b795c6a43d44b2c498df8ad3225f8c8849728/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-tree-ensembles/ss1.png)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/Readme.md",
    "content": "## Unsupervised Learning, Recommenders, Reinforcement Learning\n\n- [Week 1](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/20e9e2fafcabd86aeeabdda2f79316caba6a5213/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1)\n    - [Practice quiz : Clustering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/20e9e2fafcabd86aeeabdda2f79316caba6a5213/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/Practice%20Quiz:%20Clustering)\n    - [Practice quiz : Anomaly Detection](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/50762882a48709806ca8cfae482eafdb7ccbc394/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/Practice%20Quiz%20:%20Anomaly%20Detection)\n    - [Programming Assignments](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/078956db6f34d8c9e1dda497cd613922c5146ead/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/C3W1A)\n        - [K means](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/078956db6f34d8c9e1dda497cd613922c5146ead/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/C3W1A/C3W1A1/C3_W1_KMeans_Assignment.ipynb)\n        - [Anomaly Detection](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/3f7a43ce32bc6bea2fca7bc815ad2a5883422c9b/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/C3W1A/C3W1A2/C3_W1_Anomaly_Detection.ipynb)\n\n<br/>\n\n- [Week 2](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/20e9e2fafcabd86aeeabdda2f79316caba6a5213/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2)\n    - [Practice quiz : Collaborative Filtering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Collaborative%20Filtering)\n    - [Practice quiz : Recommender systems implementation](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Recommender%20systems%20implementation)\n    - [Practice quiz : Content-based filtering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Content-based%20filtering)\n    - [Programming Assignments](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/C3W2)\n        - [Collaborative Filtering RecSys](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/C3W2/C3W2A1/C3_W2_Collaborative_RecSys_Assignment.ipynb)\n        - [RecSys using Neural Networks](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/C3W2/C3W2A2/C3_W2_RecSysNN_Assignment.ipynb)\n\n<br/>\n\n- [Week 3](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/20e9e2fafcabd86aeeabdda2f79316caba6a5213/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3)\n    - [Practice quiz : Reinforcement learning introduction](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/eb7aab8b6964336d3d8569f6e9380ca83775969e/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20quiz%20:%20Reinforcement%20learning%20introduction)\n    - [Practice Quiz : State-action value function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/eb7aab8b6964336d3d8569f6e9380ca83775969e/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20Quiz%20:%20State-action%20value%20function)\n    - [Practice Quiz : Continuous state spaces](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/eb7aab8b6964336d3d8569f6e9380ca83775969e/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20Quiz%20:%20Continuous%20state%20spaces)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/84846129ed17898a3542fd1e5abc7605679fcfd8/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/C3W3A1)\n        - [Deep Q-Learning - Lunar Lander](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/84846129ed17898a3542fd1e5abc7605679fcfd8/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/C3W3A1/C3_W3_A1_Assignment.ipynb)\n#### [Certificate of Completion](https://coursera.org/share/5bf5ee456b0c806df9b8622067b47ca6)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/C3W1A/C3W1A1/.ipynb_checkpoints/C3_W1_KMeans_Assignment-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# K-means Clustering \\n\",\n    \"\\n\",\n    \"In this this exercise, you will implement the K-means algorithm and use it for image compression. \\n\",\n    \"\\n\",\n    \"* You will start with a sample dataset that will help you gain an intuition of how the K-means algorithm works. \\n\",\n    \"* After that, you wil use the K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common in that image.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Implementing K-means](#1)\\n\",\n    \"  - [ 1.1 Finding closest centroids](#1.1)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 1.2 Computing centroid means](#1.2)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"- [ 2 - K-means on a sample dataset ](#2)\\n\",\n    \"- [ 3 - Random initialization](#3)\\n\",\n    \"- [ 4 - Image compression with K-means](#4)\\n\",\n    \"  - [ 4.1 Dataset](#4.1)\\n\",\n    \"  - [ 4.2 K-Means on image pixels](#4.2)\\n\",\n    \"  - [ 4.3 Compress the image](#4.3)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import *\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Implementing K-means\\n\",\n    \"\\n\",\n    \"The K-means algorithm is a method to automatically cluster similar\\n\",\n    \"data points together. \\n\",\n    \"\\n\",\n    \"* Concretely, you are given a training set $\\\\{x^{(1)}, ..., x^{(m)}\\\\}$, and you want\\n\",\n    \"to group the data into a few cohesive “clusters”. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* K-means is an iterative procedure that\\n\",\n    \"     * Starts by guessing the initial centroids, and then \\n\",\n    \"     * Refines this guess by \\n\",\n    \"         * Repeatedly assigning examples to their closest centroids, and then \\n\",\n    \"         * Recomputing the centroids based on the assignments.\\n\",\n    \"         \\n\",\n    \"\\n\",\n    \"* In pseudocode, the K-means algorithm is as follows:\\n\",\n    \"\\n\",\n    \"    ``` python\\n\",\n    \"    # Initialize centroids\\n\",\n    \"    # K is the number of clusters\\n\",\n    \"    centroids = kMeans_init_centroids(X, K)\\n\",\n    \"    \\n\",\n    \"    for iter in range(iterations):\\n\",\n    \"        # Cluster assignment step: \\n\",\n    \"        # Assign each data point to the closest centroid. \\n\",\n    \"        # idx[i] corresponds to the index of the centroid \\n\",\n    \"        # assigned to example i\\n\",\n    \"        idx = find_closest_centroids(X, centroids)\\n\",\n    \"\\n\",\n    \"        # Move centroid step: \\n\",\n    \"        # Compute means based on centroid assignments\\n\",\n    \"        centroids = compute_means(X, idx, K)\\n\",\n    \"    ```\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* The inner-loop of the algorithm repeatedly carries out two steps: \\n\",\n    \"    * (i) Assigning each training example $x^{(i)}$ to its closest centroid, and\\n\",\n    \"    * (ii) Recomputing the mean of each centroid using the points assigned to it. \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* The $K$-means algorithm will always converge to some final set of means for the centroids. \\n\",\n    \"\\n\",\n    \"* However, that the converged solution may not always be ideal and depends on the initial setting of the centroids.\\n\",\n    \"    * Therefore, in practice the K-means algorithm is usually run a few times with different random initializations. \\n\",\n    \"    * One way to choose between these different solutions from different random initializations is to choose the one with the lowest cost function value (distortion).\\n\",\n    \"\\n\",\n    \"You will implement the two phases of the K-means algorithm separately\\n\",\n    \"in the next sections. \\n\",\n    \"* You will start by completing `find_closest_centroid` and then proceed to complete `compute_centroids`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1.1\\\"></a>\\n\",\n    \"### 1.1 Finding closest centroids\\n\",\n    \"\\n\",\n    \"In the “cluster assignment” phase of the K-means algorithm, the\\n\",\n    \"algorithm assigns every training example $x^{(i)}$ to its closest\\n\",\n    \"centroid, given the current positions of centroids. \\n\",\n    \"\\n\",\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Your task is to complete the code in `find_closest_centroids`. \\n\",\n    \"* This function takes the data matrix `X` and the locations of all\\n\",\n    \"centroids inside `centroids` \\n\",\n    \"* It should output a one-dimensional array `idx` (which has the same number of elements as `X`) that holds the index  of the closest centroid (a value in $\\\\{1,...,K\\\\}$, where $K$ is total number of centroids) to every training example .\\n\",\n    \"* Specifically, for every example $x^{(i)}$ we set\\n\",\n    \"$$c^{(i)} := j \\\\quad \\\\mathrm{that \\\\; minimizes} \\\\quad ||x^{(i)} - \\\\mu_j||^2,$$\\n\",\n    \"where \\n\",\n    \" * $c^{(i)}$ is the index of the centroid that is closest to $x^{(i)}$ (corresponds to `idx[i]` in the starter code), and \\n\",\n    \" * $\\\\mu_j$ is the position (value) of the $j$’th centroid. (stored in `centroids` in the starter code)\\n\",\n    \" \\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED FUNCTION: find_closest_centroids\\n\",\n    \"\\n\",\n    \"def find_closest_centroids(X, centroids):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the centroid memberships for every example\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray): (m, n) Input values      \\n\",\n    \"        centroids (ndarray): k centroids\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"        idx (array_like): (m,) closest centroids\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    # Set K\\n\",\n    \"    K = centroids.shape[0]\\n\",\n    \"\\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    idx = np.zeros(X.shape[0], dtype=int)\\n\",\n    \"\\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"\\n\",\n    \"    ### END CODE HERE ###\\n\",\n    \"    \\n\",\n    \"    return idx\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def find_closest_centroids(X, centroids):\\n\",\n    \"    \\n\",\n    \"        # Set K\\n\",\n    \"        K = centroids.shape[0]\\n\",\n    \"    \\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        idx = np.zeros(X.shape[0], dtype=int)\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        for i in range(X.shape[0]):\\n\",\n    \"            # Array to hold distance between X[i] and each centroids[j]\\n\",\n    \"            distance = [] \\n\",\n    \"            for j in range(centroids.shape[0]):\\n\",\n    \"                norm_ij = # Your code to calculate the norm between (X[i] - centroids[j])\\n\",\n    \"                distance.append(norm_ij)\\n\",\n    \"            \\n\",\n    \"            idx[i] = # Your code here to calculate index of minimum value in distance\\n\",\n    \"        ### END CODE HERE ###\\n\",\n    \"        return idx\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `norm_ij` and `idx[i]`.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate norm_ij</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html\\\">np.linalg.norm</a> to calculate the norm \\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate norm_ij</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute norm_ij as <code>norm_ij = np.linalg.norm(X[i] - centroids[j]) </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate idx[i]</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can use <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.argmin.html\\\">np.argmin</a> to find the index of the minimum value\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate idx[i]</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute idx[i] as <code>idx[i] = np.argmin(distance)</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's check your implementation using an example dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load an example dataset that we will be using\\n\",\n    \"X = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The code below prints the first five elements in the variable `X` and the dimensions of the variable\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"First five elements of X are:\\\\n\\\", X[:5]) \\n\",\n    \"print('The shape of X is:', X.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Select an initial set of centroids (3 Centroids)\\n\",\n    \"initial_centroids = np.array([[3,3], [6,2], [8,5]])\\n\",\n    \"\\n\",\n    \"# Find closest centroids using initial_centroids\\n\",\n    \"idx = find_closest_centroids(X, initial_centroids)\\n\",\n    \"\\n\",\n    \"# Print closest centroids for the first three elements\\n\",\n    \"print(\\\"First three elements in idx are:\\\", idx[:3])\\n\",\n    \"\\n\",\n    \"# UNIT TEST\\n\",\n    \"from public_tests import *\\n\",\n    \"\\n\",\n    \"find_closest_centroids_test(find_closest_centroids)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>First three elements in idx are<b></td>\\n\",\n    \"    <td> [0 2 1] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1.2\\\"></a>\\n\",\n    \"### 1.2 Computing centroid means\\n\",\n    \"\\n\",\n    \"Given assignments of every point to a centroid, the second phase of the\\n\",\n    \"algorithm recomputes, for each centroid, the mean of the points that\\n\",\n    \"were assigned to it.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Please complete the `compute_centroids` below to recompute the value for each centroid\\n\",\n    \"\\n\",\n    \"* Specifically, for every centroid $\\\\mu_k$ we set\\n\",\n    \"$$\\\\mu_k = \\\\frac{1}{|C_k|} \\\\sum_{i \\\\in C_k} x^{(i)}$$ \\n\",\n    \"\\n\",\n    \"    where \\n\",\n    \"    * $C_k$ is the set of examples that are assigned to centroid $k$\\n\",\n    \"    * $|C_k|$ is the number of examples in the set $C_k$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* Concretely, if two examples say $x^{(3)}$ and $x^{(5)}$ are assigned to centroid $k=2$,\\n\",\n    \"then you should update $\\\\mu_2 = \\\\frac{1}{2}(x^{(3)}+x^{(5)})$.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: compute_centpods\\n\",\n    \"\\n\",\n    \"def compute_centroids(X, idx, K):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Returns the new centroids by computing the means of the \\n\",\n    \"    data points assigned to each centroid.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):   (m, n) Data points\\n\",\n    \"        idx (ndarray): (m,) Array containing index of closest centroid for each \\n\",\n    \"                       example in X. Concretely, idx[i] contains the index of \\n\",\n    \"                       the centroid closest to example i\\n\",\n    \"        K (int):       number of centroids\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"        centroids (ndarray): (K, n) New centroids computed\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Useful variables\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    centroids = np.zeros((K, n))\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"\\n\",\n    \"    ### END CODE HERE ## \\n\",\n    \"    \\n\",\n    \"    return centroids\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_centroids(X, idx, K):\\n\",\n    \"        # Useful variables\\n\",\n    \"        m, n = X.shape\\n\",\n    \"    \\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        centroids = np.zeros((K, n))\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        for k in range(K):   \\n\",\n    \"            points = # Your code here to get a list of all data points in X assigned to centroid k  \\n\",\n    \"            centroids[k] = # Your code here to compute the mean of the points assigned\\n\",\n    \"    ### END CODE HERE ## \\n\",\n    \"    \\n\",\n    \"    return centroids\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `points` and `centroids[k]`.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate points</b></font></summary>\\n\",\n    \"           &emsp; &emsp; Say we wanted to find all the values in X that were assigned to cluster <code>k=0</code>. That is, the corresponding value in idx for these examples is 0. In Python, we can do it as <code>X[idx == 0]</code>. Similarly, the points assigned to centroid <code>k=1</code> are <code>X[idx == 1]</code>\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate points</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute points as <code>points = X[idx == k] </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate centroids[k]</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can use <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.mean.html\\\">np.mean</a> to find the mean. Make sure to set the parameter <code>axis=0</code> \\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate centroids[k]</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute centroids[k] as <code>centroids[k] = np.mean(points, axis = 0)</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now check your implementation by running the cell below\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"K = 3\\n\",\n    \"centroids = compute_centroids(X, idx, K)\\n\",\n    \"\\n\",\n    \"print(\\\"The centroids are:\\\", centroids)\\n\",\n    \"\\n\",\n    \"# UNIT TEST\\n\",\n    \"compute_centroids_test(compute_centroids)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"\\n\",\n    \"2.42830111 3.15792418\\n\",\n    \"\\n\",\n    \"5.81350331 2.63365645\\n\",\n    \"\\n\",\n    \"7.11938687 3.6166844 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - K-means on a sample dataset \\n\",\n    \"\\n\",\n    \"After you have completed the two functions (`find_closest_centroids`\\n\",\n    \"and `compute_centroids`) above, the next step is to run the\\n\",\n    \"K-means algorithm on a toy 2D dataset to help you understand how\\n\",\n    \"K-means works. \\n\",\n    \"* We encourage you to take a look at the function (`run_kMeans`) below to understand how it works. \\n\",\n    \"* Notice that the code calls the two functions you implemented in a loop.\\n\",\n    \"\\n\",\n    \"When you run the code below, it will produce a\\n\",\n    \"visualization that steps through the progress of the algorithm at\\n\",\n    \"each iteration. \\n\",\n    \"* At the end, your figure should look like the one displayed in Figure 1.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 1.png\\\" width=\\\"500\\\" height=\\\"500\\\">\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Note**: You do not need to implement anything for this part. Simply run the code provided below\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# You do not need to implement anything for this part\\n\",\n    \"\\n\",\n    \"def run_kMeans(X, initial_centroids, max_iters=10, plot_progress=False):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Runs the K-Means algorithm on data matrix X, where each row of X\\n\",\n    \"    is a single example\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Initialize values\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    K = initial_centroids.shape[0]\\n\",\n    \"    centroids = initial_centroids\\n\",\n    \"    previous_centroids = centroids    \\n\",\n    \"    idx = np.zeros(m)\\n\",\n    \"    \\n\",\n    \"    # Run K-Means\\n\",\n    \"    for i in range(max_iters):\\n\",\n    \"        \\n\",\n    \"        #Output progress\\n\",\n    \"        print(\\\"K-Means iteration %d/%d\\\" % (i, max_iters-1))\\n\",\n    \"        \\n\",\n    \"        # For each example in X, assign it to the closest centroid\\n\",\n    \"        idx = find_closest_centroids(X, centroids)\\n\",\n    \"        \\n\",\n    \"        # Optionally plot progress\\n\",\n    \"        if plot_progress:\\n\",\n    \"            plot_progress_kMeans(X, centroids, previous_centroids, idx, K, i)\\n\",\n    \"            previous_centroids = centroids\\n\",\n    \"            \\n\",\n    \"        # Given the memberships, compute new centroids\\n\",\n    \"        centroids = compute_centroids(X, idx, K)\\n\",\n    \"    plt.show() \\n\",\n    \"    return centroids, idx\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load an example dataset\\n\",\n    \"X = load_data()\\n\",\n    \"\\n\",\n    \"# Set initial centroids\\n\",\n    \"initial_centroids = np.array([[3,3],[6,2],[8,5]])\\n\",\n    \"K = 3\\n\",\n    \"\\n\",\n    \"# Number of iterations\\n\",\n    \"max_iters = 10\\n\",\n    \"\\n\",\n    \"centroids, idx = run_kMeans(X, initial_centroids, max_iters, plot_progress=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Random initialization\\n\",\n    \"\\n\",\n    \"The initial assignments of centroids for the example dataset was designed so that you will see the same figure as in Figure 1. In practice, a good strategy for initializing the centroids is to select random examples from the\\n\",\n    \"training set.\\n\",\n    \"\\n\",\n    \"In this part of the exercise, you should understand how the function `kMeans_init_centroids` is implemented.\\n\",\n    \"* The code first randomly shuffles the indices of the examples (using `np.random.permutation()`). \\n\",\n    \"* Then, it selects the first $K$ examples based on the random permutation of the indices. \\n\",\n    \"    * This allows the examples to be selected at random without the risk of selecting the same example twice.\\n\",\n    \"\\n\",\n    \"**Note**: You do not need to make implement anything for this part of the exercise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# You do not need to modify this part\\n\",\n    \"\\n\",\n    \"def kMeans_init_centroids(X, K):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    This function initializes K centroids that are to be \\n\",\n    \"    used in K-Means on the dataset X\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray): Data points \\n\",\n    \"        K (int):     number of centroids/clusters\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"        centroids (ndarray): Initialized centroids\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Randomly reorder the indices of examples\\n\",\n    \"    randidx = np.random.permutation(X.shape[0])\\n\",\n    \"    \\n\",\n    \"    # Take the first K examples as centroids\\n\",\n    \"    centroids = X[randidx[:K]]\\n\",\n    \"    \\n\",\n    \"    return centroids\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Image compression with K-means\\n\",\n    \"\\n\",\n    \"In this exercise, you will apply K-means to image compression. \\n\",\n    \"\\n\",\n    \"* In a straightforward 24-bit color representation of an image$^{2}$, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often refered to as the RGB encoding.\\n\",\n    \"* Our image contains thousands of colors, and in this part of the exercise, you will reduce the number of\\n\",\n    \"colors to 16 colors.\\n\",\n    \"* By making this reduction, it is possible to represent (compress) the photo in an efficient way. \\n\",\n    \"* Specifically, you only need to store the RGB values of the 16 selected colors, and for each pixel in the image you now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\\n\",\n    \"\\n\",\n    \"In this part, you will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image.\\n\",\n    \"* Concretely, you will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3- dimensional RGB space. \\n\",\n    \"* Once you have computed the cluster centroids on the image, you will then use the 16 colors to replace the pixels in the original image.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 2.png\\\" width=\\\"500\\\" height=\\\"500\\\">\\n\",\n    \"\\n\",\n    \"$^{2}$<sub>The provided photo used in this exercise belongs to Frank Wouters and is used with his permission.</sub>\\n\",\n    \"\\n\",\n    \"<a name=\\\"4.1\\\"></a>\\n\",\n    \"### 4.1 Dataset\\n\",\n    \"\\n\",\n    \"**Load image**\\n\",\n    \"\\n\",\n    \"First, you will use `matplotlib` to read in the original image, as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load an image of a bird\\n\",\n    \"original_img = plt.imread('bird_small.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Visualize image**\\n\",\n    \"\\n\",\n    \"You can visualize the image that was just loaded using the code below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Visualizing the image\\n\",\n    \"plt.imshow(original_img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Check the dimension of the variable**\\n\",\n    \"\\n\",\n    \"As always, you will print out the shape of your variable to get more familiar with the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Shape of original_img is:\\\", original_img.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, this creates a three-dimensional matrix `original_img` where \\n\",\n    \"* the first two indices identify a pixel position, and\\n\",\n    \"* the third index represents red, green, or blue. \\n\",\n    \"\\n\",\n    \"For example, `original_img[50, 33, 2]` gives the blue intensity of the pixel at row 50 and column 33.\\n\",\n    \"\\n\",\n    \"#### Processing data\\n\",\n    \"\\n\",\n    \"To call the `run_kMeans`, you need to first transform the matrix `original_img` into a two-dimensional matrix.\\n\",\n    \"\\n\",\n    \"* The code below reshapes the matrix `original_img` to create an $m \\\\times 3$ matrix of pixel colors (where\\n\",\n    \"$m=16384 = 128\\\\times128$)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Divide by 255 so that all values are in the range 0 - 1\\n\",\n    \"original_img = original_img / 255\\n\",\n    \"\\n\",\n    \"# Reshape the image into an m x 3 matrix where m = number of pixels\\n\",\n    \"# (in this case m = 128 x 128 = 16384)\\n\",\n    \"# Each row will contain the Red, Green and Blue pixel values\\n\",\n    \"# This gives us our dataset matrix X_img that we will use K-Means on.\\n\",\n    \"\\n\",\n    \"X_img = np.reshape(original_img, (original_img.shape[0] * original_img.shape[1], 3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.2\\\"></a>\\n\",\n    \"### 4.2 K-Means on image pixels\\n\",\n    \"\\n\",\n    \"Now, run the cell below to run K-Means on the pre-processed image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Run your K-Means algorithm on this data\\n\",\n    \"# You should try different values of K and max_iters here\\n\",\n    \"K = 16                       \\n\",\n    \"max_iters = 10               \\n\",\n    \"\\n\",\n    \"# Using the function you have implemented above. \\n\",\n    \"initial_centroids = kMeans_init_centroids(X_img, K) \\n\",\n    \"\\n\",\n    \"# Run K-Means - this takes a couple of minutes\\n\",\n    \"centroids, idx = run_kMeans(X_img, initial_centroids, max_iters) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Shape of idx:\\\", idx.shape)\\n\",\n    \"print(\\\"Closest centroid for the first five elements:\\\", idx[:5])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.3\\\"></a>\\n\",\n    \"### 4.3 Compress the image\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"After finding the top $K=16$ colors to represent the image, you can now\\n\",\n    \"assign each pixel position to its closest centroid using the\\n\",\n    \"`find_closest_centroids` function. \\n\",\n    \"* This allows you to represent the original image using the centroid assignments of each pixel. \\n\",\n    \"* Notice that you have significantly reduced the number of bits that are required to describe the image. \\n\",\n    \"    * The original image required 24 bits for each one of the $128\\\\times128$ pixel locations, resulting in total size of $128 \\\\times 128 \\\\times 24 = 393,216$ bits. \\n\",\n    \"    * The new representation requires some overhead storage in form of a dictionary of 16 colors, each of which require 24 bits, but the image itself then only requires 4 bits per pixel location. \\n\",\n    \"    * The final number of bits used is therefore $16 \\\\times 24 + 128 \\\\times 128 \\\\times 4 = 65,920$ bits, which corresponds to compressing the original image by about a factor of 6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Represent image in terms of indices\\n\",\n    \"X_recovered = centroids[idx, :] \\n\",\n    \"\\n\",\n    \"# Reshape recovered image into proper dimensions\\n\",\n    \"X_recovered = np.reshape(X_recovered, original_img.shape) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, you can view the effects of the compression by reconstructing\\n\",\n    \"the image based only on the centroid assignments. \\n\",\n    \"* Specifically, you can replace each pixel location with the mean of the centroid assigned to\\n\",\n    \"it. \\n\",\n    \"* Figure 3 shows the reconstruction we obtained. Even though the resulting image retains most of the characteristics of the original, we also see some compression artifacts.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 3.png\\\" width=\\\"700\\\" height=\\\"700\\\">\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Display original image\\n\",\n    \"fig, ax = plt.subplots(1,2, figsize=(8,8))\\n\",\n    \"plt.axis('off')\\n\",\n    \"\\n\",\n    \"ax[0].imshow(original_img*255)\\n\",\n    \"ax[0].set_title('Original')\\n\",\n    \"ax[0].set_axis_off()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Display compressed image\\n\",\n    \"ax[1].imshow(X_recovered*255)\\n\",\n    \"ax[1].set_title('Compressed with %d colours'%K)\\n\",\n    \"ax[1].set_axis_off()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/C3W1A/C3W1A1/C3_W1_KMeans_Assignment.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# K-means Clustering \\n\",\n    \"\\n\",\n    \"In this this exercise, you will implement the K-means algorithm and use it for image compression. \\n\",\n    \"\\n\",\n    \"* You will start with a sample dataset that will help you gain an intuition of how the K-means algorithm works. \\n\",\n    \"* After that, you wil use the K-means algorithm for image compression by reducing the number of colors that occur in an image to only those that are most common in that image.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Implementing K-means](#1)\\n\",\n    \"  - [ 1.1 Finding closest centroids](#1.1)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 1.2 Computing centroid means](#1.2)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"- [ 2 - K-means on a sample dataset ](#2)\\n\",\n    \"- [ 3 - Random initialization](#3)\\n\",\n    \"- [ 4 - Image compression with K-means](#4)\\n\",\n    \"  - [ 4.1 Dataset](#4.1)\\n\",\n    \"  - [ 4.2 K-Means on image pixels](#4.2)\\n\",\n    \"  - [ 4.3 Compress the image](#4.3)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import *\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Implementing K-means\\n\",\n    \"\\n\",\n    \"The K-means algorithm is a method to automatically cluster similar\\n\",\n    \"data points together. \\n\",\n    \"\\n\",\n    \"* Concretely, you are given a training set $\\\\{x^{(1)}, ..., x^{(m)}\\\\}$, and you want\\n\",\n    \"to group the data into a few cohesive “clusters”. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* K-means is an iterative procedure that\\n\",\n    \"     * Starts by guessing the initial centroids, and then \\n\",\n    \"     * Refines this guess by \\n\",\n    \"         * Repeatedly assigning examples to their closest centroids, and then \\n\",\n    \"         * Recomputing the centroids based on the assignments.\\n\",\n    \"         \\n\",\n    \"\\n\",\n    \"* In pseudocode, the K-means algorithm is as follows:\\n\",\n    \"\\n\",\n    \"    ``` python\\n\",\n    \"    # Initialize centroids\\n\",\n    \"    # K is the number of clusters\\n\",\n    \"    centroids = kMeans_init_centroids(X, K)\\n\",\n    \"    \\n\",\n    \"    for iter in range(iterations):\\n\",\n    \"        # Cluster assignment step: \\n\",\n    \"        # Assign each data point to the closest centroid. \\n\",\n    \"        # idx[i] corresponds to the index of the centroid \\n\",\n    \"        # assigned to example i\\n\",\n    \"        idx = find_closest_centroids(X, centroids)\\n\",\n    \"\\n\",\n    \"        # Move centroid step: \\n\",\n    \"        # Compute means based on centroid assignments\\n\",\n    \"        centroids = compute_means(X, idx, K)\\n\",\n    \"    ```\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* The inner-loop of the algorithm repeatedly carries out two steps: \\n\",\n    \"    * (i) Assigning each training example $x^{(i)}$ to its closest centroid, and\\n\",\n    \"    * (ii) Recomputing the mean of each centroid using the points assigned to it. \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* The $K$-means algorithm will always converge to some final set of means for the centroids. \\n\",\n    \"\\n\",\n    \"* However, that the converged solution may not always be ideal and depends on the initial setting of the centroids.\\n\",\n    \"    * Therefore, in practice the K-means algorithm is usually run a few times with different random initializations. \\n\",\n    \"    * One way to choose between these different solutions from different random initializations is to choose the one with the lowest cost function value (distortion).\\n\",\n    \"\\n\",\n    \"You will implement the two phases of the K-means algorithm separately\\n\",\n    \"in the next sections. \\n\",\n    \"* You will start by completing `find_closest_centroid` and then proceed to complete `compute_centroids`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1.1\\\"></a>\\n\",\n    \"### 1.1 Finding closest centroids\\n\",\n    \"\\n\",\n    \"In the “cluster assignment” phase of the K-means algorithm, the\\n\",\n    \"algorithm assigns every training example $x^{(i)}$ to its closest\\n\",\n    \"centroid, given the current positions of centroids. \\n\",\n    \"\\n\",\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Your task is to complete the code in `find_closest_centroids`. \\n\",\n    \"* This function takes the data matrix `X` and the locations of all\\n\",\n    \"centroids inside `centroids` \\n\",\n    \"* It should output a one-dimensional array `idx` (which has the same number of elements as `X`) that holds the index  of the closest centroid (a value in $\\\\{1,...,K\\\\}$, where $K$ is total number of centroids) to every training example .\\n\",\n    \"* Specifically, for every example $x^{(i)}$ we set\\n\",\n    \"$$c^{(i)} := j \\\\quad \\\\mathrm{that \\\\; minimizes} \\\\quad ||x^{(i)} - \\\\mu_j||^2,$$\\n\",\n    \"where \\n\",\n    \" * $c^{(i)}$ is the index of the centroid that is closest to $x^{(i)}$ (corresponds to `idx[i]` in the starter code), and \\n\",\n    \" * $\\\\mu_j$ is the position (value) of the $j$’th centroid. (stored in `centroids` in the starter code)\\n\",\n    \" \\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED FUNCTION: find_closest_centroids\\n\",\n    \"\\n\",\n    \"def find_closest_centroids(X, centroids):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Computes the centroid memberships for every example\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray): (m, n) Input values      \\n\",\n    \"        centroids (ndarray): k centroids\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"        idx (array_like): (m,) closest centroids\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    # Set K\\n\",\n    \"    K = centroids.shape[0]\\n\",\n    \"\\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    idx = np.zeros(X.shape[0], dtype=int)\\n\",\n    \"\\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    for i in range(X.shape[0]):\\n\",\n    \"          # Array to hold distance between X[i] and each centroids[j]\\n\",\n    \"          distance = [] \\n\",\n    \"          for j in range(centroids.shape[0]):\\n\",\n    \"              norm_ij = np.linalg.norm(X[i] - centroids[j])\\n\",\n    \"              distance.append(norm_ij)\\n\",\n    \"\\n\",\n    \"          idx[i] = np.argmin(distance)\\n\",\n    \"\\n\",\n    \"    ### END CODE HERE ###\\n\",\n    \"    \\n\",\n    \"    return idx\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def find_closest_centroids(X, centroids):\\n\",\n    \"    \\n\",\n    \"        # Set K\\n\",\n    \"        K = centroids.shape[0]\\n\",\n    \"    \\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        idx = np.zeros(X.shape[0], dtype=int)\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        for i in range(X.shape[0]):\\n\",\n    \"            # Array to hold distance between X[i] and each centroids[j]\\n\",\n    \"            distance = [] \\n\",\n    \"            for j in range(centroids.shape[0]):\\n\",\n    \"                norm_ij = # Your code to calculate the norm between (X[i] - centroids[j])\\n\",\n    \"                distance.append(norm_ij)\\n\",\n    \"            \\n\",\n    \"            idx[i] = # Your code here to calculate index of minimum value in distance\\n\",\n    \"        ### END CODE HERE ###\\n\",\n    \"        return idx\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `norm_ij` and `idx[i]`.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate norm_ij</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html\\\">np.linalg.norm</a> to calculate the norm \\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate norm_ij</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute norm_ij as <code>norm_ij = np.linalg.norm(X[i] - centroids[j]) </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate idx[i]</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can use <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.argmin.html\\\">np.argmin</a> to find the index of the minimum value\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate idx[i]</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute idx[i] as <code>idx[i] = np.argmin(distance)</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's check your implementation using an example dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load an example dataset that we will be using\\n\",\n    \"X = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The code below prints the first five elements in the variable `X` and the dimensions of the variable\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"First five elements of X are:\\n\",\n      \" [[1.84207953 4.6075716 ]\\n\",\n      \" [5.65858312 4.79996405]\\n\",\n      \" [6.35257892 3.2908545 ]\\n\",\n      \" [2.90401653 4.61220411]\\n\",\n      \" [3.23197916 4.93989405]]\\n\",\n      \"The shape of X is: (300, 2)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"First five elements of X are:\\\\n\\\", X[:5]) \\n\",\n    \"print('The shape of X is:', X.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"First three elements in idx are: [0 2 1]\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Select an initial set of centroids (3 Centroids)\\n\",\n    \"initial_centroids = np.array([[3,3], [6,2], [8,5]])\\n\",\n    \"\\n\",\n    \"# Find closest centroids using initial_centroids\\n\",\n    \"idx = find_closest_centroids(X, initial_centroids)\\n\",\n    \"\\n\",\n    \"# Print closest centroids for the first three elements\\n\",\n    \"print(\\\"First three elements in idx are:\\\", idx[:3])\\n\",\n    \"\\n\",\n    \"# UNIT TEST\\n\",\n    \"from public_tests import *\\n\",\n    \"\\n\",\n    \"find_closest_centroids_test(find_closest_centroids)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>First three elements in idx are<b></td>\\n\",\n    \"    <td> [0 2 1] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1.2\\\"></a>\\n\",\n    \"### 1.2 Computing centroid means\\n\",\n    \"\\n\",\n    \"Given assignments of every point to a centroid, the second phase of the\\n\",\n    \"algorithm recomputes, for each centroid, the mean of the points that\\n\",\n    \"were assigned to it.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"Please complete the `compute_centroids` below to recompute the value for each centroid\\n\",\n    \"\\n\",\n    \"* Specifically, for every centroid $\\\\mu_k$ we set\\n\",\n    \"$$\\\\mu_k = \\\\frac{1}{|C_k|} \\\\sum_{i \\\\in C_k} x^{(i)}$$ \\n\",\n    \"\\n\",\n    \"    where \\n\",\n    \"    * $C_k$ is the set of examples that are assigned to centroid $k$\\n\",\n    \"    * $|C_k|$ is the number of examples in the set $C_k$\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* Concretely, if two examples say $x^{(3)}$ and $x^{(5)}$ are assigned to centroid $k=2$,\\n\",\n    \"then you should update $\\\\mu_2 = \\\\frac{1}{2}(x^{(3)}+x^{(5)})$.\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: compute_centpods\\n\",\n    \"\\n\",\n    \"def compute_centroids(X, idx, K):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Returns the new centroids by computing the means of the \\n\",\n    \"    data points assigned to each centroid.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray):   (m, n) Data points\\n\",\n    \"        idx (ndarray): (m,) Array containing index of closest centroid for each \\n\",\n    \"                       example in X. Concretely, idx[i] contains the index of \\n\",\n    \"                       the centroid closest to example i\\n\",\n    \"        K (int):       number of centroids\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"        centroids (ndarray): (K, n) New centroids computed\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Useful variables\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    # You need to return the following variables correctly\\n\",\n    \"    centroids = np.zeros((K, n))\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    for k in range(K):   \\n\",\n    \"          points = X[idx == k]  \\n\",\n    \"          centroids[k] = np.mean(points, axis = 0)\\n\",\n    \"\\n\",\n    \"    ### END CODE HERE ## \\n\",\n    \"    \\n\",\n    \"    return centroids\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"* Here's how you can structure the overall implementation for this function\\n\",\n    \"    ```python \\n\",\n    \"    def compute_centroids(X, idx, K):\\n\",\n    \"        # Useful variables\\n\",\n    \"        m, n = X.shape\\n\",\n    \"    \\n\",\n    \"        # You need to return the following variables correctly\\n\",\n    \"        centroids = np.zeros((K, n))\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ###\\n\",\n    \"        for k in range(K):   \\n\",\n    \"            points = # Your code here to get a list of all data points in X assigned to centroid k  \\n\",\n    \"            centroids[k] = # Your code here to compute the mean of the points assigned\\n\",\n    \"    ### END CODE HERE ## \\n\",\n    \"    \\n\",\n    \"    return centroids\\n\",\n    \"    ```\\n\",\n    \"  \\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `points` and `centroids[k]`.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate points</b></font></summary>\\n\",\n    \"           &emsp; &emsp; Say we wanted to find all the values in X that were assigned to cluster <code>k=0</code>. That is, the corresponding value in idx for these examples is 0. In Python, we can do it as <code>X[idx == 0]</code>. Similarly, the points assigned to centroid <code>k=1</code> are <code>X[idx == 1]</code>\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate points</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute points as <code>points = X[idx == k] </code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"     <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate centroids[k]</b></font></summary>\\n\",\n    \"          &emsp; &emsp; You can use <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.mean.html\\\">np.mean</a> to find the mean. Make sure to set the parameter <code>axis=0</code> \\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate centroids[k]</b></font></summary>\\n\",\n    \"              &emsp; &emsp; You can compute centroids[k] as <code>centroids[k] = np.mean(points, axis = 0)</code>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    </details>\\n\",\n    \"\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now check your implementation by running the cell below\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The centroids are: [[2.42830111 3.15792418]\\n\",\n      \" [5.81350331 2.63365645]\\n\",\n      \" [7.11938687 3.6166844 ]]\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"K = 3\\n\",\n    \"centroids = compute_centroids(X, idx, K)\\n\",\n    \"\\n\",\n    \"print(\\\"The centroids are:\\\", centroids)\\n\",\n    \"\\n\",\n    \"# UNIT TEST\\n\",\n    \"compute_centroids_test(compute_centroids)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"\\n\",\n    \"2.42830111 3.15792418\\n\",\n    \"\\n\",\n    \"5.81350331 2.63365645\\n\",\n    \"\\n\",\n    \"7.11938687 3.6166844 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - K-means on a sample dataset \\n\",\n    \"\\n\",\n    \"After you have completed the two functions (`find_closest_centroids`\\n\",\n    \"and `compute_centroids`) above, the next step is to run the\\n\",\n    \"K-means algorithm on a toy 2D dataset to help you understand how\\n\",\n    \"K-means works. \\n\",\n    \"* We encourage you to take a look at the function (`run_kMeans`) below to understand how it works. \\n\",\n    \"* Notice that the code calls the two functions you implemented in a loop.\\n\",\n    \"\\n\",\n    \"When you run the code below, it will produce a\\n\",\n    \"visualization that steps through the progress of the algorithm at\\n\",\n    \"each iteration. \\n\",\n    \"* At the end, your figure should look like the one displayed in Figure 1.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 1.png\\\" width=\\\"500\\\" height=\\\"500\\\">\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Note**: You do not need to implement anything for this part. Simply run the code provided below\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# You do not need to implement anything for this part\\n\",\n    \"\\n\",\n    \"def run_kMeans(X, initial_centroids, max_iters=10, plot_progress=False):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Runs the K-Means algorithm on data matrix X, where each row of X\\n\",\n    \"    is a single example\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Initialize values\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    K = initial_centroids.shape[0]\\n\",\n    \"    centroids = initial_centroids\\n\",\n    \"    previous_centroids = centroids    \\n\",\n    \"    idx = np.zeros(m)\\n\",\n    \"    \\n\",\n    \"    # Run K-Means\\n\",\n    \"    for i in range(max_iters):\\n\",\n    \"        \\n\",\n    \"        #Output progress\\n\",\n    \"        print(\\\"K-Means iteration %d/%d\\\" % (i, max_iters-1))\\n\",\n    \"        \\n\",\n    \"        # For each example in X, assign it to the closest centroid\\n\",\n    \"        idx = find_closest_centroids(X, centroids)\\n\",\n    \"        \\n\",\n    \"        # Optionally plot progress\\n\",\n    \"        if plot_progress:\\n\",\n    \"            plot_progress_kMeans(X, centroids, previous_centroids, idx, K, i)\\n\",\n    \"            previous_centroids = centroids\\n\",\n    \"            \\n\",\n    \"        # Given the memberships, compute new centroids\\n\",\n    \"        centroids = compute_centroids(X, idx, K)\\n\",\n    \"    plt.show() \\n\",\n    \"    return centroids, idx\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"K-Means iteration 0/9\\n\",\n      \"K-Means iteration 1/9\\n\",\n      \"K-Means iteration 2/9\\n\",\n      \"K-Means iteration 3/9\\n\",\n      \"K-Means iteration 4/9\\n\",\n      \"K-Means iteration 5/9\\n\",\n      \"K-Means iteration 6/9\\n\",\n      \"K-Means iteration 7/9\\n\",\n      \"K-Means iteration 8/9\\n\",\n      \"K-Means iteration 9/9\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Load an example dataset\\n\",\n    \"X = load_data()\\n\",\n    \"\\n\",\n    \"# Set initial centroids\\n\",\n    \"initial_centroids = np.array([[3,3],[6,2],[8,5]])\\n\",\n    \"K = 3\\n\",\n    \"\\n\",\n    \"# Number of iterations\\n\",\n    \"max_iters = 10\\n\",\n    \"\\n\",\n    \"centroids, idx = run_kMeans(X, initial_centroids, max_iters, plot_progress=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Random initialization\\n\",\n    \"\\n\",\n    \"The initial assignments of centroids for the example dataset was designed so that you will see the same figure as in Figure 1. In practice, a good strategy for initializing the centroids is to select random examples from the\\n\",\n    \"training set.\\n\",\n    \"\\n\",\n    \"In this part of the exercise, you should understand how the function `kMeans_init_centroids` is implemented.\\n\",\n    \"* The code first randomly shuffles the indices of the examples (using `np.random.permutation()`). \\n\",\n    \"* Then, it selects the first $K$ examples based on the random permutation of the indices. \\n\",\n    \"    * This allows the examples to be selected at random without the risk of selecting the same example twice.\\n\",\n    \"\\n\",\n    \"**Note**: You do not need to make implement anything for this part of the exercise.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# You do not need to modify this part\\n\",\n    \"\\n\",\n    \"def kMeans_init_centroids(X, K):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    This function initializes K centroids that are to be \\n\",\n    \"    used in K-Means on the dataset X\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray): Data points \\n\",\n    \"        K (int):     number of centroids/clusters\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"        centroids (ndarray): Initialized centroids\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Randomly reorder the indices of examples\\n\",\n    \"    randidx = np.random.permutation(X.shape[0])\\n\",\n    \"    \\n\",\n    \"    # Take the first K examples as centroids\\n\",\n    \"    centroids = X[randidx[:K]]\\n\",\n    \"    \\n\",\n    \"    return centroids\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Image compression with K-means\\n\",\n    \"\\n\",\n    \"In this exercise, you will apply K-means to image compression. \\n\",\n    \"\\n\",\n    \"* In a straightforward 24-bit color representation of an image$^{2}$, each pixel is represented as three 8-bit unsigned integers (ranging from 0 to 255) that specify the red, green and blue intensity values. This encoding is often refered to as the RGB encoding.\\n\",\n    \"* Our image contains thousands of colors, and in this part of the exercise, you will reduce the number of\\n\",\n    \"colors to 16 colors.\\n\",\n    \"* By making this reduction, it is possible to represent (compress) the photo in an efficient way. \\n\",\n    \"* Specifically, you only need to store the RGB values of the 16 selected colors, and for each pixel in the image you now need to only store the index of the color at that location (where only 4 bits are necessary to represent 16 possibilities).\\n\",\n    \"\\n\",\n    \"In this part, you will use the K-means algorithm to select the 16 colors that will be used to represent the compressed image.\\n\",\n    \"* Concretely, you will treat every pixel in the original image as a data example and use the K-means algorithm to find the 16 colors that best group (cluster) the pixels in the 3- dimensional RGB space. \\n\",\n    \"* Once you have computed the cluster centroids on the image, you will then use the 16 colors to replace the pixels in the original image.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 2.png\\\" width=\\\"500\\\" height=\\\"500\\\">\\n\",\n    \"\\n\",\n    \"$^{2}$<sub>The provided photo used in this exercise belongs to Frank Wouters and is used with his permission.</sub>\\n\",\n    \"\\n\",\n    \"<a name=\\\"4.1\\\"></a>\\n\",\n    \"### 4.1 Dataset\\n\",\n    \"\\n\",\n    \"**Load image**\\n\",\n    \"\\n\",\n    \"First, you will use `matplotlib` to read in the original image, as shown below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load an image of a bird\\n\",\n    \"original_img = plt.imread('bird_small.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Visualize image**\\n\",\n    \"\\n\",\n    \"You can visualize the image that was just loaded using the code below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7fec91c97650>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Visualizing the image\\n\",\n    \"plt.imshow(original_img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Check the dimension of the variable**\\n\",\n    \"\\n\",\n    \"As always, you will print out the shape of your variable to get more familiar with the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Shape of original_img is: (128, 128, 3)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Shape of original_img is:\\\", original_img.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, this creates a three-dimensional matrix `original_img` where \\n\",\n    \"* the first two indices identify a pixel position, and\\n\",\n    \"* the third index represents red, green, or blue. \\n\",\n    \"\\n\",\n    \"For example, `original_img[50, 33, 2]` gives the blue intensity of the pixel at row 50 and column 33.\\n\",\n    \"\\n\",\n    \"#### Processing data\\n\",\n    \"\\n\",\n    \"To call the `run_kMeans`, you need to first transform the matrix `original_img` into a two-dimensional matrix.\\n\",\n    \"\\n\",\n    \"* The code below reshapes the matrix `original_img` to create an $m \\\\times 3$ matrix of pixel colors (where\\n\",\n    \"$m=16384 = 128\\\\times128$)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Divide by 255 so that all values are in the range 0 - 1\\n\",\n    \"original_img = original_img / 255\\n\",\n    \"\\n\",\n    \"# Reshape the image into an m x 3 matrix where m = number of pixels\\n\",\n    \"# (in this case m = 128 x 128 = 16384)\\n\",\n    \"# Each row will contain the Red, Green and Blue pixel values\\n\",\n    \"# This gives us our dataset matrix X_img that we will use K-Means on.\\n\",\n    \"\\n\",\n    \"X_img = np.reshape(original_img, (original_img.shape[0] * original_img.shape[1], 3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.2\\\"></a>\\n\",\n    \"### 4.2 K-Means on image pixels\\n\",\n    \"\\n\",\n    \"Now, run the cell below to run K-Means on the pre-processed image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"K-Means iteration 0/9\\n\",\n      \"K-Means iteration 1/9\\n\",\n      \"K-Means iteration 2/9\\n\",\n      \"K-Means iteration 3/9\\n\",\n      \"K-Means iteration 4/9\\n\",\n      \"K-Means iteration 5/9\\n\",\n      \"K-Means iteration 6/9\\n\",\n      \"K-Means iteration 7/9\\n\",\n      \"K-Means iteration 8/9\\n\",\n      \"K-Means iteration 9/9\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Run your K-Means algorithm on this data\\n\",\n    \"# You should try different values of K and max_iters here\\n\",\n    \"K = 16                       \\n\",\n    \"max_iters = 10               \\n\",\n    \"\\n\",\n    \"# Using the function you have implemented above. \\n\",\n    \"initial_centroids = kMeans_init_centroids(X_img, K) \\n\",\n    \"\\n\",\n    \"# Run K-Means - this takes a couple of minutes\\n\",\n    \"centroids, idx = run_kMeans(X_img, initial_centroids, max_iters) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Shape of idx: (16384,)\\n\",\n      \"Closest centroid for the first five elements: [1 2 2 1 1]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Shape of idx:\\\", idx.shape)\\n\",\n    \"print(\\\"Closest centroid for the first five elements:\\\", idx[:5])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4.3\\\"></a>\\n\",\n    \"### 4.3 Compress the image\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"After finding the top $K=16$ colors to represent the image, you can now\\n\",\n    \"assign each pixel position to its closest centroid using the\\n\",\n    \"`find_closest_centroids` function. \\n\",\n    \"* This allows you to represent the original image using the centroid assignments of each pixel. \\n\",\n    \"* Notice that you have significantly reduced the number of bits that are required to describe the image. \\n\",\n    \"    * The original image required 24 bits for each one of the $128\\\\times128$ pixel locations, resulting in total size of $128 \\\\times 128 \\\\times 24 = 393,216$ bits. \\n\",\n    \"    * The new representation requires some overhead storage in form of a dictionary of 16 colors, each of which require 24 bits, but the image itself then only requires 4 bits per pixel location. \\n\",\n    \"    * The final number of bits used is therefore $16 \\\\times 24 + 128 \\\\times 128 \\\\times 4 = 65,920$ bits, which corresponds to compressing the original image by about a factor of 6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Represent image in terms of indices\\n\",\n    \"X_recovered = centroids[idx, :] \\n\",\n    \"\\n\",\n    \"# Reshape recovered image into proper dimensions\\n\",\n    \"X_recovered = np.reshape(X_recovered, original_img.shape) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, you can view the effects of the compression by reconstructing\\n\",\n    \"the image based only on the centroid assignments. \\n\",\n    \"* Specifically, you can replace each pixel location with the mean of the centroid assigned to\\n\",\n    \"it. \\n\",\n    \"* Figure 3 shows the reconstruction we obtained. Even though the resulting image retains most of the characteristics of the original, we also see some compression artifacts.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure 3.png\\\" width=\\\"700\\\" height=\\\"700\\\">\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Display original image\\n\",\n    \"fig, ax = plt.subplots(1,2, figsize=(8,8))\\n\",\n    \"plt.axis('off')\\n\",\n    \"\\n\",\n    \"ax[0].imshow(original_img*255)\\n\",\n    \"ax[0].set_title('Original')\\n\",\n    \"ax[0].set_axis_off()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Display compressed image\\n\",\n    \"ax[1].imshow(X_recovered*255)\\n\",\n    \"ax[1].set_title('Compressed with %d colours'%K)\\n\",\n    \"ax[1].set_axis_off()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/C3W1A/C3W1A1/public_tests.py",
    "content": "import numpy as np\n\ndef compute_centroids_test(target):\n    # With 3 centroids\n    X = np.array([[-1, -1], [-1.5, -1.5], [-1.5, 1],\n                  [-1, 1.5], [2.5, 1.5], [-1.1, -1.7], [-1.6, 1.2]])\n    idx = np.array([1, 1, 1, 0, 0, 0, 2])\n    K = 3\n    centroids = target(X, idx, K)\n    expected_centroids = np.array([[0.13333333,  0.43333333],\n                                   [-1.33333333, -0.5      ],\n                                   [-1.6,        1.2       ]])\n    \n    assert type(centroids) == np.ndarray, \"Wrong type\"\n    assert centroids.shape == (K, X.shape[1]), f\"Wrong shape. Expected: {(len(X),)} got: {idx.shape}\"\n    assert np.allclose(centroids, expected_centroids), f\"Wrong values. Expected: {expected_centroids}, got: {centroids}\"\n    \n    X = np.array([[2, 2.5], [2.5, 2.5], [-1.5, -1.5],\n                  [2, 2], [-1.5, -1], [-1, -1]])\n    idx = np.array([0, 0, 1, 0, 1, 1])\n    K = 2\n    centroids = target(X, idx, K)\n    expected_centroids = np.array([[[ 2.16666667,  2.33333333],\n                                    [-1.33333333, -1.16666667]]])\n    \n    assert type(centroids) == np.ndarray, \"Wrong type\"\n    assert centroids.shape == (K, X.shape[1]), f\"Wrong shape. Expected: {(len(X),)} got: {idx.shape}\"\n    assert np.allclose(centroids, expected_centroids), f\"Wrong values. Expected: {expected_centroids}, got: {centroids}\"\n    \n    print(\"\\033[92mAll tests passed!\")\n    \ndef find_closest_centroids_test(target):\n    # With 2 centroids\n    X = np.array([[-1, -1], [-1.5, -1.5], [-1.5, -1],\n                  [2, 2],[2.5, 2.5],[2, 2.5]])\n    initial_centroids = np.array([[-1, -1], [2, 2]])\n    idx = target(X, initial_centroids)\n    \n    assert type(idx) == np.ndarray, \"Wrong type\"\n    assert idx.shape == (len(X),), f\"Wrong shape. Expected: {(len(X),)} got: {idx.shape}\"\n    assert np.allclose(idx, [0, 0, 0, 1, 1, 1]), \"Wrong values\"\n    \n    # With 3 centroids\n    X = np.array([[-1, -1], [-1.5, -1.5], [-1.5, 1],\n                  [-1, 1.5], [2.5, 1.5], [2, 2]])\n    initial_centroids = np.array([[2.5, 2], [-1, -1], [-1.5, 1.]])\n    idx = target(X, initial_centroids)\n    \n    assert type(idx) == np.ndarray, \"Wrong type\"\n    assert idx.shape == (len(X),), f\"Wrong shape. Expected: {(len(X),)} got: {idx.shape}\"\n    assert np.allclose(idx, [1, 1, 2, 2, 0, 0]), f\"Wrong values. Expected {[2, 2, 0, 0, 1, 1]}, got: {idx}\"\n    \n    # With 3 centroids\n    X = np.array([[-1, -1], [-1.5, -1.5], [-1.5, 1],\n                  [-1, 1.5], [2.5, 1.5], [-1.1, -1.7], [-1.6, 1.2]])\n    initial_centroids = np.array([[2.5, 2], [-1, -1], [-1.5, 1.]])\n    idx = target(X, initial_centroids)\n    \n    assert type(idx) == np.ndarray, \"Wrong type\"\n    assert idx.shape == (len(X),), f\"Wrong shape. Expected: {(len(X),)} got: {idx.shape}\"\n    assert np.allclose(idx, [1, 1, 2, 2, 0, 1, 2]), f\"Wrong values. Expected {[2, 2, 0, 0, 1, 1]}, got: {idx}\"\n    \n    print(\"\\033[92mAll tests passed!\")"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/C3W1A/C3W1A1/utils.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef load_data():\n    X = np.load(\"data/ex7_X.npy\")\n    return X\n\ndef draw_line(p1, p2, style=\"-k\", linewidth=1):\n    plt.plot([p1[0], p2[0]], [p1[1], p2[1]], style, linewidth=linewidth)\n\ndef plot_data_points(X, idx):\n    # plots data points in X, coloring them so that those with the same\n    # index assignments in idx have the same color\n    plt.scatter(X[:, 0], X[:, 1], c=idx)\n    \ndef plot_progress_kMeans(X, centroids, previous_centroids, idx, K, i):\n    # Plot the examples\n    plot_data_points(X, idx)\n    \n    # Plot the centroids as black 'x's\n    plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', c='k', linewidths=3)\n    \n    # Plot history of the centroids with lines\n    for j in range(centroids.shape[0]):\n        draw_line(centroids[j, :], previous_centroids[j, :])\n    \n    plt.title(\"Iteration number %d\" %i)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/C3W1A/C3W1A2/C3_W1_Anomaly_Detection.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Anomaly Detection\\n\",\n    \"\\n\",\n    \"In this exercise, you will implement the anomaly detection algorithm and apply it to detect failing servers on a network. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Packages ](#1)\\n\",\n    \"- [ 2 - Anomaly detection](#2)\\n\",\n    \"  - [ 2.1 Problem Statement](#2.1)\\n\",\n    \"  - [ 2.2  Dataset](#2.2)\\n\",\n    \"  - [ 2.3 Gaussian distribution](#2.3)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"    - [ Exercise 2](#ex02)\\n\",\n    \"  - [ 2.4 High dimensional dataset](#2.4)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages \\n\",\n    \"\\n\",\n    \"First, let's run the cell below to import all the packages that you will need during this assignment.\\n\",\n    \"- [numpy](www.numpy.org) is the fundamental package for working with matrices in Python.\\n\",\n    \"- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\\n\",\n    \"- ``utils.py`` contains helper functions for this assignment. You do not need to modify code in this file.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from utils import *\\n\",\n    \"\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Anomaly detection\\n\",\n    \"\\n\",\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Problem Statement\\n\",\n    \"\\n\",\n    \"In this exercise, you will implement an anomaly detection algorithm to\\n\",\n    \"detect anomalous behavior in server computers.\\n\",\n    \"\\n\",\n    \"The dataset contains two features - \\n\",\n    \"   * throughput (mb/s) and \\n\",\n    \"   * latency (ms) of response of each server.\\n\",\n    \"\\n\",\n    \"While your servers were operating, you collected $m=307$ examples of how they were behaving, and thus have an unlabeled dataset $\\\\{x^{(1)}, \\\\ldots, x^{(m)}\\\\}$. \\n\",\n    \"* You suspect that the vast majority of these examples are “normal” (non-anomalous) examples of the servers operating normally, but there might also be some examples of servers acting anomalously within this dataset.\\n\",\n    \"\\n\",\n    \"You will use a Gaussian model to detect anomalous examples in your\\n\",\n    \"dataset. \\n\",\n    \"* You will first start on a 2D dataset that will allow you to visualize what the algorithm is doing.\\n\",\n    \"* On that dataset you will fit a Gaussian distribution and then find values that have very low probability and hence can be considered anomalies. \\n\",\n    \"* After that, you will apply the anomaly detection algorithm to a larger dataset with many dimensions. \\n\",\n    \"\\n\",\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2  Dataset\\n\",\n    \"\\n\",\n    \"You will start by loading the dataset for this task. \\n\",\n    \"- The `load_data()` function shown below loads the data into the variables `X_train`, `X_val` and `y_val` \\n\",\n    \"    - You will use `X_train` to fit a Gaussian distribution \\n\",\n    \"    - You will use `X_val` and `y_val` as a cross validation set to select a threshold and determine anomalous vs normal examples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Load the dataset\\n\",\n    \"X_train, X_val, y_val = load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### View the variables\\n\",\n    \"Let's get more familiar with your dataset.  \\n\",\n    \"- A good place to start is to just print out each variable and see what it contains.\\n\",\n    \"\\n\",\n    \"The code below prints the first five elements of each of the variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first 5 elements of X_train are:\\n\",\n      \" [[13.04681517 14.74115241]\\n\",\n      \" [13.40852019 13.7632696 ]\\n\",\n      \" [14.19591481 15.85318113]\\n\",\n      \" [14.91470077 16.17425987]\\n\",\n      \" [13.57669961 14.04284944]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Display the first five elements of X_train\\n\",\n    \"print(\\\"The first 5 elements of X_train are:\\\\n\\\", X_train[:5])  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first 5 elements of X_val are\\n\",\n      \" [[15.79025979 14.9210243 ]\\n\",\n      \" [13.63961877 15.32995521]\\n\",\n      \" [14.86589943 16.47386514]\\n\",\n      \" [13.58467605 13.98930611]\\n\",\n      \" [13.46404167 15.63533011]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Display the first five elements of X_val\\n\",\n    \"print(\\\"The first 5 elements of X_val are\\\\n\\\", X_val[:5])  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The first 5 elements of y_val are\\n\",\n      \" [0 0 0 0 0]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Display the first five elements of y_val\\n\",\n    \"print(\\\"The first 5 elements of y_val are\\\\n\\\", y_val[:5])  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Another useful way to get familiar with your data is to view its dimensions.\\n\",\n    \"\\n\",\n    \"The code below prints the shape of `X_train`, `X_val` and `y_val`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of X_train is: (307, 2)\\n\",\n      \"The shape of X_val is: (307, 2)\\n\",\n      \"The shape of y_val is:  (307,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of X_train is:', X_train.shape)\\n\",\n    \"print ('The shape of X_val is:', X_val.shape)\\n\",\n    \"print ('The shape of y_val is: ', y_val.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Visualize your data\\n\",\n    \"\\n\",\n    \"Before starting on any task, it is often useful to understand the data by visualizing it. \\n\",\n    \"- For this dataset, you can use a scatter plot to visualize the data (`X_train`), since it has only two properties to plot (throughput and latency)\\n\",\n    \"\\n\",\n    \"- Your plot should look similar to the one below\\n\",\n    \"<img src=\\\"images/figure1.png\\\" width=\\\"500\\\" height=\\\"500\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Create a scatter plot of the data. To change the markers to blue \\\"x\\\",\\n\",\n    \"# we used the 'marker' and 'c' parameters\\n\",\n    \"plt.scatter(X_train[:, 0], X_train[:, 1], marker='x', c='b') \\n\",\n    \"\\n\",\n    \"# Set the title\\n\",\n    \"plt.title(\\\"The first dataset\\\")\\n\",\n    \"# Set the y-axis label\\n\",\n    \"plt.ylabel('Throughput (mb/s)')\\n\",\n    \"# Set the x-axis label\\n\",\n    \"plt.xlabel('Latency (ms)')\\n\",\n    \"# Set axis range\\n\",\n    \"plt.axis([0, 30, 0, 30])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.3\\\"></a>\\n\",\n    \"### 2.3 Gaussian distribution\\n\",\n    \"\\n\",\n    \"To perform anomaly detection, you will first need to fit a model to the data’s distribution.\\n\",\n    \"\\n\",\n    \"* Given a training set $\\\\{x^{(1)}, ..., x^{(m)}\\\\}$ you want to estimate the Gaussian distribution for each\\n\",\n    \"of the features $x_i$. \\n\",\n    \"\\n\",\n    \"* Recall that the Gaussian distribution is given by\\n\",\n    \"\\n\",\n    \"   $$ p(x ; \\\\mu,\\\\sigma ^2) = \\\\frac{1}{\\\\sqrt{2 \\\\pi \\\\sigma ^2}}\\\\exp^{ - \\\\frac{(x - \\\\mu)^2}{2 \\\\sigma ^2} }$$\\n\",\n    \"\\n\",\n    \"   where $\\\\mu$ is the mean and $\\\\sigma^2$ controls the variance.\\n\",\n    \"   \\n\",\n    \"* For each feature $i = 1\\\\ldots n$, you need to find parameters $\\\\mu_i$ and $\\\\sigma_i^2$ that fit the data in the $i$-th dimension $\\\\{x_i^{(1)}, ..., x_i^{(m)}\\\\}$ (the $i$-th dimension of each example).\\n\",\n    \"\\n\",\n    \"### 2.2.1 Estimating parameters for a Gaussian\\n\",\n    \"\\n\",\n    \"**Implementation**: \\n\",\n    \"\\n\",\n    \"Your task is to complete the code in `estimate_gaussian` below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Please complete the `estimate_gaussian` function below to calculate `mu` (mean for each feature in `X`)and `var` (variance for each feature in `X`). \\n\",\n    \"\\n\",\n    \"You can estimate the parameters, ($\\\\mu_i$, $\\\\sigma_i^2$), of the $i$-th\\n\",\n    \"feature by using the following equations. To estimate the mean, you will\\n\",\n    \"use:\\n\",\n    \"\\n\",\n    \"$$\\\\mu_i = \\\\frac{1}{m} \\\\sum_{j=1}^m x_i^{(j)}$$\\n\",\n    \"\\n\",\n    \"and for the variance you will use:\\n\",\n    \"$$\\\\sigma_i^2 = \\\\frac{1}{m} \\\\sum_{j=1}^m (x_i^{(j)} - \\\\mu_i)^2$$\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED FUNCTION: estimate_gaussian\\n\",\n    \"\\n\",\n    \"def estimate_gaussian(X): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Calculates mean and variance of all features \\n\",\n    \"    in the dataset\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        X (ndarray): (m, n) Data matrix\\n\",\n    \"    \\n\",\n    \"    Returns:\\n\",\n    \"        mu (ndarray): (n,) Mean of all features\\n\",\n    \"        var (ndarray): (n,) Variance of all features\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    m, n = X.shape\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    mu = 1 / m * np.sum(X, axis = 0)\\n\",\n    \"    var = 1 / m * np.sum((X - mu) ** 2, axis = 0)\\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"        \\n\",\n    \"    return mu, var\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"  \\n\",\n    \"   * You can implement this function in two ways: \\n\",\n    \"      * 1 - by having two nested for loops - one looping over the **columns** of `X` (each feature) and then looping over each data point. \\n\",\n    \"      * 2 - in a vectorized manner by using `np.sum()` with `axis = 0` parameter (since we want the sum for each column)\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"   * Here's how you can structure the overall implementation of this function for the vectorized implementation:\\n\",\n    \"     ```python  \\n\",\n    \"    def estimate_gaussian(X): \\n\",\n    \"        m, n = X.shape\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        mu = # Your code here to calculate the mean of every feature\\n\",\n    \"        var = # Your code here to calculate the variance of every feature \\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"        \\n\",\n    \"        return mu, var\\n\",\n    \"    ```\\n\",\n    \"\\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate `mu` and `var`.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate mu</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.sum.html\\\">np.sum</a> to with `axis = 0` parameter to get the sum for each column of an array\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate mu</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute mu as <code>mu = 1 / m * np.sum(X, axis = 0)</code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate var</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can use <a href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.sum.html\\\">np.sum</a> to with `axis = 0` parameter to get the sum for each column of an array and <code>**2</code> to get the square.\\n\",\n    \"          <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate var</b></font></summary>\\n\",\n    \"               &emsp; &emsp; You can compute var as <code> var = 1 / m * np.sum((X - mu) ** 2, axis = 0)</code>\\n\",\n    \"           </details>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can check if your implementation is correct by running the following test code:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Mean of each feature: [14.11222578 14.99771051]\\n\",\n      \"Variance of each feature: [1.83263141 1.70974533]\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Estimate mean and variance of each feature\\n\",\n    \"mu, var = estimate_gaussian(X_train)              \\n\",\n    \"\\n\",\n    \"print(\\\"Mean of each feature:\\\", mu)\\n\",\n    \"print(\\\"Variance of each feature:\\\", var)\\n\",\n    \"    \\n\",\n    \"# UNIT TEST\\n\",\n    \"from public_tests import *\\n\",\n    \"estimate_gaussian_test(estimate_gaussian)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Mean of each feature: <b>  </td> \\n\",\n    \"    <td> [14.11222578 14.99771051]</td> \\n\",\n    \"   </tr>    \\n\",\n    \"   <tr>\\n\",\n    \"    <td> <b>Variance of each feature: <b>  </td>\\n\",\n    \"     <td> [1.83263141 1.70974533] </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that you have completed the code in `estimate_gaussian`, we will visualize the contours of the fitted Gaussian distribution. \\n\",\n    \"\\n\",\n    \"You should get a plot similar to the figure below. \\n\",\n    \"<img src=\\\"images/figure2.png\\\" width=\\\"500\\\" height=\\\"500\\\">\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"From your plot you can see that most of the examples are in the region with the highest probability, while the anomalous examples are in the regions with lower probabilities.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Returns the density of the multivariate normal\\n\",\n    \"# at each data point (row) of X_train\\n\",\n    \"p = multivariate_gaussian(X_train, mu, var)\\n\",\n    \"\\n\",\n    \"#Plotting code \\n\",\n    \"visualize_fit(X_train, mu, var)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.2.2 Selecting the threshold $\\\\epsilon$\\n\",\n    \"\\n\",\n    \"Now that you have estimated the Gaussian parameters, you can investigate which examples have a very high probability given this distribution and which examples have a very low probability.  \\n\",\n    \"\\n\",\n    \"* The low probability examples are more likely to be the anomalies in our dataset. \\n\",\n    \"* One way to determine which examples are anomalies is to select a threshold based on a cross validation set. \\n\",\n    \"\\n\",\n    \"In this section, you will complete the code in `select_threshold` to select the threshold $\\\\varepsilon$ using the $F_1$ score on a cross validation set.\\n\",\n    \"\\n\",\n    \"* For this, we will use a cross validation set\\n\",\n    \"$\\\\{(x_{\\\\rm cv}^{(1)}, y_{\\\\rm cv}^{(1)}),\\\\ldots, (x_{\\\\rm cv}^{(m_{\\\\rm cv})}, y_{\\\\rm cv}^{(m_{\\\\rm cv})})\\\\}$, where the label $y=1$ corresponds to an anomalous example, and $y=0$ corresponds to a normal example. \\n\",\n    \"* For each cross validation example, we will compute $p(x_{\\\\rm cv}^{(i)})$. The vector of all of these probabilities $p(x_{\\\\rm cv}^{(1)}), \\\\ldots, p(x_{\\\\rm cv}^{(m_{\\\\rm cv)}})$ is passed to `select_threshold` in the vector `p_val`. \\n\",\n    \"* The corresponding labels $y_{\\\\rm cv}^{(1)}, \\\\ldots, y_{\\\\rm cv}^{(m_{\\\\rm cv)}}$ is passed to the same function in the vector `y_val`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"Please complete the `select_threshold` function below to find the best threshold to use for selecting outliers based on the results from a validation set (`p_val`) and the ground truth (`y_val`). \\n\",\n    \"\\n\",\n    \"* In the provided code `select_threshold`, there is already a loop that will try many different values of $\\\\varepsilon$ and select the best $\\\\varepsilon$ based on the $F_1$ score. \\n\",\n    \"\\n\",\n    \"* You need implement code to calculate the F1 score from choosing `epsilon` as the threshold and place the value in `F1`. \\n\",\n    \"\\n\",\n    \"  * Recall that if an example $x$ has a low probability $p(x) < \\\\varepsilon$, then it is classified as an anomaly. \\n\",\n    \"        \\n\",\n    \"  * Then, you can compute precision and recall by: \\n\",\n    \"   $$\\\\begin{aligned}\\n\",\n    \"   prec&=&\\\\frac{tp}{tp+fp}\\\\\\\\\\n\",\n    \"   rec&=&\\\\frac{tp}{tp+fn},\\n\",\n    \"   \\\\end{aligned}$$ where\\n\",\n    \"    * $tp$ is the number of true positives: the ground truth label says it’s an anomaly and our algorithm correctly classified it as an anomaly.\\n\",\n    \"    * $fp$ is the number of false positives: the ground truth label says it’s not an anomaly, but our algorithm incorrectly classified it as an anomaly.\\n\",\n    \"    * $fn$ is the number of false negatives: the ground truth label says it’s an anomaly, but our algorithm incorrectly classified it as not being anomalous.\\n\",\n    \"\\n\",\n    \"  * The $F_1$ score is computed using precision ($prec$) and recall ($rec$) as follows:\\n\",\n    \"    $$F_1 = \\\\frac{2\\\\cdot prec \\\\cdot rec}{prec + rec}$$ \\n\",\n    \"\\n\",\n    \"**Implementation Note:** \\n\",\n    \"In order to compute $tp$, $fp$ and $fn$, you may be able to use a vectorized implementation rather than loop over all the examples.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"If you get stuck, you can check out the hints presented after the cell below to help you with the implementation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: select_threshold\\n\",\n    \"\\n\",\n    \"def select_threshold(y_val, p_val): \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Finds the best threshold to use for selecting outliers \\n\",\n    \"    based on the results from a validation set (p_val) \\n\",\n    \"    and the ground truth (y_val)\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"        y_val (ndarray): Ground truth on validation set\\n\",\n    \"        p_val (ndarray): Results on validation set\\n\",\n    \"        \\n\",\n    \"    Returns:\\n\",\n    \"        epsilon (float): Threshold chosen \\n\",\n    \"        F1 (float):      F1 score by choosing epsilon as threshold\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"\\n\",\n    \"    best_epsilon = 0\\n\",\n    \"    best_F1 = 0\\n\",\n    \"    F1 = 0\\n\",\n    \"    \\n\",\n    \"    step_size = (max(p_val) - min(p_val)) / 1000\\n\",\n    \"    \\n\",\n    \"    for epsilon in np.arange(min(p_val), max(p_val), step_size):\\n\",\n    \"    \\n\",\n    \"        ### START CODE HERE ### \\n\",\n    \"        predictions = (p_val < epsilon)\\n\",\n    \"        tp = np.sum((predictions == 1) & (y_val == 1))\\n\",\n    \"        fn = np.sum((predictions == 0) & (y_val == 1))\\n\",\n    \"        fp = sum((predictions == 1) & (y_val == 0))\\n\",\n    \"        prec = tp / (tp + fp)\\n\",\n    \"        rec = tp / (tp + fn)\\n\",\n    \"        F1 = 2 * prec * rec / (prec + rec)\\n\",\n    \"        ### END CODE HERE ### \\n\",\n    \"        \\n\",\n    \"        if F1 > best_F1:\\n\",\n    \"            best_F1 = F1\\n\",\n    \"            best_epsilon = epsilon\\n\",\n    \"        \\n\",\n    \"    return best_epsilon, best_F1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"\\n\",\n    \"   * Here's how you can structure the overall implementation of this function for the vectorized implementation:\\n\",\n    \"     ```python  \\n\",\n    \"    def select_threshold(y_val, p_val): \\n\",\n    \"        best_epsilon = 0\\n\",\n    \"        best_F1 = 0\\n\",\n    \"        F1 = 0\\n\",\n    \"    \\n\",\n    \"        step_size = (max(p_val) - min(p_val)) / 1000\\n\",\n    \"    \\n\",\n    \"        for epsilon in np.arange(min(p_val), max(p_val), step_size):\\n\",\n    \"    \\n\",\n    \"            ### START CODE HERE ### \\n\",\n    \"            predictions = # Your code here to calculate predictions for each example using epsilon as threshold\\n\",\n    \"        \\n\",\n    \"            tp = # Your code here to calculate number of true positives\\n\",\n    \"            fp = # Your code here to calculate number of false positives\\n\",\n    \"            fn = # Your code here to calculate number of false negatives\\n\",\n    \"        \\n\",\n    \"            prec = # Your code here to calculate precision\\n\",\n    \"            rec = # Your code here to calculate recall\\n\",\n    \"        \\n\",\n    \"            F1 = # Your code here to calculate F1\\n\",\n    \"            ### END CODE HERE ### \\n\",\n    \"        \\n\",\n    \"            if F1 > best_F1:\\n\",\n    \"                best_F1 = F1\\n\",\n    \"                best_epsilon = epsilon\\n\",\n    \"        \\n\",\n    \"        return best_epsilon, best_F1\\n\",\n    \"    ```\\n\",\n    \"\\n\",\n    \"    If you're still stuck, you can check the hints presented below to figure out how to calculate each variable.\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate predictions</b></font></summary>\\n\",\n    \"           &emsp; &emsp; If an example  𝑥  has a low probability  $p(x) < \\\\epsilon$ , then it is classified as an anomaly. To get predictions for each example (0/ False for normal and 1/True for anomaly), you can use <code>predictions = (p_val < epsilon)</code>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate tp, fp, fn</b></font></summary>\\n\",\n    \"           &emsp; &emsp; \\n\",\n    \"        <ul>\\n\",\n    \"          <li>If you have several binary values in an $n$-dimensional\\n\",\n    \"binary vector, you can find out how many values in this vector are 0 by using:  `np.sum(v == 0)`</li>\\n\",\n    \"          <li>You can also apply a logical *and* operator to such binary vectors. For instance,  `predictions` is a binary vector of the size of your number of cross validation set, where the $i$-th element is 1 if your algorithm considers $x_{\\\\rm cv}^{(i)}$ an anomaly, and 0 otherwise. </li>\\n\",\n    \"          <li>You can then, for example, compute the number of false positives using:  \\n\",\n    \"<code>fp = sum((predictions == 1) & (y_val == 0))</code>.</li>\\n\",\n    \"        </ul>\\n\",\n    \"         <details>\\n\",\n    \"              <summary><font size=\\\"2\\\" color=\\\"blue\\\"><b>&emsp; &emsp; More hints to calculate tp, fn</b></font></summary>\\n\",\n    \"               &emsp; &emsp;\\n\",\n    \"             <ul>\\n\",\n    \"              <li>You can compute tp as <code> tp = np.sum((predictions == 1) & (y_val == 1))</code></li>\\n\",\n    \"              <li>You can compute tn as <code> fn = np.sum((predictions == 0) & (y_val == 1))</code></li>  \\n\",\n    \"              </ul>\\n\",\n    \"          </details>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate precision</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can calculate precision as <code>prec = tp / (tp + fp)</code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate recall</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can calculate recall as <code>rec = tp / (tp + fn)</code>\\n\",\n    \"    </details>\\n\",\n    \"        \\n\",\n    \"    <details>\\n\",\n    \"          <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Hint to calculate F1</b></font></summary>\\n\",\n    \"           &emsp; &emsp; You can calculate F1 as <code>F1 = 2 * prec * rec / (prec + rec)</code>\\n\",\n    \"    </details>\\n\",\n    \"    \\n\",\n    \"</details>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You can check your implementation using the code below\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best epsilon found using cross-validation: 8.990853e-05\\n\",\n      \"Best F1 on Cross Validation Set: 0.875000\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"p_val = multivariate_gaussian(X_val, mu, var)\\n\",\n    \"epsilon, F1 = select_threshold(y_val, p_val)\\n\",\n    \"\\n\",\n    \"print('Best epsilon found using cross-validation: %e' % epsilon)\\n\",\n    \"print('Best F1 on Cross Validation Set: %f' % F1)\\n\",\n    \"    \\n\",\n    \"# UNIT TEST\\n\",\n    \"select_threshold_test(select_threshold)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Best epsilon found using cross-validation: <b>  </td> \\n\",\n    \"    <td> 8.99e-05</td> \\n\",\n    \"   </tr>    \\n\",\n    \"   <tr>\\n\",\n    \"    <td> <b>Best F1 on Cross Validation Set: <b>  </td>\\n\",\n    \"     <td> 0.875 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we will run your anomaly detection code and circle the anomalies in the plot (Figure 3 below).\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/figure3.png\\\" width=\\\"500\\\" height=\\\"500\\\">\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x7f9000717290>]\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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ilTiqlTgYHB+ekmBqNRqPJgBxVEiJiEpFGQEWghVKqng15fxaRZiLSzM3NLeeE1Gg0Gk2G5Ip1k4hEAHuB9kCgUqocgOVvUG7IoNFoNBrbyUnrJjellIvl/0LA88BVYCPQz5KsH7Ahp2TQaDQaTfZwzMGyywGLlFL5MJTRahHZpJQ6AqxWSg0EvIEuOSiDRqPRaLJBjikJETkPNE5nfyjwXE7Vq9FoNBr7oT2uNRqNRpMhWkloNBqNJkO0ktBoNBpNhmglodFoNJoM0UpCo9FoNBmilYRGo9FoMkQrCY1Go9FkiFYSGo1Go8kQrSQ0Go1GkyFaSWg0Go0mQ3IydpNG81AS4xPxunSPUL8wIgIjCQuIICwggqjQKOJjEkiITbRsCSQnpmBKMVk2M6YUE4igHBxQCpSDAw4OCsf8jjgVcCJ/QSecCjhRoFB+nF0KU8TFmSIuzhQtUQSX0sUpX6Ms5auXoUxlN/I55nvUl0KjybNoJaHJFcxmMzdO3ebK0RtcP32LG6du438rkAo1y+HmXooSZVwoWdaFSnUqUNztMQoVKUhB5wIUdC5AgcIFyF/QCUcnR/I5OpDPMR8O+RxQSiEimM0CIphMZkzJJpITk0lKSCY5MZmEuERiI+KIDo8hJjyW6PAYbp/z4uC6Y/jdDCDMPxy3Sq7UalqNJ15tSvP2jSjuWuxRXy6NJs+glYQmy4gIsZFxhPqHExUSTUJcIknxSSTGJ5EYl0hKUgopySZ8rvlxeMNxChcrTIOnHqNuy9p0HPEyVetXIn8Bp0d6DkmJyfjfDuTy4WscXHuUWW//QpV6lWj2QkOKuRbFKb8jjpbNqYATzsUK4eziTBGXwjgXN3oojk76NdL8d1Ei8qhlyJRmzZrJyZMnH7UY/68REW6fv8vhDSc4u+ciIfdCCfULJ59TPkqVK0Ex16IUKFyAAoXyG38L5sepgCOOTo6UqlCSVh2bU6lOhUd9GpmSlJjM+X2XObv7AnHRCRZFZ2xJCcnERcUTExFLbEQsMRFxxMckUKFmWao3rEL1hlWo1rAydVrUwLm486M+FY0GpdQpEWmWrTK0ktBkhIhw6dBVDqw5xuENx0EpWndsTrP2jSlbtTSlyrlQqEihRy3mIyUpIYm7l+9x66wXt855cfv8XW6fu8tzvZ6i85jXKFetzKMWUfP/GK0kNHbDbDYT5B1CeGAksZFxhPiG8efc7cRFxfFsz6do/UYLqtavhFLqUYua5wnxC2PDrK1s+WUXjZ6tR9suLXEpXdyyFaOIizMODtqwUJPzaCWhyTK3z9/l6KZTeF+5h/eVe/hc9aNoySKULOdijLWXKMKzPdrQskOzXPmgiQgxEbEE+4QSFhBBXFQccVHxxEXHEx+dQEJcIqZkY47DlGLClGxCLNZNDg4KlMLBQZG/oBP5C+WnQCFj6KugcwGKliqKi1sxirkWpbhrUYqWLEK+fDlv0RQXHc+2X3dzdu9FYsJjiQiKJCIokviYBIq7FaPZi414oW9b6j/9mFYamhxBKwmNzXhf9WXxp6u4sP8Kz/Z8iir13Kn8eEXc61TAuVjhXJEhPjaBK0dvcPHAFa4cu06gVzDB90JxyOdAaXdXSpZzoXCxwhQuVojCRQpRqGhBChQugKOTI45O+cjnmI98TvnuWzeJWTCbzYhZSEpItkyeJ5IYn0RCbCLRYdFEBEcRGRxFZEg0iXGJVKxdnqr1KlH5cXeq1HOnTosalCjjkivnn5yUTIhvGAfXHmfn4r3ERcXzfO+neb5vWyrWLJcrMmj+f6CVhMYqYqPi8Lnqy8a52zm++TQe775Ox5EvU8i5oN3rEhESYhOIjYwjITbRMEdNTCE2Mo7TO89xbu8lvC76UK1RFeq3qUPd1nUoX70MrhVL5Z6SionH+4ovXpd88Lrog9clb64cvYF77fI88WpT6j/9GAWdC1om4fNTsHCB+5PyOeFTceucFzsX72PXsgM0fq4eb4x8hXJVS1PcrZjuYWiyhVYSmv8hMT6RvasOc/vcXe5euYf35XtEh8VQvkZZWnZohse7r1PEJXuWN2EB4XhdMsr2vnKPu1fuEXQ3mNjIOGKj4slfwInCxQtT0LkAThbT0QKF89Pg6cdp+kJD6jxRgwKFCtjpjO1DclIyFw5c5dimU1w9fsPokSQkkRhnmPMmWkx7i5ZwplT5kpQqX4JS5UpQtmoZmrVvRM0mVbP9QY+LjmftjM0cWHuUUN8w4qLica9TgVeHvMALfZ/+f28koLEdrSQ09zGbzexefpAFE5dTrUFlGjz9OJXrGkNJpSu5ZvsDZjabObHtLGtnbOLGqdtUqV+Jyo9VpPLj7lR6rAJlq5amiIszhYsV+s/6DZjNZiKDowj1CyfUP5xQv3C8r9zj+JbTxEbF88QrTXjy9aY0eb4BBQtnXwkmJSRx+ch1NszZxrm9l3ixb1s6jGhP+epl7XA2mv8PaCWhwWQycfHAVX7+YAlKwdBv+1H/qcfsU3aKiYjgKI5sPMnamZvJX9CJzqNfo223Vo/cCS6vce+GP8c2neLoppPcPu/Nq0Oe5+WBz1GynItdek2Bd4P5c+52ti3YTcN29ej/WTcq1iqvrc00DyVPKwmllDuwGCgLmIGfRWSmUupTYDAQbEk6QUS2PKwsrST+SXxsAie3neXA2qMc33KGkmVd6PWRB+16tM5Sj8FsNnP58DX2rDyE91VfwwonMJLo8FiKuBTm8Va16Tz6NRq0fVx/lKzA96Y/a77fxOGNJ4gKiUY5KMpVK0P1RobDXfVGVajRuGqWwn8kxCWyZvom1s/aiinFRJV67lStV4kX+7ejdrPqOXA2mn8zeV1JlAPKichppVRR4BTwBtAViBGRb60tSysJY0L4+NYzbP11F2d2XaDOEzVp8+YTtH6jOSXLlshSmXcuerN72QH2rDxEoSIFadejDbWbV6dEGRdcShejuGuxRxr8LjXsR7BPCJEh0STEJpIYl0i85a+IGJZOjvnux3Qq4uJ839S1WKmiOBcv/Egnf0WEhLhEfG/4Gw53Fqe7W2e9aNSuLv0md6Nq/cpZKjs8MII7F324ceo2a2dupkX7RgyY2jPXrLQ0eZ88rST+pyKlNgCzgdb8l5WEtzds3AgREeDiAh07grt7toq8d8OfH0cvJOBOEN0+6EjLDs0oVrJolsu7ePAKCz5aQcDtINp1b82zvZ6iWoPKj6yXICKE+IZx9fhNrh67wZ0LdwnyDiHIOwQAN/dSuJQufj/YX0HnAhQsVADloO5HhDWZTKQkpRATEUd0aDSRIdFEhkSRnJiCe+3yVK1fiSr1KlG1njvVG1elVLkSj7RXlBifyKafdrLq6/U0bFePvp90wb121sOWxEbGsnTKGnYu3kuP8Z3o+Hb7/+zckMZ6/jVKQilVBdgP1APeBfoDUcBJYKyIhKeTZwgwBKBSpUpN7969m+NyZouAAHj7bVi3Dszmv/c7OMCbb8Ls2VDW+gnHe9f92L5wD2f3XMT3hj89JnTmjZHtccpv21yA2Wzm8IYT7F11CL9bgQTcDqRwscL0+aQLz/d52q5OZSJCWEAEXhe98broQ7BPCNERsUSHWSKwhsWQEJf4d5TWhGSSEpMxm8y4uBWjdosa1GlRkxqNq1Kmsitu7q44Fy+crY95Qlwidy/fw+uiN3cueON1yWh5R4VG4+iUzwje5+RI/kL5KVSk4N9b0YKUKO2Ca8WSlHZ3xc29FKUru+Feu7xdeybxMfGsn7WN37/biFN+R2q3qMHjLWtTt1VtajWrZvN8hvdVX+aOWcj1k7d5eeCzvPnOq5Qql7Wepubfz79CSSiligD7gC9EZK1SqgwQAggwBWNIasDDysjzPYmAAGjdGm7fBicn6NABateGa9dgwwZISYFq1eDwYSjz8Fg+CXGJrPxyHX/+tINXBj9Ps5ca8tiTtWyeKDalmNiz8hArp60jf6H8dBjensqPV6RctdIUdy1mt1Z0ZEgU2xfu4ejmU3hd9MHBQRmt9rqVKF3JlaIli1C0ZBGKlDDWcijobIT9zm8JAOhUwIl8jvlyvVUvIphSTCQnpdxXWgkxCcRbtrioeMICIgi5F0qwTwhB90LxuxlAUnwSLV5pwpOvNaXJ8/XtZpZqNpsJ9gnl6rEbXD5ynctHruF10YfHWtbC8/MePPZETZvK87sVwNoZm9m9/ABPdX6SLu91oGKt8naRVfPvwR5KwvBYzaENcAK2A+9mcLwKcDGzcpo2bSp5ms6dRUD8KzSRQ6t8/nHo0Cof8a/QRASMdA/hyJ8npXfVYTKl23cSfC8kS6IkJyXL5p93Sp/qI2RM20lyYvtZMZvNWSorI8xms1w6ck2+6jdL3ijRT77qN0tObD8rYYERdq0nL+Jz3U/WTN8k7z8/WV4v2lsmvjZVjm4+JSaTye51JcQlyNYFu6V7xSHyRc/pEuAVZHMZ4UER8tvHK8Wj9AD5rOt3WSpD8+8FOCnZ/I5n2pNQSpXGmEcoD8QDFy0VmzPJp4BFQJiIjE6zv5yI+Fv+HwM8ISLdH1ZWnu5JeHtD1aqQLx+Hl96m44iKrF4N7drBnj3QtStsmO1Dq97VjGEoL6//maM4t+8SCz9aQURQFCNnD6TpCw2trj4pMZmbZ+5w9egNrhw3Ql2416lA30+6UK9N9kxho8KiObb5NAF3ggj1CycswPANCPENo0Ch/Lz+1ou85NmOYqWyPj/ybyY2Ko6Da4+xftZWQu6FUrJ8CYq7FsPNvRQ1G1ejRpOqVG9UJdue7fGxCfz+zUbWz9pClXqVaPl6MzqMeMmmoaj42ATWTt/MmhmbaNWhOe0HtKNu6zraWu0/To4ONyml2gHjgJLAGSAIKAjUAqoDfwDfiUhUBvnbAAeACxgmsAATgB5AI4zhJi9gaKrSyIg8rSRmz4aRI6FzZ/jjj/uKYdgwmDuX+woDDw9Ys8ZIP2IEAFGh0fz8/hJO/3WewV/3oU2nFlbPOZhMJjbP+4slk1fjWrEUdVrU4LEna1HniZq41866/Xx8bAJH/zzF7hUHOL/vMo2fq0/lxypSqnxJSpZzoWS5EpQs62IXB73/CmKZeDdiQ0XhfzuIm6dvc+PMHe5e8qFMFTee6vQknca8mi2Dg5iIWK6duMmmeTu5euwGfT7uwkue7WyyQAsLCOevJfvZtnAPZpOZN95+mdeHvaiXcP2PkqPDTcA3QKUMjjlimLN2zm5XxpotTw83TZliDCVNmHB/16RJxq5Jk9KkGz/e2DllipjNZtmxaK90KTtQ5ryzQGKj4myq8vz+yzKk0Vh595mP5dY5L7ucht+tAPl24I/S0aWvjGs/RXYs2isxkbF2Kfv/M8lJyXL91C35btBcebNUf1n40QqJDI3KdrlXjl2X9579RPrVGil7Vx2yebjLbDbLpcNX5b3nPpVhTd+XaydvZlsmTd6D3Bhuygv8l3oSoR9NYerBZOKj43nnp6FWO0CJCAfWHGXbgt14XfRhyDd9aNu1VZZ7DD7XfDm5/Rw+1/y4d92Pm2fu0HFEezoMfynX7ezF4g8RFRpNVGg0MRFx9+MlpS6HmpKUgtmUau5qxmwyOqdKKWNzMEKF53Ny/MeSo/kLOFKwSEGcixWmUNGCFC5WmGKWyfTcHmrxvxPIiqnrOLjuGHWeqEmd5jVo0PZxHnuyZpa8skWE03+d59cJy0GE53o9zVOdn6B0JTebyti5eB/zP1xKmzdb0GuSB67lS9osiyZvkivWTUqpd4CFQDTwC9AYGCciO7JTsS3kaSVh5ZxEy97VEJOJYSU8eHmyJ6+/ZX0X3+eaL7Pe/pXI4Ci6j3uTJ19vmuVxbq9LPiz7Yg1nd12gTacnqPRYRSrWKkedJ2pStESRLJVpLeGBEdw6d9eyhoUv3lfv4Xvdn8iQaAoUyk+xUkUoWqooRUs434+6mr9QfgoUzI9jfkcc8hkOcw75HMiXzxjq+rvFw99KJNmwWkpJSiEpMYn46ATiouON9Smi4ogMiSYlKQXXiqVwcze2qnUr0aDt49RoXDXHh15CfEO5fvI2lw5f4/z+y3hd9KZmk2o0fq4+HUe0t3mOx2w2c2rHOfb/cZQjG09QtmppnvZoyWtvvUjhotZZX0WFRrNy2rFKW+8AACAASURBVDq2LdxDh+Ev0XNiZx165T9AbimJcyLSUCn1EjACmAQsFJEm2anYFvK0koD7vYSACk24/d16WnX7e2L68Cof3Ee+hnvweS6Vq4fbsT2Udne1qtiEuERWTF3Lpnk76TWxMx3fbp/lD9jt83dZ+vkfXDxwhc5jXuf1YdZ/QLJDiF8YB9ccY/8fR7hzwZsajavgXqcilR6rQKXHKuJeuzwupYvZ7P+RXeKi4wm+F0qwj2HievPMHc7vu0yQTwj12tShYdu6tOrYPFfMRuOi47l0+BqH1h7j4LpjdH2/I2+MfJn8BfPbXJYpxcS5fZfZvnA3F/ZfYdj0/rTp9ITVvaYQ31Bmj/wVn2t+vDt/GHVb1bZZBk3eIbeUxHkRaaCUmgnsFZF1SqkzItI4OxXbQp5XEmn9JBwdDS/rWrUwXb4Cf/5JPrOJ+NLlKXjuFMoKhzoRYf8fR/ll3FLqtKjB0G/74lqhlE0iiQg+1/w4t/cSJ7ad4drxm3R5rwOvDn3BrutIiAh+twK4e/keEUGWhX2CI4mwTOD6XLnHk68342mPljR5oUGeb51GBEdyYf8Vzuy+yMG1R3GtWAr32uUpWqIIxUoVpUwVNyrULEeFGmVxKV3c7kNW3ld9+XX8Mm6d9aJt11a0eLkxDZ+pm6Wyzu27xA/D51O6kis9xnei/lOPWSVv6tDmnHcW0rZLSwZ+2TPPhXbXWEduKYmFQAWgKtAQyIehLJpmp2JbyPNKAjL0uDYrhem1DjjNn5epIx3A3Sv3mDNqARFBkQyf4UmjdvVsEsP7qi8rvlzL6b8ukM/RgUbt6tHwmbo87fGkXRy/RIQrR69z8eBVLh+5xqXD13F0ykf1RlVwcStOcbdixlrObsUoVb4E9Z56LM8rhowwpZi4ePAqQT4hxITHEhkSRYBXEH43A/C9EUByYjJVG1Tm5QHP0q5Ha7t+SK8cu8HJbWf5a9l+3GuX563v+mWpV5OclMy2X3ezZsZmipUqwsjZg6jZpJpVeaPCovlh+HzuXrrHh4tHUqNxVZvr1zxactq6ydHy1wFoArhYfpcCGmR3xtyWLU9bNz1A7KVrsrtNd1lVrLncfusDEW9v6/JFxcm89xZJZzdPWTtzs6Qkp9hUb0xkrPw0dpF0cvWUlV+tF79bAXZ1oktOSpYdi/bKoPpjZMDj78jskb/K7hUHJfDu/1/nrKiwaDm66aRMfG2qdHbzlJ/GLhLfm/52rSMpMUlWf7NBOrl6yrz3FklMREyWyklJSZGtC3aLR5mBMu+9RRIfm2BVPrPZLDsW7xWP0gNkwcTlkpiQlKX6NY8GctK6SSl1ErgHbAO2iYhXtrRRNvhX9CSAs3su8o3nHJo834Ch3/a1egW4g+uOMWfUAho/X5/B03rbZF0U6h/OnhUH+f27P2n+UiMGfmm/KKD+twM5t+8yPlfusWfVISrUKEvXD96g2YsNtRPWA/jfDuTPudvZsWgvdZ6oSYOnHzfmXOqUp2yV0tmeDA8PjGDBhOUc33qGru935IW+bbPkxBgeFMncMQu5cvQGo+YMonl760aNQ/3DmTnsZ/xvBfLJmvd0iI9/CTk+3KSUqgy8DLTHGHI6CGwF9olIYnYqtoW8riQS4hL5dfwyDqw5yrvzh9HiZetevIjgSGaP/JVbZ714d/4wmxYLSkpM5vdvNrJmxiZadmhGh2EvUbt5jayeAmBYuJzccY4zuy5wdvcFEuOTaNiuHlXrVaLpCw2yXf7/BxLiEjnwx1FunrmDzzVffK76EhYQwROvNqHf5G5Ufjx7EYGvn7rF2hmbObrpFE+82oRXBj9Pg6dtX+fjxLYz/DDiF2o3r86IHwZSonTxTPOICJt//oulU35n2vZJVKmbvXPR5Dy5GrsJIw7Ts8DXwHFgc3a7MdZueXm46eKhq9Kv1kiZ2muG1U5SZrNZ9qw8KF3KGl3/hDjruv6pnPrrvHjWGSWTOk6zSyyemIgYWTBxubxZqr9M6jhN1s7cLHcuets95tP/V2Kj4mTV1+vFo/QAmdb3B/G7FZDtMiNDo2TNjE0ysO5o6V97pPy1dL/N9ys+NkF+fn+xdCk7UHYtP2B1/l3L9kuXsgPl4LpjWRFdk4uQW850Sqn8QB2MUBrXRCRJKVVBRHyzpaGsJC/2JESEBRNXsOO3Pbw9exBPdXrCqnzhQZH8MHw+Pld9GfvrcJuiewbeDebXCcu4fPg6w2d60qpD86yKj8lk4ubpO5zYdpYNs7fyxKtN6fNJF8pUtt4RK6cQEZKTUkiITSAxLomE2ASSE1MMH4gUk8UfwoyI4OCgQBmOdMrBAUenfDgVcDIizOY3QoA7Fy+c6ya26REbFcfa6ZtZP3srbd5swbM9n6Ju69rZWvdBRDi//zI/jl5IcddiDJ/haXML/9qJm3w74EfKVS/D2F+GWbVi3sWDV/jGcw51W9dh9Lyh/1rjhP86uWXd9CrwE3ALUBhWTkNFZGt2KraFvKYkUq0+Ar2C+WzjOKu66gBH/jzJjKHzeKFvW/p+2tVqO3hTiom1M7ewcto6Xn/rRbqNeyNbZqwntp3hp7GLUErR8Jm6dBj+UraHQWwhKTGZu5d8uHXuLl4XvQkLCCc8MNJYNjUoiqjQaBwcFAWdC1KgcH4KOhfEqYCx7oPhUOeAQz4HlFJGa8dstHjMJjMpySaSE5NJTjRCgCfGJxEXFY+jUz6cXZwp4lKY4m7FKFu1NOWqlqFcNWOrUs8d52KFc+X8o0Kj2TBnG0c3ncL3hj+Nn6tPi5cb0/zlxln2djalmNgwZxsrpq6l+SuN6fdpN5sUflJiMr99tIJD64/z6boPqFqvUqZ54mMTmNb7Bwo6F2DcklF6nioPkltK4irwmojctPyujjHUVCc7FdtCXlISJ7af5ftBc3naoyUDpvawyuwxPiaeeWMXc2rnOT5YNNKmuYebZ+/w/eCfcC5emNE/DaFCjXJZlv3uZR/mvbcY/9uBDPmmL0++1jRXXmyTycS5PZfYu+owV4/fwPeGP+VrlKV6wypUqVcJt4qlcCltmM6WKFOcYqWK2nVVNRFjCdHYiFhiIuIID4wg4E4QAXeC8L8TiP+tQO5evkft5tVp8XITWrzahEp1KuTKtQkPjODk9nMc33aGUzvO8VzPp+jzaZcsBwKMjYzl92//ZOPc7XR651W6fdjRpl7UzsX7mPfeIrp98Aadxrya6aJUCXGJvP/cZJo+34D+Ux4azFnzCMgtJbFfRJ5O81thTFw//ZBsdiUvKAlTiom5Y37j8MYTvLdgBE2eq29VvmsnbzG15wzqtq7NiJkDrG6tJsYnsmTy72xfuIeB03rzUv9nsvzRCvIJYdVX69n/+xF6jO/E68NfzNHhl5iIWO5d9yPIO4SLB6+yb/VhXCuWol331jRo+zhV6rpnyZs4J4mPTeDs7osc33KaY1tOky+fA093aUXdVrVxcy+Fa8VSFHctmqORbyNDolj0yWoOrDlKr4mdebH/M1n2ig/yDmbm8PkE+4Qy9tfhVscIA8NS69uBP2JKMTF+6TuZ9kjCgyIZ3Xoi3T54g1cGP58leTU5Q06HCu9k+fcFoDKwGmNOogvGvMTY7FRsC3lBSayduZn9fxxhysZxVsU4MplMrJ2+mVVfr2fk7EG07drK6rouH73Ot55zqFK/Em//MICSZW1fflJEOLP7Iht/3Mb5vZd4sd8z9JzY2e5rP6Qkp3Bk40mObzmNz3U/fK/7kxifRMVa5Shd2Y3qDavQrnvrf5XJpIhw54I3e1Yc5M5Fb4LvhRJyL4zEuEQea1mLRu3q0fjZetRuXiNH4jzduXCXRZ+s4tzeyzzTrRX9p3S3ap4gvfPYvfwgP737G10/eIPOY161WsmZzWZWfbWBHYv2MPPQF5k+N/eu+/HBC5/RumMLBn3VS3to5xFyWkksfEg+kUyWHLUnj1pJnN51gS+6T+f7/Z9R+bGKmaYPvBvM1/1nI2bh/d9GUK5q5p7WYPQeFn28il3LDjDihwE87dHSZlmTk5LZMn8XG+ZsI5+jAx2Gt+f53k/ZbZnNVEL9w9ky/y+2zP+LslVL0657Gyo/XpGKtctTsqzLf3J8OjYylosHr3Jm90XO7L5AwJ0gGj9XH48xr2V7gaf0CA+MYOW09execRDPz3vQfkC7LPVk/O8EMrXnTIqWLMLYX4bZtOb1z+8v5urxm3y5bWKmH/7o8BhmvPUz3lfuMWHZO1StX9lmWTX2Jc8vX2qv7VGZwCYnJcvPHyyR7hWHyMkdZ63Ks3PJPunsZng9p6RY7zV9+eh18awzSqZ0+07Cg7K2DOj5/ZdlYN3RMv7lz+Xs3ot2N2FNTEiSc/suyZRu38kbJfrJjLfm2W09i38jEcGR8udPO4ylYp+eJIfWH5fo8Kx5RD+MG2duy8iW42VUqwly48ztLJWRnJQsCyetEI8yhrmrtZhMJvmq3ywZVG+MVffabDbL9t/2SGc3T1n3w5YsyaqxH9jBBNYa/4iqwPfAWmBj6pbdim3ZHpWSmPPOAhnb7hOrP9oH1x2THu5D5ebZO1bXYTabZf3sreJReoDs+/1wluQM9A6WL3pOlx7uQ2Xf74ftphzMZrOc2nlOfhm3VMY8PUleK9JL3mryvqyZvinL4SH+i6Qkp8iu5Qfk3Wc+lteK9JIRLT6UrQt22+z/8jBMJpNsmrdDupQdKN8PnptlZXTt5E3pU32ErJm+yeo8qR9+j9ID5NKRa1bl8b3pL72rDpOzey9mSU6NfcgtJXEOGAW0A9qmbtmt2JbtUSiJXcsPSJ/qIyQqLDrTtEmJSTL/wyXSrcJguXT4qtV1BPuGyrj2U2REiw/F++o9m2VMSkySpZ//cX/Fs7iYeJvLyIjz+y/LyJbjZVD9MbLok1VycsdZm1fQ+/9ISnKKHNtyWia8+oV0djPiLdnDeS6VmMhYmTl8vnSvOCTLzmwBXkHSu+owWT97q035jm4+ZSgKK5/xXcv2Sw/3oXJu36WsiKmxA7mlJI5lt5LsbrmtJG6f95LObp5W9Qju3fCT4c0/lI9e/9KmYaK9qw+LR5mBsvjT1ZKclGyzjNdO3pQhDcfKhFe/EL/b9vsI3TrnJR+9/qX0qjJMdi7ZZ/OymJq/8b3pfz/o4uc9pttVWZzbd0n61x4pk7t8K6H+YTbn97sdID0rvyVrZmyyqed5fOtp8Sg9QC4esk5RHN18SrqUHSiLJ6+2afhVYx9yS0n0BD4BWmJEg20CNMluxbZsuakk4mMT5O0nx8umeTsyTZuSnCJ9a74ta6bb9qLtXnFQelZ+S64cu54lGTf/vFM8ygyUnUv22W1o6dKRa/JOm4nStfxgWTN9k472aUfiYuJl8eTV8map/vLBi5/JruUH7DIUlRifKL+MWypdyw+W41tP25zf96a/DGk4Vqb2mmF1VFgRkWNbTkuXsgPl9oW7VqUP9g2Vt58cLyumrbNZRk32yC0l8SVGNNh9wB7Ltju7Fduy5ZaS8L56TwbVHyNf9p5pVet++297ZGy7T6wu32w2y6Z5O6Szm2eWJiCTk5LlhxHzxbPOKPG55mtz/vQI9A6Wqb1mSLcKg2X7b3t0ay8HSYhLkN0rDsqHL02RN0v1lxXT1klSYvaV8dk9F6VXlWEyfeg8iYu2bUgwPjZBvuw9U4Y2fs+mHumORXuls5unbP11l1UNFb9bAdLJ1dMuscY01mMPJWGtx3UDEUnKlhlVNsgNE9i9qw4xe+SveH7eg1cGP5+pCacpxcSAx97h3fnDrFo5LDo8hulDfsL3ZgATV4yhUp0KNsnnfzuQaX1nUcSlMBOWvYNzcevCkGdEfGwCq7/ewIY52+gw7CW6fdjR7mayWUVESIxPIvBuMHcv+XD38j2C7gYTHRFLbEQs0eHGX7NZcMxvxGhyzO+IUwEnSpUvQZlKbpSp4kaZym6Uq1aGCjXL5jm7fd+b/vw4eiH+t4N4e9ZAq50zMyI2Mpa5YxZxfv9l3l84wiavfhFh3cwtrPxqHR8uHknTFxpale/uZR+mdP2emk2rMWrOoEyfn2Wfr+HayZt8tv5Dq2XTZI/c8rheBYwUkSAbhXMHFgNlATPws4jMVEqVBFYBVQAvoKuIhD+srJxWEuf2XuKrvrP4bMOHVq2+lRCXyIy35hEeGMlX2ydlmt6UYmJUq4n3lyK11dv44sErTPb4ju4fvsGb77ySLa9fEWNp1J/fX8zjLWsxaFrvXA/qdz9201kvvC56ExoQYcRtssRvigqNxjG/I64VS1GlrjuVH69I2SqlKVqyCM4uzhQt4Yxz8cLkc8xHcpIRoyklKYXE+CRC/cIJuhtMoGXzveFPwJ0gUMrI5+KMS+l/xm4qW7U0VetXyrXYTamICEf+PMncMb9RtX4lBk7tme0YWoc3nmDG0HkMm+5Ju+6tbcp7bu8lPu/2PZ9tHGd14MmEuESmD/kJEWHCstEPTZuUmMzAx0czfukoHm+p187ODXJLSewFGgAngPtrSIhIh0zylQPKichppVRR4BTwBtAfCBORaUqpcUAJEXlo0yKnlcSnnb6m2UuNeW3oC5mm9bsVwGSPb6lS153R84ZaFWjvj+//5NiW03y982ObncyObz3DN/1nM37ZOzR5voFNeR/E65IPc95ZQGRwFCN+GEDDtllbO9lWRIwlT3f8tpfLR6/jdzOA8jXKUq1hZarWq4xbxZLGkqdpYjfZ05NZREhKSCI6PJaY8Ng8FbsJICkhiQ1ztrP66/W0H/gcfT72yFbYkjsX7vLhi1Po9oHtjYpD648zfchPDJ/hybM9n7IqT0xELL2rDmfxzdmZemZvW7CbhZNW8tHKMTb1djRZI1ec6Uhj9ko2TGCBDRghPq5hKA+AchghPh7ZnESAV5C8Waq/VWO5R/48KR6lB8i6WVusnjD2vxMonVw95d4NP5tl27PyoHiUGWi1bXpGxETGyo+jF953cLJ1adSsEhkSJWumb5JB9cZIv1ojZcW0dXL91C1JjE/MlfptIS4mXg5vPCEz3ponPSu/Jb2rDpOFk1ZI8L2QXJMh1D9MPu38jXjWGSUXDl7JVll+twLk7SfHy/hXvpCwQNucM2+evSP9ao2U6UPnWX2vvuw9U9bMsM734vi2M+JRZqCsnblZr1mSw5CTE9dYehkPzWxFGku6KoA3UAyIeOBYeGb5c1JJLP50tcwe+Wum6S4cuCxdyw+22vRPRCQ6PEZGP/WRLPtijc1ybf11l3QtPzjbHs1Xj9+Q3lWHyTeec7LsyW0tKSkpcunINVk8ebW802aidCjWR6b2miFn99jf+zsnMZvNcuucl/wwYr68UaKfvP3EOPlx9ELZ/8eRXLH62v/HEelafrAs/GhFtgwJkpOS5dcJy6RXlWE2O9/FRMbKZI9v5JM3v7Iq/bl9l6R7xSHiddnHqvR+twJkcIN3rVYsmqyR00piLzASqPTA/vwYK9QtAvpnWgEUwRhq6mT5bZWSAIYAJ4GTlSpVypELmLrA+43TmVsa/Th6oSyd8ofVZd+56C19a74ts0f9alPLPTE+Ub4fPFc864wSr0veVud7kLSe3PvXHM1yOdbUc2L7WZnc5Vt5s2Q/GVR/jPw0dpGc2H7Wrs59j4qEuAQ5v/+yrJi2Tt577lPp4T5U1kzflOPnFhYQLmPaTpIPX5oiIX62+0GkZebw+fKN5xyb8yXGJ0q3CoOtej9ERLYu2C0epQfI0U0nrUp/48xt6VZhsDa3zkFyWkkUBIYDhwA/4DJwG7gLzAcaZVq4seTpduDdNPse+XBTSkqKzP9wifSpPkLuXLTuQ9yv1kirX5b9fxyRzm6esmPRXpvk8r3pL0Mbvyefd/8+W97NsVFx8nmP6TKk0dgsDXNZy4UDl2VM20niWWeUbJ7/lwT7huZYXXmFaydvyuQu34pHmYGy5LPfrfLIzyqp8Zayu1RobFSc9KoyLEu+FGumb5LJHt9Ynf7S4avSrcJgWTFtnVW9x/GvfCGb5/9ls1wa68hRJfGPRMbHvhzgYnXBxip2i4EZD+z/Bhhn+X8c8HVmZdlbScx6+xcZ03aSRARHZprWbDbLL+OWyqB6Y6x66PeuPiw9K78l107etEkmv9sBNs93pIfPNV8ZWHe0fDdorl1jB6WSkpIie1YelPee/UR6Vx0m2xbuzrU5jrzE3Sv35GvP2dLJ1VPmf7gkR+cuLh66Kr2rDZeFH63Ichmndp6TruUHy/VTt2zKFx+bIB5lBto0RxLkEyLDmn0gSz77PdO0Z/dclEH1x9gkk8Z6ck1JZKlgaIOx/sR54KxlewUoBewCblj+lsysLHsqiWDfUHmjRD+JDI3KNK3JZJIZb82T4c0/tEqhiIi8//xk2f/HEZvl+sZzjvz28Uqb86Vl94qD0tnNU/78KXNv8axw6fBVeavJ+zKq1QTZveKgXRzB/u343Q6Q2aN+lTdL9pMv+8zMcpTWzIgIjpR+tUbKxh+3ZbmM1B7u3tW2BZI88udJ6ezmaVMAyuunbkn/2iMzTZeSnCKvFOphk8e3xnrytJKw52ZPJfHLuKUy6+1fMk2XnJQsU3vNkHef+VhiImOtKjssIFw6uvS1uQV/fNsZ6ezmmeXInonxiTLjrXnSt+bbVg+J2UJYQLh81X+WdKswWHYt2/+vmoTOLaLDY2TlV+ule8UhMuHVL8T3pr/d6/C96S9dyw2y+SOflhunb0vPym/Jok9W2RSX6/qpW9LDfagsn7rWqvtvNpulW4XBVkUG+PClKfLtgDlZimGmeThaSdhIUmKSdHL1tOoF/tpztkx8bapNH/wln/0uU3vNsEmmVCumCwcu25QvlfjYBBnW7AP5rOt3ViszazGZTLLuhy33o5nau/z/IkmJSbLq6/XSydVTVkxbZ/cwJ9dP3ZIelYbKks9+z7KyDvUPk5Etx8vM4fNtyhd8L0SGNX1ffhm31Kr03w+ea1VI8rjoOJnw6hfy8Rtf6YCSdiZXlATwlTX7cnKzl5Lwue4nfaqPyDRdXEy8vF60t01rJqz6er30rPyWTTGVjm81AqX5XM/a5LLZbJZZb/8in/eYbvfWvd+tABnTdpK802Zitqys/r8SePCUrK31smxyf1rCJn0u4m2/axgWEC6D6o+RBROXZ/m+R4fHSEeXvhIZkvmwa1pC/MLkjRL9rBoeOr71tPRwH2qVcUhyUrKMajXBpnUuNJmTW0ridDr7zme3Yls2eymJkzvOynvPfZppuiN/nrQ6cJ/JZJKfxi6SgXVHS6B3sNWy3DpnhCPPqtNUYkKSfNV/lgxt/J7V8yXWYDabZePc7dLJ1VNWf7tRB/yzFX9/kc6dRRwcjNfLspmVg5g7dzaO24HwoAgZ3OBd+WX8siwriqm9ZmRp9bjxL38uu5bttyrtziX7pEvZgVZFPPa7ZRhv2LJol+bh5LQJ7DDgAhBrmXxO3e4AS7NbsS2bvZTEgonL5duBP2aa7ocR82XlV+utKnPm8PkyqvVEqybCU4mJiJEelYbK7hUHrc6TloS4BHmnzUSZ7PGNXe31o8Nj7i+CZK1TlCYN/v4i1aoZr5WTk6EsJkyQ2JdekRRlKA1T1aoiAfZZVyIiOFKGNBprk/9OWi4dvioepQfY3FDZtfyAjH/5c6vTH954Qjq7ecrVE5lb/O1YvFcG1h2th53sRE4rieIWT+kVQOU0W6bWSPbe7KEkdizaKz3ch1q18MuYpyfJmd0XMk2XOlFt61j93lWHZMKrX9iUJy1zxyyUz7p+Z9cXyeearwyqN0Zmj7TN+U+Ths6dRUD8KzSRQ6v+qWT3L/OSG4Vri4Akd3zDblUG+4bKmyX7ZWnhIRFLiIzSA+T2eS+r80SGRkmHYn1sCku+ce52+ej1LzNNZzabpU/1EXqI007k1nBTpfS27FZsy5ZdJbH/jyPStdwgq1rHh9YfF48yA60awlk7c7NM6/uDzfJ84zkny4vEn917UbpVGGzzWPLDOLD2qHiUHiCb5u3411guffWVyO7d/9y3e7ex/5Fw964xxOTkJIdW+Yir69/y7d4t4uoqcmC5l6QoBzGhJPZS9uJxpeW3j1fKkEZjs+yZvWv5AelecYj43wm0Os+Mt+bJO20mWu30mRCXIB6lB1g1Z/dl75my9dddVsuiyZjcUhIXLMNMFyy+DSnApexWbMuWHSVxZvcF8Sgz0CrT0NTAY9Y6wo1o8aGc2H7WJnnCgyKsflkeJD42QXpXGy5H/rQu7IE1/PbxSulVZZhcPX7DbmXmBqkf3gc/xA8qjlxj1izjderc+R/yTJr0T7nMnTqJgKyu+qLd1gw3m82y9PM/pHe14RJ4N2uL+qyduVn61RpptbGGyWSS6UN+klGtJ1odVmPhRyussqhaN2uLfD94rlVlah6OPZREpjGERaS+iDSw/K0JtAAOZpYvr7Dq6/UM/bZvputExMfEM33wT0xcMZpaTatnWm5yUjK3z92lUTvrw23HxyYw6fVptB/4HBVrlbc6XypHNp6kQs1yPPlaU5vzpsfhDSfYtewAc05Mo3bzGnYpMzcQSeaZp4NYtSKMrl3NTJqURNeuwurV0K7dIxIqIsL4W9tYJ6FdOxg2DKZMMf6myqUsxyu6FWLsM58Q6v/QpVSsQilFr4mdebHfM3w/ZF5q484m3hz1Cu51ynNw3XGr0js4ODBq7mCcixfmzx+3W5Xn6S4tObf3Yqbp3GuXx/+OTcvXaHIQm1evEZHTQPMckMXuRIVFc/nIdVq/kbm4y6euo0Hbx2nUrp5VZQd6BeNasRSOTo5WpTelmPii+3TcH6vAgC96WJUnLUHewSyYsMyqNS+sITwokpnDfuaDRW9T3LWYXcq0N2LyQ+I3Yo78GHNoD8zBL2EObI4ENkRCO/JMg4681WcBn3+en7f6/Ejbx+oaacKHYY7+DolfjyRfRsSc88K6uBh/r10DYM8emDsXJk0yIU42owAAIABJREFU/u7ZY0l3/ToAT/Z5njadnmB064l4X/W1iwg9xr9JRFAkfy3Zn6X8bbu04tB665QEGIpiyNd9WDltHTERsZmmr1CzLAF3gjClmB6arrhrMaJCo62WQ5PDZNbVAN5Ns70HLAe2Z7cLY8uW1eGmrQt2y6edMw9Odu+Gn3Ry9bQpQN3qbzdaNRGXyvQhP8m49lOy5FUa4hcmfWuMsJsNeXhQhAxr+n62YgHlBGZTjJjjt4op/D0xBT4jpsAnxBQ2QswxC8WccFTMyTfEbAqVadNMsnv3P4d0ihc3y+DBKWJOui7m+K1ijp4l0z7dKH+tfU9MgW3EFPmJmON3y66/4nJm3sKKOYlDK71FHB2NdBa/ie2/7ZEuZQfK5aOZm4haw7WTN8WjzECb15AQEYkKi7Z5QlpE5NsBc+SX8cusStu76rBM/YIC7wZJ94pDbJJBkz7k0pzEJ2m2iUAvoGB2K7Zly6qS+H7wXNkwJ/NYN0un/CFzxyy0utyrJ26KR+kBVodeSLX/tvXlS+Wzrt9Z7eWaGYkJSTKq9UT5+f3FeWKS2mwKFXPsajGFDRFTQCMxhXqKOXaZmJNvZSjf7t0ixYqJFC8u95VF2t9p07m6iuzaeU/M0T/JX+unimvJUPlr/Rdijt8lZrOdzSzTWjet/Kd1zqGV3uJfock/5i1S2bX8gAxt/J7d7seXvWdmOcbTyJbjbfb+97rkLb2rDbcq7dh2n8ipv84/NE1iQpJ4lBmYaTpN5thDSVgzJzFZRCYD04EfRGSZiCTYv09jfwK8gihXrXSm6S4eukIDK5fyjI+J58teMxnxwwDKVy9rVZ49Kw/xVOcnM10oPj3O7rnI9RM36f2xh815H0REmD7kJ0qVL8HAab1ybXnOdGUxhWCO+gwJfhFJOogq+DrKbR/f/rKAvcd6ohyr3Zdvzx74+uu/87ZrB927G15qe/ZA166wfj2sWwcnTvwz3erV0K1HBT75eijdB41n1e9FePbFhkjMLCTkVSRuDSJJ9jmp2bOhWjXK+p6mVe9q4OEBEyaAhwetehv7qVYN5sz5R7ZnurXClGLi5I5zdhGjyfMNOLfvUpbyVqxdHu8rtg1/udepQHRYDBHBkZmmLVaqCNGZDCXlL+DE+KWj+KrPD4T4htokiyYHyEyLAM0wLJu8LNs5oGl2tZMtW1Z7En2qjxDvq/cemiYlJUU6uvS1etW2bwf+KF97zrZJjkH1x8j5/bbHZkpJTpFB9cfYbdGgFV+ulWHNPnikETfNplgxRc0QU0BzmTb5L9n11z9Njb/7TsTZOWOrpZdfNtKIiDz3nNEwf+EFY39q+tThpFQz2UmTjHR9+vx93Gw2y65tF2XaJ6vFFNhazDFL7NOzyMDjWhwcjP0ZONLtXLJPxrSdZJfeRIBXkHiUGZilso5uOik9Kg2V8JPnDYutKVOMv5mEFXn/+clWLTa07oct8vaT462KILx0yh/yTpuJOvBfNiCXhpvOA0+l+d2Gf0lYjlcK9ch0iMfvdoD0cP8/9s47LIqri8O/oQtIB0EBBRV7b7FExRqNHcSGUcSGNfaoQWOLXWPsGjXG3hF7xYqKBWyoqPTe2wJbz/fHsIoE3JndBfXLvs8zrrvce+ay7M6Ze+o4TvKSY1Kpv+VIXqGL6UmZ1M98hFKJb0+uPSOfZrPUcuHITs+hPqbDy7Vnc3FkBXdImuxK0oyfSSaOLjWMde3af4ePyi/4a9eyn9o+fYgMDYm0tT8+HzuWHT927EczlKkpkb4+UbNmH6/VPj6fKqM1qxKoR+cnJE0bRTKpmhonRUcTbdrEXmQ3bVJ4kRWLxDSuyUw6ueGcWk4/stZkun/uMf+JCQn0vmZzkjJMyUqulLIiW6ftoSOrFFcpkMlkNKf7Ek7+NalUSpNbz6XbJ8uus+L/O+WlJO5yea0sD2WUhEgoom46HgovsC/uvqbJredyknl680XeyXPPb4dyll+cPb6HODsEFXF2+2VaNHCNWmTxRSaTsLuHpLYkK7jxyc9KyidYuZK96wfY3cLYseyF3dCQ/XmbNuzP5NcxuaLQ0SHq1YtVACYmRK1asdUx5Df1xW/ufXzYAyAaP15CV88eohW+W0gmfPhFEvPi3yfybvBTGk+uPaPB9mP51fUqUlZEDC3K7vwD0bx5rHLQ0WHfKGfnEndDh1f60bYZezmdJvx5FLlX8uaUk3F2+2Va7LGW+++g4RPUoSS4hMAGMQyznWGYjgzDdGAYZguAGwzDNGUYpqn6DF/qJT+nAEYmFRTa3dPi02FhZ85JZqD/Q7Tpwy/6NzYsQamcCAB4djMUjTpyz8P4HNcO3EYXz/ZqkcUHkqWDMkYD4kdgLE+B0e/wyc+L5hM0asS+pqMD7N8PDB8O3L8P7NrFhpF26QL07Ak8eFAomwAjI0Cr8FMskQAXLgAzZwICATtOIgFkso/ji34ctm5lD0dHYNcubbgNGwwd4y4YNzoG/fsJ8e4d/3wDVbBzroSZu3ywbPB6ZCRlqiSrSacGcB3cFuvHbpPf2Clm0iQgPByJVZpii/dpLMZ3oKVLgePHEXggAolVmgLh4cDEif+aamFrhoxkbmt2qu+IFj0a49iaMwrHfu/2HR5dCoEgO4/b76BB7XBREo0BuICNbvoNQB0AbQCsBbCmzFamIvm5BTAwNlA4LislG2bWpgrHSaVSvLj9Ck27NuS1jsSIZNhWU+w8Lw4RIezRe9Rt7cJ7bnGy03PwPiQSLX5orLIsPhCJQBnjAZ0aYMz3gNG2/teYovkEjx4BvXoBixcDP/4InDwJiMWAVAro6gL+/oBQyD6XIxCwY+zt2edSKasM5GOKXh8Z5tPncmJjWRkCAeD7W00cPNkXBDEG9T2ixneDG61+bIYOHm1wcNlJlWWNXDoEES9iEHovTPHg6GjW86+ri/B1p7Hk9I8IfmGCyJcxCAgA+k60R/haP1aDnzoFxMR8Mt3U2gRZKdmc1zZ8wUD4b7kIqfTzORMmlhVRu2UNPL/1irNsDeqFS3ST62eOTuWxSGUQC8XQ1ddVOC4/twCGFRUrk9TYdFS0MIaRiSGvdWSn58LEsiKvOQCQmZINAyN9GFbkHxFVnEC/h6jT2gW6eorfD3VCOesALTMwFeeCYf6ddCiPTDp6lFUMp06xd/1iMWBuzl60RSKgSRPApDDfr7Sb4thYxeuRlZJTJ39dIgHy8wGZTAunTsrg2vJvkGAXh99UvfT26YYbRwMVJp0pQk9fF1082+PGkbuKB/v7s29Enz5o42GPo0cZ3M8ajzkzhR/+Rm0GOQB9+7Lj/P0/mV7B2AAFAiHntdk5VYKptQkinkcrHOtU3xFRoRz+wBrKBIVKgmEYfYZhhjIMM49hmAXyozwWpwpikQS6eoqzofNzCziFpsa9S0TlGtxCXouSm5mLihbGvOclR6fC2sGK97ziCLIE2ON7GCMXD1JZFh+o4ApQcBGM6UowTMkfs9Wrgblz2bDVgADW9NSlC5u8vG8fa0bS0gKePQPSSomE1NZW35rlpiiJBGC0TMBY7AEJ9oHyTqjvJByoUsMOtk42eHL1mcqyOg5qg1vH7yu8Yy+prEj/Hik4dqnmJ2VF4FK4s834tJyIiVVFJEWm8FJsDdvXxbOboQrHOdaxR/RrjZL4UnAxN50G0BdsYT9BkeOrRiKSQIeDkigQCKFvqKdwXFJkMipV+7e5RBFZqTkwNjfiPS8tLh1WVSx4zyvO2e1X0bRLA9RuWVNlWVwhWToo61cwZhvAaH3096xa9THnISAAcHAAFiwAbt8G+vVjTU1XrwLx8YVy6FPTUUkouvbxWjexSkkqBfr3B27csgNjsQuUsxIkjVffiTjQ3r017vk/UlmOQ60qqGhuhPchkZ8fWEJZkQs37VHX+EyJZUVg/qkfr2ode1g7WuHBuSec11avbW28DnqrcFwVFzvEv0vkLFeDeuGiJOyJaBARrSKitfKjzFemIhHPo2HrpNgXwFWZ5GYIYGLBz2xUkCfE6wdvUbsl/+J56YmZMLHib6YqTuDpIHQeVr4Oa8rdBlT4EYxeo09eb9GCNS/p6LCP2tqsSenqVdbXcO4cUFAkTVOuJMoDhmHXI5OxN9NEwOHDAKNTHajQB5R3uHwWUkj1xtUQGRqjeCAHnBo4Kk6Q69OH1ZD+/gg8GgsPD+DwEYKj6AAO7JfAwwMIPBIDnD7NjuvT518i2rt9h/tnH3NeV6Vq1kiJUZwsp6nl9GXhoiQCGYZpUOYrUTOBp4PQtm9LheMkYgmnIn25mQIYm/HbETy8EIzaLWsoVUAv0P8hmnVtpHjgZ8hIykT0qzg06lhXJTl8IEkMkO8HxujfETDyDOjly9ldxLZtQNeurGIQFjFnV6lSbsv9gHzHoqMDVK/OZnBXLywGfCNoFFav1ofaMrM54Fi7MmJeq2f3Yu9SGbFvFMhydGS3T2IxnKf3xelNMejSRRuWlS1Qv0YqTm+KgfOMfqwtrn9/9g9YjFY/NkXQhSeco6ms7S2RGqtYSZhYGiM7LZeTTA3qp1QlwTDMc4ZhnoFNnnvCMMwbhmGeFXn9szAMs5thmGSGYV4Uee03hmHiGIYJKTx6qufX+BQiwuMrz9DqR8URulKJDNo6inWlICsPhib8nMhB55+gNc+QWQAQ5gvx/GYoWvdWrSR48PUXaORar1wc1nITEuXtAwwHgdG2/Fc5DYD1P/ToAQQHs8+vXPk0LNXFBYhTT1FUhTAM0KzYW9y8ORt2CwCzZ7O/06AhldG8aSYgLL8K+ZaVLVAgKFBL6GflGraID+dgrimhrMgIyVMYeY/4bFkROfYulaGrp4MYRQqpEMvK5kiLV1wq3djMCLkZGiXxpfjc1bEXgN4AegCoAaBb4XP564r4G8APJby+nogaFx7n+S2XGzkZudDR1eEUVUQyGRgtxUpCIpJwipYqSnJMKqrUtOM1BwBS49JhVslUqVpPn8iJTVMq/FYZ5KakgAAtMHotPkQutSimI+U5EF27fjQlFb3xDOMQrakuiFineFFCQlhnuocH6y+RR/a4dpIB0shyWxvDMDA2M4IgS3UlYWxmhPwcDuXWbG2Bu3cBNzfW7nbiBLrGBsL05mX2uZsbEBgIVKpUqgibqtZI59gjQ89ADxKxVKGzW1dfFxKxlHu+hwa1UqqdhYiiAIBhmJK8pwoNhER0i2GYakqvTAXSEzJhbmvGaayMCFpaigvdScRS6OjyC6VJT8iEJcdEvaKkxqbD2t6S97ziZCRlwbwSt/dBVeSmJI+B4zF+vBa2bce/mgCNGwf88w/Quzdw5gxgZQWkppbL8kpES4sNt5VHSDEMa/oKCPiY4Ofry/4OJKgMkiagPEsiGhjp8wor/Zyc/FyONTltbYHjx9k8CH9/XNt0Gk7tG8P518klmpiKY2ZjisxkxYX+AFYR6hnoQiQUo4JO6d8thmGgpa0FqUTKuX+LBvXBxSfxBEAKgDCw7UtTAEQwDPOEYRhl7CGTCs1WuxmG4X8F5UBWajZMOTp9qXgabilIpVJof+aDXBKZKdkwtebvj0hPzIRZJcUJforITs+BiSX/8Ftl6dhRhPE/HcTSZSbw8WFNS+PGFYmMKeTMGaBy5S+nIBiG3dHI8yOkUmDoUODyZcDAgM3U/lfDIK1KgDSpXNepb6iPAoHqBZf1K+hBmMdT2Tg4ABMnIrDhD4ju1JeTggCAiub8/Ae6+roQC8UKx+noakMiVmMomwbOcFESFwH0JCIrIrIEa346CmACgC08z7cVQHWwWdwJYLO2S4RhmLEMwzxiGOZRSkoKr5MYmRgiLzuf01imtDTcYmhpa0Em5dfhzMjUUClzgbG5EQQcOn0pwrBiBW5mBiWQ+yCKsn69HtZtG4lf5+dh61b2Qnz4MOvnDAhgy3tra7Nvd1G/gzpzHRShqwsYGrJrk9O1K1vOIziY3V3k5HxM8Dt6VG5G0we0yuSeplTEQjH0DBSHZyuUo4SpVI5UzC36T05+bj4qcEhOlSMRc8tn4uo71KB+uLzrzYnoQxNbIroMoD0R3Qegz+dkRJRERFJi+0nuBNsvu7SxO4ioORE1t7bml59gbmuG9ERudWQYMJxsnTo62rwzYC3szJDBcR1FsapigdS4dN7zimNmY6pyDaDS+OiDYJ+vW8fWTFo89zgWL3j5IYpp4UJWKfTqBXTv/rHEhhyGUW+ugyLEYnYXI5Gwz7t2ZZVDjx7s+l1dgfPnP5rJ5Ga0h4/1wGjz9y+pQoFAiAocSstwkWNgxOur+gFRAT9FlZmSDTMeu2cu8olIY2r6gnBREukMw8xhGKZq4TEbQAbDMNoAeN1aMwxT9FvWH4DiruhKYGZtgtyMXIgKFIcsMloMSMZBSejpQCyS8FqHZWULJEfzt6lY21siJSZN5bIMFrZmSI1XXdmUxAcfRKGDd8ECYM0aYNqUV4A49MPPJRJg6lQgL4+9QIvFrJlHrijk+plhOFn91MLbt+y6iiqIffsAT0+gfftP/Sjy33XWpKOAdvnG5ubn5KtHSXCsY1YSwnwR9Ay470LSEzJgZsPNVCoRSwAihWZcqUQKLW2tL9ok678MFyUxFIA9AD+w2deOha9pA/AobRLDMIcA3ANQi2GYWIZhvAGsKhJC6wpgmorrLxFtHW3UblUTwdcV6yBtjjsEZUw3jTrUw8NLwbzmAGw0ikPtKnjKoWTB56jXtjZCrr8os6iQohVcp09nD6aCO0iwB0RCuLqyO44NG1gTj74+u2uQF+ErSnkkzhW9xhgaslFMc+d+rDh74cK/o7EANoMcovuAfruyXWARcjJyIRFLlSrpUpyEiGTYKFniJSORexBIZkoWUmLSUK0+N/9FRlIWzDgEVgiy8mBkyq9mmgb1waXAXyoRTSaiJoVhq5OIKIWIRET07jPzhhCRHRHpEpE9Ee0iouFE1ICIGhJRHyJKUO+v85E2fVsi0C9I4TiuDjEjMyPk8vQTtO3XAkHngzntaIrT3r017p56wHteURxqVYa+oT7eBUeoJKc0ilZwlTt4Gb1mgG5tIO8gAgLYchsMA5w9C4wYwe4gdHQ+mns4RB+rDbkS0tJiFVu/fqzfYc0aoH79ov6HYhPzTgD6XT4pMVLWxLyJh0PtKmq5e459Ew+HWvzL1RMRUmLSOEfaPbwQgiadG3DOy0mJTYO1veLSMznpytU/06AeuBT4C2AY5nrxozwWpwoNvq+Nt0/CFY7T0dWGhIMZSZmEHvNKZqha1x4vA9/wmgcAlatXQhrHePPSYBgGrXs3x63j91WSUxLFK7gWvcAyxtNBgh0IepCLwYPZ6q6ursD27cCKFay5SUeHVRilVWYtK4YPBypWZEsUDR4MDBrE7oBmzy7ifyjSJ5tkuaC8g2CMhpXrOqNexsChtnJ9SIoT/ToW9kooiZz0XGjranOuRHzvzEO0+KEJZ/kpMWmc6pNlp+XARKMkvhhc7uNmAphVePgCCAGgeuWxMka/gh4nH4K+oT6E+Yrv9K3tLZAcw9+/YFnFAtmp/OvOmNuacU5K+hy9fbrh/M6ram8oL6/gWtTBO3cu+zqj6wJUGIxZY8Zj2zbJhzEBAawze/RoYOXKTx3YZUXRnYquLuDlxSqtkydZJbF9+6fjXV1ZhQEAREJQ5gRAvwMYXX59RFTl3plHaNpZ9XNmpmQhKjQWtVrwrx/26sFb1GzqxGlsSmwaQgJeor37d5zlhz18h+qNFctPCFeuuKYG9cDF3PS4yHGXiKYDaFUOa1MJHT0diIWKlYSBkQGnWPTKNWwR/55/nLyJuTGy0/mXFLBxsORU/EwRlavbote4rtjjq94CdbNmsRd8uWlGrgBmzWKfM8aTAEYHlLvpQ7jsw4fsnfrgwcCiRayJx8fn44W8LJzXMhkr09KSPU///uzrxXcMxSGSgjJnAFrmYEx81bsoBWSn5+DpjZdo219x7TFF3D7xAC17NIGBIf/opmc3XqJhB26dEZOiUmDvYservtmzW6Fo2EFxXbHoV7FwrG3PWa4G9cLF3GRR5LBiGKY7AP6NFcoZVkkoTtKpYGyAPA4O6UrVbJASk8ZGZPDA2NwIOUooCcvKFshMzoJYpPh3UMSPY7sg6HywWh3YxaObPpSvKNw1MIw2GNM1QP5xNG/08EOJDlfXwuqqDGvqOXiQvcOvWfNj5zhd3X/XVOKKXpFoSnmjIh0d4Ngx1jEtr+5adMdQHCIRKHs+QLlgTFeDDeQrP64fvIPm3RvzbnBVHCJCwKE76ODRRqn5wdefo7FrfU5j2TBb7hFUguw8RL6MQZ1WikvYR7+Og2OdL1D1UQMAbuamx2DNS4/BRivNAOBdlotSBwZGBpyyTE0sjZGTrtgcpKevi2r1HfDyLj//gpW9JVKUMFNp62jDvlZlxX0AOGDtYAVjM0O8eqC4dj8fikY3fdKYphBG2wqM+RY8vPsIv0y/AQ8PwoIFrKlnwQLgyBH2ov3770ByMptUp60N1KgBPC6sOM1nZ6GlxXayA9hK1nZ2wNq1bCSTXDEUre5aEiSNB6V7ArIMMGabwDCqJ7PxIeZNHPYvPobBc/qpLOvKPzeRk5GLlj35t6J/fOUpBNn5qPMdtz4k2Wk5vJzLpzacR5u+LRTmSIiEYjy98RL129XmLFuDeuFibnIiIufCx5pE1I2Iyq8cppIYmVTglHVtYWfO2UHcundz3PP/jI2iBOxdKiP2rXJBXI071sfTG6qFwQKsA7vzsPa4fuC2yrKKUlJ007/OrdsQLdoPxYq1TfFDp7tYsoTNS1i+HGjcmL1oT5/O+gkMDVml8OoVu5vo00dxWKw8g7pmzY9O8CZN2LYHr19/lC1XDJ/dQQhvgtLcwRh0A2O2FYxW+TpL8wUFWOy+Fl5Lh6BmU2eVZCWEJ2HHrH8wd/9U6PHMtpZKpdgxex9GLx/GOYEtMykLFhzrhGWmZOHUn+cxYpHibomPLobAqYEjrKqoXstMg3JwMTfpMgwzhWGY44XHJIZhyrdZshLo6uuCiBSGn1rYcStXDABt+rZAoP8jyHiE5Ni72Cmu5V8KDTvWw9Mb6sk37Dzse9w8GqhUOG5JfC66qTidupii1XfGOHCsLdq1Csb+/YRffpFi8OCPfoGHD1mlIA+NHTyYraVUuzbQqhWrDEpydIvFbGhtcjJQoQKrMN6//3Qdn1MMAOugluWs+dhNz2h0qS1XywoiwgafHajZzBk9x3RRWd66sdswaE5/ODesynvulX9uwcBQH+0GcHc9ZiRxrzd2cNlJdBzUBpWrK7ZaXzt4Gx0HteW8Dg3qh8s3YSuAZmDrNG0p/P/WslyUOmAYprAsxecrUtpWs0Z6fAbyOTivnRtWhYWdGU7+cY7zOmwcrSCTEWLe8G+S0LRLA7wLjsC7ENXzHOycK6FJlwb4Y/wOtfgm5E7of5WvKGGjFRAA3LmjBT09BnceNMEw9ztYsjgf/fqJ0bw5uxZ5CXE9vY/mIW1tYMsWYMAANiJKLP7o5C4atbR1K6tczp1j8zEYhs2BKElhFYVkWaDcbaCUzoAkEoylHxg9/v0/1MGLO68RGhiGKVvGqJwbkZ2egzdB79BvckmV+j9PVGgM/pqzDxP/HMVrHTFv4mDnXHoJcTmPrzzFreP34LlgoMKxgf4P8ep+GFwHa5TEF4WIPnsAeMrltbI8mjVrRsowtd18Cgl4oXDc5NZzOY0jIooPTyQ3ay96GxzOeR2bp+6mf347ynl8Uc5uv0zT2vuSTCZTan5R8gUF5NNsFh1e6aeyLD6sXEm0di2RiQmRoSFRhQoyMjKS0JifzpM01Y2unbtMBgYy0tcnMjUlGj6ciGGI9PWJxo4lun6dyMiIqFcv9jAwYH/etSuRlhZ79Or18XzXr7PzVq4seT0ySRxJs5aRNLE5STNmkUz0unzeiM8w78dldHb7ZbXIuusXRLO7LVZq7gafHbR/yXHe8wbbj6W4dwkKxy0dsp7O/3VV4bj494nkbjOKXgZ++b/NtwyAR6Ti9ZfLTkLKMMwHVx/DMM4AvomavbZONkiMTFY4rl6b2nhx9zUnmXZOlTB+3UgsH7YBBRzLL3cc3BYBh+8odQf/g3cn5OcWIOCQ6m4gA0N9LPKbg1N/nkPgaX6+FVVo0YL1Qfj5ATNmAPn5DGQybQwe0R2M0Xg8fChC53a3YaAvQO8eb7BvH9C5M4Fh2CZAhw+z5cXlJca1tICePYEuXdj+2MbG7Oty5Il7H/MdZCBRCCh3I2RpnqDUvgC0wFj5Q8tsFRjdWuX2XpTEu5AIvHsSga4/dVCLvKc3XqIRx9DV4sSExaMWz57sqfHpEOaLOO0kstNyYKUgg1sqlWKJx1oMmTcAdVt/2b+NBnDaSXQCEA3gBoCbACIBuKqqnfgcyu4kjq7xp/Vjtykc9+xWKHk6+VBBXgEnuTKZjFaO3MhJtnz8lLbzaff8g5zGF+fVgzBys/aiZ7dClZpfnNdBb8m9kjfd9QtSizxFrFzJ3t1fv05kZUXk68vuGMaO/ThGJhPRmpXhxDAyatcqmBhGSmuWnqBr5y6RlZWYrl2TEBE7x9SUlSXn+vVPdw0yaRrJhEEkExwiaaYvSZPakjT5B5JmrSBZQQDJpDnl8ntz4W1wOA22H0uX/g5Qi7y0hHRytxlFkaExvOeKhCLqZz6C0hMzeM3bv+Q4LR++gdNY73o/0/unkZ8dkxqfTgOsvNSye/6vAzXsJBQpCG2wRfj0ATQE0AiAvqon5XsoqyTi3yeSeyVvkkgkCscucl9N+xYf4yz7XUgEja4/jfP4jORM+qnmJDqzTTmTwuMrT8ndZhS9fcLdzPU5Xge9JXebUZQck6oWeYqQKwj5xb2058OHs59Kz2E5ZGVVQFf9d9LVk7PIyiKNfp3+F1lZZtJVv2UkzZhC0ozpJE2fQNI0b5KmDSdpqhtJE1uQNLEpSVMHkjTzF5Ll7iYhHXgsAAAgAElEQVSZ+H25/I58kf9NbxwNVJvMxR5r6a9f9is1N+jCE5rSdj6vOZkpWTTAyoti38YrHCuRSKhnhSEKb8bCn0Xy+m5pKJ0yVxLsORCg6klUPZRVEkREPs1mcfI3JEYm0wArL0qKSuYkV5CdRz8aDiWpVMp5LXHvEsjDbjQF+j/kPKcox9b608oRG5WaWxL7lxyn2V0X8fodlEW+myhK0R2A3G8h32lYWbHP5T//9VcpAUS/zkshWcFdkuWdIVneaZLlXyRZ/nX2NeFjkkmSvok70GsHbpF7JW96evOl2mTePR1EP9WcxHlHXJz147bTkVX8/FVbft5Df07cyWls3LsEGlbNR+G4kIAXNK2DL691aCiZ8lISywBsAvA9gKbyQ9UT8zlUURJnt1+m0Q2mUVZatsKxR1b50dhGMygrVfFYIqLh1SfyvrN/9SCM+pmPoIzkTF7ziIjSEzPIzdqLIl9G855bEhKxhGZ1WURzey7j9P6UJZ/baRQ1UxUd861ydf8t8qg8hiJeqOfvSMTuStysvZRWOs9uhZK7zShKjOR2k0REdHnvDfKoPIazeerMtsu0yH21wnGX/g6gZUPXc16HhtIpt51ECcd1VU/M51BFSchkMto+cy9NavULCbLzFI7dMXsfjW08g9Pd2K55B2j7zL281+TbdwVdO3CL9zwi9os2ocUckogVm9C4IBaJaeu0PeTp5ENhj7+cWaa0ncbYsZ83U31rlIWCuHPqAbnbjFJaQYQ/jyL3St706HII5zmX/g6gQVXGUNSrWE7jpVIpedWZSsHXnyscu8FnB51Yf5bzWjSUTrkoia/hUEVJELEX//Vjt9HMTgsVXvxlMhmNazKTU+hd5MtoGlRlDCefR1H8t1xU2mwkk8lodtdFdGDZCaXml8aNI3fJzdqLbhy5q1a5qqLITPUtURYKoiCvgDzsRlPo/TCl5idFJdMQh3F07eBtXufsZTyMs4IgYv0dYxvP4GQKHN90Fr2894azbA2lU147CX2wnejmAVggP1Q9MZ9DVSVBxDrNFg5YRTvn7FM4dvnwDZxj1qd18KWNk/7iZddPimL9H+9CIjjPKT7fw240ndxwTq329/dPI2mIwzjaNGUX5QuUs2tr+DfCfCFtnf43DbYfq1YFIZFIaPnwDbRk0Fql5idFJZOn8wTed+2Prz6jiS3ncB4f9y6BPJ0nUMDhOwrHvnoQRu42o0iYL+S1Jg0low4lwSVP4jSAvgAkAARFjm8KbW1tdBvREREvohWObdalER5dfspJ7pLTcxD+PArLh22AiEPVWQCwcbTG5E3e8O2zQqke1DaO1vjj7lKc/+sq1o/drnIvbDnODatiW8hqZKflwKfpLITe498sScOnvAuOwMSWvyA5OgXbQ9agWr1SWntGRwObNgFLl7KPMTGflSuVSrHaazPS4jMwc/dEpdZ26s8LaNu3BQb8/COvedcO3OJcKiP8WRSmd1gAj1l9Fc7Jz83HiuEbMXnzaIWF/zSUI4q0CIAXqmoiVQ917CSI2NA673o/KxyXnphB/cxHkFgk5iRXmC+khQNW0eyuixT6PYqyf+lx8mk+m/Jy8znPKYogO4+md1xA+5fyz5BVxM1jgTTQ1pt2zN6ndLTMfxmJWEL7Fh8jd5tRdGXfzdJ3fAkJRG5ubNr4x1bf7HM3N/bnxWVLJLTipz9pZufflN7xiUViGmjrTTFv4njNyxcUUD/zEZQan65wbPizSHKv5M1pB0FEtG7MVlo5Un3RexrKbycRyDBMg7JVVeWDrZMNkqNTkaOgDal5JTNUq++AQ8tPyRXlZ9Ez0IPv0ekwsTLBP78d5byeofMGwKmBI+b3/B2ZKZ+vMVUShhUrYM7eSfDbeAGn/jzPaa1cae/eGttD1iAhIgljGszAvTNffTPCr4bXQW8xseUveHH3NbY8XoUunu1LroOUmAi0bQucOMEWqnJzA+bNYx+1tNjX27YFkj42u8rJyMXCfquQGpeOJf6/KNVMSCqRYvuMf+BYxx72LtzbmopFYmyatAsNO9SFpZ3ift9X993CD6M6KdxBEBEOLT+FkIAXmLhhFOf1aCgnStMeAF4AeAYgFIAYwJvC588BPFNVO/E51LWTIGL9DVxqF6XGp9Po+tNo55x9nO3+kS+jabD9WF7+CalUSn/NPUDe9X5WOmIp7l0C+TSbRYvcV1NuZq5SMj7Hw0sh5FV7Cs3v9TunpKn/Kq+D3tLSwevIw240Xd1/S/Hnxs2NCKCEKk3p7pFPM6TvHomhhCpN2V2FmxsRsXfxk76bSxsm7OS8yy1ObmYuze25jGZ3XUTZ6dwzz7PTc2hmp4X0a+/llJfDbbc8usE0TgEg22fupXFNZlJKXBrn9WjgBsrScQ0gA0DV0g5VT8znUKeSCHv8ngbbj+X0JctKzaYJLebQnxN3cr7wj24wjZ7fecV7XTNcF6pUmkFYIKI/J+6k4dUn0ptH75SWUxoioYiOrPKjAVZetHPOPkqKTlH7Ob5FJBIJ3fULomntfWlo1fF0bK0/5WYJFE+MimJNSrq6dPdITIlhvncPRxPp6BBpaZEkIoIW9l9JK376U+lghfj3ieRd72feSib2bTx51Z5CW6ft4RzJlxSVTG7WXgrHy2Qy6mMynHcpEA3cKGsl8URV4eo61KkkiIhmdv6NtvzM7QOfmyWgae19OYesHvz9JE3vuIB3ctrTmy/Jo/IYevVAuVBGOfJQ1st7b6gkpzRSYlPpz4k7qb/FCFo2dD3FhP03dxYSsYSu7r9FXnWm0sSWcyjg8B1+O8GNGz/ZJZSaMFi42zjXuB/N7LSQREKRUuuNf59IHnaj6dTG87zmSaVSGlV3Kp3ccI7zHJFQRCt++pNWeW1SODYyNIYGVRnDa00auFPWSiIWwPTSDoWCgd0AklHE8Q3AAsAVAG8LH825LFLdSiIzJYumdfClX/ss5+RoLsgrIDdrbiU7JGIJbZ22h36qMZHCn0XyWtetE/fJ3WYUndp4XqXQ1vDnUTSy1mRaP257mYUSCrLzaP/S4+Rm7UVrvLf8Z8xQ+YIC8tt0gTydfGiG60J6eClEub/VkiXs12/evA8v+fqyL/kWqUgRP3gUEUAP2vRX+m8pk8loZqeFvEtuELGfyYkt53D+HdOTMmlae/a7xWVHtX7sNtrje4j3ujRwo6yVRALYnIiFJR0KBQPtwZbwKKokVgH4pfD/vwBYyWWR6lYSROzdztrRW2lMw+mcIjWWe24g/62XOMu/uv+WUslpsW/jaVyTmbR08DpekVLFyc0S0CL31TShxRxKiEhSWo4istNzaNe8A+ReyZtGuEymPyfupLt+QdxMLiURFcXeZS9Zwj5Gqy+vQFmE+UJ6cfc17V14hNwredOCfitVT/ZSsJO4ckVCu+YdoPsVnNhxmxTflZfGmW2XaVKrX3gnfcpkMvJpPpvunHrAafzb4HAaVs2Hds8/yMk8m5aQzladTeJfokYDN756cxOAasWUxBsAdoX/twPwhoucslASROyXYLXXZvpnkeKGQAGH79C4JjN5faDfPgmnIY7j6Nbxe7zWVZBXQKu9NtPElnMoJ0N5R7RMJqOja/zJvZI3XTvAwZGqAlKplN6FRNCRVX40u9ti6l3Rk6a2m0/ndlzhpuyUCAUtSyRiCd0+eZ9md11EvYyGkU/z2bRpyi71JcN9xidx/GA6VdDNJZ96a0hW6JNQVlkGXXhCbtZevNctk8no6OrT5F3vZ04X/Jg3cTTAyotzuKswX0hLBq2lDRO4FQfUoBxlrSSCVRb+byWRWeznGZ+ZOxbAIwCPHB0d1f7myXkd9JY8nScovMuSSNg7uyGO43jdRT67FUqD7cfyvrOWSqW0afIuGt90llLFAIsSej+MxjaaQTNcF1L48yiVZHGlIK+A7p15RAv6raR+5iNo7eitFHo/rOQLTkICkbMz+3HU1WWVwrx57KOODvu6szNRYmKZrjk7PYcCDt+hlSM30kBbb5rSdj5dO3ibczQPb4pGNx2OJolEQic3nKMBVl60ashxSqj8aXQTH4QFIto6/W8a4jCOc9dFOblZAlrssZbGN53FqdscERuhxKWaARFRUnQKTWw5hxYNXFN2760GIip7JWGhsnAVlETRo6x2EnImtJhD98895jT2rl8QuduMotObL3K+M187eittmryL97pkMhntmneARtWdSimxqvV9kIgl5LfpArnbjKLNU3ertEPhS2p8Oh38/SSNcJlMfUyG09R282nDhJ10bscVennvDeX36PXxYskhFFRVhAUiinkTR0EXnpD/lou0Y9Y/NLXdfOpjMpzm9/qd/DZdoPj3ZauQiOgT5SjT1qFgi1p02aENCbr1VEk55mTk0qRWv9CCfis5VzSWE/kymka48PNn8UnMexn4mjzsRtPhlX7fREn3bx11KAmGlVM2MAxTDcBZIqpf+PwNgI5ElMAwjB2AG0SksD9h8+bN6dGjskvmurgnAOd3XsHv5+fD2MxI4fi4dwlY5LYGTbs0xPi1IxSOz07PwZj60zFhwyh0GNia9/oOrziFczuv4tcj01GreXXFEz5DZkoW9sw/hPtnH8Nr6RB0HdEB2traKsnkQ3ZaDsKfReF9SCTeP4tE9pNQLH62DVIwGFhhFi6K5qOHyxG4OCRAoN8Y+wK6Y+vPDzFkRUdAJgOFh0OratVS5edmCpAYmYzkqFQkRaUg/n0iol/FIic9FzkZAuRmCCDME8LK3hJ2zjawrWYDW6dKqNHUCQ3b14F+Bf7JaaqQFhyK5L5DUCvmObRQ5LuopQX07w9s3gxUUtwWVE5SVAqWDl6POq1qwmf9yJKT+EpbS0IGZndZhH6TeqC3T3fO807+cQ43j9/DhjtLPzuOiDClzXz08emutlatGj4PwzCPiai5SjLKWUmsBpBGRCsYhvkF7G5ltiI5Za0kJGIJtk3fi4cXg+F7dAZqNHFSOCcnIxcjXabgz3vLUKWGncLxbx69x7LB69Gsa0OMXzeC98Uo4PBdbJm6Gx6z+8F9ei9eX/4S1/PwHbZM+xvCPCHGrx2Bxq71VZKnNJs2AZMng9zckLdrL86dzsf4qWYY0CMFx/wt4el6CXpZQRjy+CBa5oVjk1YTXDdrCCMzI2hpa0EikkAsFEMikkBUIIaWthYqVbWGTVUrVHK0hp1zJVSr7wATKxNUNDeCsZkRjMwMy1UxloRELMGpDedxeKUfeo3riiHDm8Pg6mUgIwMwNwf69AEcSqnzVAJEhEt/38Bfc/bBfUYfeMzqAy0tLgUVWF49eIslA9ei55guGParG6fPV35uPjZO2oWwR++x4PhMONau8tnxN4/dw/4lx7D18Sro6OpwXpsG5VGHklBpG/K5A8AhsBFSYrDhtN4ALAFcAxsCew0cTVplbW6SE3D4DrlZe3GuALt34RFaM2ozZ/m5WQJaNnQ9ja4/jTJTsnivLykqmcY3nUXLh29QS2irTCajG0cDydN5Avn2XUHRr7mXflYbHENBae5cIoCkixZRVlo2xb1LoJiweEqMTKbU+HTKSs2mvJy8r96EkRKbSoeWn6SfakykX35YopY8k/TEDPLtu4LGNp7BO+yaiOjinuvkbjOK7p7m3vM8/HkUedWZSqu9NnOqPSbIzqMhDuPU1qddAzeg6SehfqJfx9IQx3H05NozhWOz03Oov+VIXrX85U2QZnZayKssgpx8QQEtHbyOJraco7bcBGG+8EM29aKBa8okY7tUeCaVqRIK+iV5fucVze2xlPpbjKB1Y7bSy8DXalFo9889Jo/KY2jXvAO8E+2E+ULaOOkvGuEymSJDYxRPKCTuXQINsPLiXCFAIpbQ2tFb1dp6VwM3NEqijLhz6gENq+bDyeknd2QfXunHuXSHRCyhTVN20RDHcfT4ylPe65PJZHTij7M0wMqLjq7xV1uXuuz0HDq18Tx5VB5DSwevK58EOZ7lKb6GvAmuSKVSenD+Cc37cRkNcRxHF3ZdU1ufjsyULFruuYGGV5/IqdtbcYQFIprbYyn59l3B62YlISKJhlXzoTPbuO22k2NSaVp7X5rddRFvJ7oG1dEoiTJk+8y9NLfnMk4X/sTIZJrabj7N6rKI0hIUJ+bJeXQ5hIY4jKPNU3crVY477l0Czez8G41pOF2pC0Vp5OXk0f4lx2mAlRdt8NlBT649U7qcOSeKhYIW5e7haLVHN5U1mSlZdHilH3k6TyCfZrPo/F9X1VZuXSaT0fVDd2igrTdtnf63Un8XkVBEv/ZeTovcV/O6wUiMTCZPJx/OpT1ePQijgbbedPD3k7yKXmpQH+pQEmXquFYXZe24LgmpRIpZnRehbmsXjPp9qEInoFQixa65B/Di7mssv/grjEwMOZ0nOz0HGyf+hciXMVh0ajYqV7fltU4iwu0T97Fj1j64tKiO8WtHwMbBipeM0shMyYLfnxcQfP05wp9GwbGuPRq0q40eozujal3uTlWFyEtmh4cDOjpA376AiwsQFgacPg1IJICzMxAYyCvSpzxJjknF+Z1X8ep+GMIehaNNvxboPb4barWooXKQgZyI51HYNmMv0hMyMf0vH9RpVZO3jLycfKz8aSMAwPfodM4O5NiweMzr+Tv6Te6BAVMVNykSFYgwu+ti/ODVCT+M6sR7nRrUw1ftuFbn8SV2EkRs2YDJrefSLz8s4bRDkEql9Mf47eTp5MO7Kb3fpgvkZu1Fp/48r9RdV0FeAe1deOSDCUrdSUoFeQX07FYo7fE9RO6VvGlm59/o5rFApUtW/wtFGddlnEinLNGvY2m112bqbzmStvy8h+6fe6yUr+lz5OXk0bYZe8ndZhT5bbqg9Hv+5Noz8nTyobWjt5KwgLv/4tLfAeRm7UXndlzhND78WSSNbjCNlgxaq77PhwalgMbcVPaIRWLa43uIhjiM4/zlv3fmEXlUHkM7Zu/j9WWMeRNHU9vNp2kdfJX2B8S8iaPf3FZTf8uRtGnyLl4OSa4IC0R0/dAdmt5xAXnYjabd8w9SxItopSuUfkJ0NOucXrKEffzKfBDZ6Tl059QD2jRlF41pOJ3crL1o3+JjalcMRKxp6fbJ+zTEcRyt+OlPpctpi4Qi2jHrHxpsP5YenH/Ca+6t4/dosP1YTp8jmUxGJzecIzdrL7q45/pXH2n2X0AdSkJjbuLIBp8dkEll+Hn7OE7mg8yULPwxbjsSwpPhe3Q65w5gUqkUfn9ewMHfT8LT1x19JnZXKqZfbv648Nc1ONapgv5Tf0Tr3s3VZvqQExUag7PbriDoYjBSYtJgVcUC9i52qFLTDpWqWqN2yxqo26YWr5j9rwGZTIanN17izcP3SIlJRWpcOhLCk5AYkYw6rV3QxLU+GrnWh0szZ2jrqDfnQpgvxLntV3Fh1zUAwKSN3mjUsZ5SsuLfJ2L5sA0wtTbBrD0TYWplwnnuizuv8NuA1Vh+8VfUbOr82bGC7DwsG/IHctJy8Mv+KZxyhzSUPV99Mp26+BqURHZ6Dub+sAym1iaY8ZcPp/aNRISz269g74LDGLNqOLqN6Mj5Ih37NgFrvbdAKpFi+k4fVKunnA9ALBLj7qkgHFh2AhUtjDF+7Qi4NFMta7s0JGIJEiOSERuWgNiweCRFpSDk+gvk5eTDdXBbdBzcFtUbVVO7olIXgiwBnlx9jqALwXh4MRim1iZo2rkhrB0sYe1gBRsHSzg3qgpdPd0yOT8R4dbx+9g5ex9qNHXCgKk/osH3dZR6v6QSKU5uOI/DK05h2Hw39J/ak7McsUiMA0tO4NzOq5i9dxJadG+s8Fy/9l4OqyqWmLp1jCZR7itCoyTKGYlYggNLT+Ds9iuYvMkb7d25ldiIeB6F34dtgGMde/y8bSwqmhtzmieTyXB+5zX87XsIvcZ3w9B5A6BnoKfU2qVSKS7tDsDehUfQrFsjjFo2BFZVLJWSxZeI51G4fvAObh67h5z0XDg1dIRzg6pwblQNTg0cYVnZHGY2ptDTL5uLb3HEIjGSolKRGJGMxIhkJIQn4dX9MLwLjkC9trXQ4ocmaNmjCa/+z6qQGJmMhxdDcP3gbeRl52PCH15K7xwAICTgBXbM3gcjkwqYtmM8r2CIiBfRWDH8T9g4WOHn7eMU3gwRETZO/AuJkclY4v+L2ndVGlRDoyS+EK8evMXKnzaiaZeGmLRxFCdTiqhAhJ1z9uOuXxBm/z2JVxmM1Lg0bJq8C9Gv4jBhwyg079ZI6bULsvNweIUfzu24gvZu36FJ5wZo3bdFuV2gM1OyEP40Cu+fRiHieRQiX8YgIzETmclZ0KugBzMbU5hYVoSBkT57GOpDv4I+9Ax0oaWtBW0d7cJH9j0nYv8hIshk9KFMh7jwUVQghiArD4KsPORmCiDIFKBAIIRlFQvYOslrN9mgRhMnNOpYDwaGZV+7iYgQEvAC9888xsOLwcjJEKB590Zo1bMpvnf/TumSIe9CIrBr7gHEvU3EyMWD4DqkHa9dyL0zj7DWewu8lw/DD6M6cZp7bO0ZXN4bgD/uLOUc0aeh/NAoiS9Ifm4+fPushEwqw8zdEzjfrQVdCMb6sdvQtl9LeC8figrGFTif865fEHbM3genBo6Y8IeXSqGuydEpuH3iAR6cf4KE94n4adEgdBra7ovVNCIiCLLykJmchazUHAjzhCjIE6JAIIQwTwhRgRgyqQwyqQxSiRRSiQwAwDDsP1paDBiGgY6eDnT1daGrrwNdPR3oGujB2MyQrdlk+vHxS93xhgS8wO75B5GXnQ/XIe3Q4ofGqNHESSWfTV5OPvYuOILrh+7A09cdPcd05mUSE2QJsH3mPjy6HIJfD09D3dYKa24iJyMXm6fsZm+YLvvCtpqN0uvXUHZoQmC/MBKJhI6t9acBVl50evNFzqGr2ek5tHLkRvJ0nsCp/EdRhPlC+ue3ozTAyouOrPJTS4jh05svaUrb+TS6/jQ6sf4svX8aqUl+UjOh98NoVpdF9FPNSXTtwC3eXeJK4m1wOG2c9BcNsPKi1V6blaoHdv/sIxriMI7+GL+dc8+TyJfRNMRxHG2avKtskyw1qAw00U1fB9Gv47DaazMMjPQxa/cE2Dhac5r34PwTbPDZgZY9mmLMKk9e2/XYtwnYNHkX0uLT4fZzL7Qf2BqGFbnvSopDRHh4MQSBfkEIDngBQaYADTvWQ6MO9VCtvgNqt6xR7mW0v1WSY1IR9ug9Yl7HI+ZNHCJfxiAzOQuevgPRbUQHlRy7eTn5uLb/Fi7svo6slGx0H+mK7l6uqFSV22dOTlpCBrbN2Is3Qe8wfed4zubP908jMa/HMoxe6YmuwzXlvr92NOamrwipRIojq07j1IZz8F7hie4juUUyfdjqXwrBmFXD0XFQG852ZCLC/bOPcXH3dbx5+A7ey4eh87Dv1RJumhyTiqcBL/HsViiiQmOQGJGMvpN6oM+E7pwd7/8lZDIZgq89x5ltl/H81ivUbeMCx1pV4FCbPWo2c1bJ7yOTyXB5703s+fUQ6rVxQY/RXdC0SwPe5kGZTIYLf13D376H8YN3Zwz71Y2TH6ZoaPaULWOU6ouiofzRKImvkPdPI7Fm1BaYWptg2vZxnO/wXtx5hc1T96CCsQEmbPBCjcaKe1oUJfR+GDZP2Q0dXW34rB+p1nIQABD5MgbH1vrj3umHcB3SDo07NUDd1i6cQoH/n8lOy8Glv2/g7PbLMDDUR6/x3dB52Pcq7eqKE3rvDTZP3QNtHS1M3DAKtVrUUEpO9Os4/DFuO8QiCabvGAenBqU3bypKbFg8Vo/aAm1tLczY5aPJgfiG0CiJrxSJWIJja87gxPozGLFoEH4c15XT3b1UKsXFXdexd+ERtOnbEiOXDIKZtSnn88pkMlz++wb+WXQUANDYtT4adqiHJp3q8zZHlEZKbBou772Bl4Fv8Pp+GAxNDFGntQuqN6wKMxtTmFqbwNSqIkytTWBhZ44KRgZqOe+XQpgvRHpCJnIycpGVmsOGzL5PRPz7RMS/T0JSVApa92mO3uO7o25rF7Up5oI8IULvheHy3wF4euMlvJcPQ6eh7ZTaJUa+jMGJdWcQ6P8IwxcORG+fbpx2IFQkz8fTdyD6TOz+zSVF/tfRKImvnKjQGKzx3go9A11M2z6Oc9x9TkYu/vntKAIO3YGnb+GXmkc0DhEh7m0CQgJe4umNFwi+9hyNXOvD81c3znePXJDJZIgNS0DovTBEh8YgKy0HWSnZyEplHzOTs9CwQ120d2+NNn1bcGoN+zWQmynA89uvcPNYIO6feQxjMyNUtDBGRQtj2FazQeXqlVC5hi0q17BFlRq2vCLUFCEWiXFm62Uc+v0kKte0Q8sfmmDAzz2VOkd2eg52zz2IQP+H6DuxB3qN78o54zoxMhl/jN+B7NRs/LJ/qsKucxq+TjRK4htAKpXi9KaLOLD0BNyn98bAmb05Oy6jQmOwcdIuCLLyMHnzaNT9zkWpNeQLCuC/+RJO/nEWtVrWwNB5A1C7Jf8KonwRZOfh/pnHuHX8HkKuv0Cd1i5o8H0dOBba6SvXsC23/IzSEAnFSItLx7uQSDy/FYpnt0IR/y4RtVrWQNu+LdFhUBuY23DfzSlLvqAA9888xt++h1DFpTLGrPSEU31H5WTl5uPGkUD87XsY7Qa0gtfSIZwVdNHP68AZveE+g/vnVcPXh0ZJfEMkRiZjg88OpCdmYvpOH9Rqzq00BhHh+sE72DF7H777sSmG+bornR8hzBfiwq7rOLr6NBxqV0H3ER3hULsKqtS0U6sNvSQE2Xl4cuUZ3jx8h5g38Yh5HYfEyBRYVbGAmY0JTCwrsoeFMYzMjKBfQQ96FfSgX3jo6OlAS1uLTaQrfATkpWKpMKGONfVJxVKIRZIPiXV5OQXIy85DXk4+8nLykZOei9TYNKTEpkOQKYBlZXM41LFHw/Z10bB9HdRs5lxmpTfkZKVmIzTwmLgAABeESURBVPReGJ7fCsXz268Q+TIGLs2rY9h8NzTt0pC3PEF2Hh6cfYxbJ+4j+Npz1G3tgpGLB3P2XxARgs4/wR7fwzCsWAHTdoyDQy3N7uFbR6MkvjHkF/ztM/eiXf9W8F4+FEam3O7wcjMF+Oe3o7h24DYafF8b49aOgJ2Tcr0VxCIxru67hUeXnyL2TTzi3iagTmsXjFg0CPXb1lZKprLrSIpMQWZKNnLSc5GdloPstFzkZuRCmC+CqEAMUb4IwgIRJCLJx2S6wkeATaZj2H/AMICOrg6bUKen8+H/hhUNYGhiCMOKFWBoUgHG5kawdrCCtb0FzGxMy83OLpVKcfNIIA4sO4HUuHTUblUTDdrVQYP2dZQOMRZkCXBszRn4bbqABt/Xwfdu3+G73s1gYlGRs4zYtwnY4LMDmUlZGLF4ENr2a/nV1tfSwA+NkvhGycnIxa5fDuD+ucfwWe+F9u7fcf5SigpEOLb2DE6sP4s+E7pj8C/9VS4lIRFLcOWfmziw9ARMLI3RcVBbdPBoozZn938dqVSKG4cDcWDpcZhYVcRPCz3QpHMDlS7E+YICnN54ASfWn0XLH5tixKJBvHeY8s/SyT/OYei8Aeg3uYem9tL/GRol8Y3z4u5rbBi/AzZVrTBpozevnUFyTCp2zPoHr+6/Rbv+rVC7VU3U+a4mKlW1VvriI5VI8fRmKG4cvou7fkGwd7FDe/fWcKxTBRZ25rCwM4epVUVNhMtnKMgTIjEiGe+CI/AuOALvQyLwLjgSVevZq0c55Objwq7rOLLSDw071MXwhR68nMpEhLdPwnFxdwBuHg1Eg+9rw2e9l+aG4P8UjZL4P0AsEuPEurM4suo0vuvdDGNWesLClnvuwZtH7xF87TleB73Fq3thAMNg4Ize6DW+m0o7DLFIjCdXnyPQLwiJUSlIT8hAekIm8rLzYGZjiubdG6PPhO4K+wz8v5MSmwb/LZdw82gg0hMyIJMRbBytUKNJNdRo7ITqTZxQo4mTys7vpzdfYvf8QwgPiUTTrg3x028eqN6oGi8Zr4PeYvOU3chMzkJ3r07oNqID5+oAGr5NvlklwTBMJIAcAFIAEkW/xP+zkpCTl5OPA0tP4NKe6/BcwD2WvShEhIjn0di78AhC74Wh36Qe6DNRvRnSYpEYqbHpuHEkEGe3X4aFnTl6j++GDh6t/xNlO4gIkS+i8eB8MIIuPEHEs2h08WyPnmM6w9a5EgwM9dVqz48Ni8fOOfvxPiQS3suHod2Alryc6kSEF3de49haf7wJeofRKz3VlpWv4evnW1cSzYkolcv4/4KSkBP5MgYbJ/2F/Jx8jFwyBA071FVqRxAVGoOja9gM6W4jXdHbpxtsnWzUWuVVKpUi6Hww/LdcxLObobB1skHVeg6oWsce1eo5wKaqNSqaG8HY3AgVzY2/GXu3VCJFfm4BMpIykRKbXhgJlYaE90l4cvUZtHW00LJnU7Ts2VTt5cVT49Px6l4YXt0PQ+j9MMS8jofHrL7oP6UHr14iUokUd04+wPF1Z5CTIYDbtF7o+lOHcimFruHrQaMk/k8hIlw7cBtntl1G+NNI1G9XG6OWDVXKtJMck4oT687i5rFAZKflolJVKzT4vi6Gzh+g1vLOogIRYsMSEPkyBlEvYxAVGoPUuHTkZAiQmyFAbqYA+hX0YGCkD119XegZ6H541NbVQZUatqjdsiZqt6oBpwaOag9BzUjOQuSLaES9jEXYk/eIC0uAWPQxXFaUL0KBoAD5uQWQiCQwMDKAWSVT2DhYwsreElZVLGHjaIVGHevBoVZlte4WQu+9weGVfnj7OBzCfBHqfFcTdb+rhTqtXVC7ZQ1e4clyZ/TZbZdR204fno314WRfEVoWFkDfvoCDch0ONXybfMtKIgJABgACsJ2IdpQwZiyAsQDg6OjYLCoqqnwX+ZWQn5uPgEN38feCw2jTtyWGzh+gUp5EYkQyAg7dhf/WS2jdpzk6DWmHxq71y/wuXyaTIT8nHwV5og/NgOSPEpEE0a9i8TroHV4HvUVcWAJMrU1gZW8Ja3sLWNtbwdTahG1AZKgHAyMD6BvqgWEYSCXSwh4TbJ+J3AxBYShtDrLTc5CemIno0FjIpDJUreeAavUc2I549R2ga6AHXT0daOtqQ89AF4YVK8DASB96BnrlEgL69kk4/l5wGJEvYjB03gA07dIQtk42Sp2biHDP/xG2zdiLhrUs4CN6BKPrlwCZ7OMgLS2gf39g0ybAlnu3Og3fLt+ykqhMRPEMw9gAuAJgMhHdKm38f20nURI5GbnYv/g4ruy7CTvnSmjTtwXa9muJqnXtlbqoZCRn4dr+W7hx5C6SolLR3v07uA5ui5rNnL+4b0EiliA9IQMpselIiUlFSmw6slKyIMwXQZgngjCfbUYE4EOXOm0dbWjraMPI1PBjYp5lRZjZmMCxjj0sbM2+aOy/TCZDQnhSYVe+SLwOeofIF9EYMncAeozurHTmuTBfiPchkdi3+BiSo1MxdWEfNJw7BggPB3R1gT59gFq1gDdvgNOnAYkEcHYGAgOBSsrl2Wj4dvhmlcQnC2CY3wDkEtGa0sZolMRHJGIJnt9+hUC/h7h7Ogi6+rpo1KEehsztDztn5b70ce8ScONIIG4du4eYN/HQ0dWGeSVTmNmwR6MO9dB9lKumPSVHZDIZ7p99jIBDd5CZnIXstFwkhCfB2NwI1RtVg3PDqqjeuBpa9myqlI8g7PF7HFvjj7dPIpASk4oqNe3wg1cn9JnYHTpDBgMnTiCxSlOErzuNNh72H+YFHo2F8/S+sI17Ari5AcePq/PX1vAV8k0qCYZhjABoEVFO4f+vAFhMRBdLm6NREiVDRAh/FoVAv4c4tfE8uo90xdD5A1SKZiIi5OXkIzM5CxlJWUhPyMCt4/fw5OpzdPupA/pN6alpVVkCRITIlzF4cO4JLu+9AQNDPfQa3x221axR0cIYlapZ88qCLkl+SMAL+G+5hNDANxg63w2NOtaDvYvdx9pK0dGAkxOgrY3A/eHoO9EeR48Crq5AQADg4QGc3hSDNp7OrBkqMlLjo/g/51tVEs4AThU+1QFwkIiWfW6ORkkoJj0xA3sXHEHg6YfwmNUX9b+vA8c6VdR2958cnQK/jRdwcU8A6rWphRpNnOBYxx4OtSvDoVblL26iKk/kijQ9IQMxb+Lx8EIwgi4Eg2EYtOzZFO3dv0OjjvXUYt4SZAlwee9NnNl2GdraWugzoTs6e7Yv2Zm9aRMwefKHXYJcMfj4AFu34oPCgLs7cOIEO37iRJXXqOHrRR1KotzLOxJROIBG5X3e/3csbM0xbcd49JvcA8fWncH1Q3cQ+yYeRmaGcKxjD/uadvje/Ts06dRAKfk2jtYYu/oneC4YiAfnniDqZQxun7iHmNfxiH+fCGNzYxiZGsLIpAIMCx8NjAygo6sDXX0dmFhWRIseTVCrRXW1huGqE5FQjGc3Q/E04AXycwsK60eJIMwToiCPjX6S764AwMLOHLZONmjetRF+Pz8PjnWU8w8V5c3Dd7i67xZSYllfTNzbBLT4oTF+3jYWDb6v83n5mZnsY61aAFiF4OMDLFkC+PoWKggAcCmsJpyRodJaNfw30NQA/j/DqUFVzN4zCQBrG0+OTkX0qzhEhcbij3HbYVPVGl5LhyhddtywYgW4Dm77yWtSiRQZSZkQZOVBkJ0PQVYe8rLyUJAnhFjIVmJNjUvHutFbkRqXjrqtXVCvTW3Ub1cbtVrW+CKx+0SEtIQMRIfGIvJlDJ7dCkXwteeoVs8Bzbo2goWdOVuB1lAf/2vvzoPrKs87jn9/2nfpSrJleccYGowxNiZAcAADDsOShqRDQghN6Bo6E0+WMpNmmpmWLpm2GSidSRl3SJMOgZAESgwObhqIJ2BcB2O8gPCKN7zJkhftlrU+/eMcmRujo8Vazr3y85nR6Orq6Nxn3iPpued9z3menPwc8gqCx4mqUiqmlo9q1dye7h42r6lh9ROvsHPDe9z9lTtYcNM8KqdXMG3uFEoqhjhNVVYWfN61CwimmFasCBLEihVBkrj5ZmD37mC7xIXdVdANTewL10Ph002jo7urm5effI2n/+E55iyYxScfvI1Z86YzeVbluL27b6hvYvv6Xby7bifb1u9k79YDFCWKmDS9PLjkdVoFiSllFJTkU1CcT35xPvlFeeQV5JCZnUVmVgZZ4WckMKO317Beo7e3l66O4J6HzjOdZ6+Gaj7ZQtOJZppPtNB0soUTh0/y/vbDZOdmM2vedGZeNp2PXDOXa+5cNKxOgCNhZuzetI81T6/ltWfXM2lGBcv+8CZu/9Nbzj9p+pqEO0darkmcD08So6vzTCf/8/01/Paltzi08wjNJ1qYdkk1My+bRqKqjMLSAhJVZSy99/qhv4s9T31nIcl3Np+qbeB0yxnaW9tpb2nndEsw9dPb3UN3V8/ZnhEAylBQYkKQkZFBdm7W7/ShyC3IpaS8mJLKYkorg9aq5dUJZl42bchd2kZif837bPzfrR906zveRNPxZo4fPklOXg633n8Dt95/w5C7Fg4qXG84Nu0q9j36Atff+0ESWP+zQ8x56NN+ddMFxJOEGxXtre1hI6CjNNY30dZ0miN7atn4yy0s++JN3Hr/DVyyeI7X+xkiM2PLmhqee3QV+945yI33XEf5lARlk0sonVRC2eRSEpNLz/vGuQEdOwZLlgT3SWRlBXdZX3ppMMXk90lccDxJuDHVV+F0/Ytv0trQxrV3LeZjn7qaRbde4TWAkvT29lJ34Dj73nmf/TUHWbdyA92d3dzz0Ke45QsfH/8WrceOwfLlsHJl/3dcP/64J4gLhCcJN26O7KnljV9s4o2X3mLb+t0UlRWc7TFRMaWMksqSD7Uczc7NJjM7k4vmz2D2/Jlp0+2so72Dd9buoOl4c9ACta/GU0cXbc2naWs8TVvzaVob22ioa+Lg9sMUlhUwZ8EsLpo/kwVLL2fxJxbEf+Z16BCsWhVcxZRIBHdf+xrEBcWThItFT3cPDfVNZ3tMnKptoPlkS3DJaHu4YNzeSVdnUJdp54Y9ZGZlcN0nr+bKmy9n7qKLmDS9IqWSRv2hE2xYvZkNqzdRs3YHFy+czeSZlWRmZ5IdtkHNysmisKSAwrICCksLKCorpLSymFmXzxjVcuzOjRZPEi4tmBl7tx7gjZc2sf23u9izZT893b3MXTSbOQtmUzmtPCgDUlVGoqqU0spi8ovyyMnPGfFVV32FBVsbg3f+rQ1tNNY3cXRvHUf3HuPY/jqO7q3jTFsHH71jIdfdtZjFt13p//TdhOBJwqWtk7UN7Nmyn/01BzlV20BDfRONdY001DXRdKKFM21n6DjdSXZuFnmFeUFJ8azM3ynoB0ECsl7DwkthuzuD+zL67s/o6ugityCXokQhRWVBb4vSyhKmzqmi+uIpVM+ZzNSLp4zrZcDOjZe0vOPaOYCK6gQV1QmuvfOqyG3MjI6wz0Pnma6wJHhQGrw7vAQ2I0MgkZEhJJGVk0V2bjbZuVkf9Kvwf/7OnTdPEi5lSSKvINevpHIuRn7hu3POuUieJJxzzkXyJOGccy6SJwnnnHORPEk455yL5EnCOedcJE8SzjnnInmScM45F8mThHPOuUieJJxzzkXyJOGccy5SLElC0u2SdknaI+lbccTgnHNucOOeJCRlAo8DdwDzgPskzRvvOJxzzg0ujjOJa4A9ZrbPzDqBnwJ3xxCHc865QcRRKnwacCjp68PAteduJOnLwJfDLzskvTsOsY2VSuBE3EGMQDrHn86xg8cft3SP//dGuoM4kkR/jY0/1B7PzJ4AngCQ9NZIuyvFyeOPTzrHDh5/3CZC/CPdRxzTTYeBGUlfTweOxhCHc865QcSRJDYCl0i6SFIO8HlgVQxxOOecG8S4TzeZWbek5cCvgEzgh2a2bZAfe2LsIxtTHn980jl28PjjdsHHL7MPLQc455xzgN9x7ZxzbgCeJJxzzkVK6SSR7uU7JB2QVCNp62hcijbWJP1QUn3yPSmSyiW9Ium98HMizhgHEhH/w5KOhMdgq6Q744xxIJJmSPqNpB2Stkn6Wvh8yh+DAWJPi/GXlCfpTUlvh/H/Xfh8yo89DBj/iMc/ZdckwvIdu4FPEFw2uxG4z8y2xxrYMEg6AFxtZmlxM46kG4FW4EdmNj987rvAKTP75zBRJ8zsr+KMM0pE/A8DrWb2SJyxDYWkaqDazDZLKgY2AZ8G/ogUPwYDxP450mD8JQkoNLNWSdnAOuBrwB+Q4mMPA8Z/OyMc/1Q+k/DyHePMzNYCp855+m7gyfDxkwR/+CkpIv60YWa1ZrY5fNwC7CCoUJDyx2CA2NOCBVrDL7PDDyMNxh4GjH/EUjlJ9Fe+I21+6UIGvCxpU1hmJB1VmVktBP8IgMkxx3M+lkt6J5yOSsnpgnNJmg0sAjaQZsfgnNghTcZfUqakrUA98IqZpdXYR8QPIxz/VE4SQyrfkeKWmNlVBBVvvxJOh7jxtQK4GFgI1AKPxhvO4CQVAc8DXzez5rjjGY5+Yk+b8TezHjNbSFAF4hpJ8+OOaTgi4h/x+Kdykkj78h1mdjT8XA+sJJhCSzd14Xxz37xzfczxDIuZ1YV/PL3A90nxYxDOJz8P/NjMfh4+nRbHoL/Y0238AcysEXiVYD4/LcY+WXL8ozH+qZwk0rp8h6TCcAEPSYXAbUA6VrJdBTwQPn4AeDHGWIat7w889BlS+BiEi48/AHaY2b8mfSvlj0FU7Oky/pImSSoLH+cDy4CdpMHYQ3T8ozH+KXt1E0B4uda/8UH5ju/EHNKQSZpDcPYAQfmTZ1I9fkk/AZYSlEeuA/4WeAF4FpgJHAQ+a2YpuTgcEf9SglNtAw4AD/bNMacaSR8HXgdqgN7w6b8mmNtP6WMwQOz3kQbjL2kBwcJ0JsGb52fN7O8lVZDiYw8Dxv8UIxz/lE4Szjnn4pXK003OOedi5knCOedcJE8SzjnnInmScM45F8mThHPOuUieJFzak9Q6+FZnt10q6fqxjGeQ1/+6pC+Nwn5+KumS0YjJuYF4knAXmqVALElCUhbwJ8Azo7C7FcA3R2E/zg3Ik4SbkCT9vqQNkrZI+rWkqrDw3F8A3whr698Q3qn6vKSN4ceS8OcfDguivSppn6SvJu37S2HBtLclPSWpWNL+sCwFkkoU9BLJPiesW4DNZtYdbveqpMckrVXQh+Gjkn6uoHfBP4bbFEpaHb7Wu5LuDff1OrAsTDzOjRn/BXMT1TrgOjMzSX8GfNPMHpL0HyTV15f0DPCYma2TNBP4FXBZuI+PADcDxcAuSSuAS4FvExRvPCGp3MxaJL0K3EVwh/rngefNrOucmJYQ9FlI1mlmNypo0vMisJig3PleSY8RnPkcNbO7wnhLAcysV9Ie4Mp+9uncqPEk4Saq6cDPwto1OcD+iO2WAfOC0kMAlPTV3AJWm1kH0CGpHqgiOBv4775GUkklGv6TYPrnBeCPgT/v57WqCfosJOurR1YDbOsrmSBpH0GByxrgEUn/ArxkZq8n/Ww9MBVPEm4M+XSTm6i+B/y7mV0BPAjkRWyXAXzMzBaGH9PCpjkAHUnb9RC8qRL9lKw3s/8DZku6Ccg0s/4KqbX3E0ffa/Se83q9QJaZ7SY4u6gB/knS3yRtkxfu07kx40nCTVSlwJHw8QNJz7cQTB/1eRlY3veFpIWD7HcN8Lmw8BuSypO+9yPgJ8B/RfzsDmDuoJEnkTQVOG1mTwOPAFclfftSYNtw9ufccHmScBNBgaTDSR9/CTwMPCfpdSC5x/gvgM/0LVwDXwWuDheitxMsbEcys23Ad4DXJL0NJJf0/jGQIEgU/fklMNzGU1cAbyroOPZtoG9BuwpoT8WKqm5i8Sqwzo0SSfcAd5vZFwfYZiXBIvp7I3ytbwDNZvaDkezHucH4wrVzo0DS9wja1N45yKbfIljAHlGSABqBp0a4D+cG5WcSzjnnIvmahHPOuUieJJxzzkXyJOGccy6SJwnnnHORPEk455yL9P/T4+J8ext/8QAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Find the outliers in the training set \\n\",\n    \"outliers = p < epsilon\\n\",\n    \"\\n\",\n    \"# Visualize the fit\\n\",\n    \"visualize_fit(X_train, mu, var)\\n\",\n    \"\\n\",\n    \"# Draw a red circle around those outliers\\n\",\n    \"plt.plot(X_train[outliers, 0], X_train[outliers, 1], 'ro',\\n\",\n    \"         markersize= 10,markerfacecolor='none', markeredgewidth=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.4\\\"></a>\\n\",\n    \"### 2.4 High dimensional dataset\\n\",\n    \"\\n\",\n    \"Now,  we will run the anomaly detection algorithm that you implemented on a more realistic and much harder dataset.\\n\",\n    \"\\n\",\n    \"In this dataset, each example is described by 11 features, capturing many more properties of your compute servers.\\n\",\n    \"\\n\",\n    \"Let's start by loading the dataset.\\n\",\n    \"\\n\",\n    \"- The `load_data()` function shown below loads the data into variables `X_train_high`, `X_val_high` and `y_val_high`\\n\",\n    \"    -  `_high` is meant to distinguish these variables from the ones used in the previous part\\n\",\n    \"    - We will use `X_train_high` to fit Gaussian distribution \\n\",\n    \"    - We will use `X_val_high` and `y_val_high` as a cross validation set to select a threshold and determine anomalous vs normal examples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# load the dataset\\n\",\n    \"X_train_high, X_val_high, y_val_high = load_data_multi()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Check the dimensions of your variables\\n\",\n    \"\\n\",\n    \"Let's check the dimensions of these new variables to become familiar with the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"The shape of X_train_high is: (1000, 11)\\n\",\n      \"The shape of X_val_high is: (100, 11)\\n\",\n      \"The shape of y_val_high is:  (100,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print ('The shape of X_train_high is:', X_train_high.shape)\\n\",\n    \"print ('The shape of X_val_high is:', X_val_high.shape)\\n\",\n    \"print ('The shape of y_val_high is: ', y_val_high.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Anomaly detection \\n\",\n    \"\\n\",\n    \"Now, let's run the anomaly detection algorithm on this new dataset.\\n\",\n    \"\\n\",\n    \"The code below will use your code to \\n\",\n    \"* Estimate the Gaussian parameters ($\\\\mu_i$ and $\\\\sigma_i^2$)\\n\",\n    \"* Evaluate the probabilities for both the training data `X_train_high` from which you estimated the Gaussian parameters, as well as for the the cross-validation set `X_val_high`. \\n\",\n    \"* Finally, it will use `select_threshold` to find the best threshold $\\\\varepsilon$. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best epsilon found using cross-validation: 1.377229e-18\\n\",\n      \"Best F1 on Cross Validation Set:  0.615385\\n\",\n      \"# Anomalies found: 117\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Apply the same steps to the larger dataset\\n\",\n    \"\\n\",\n    \"# Estimate the Gaussian parameters\\n\",\n    \"mu_high, var_high = estimate_gaussian(X_train_high)\\n\",\n    \"\\n\",\n    \"# Evaluate the probabilites for the training set\\n\",\n    \"p_high = multivariate_gaussian(X_train_high, mu_high, var_high)\\n\",\n    \"\\n\",\n    \"# Evaluate the probabilites for the cross validation set\\n\",\n    \"p_val_high = multivariate_gaussian(X_val_high, mu_high, var_high)\\n\",\n    \"\\n\",\n    \"# Find the best threshold\\n\",\n    \"epsilon_high, F1_high = select_threshold(y_val_high, p_val_high)\\n\",\n    \"\\n\",\n    \"print('Best epsilon found using cross-validation: %e'% epsilon_high)\\n\",\n    \"print('Best F1 on Cross Validation Set:  %f'% F1_high)\\n\",\n    \"print('# Anomalies found: %d'% sum(p_high < epsilon_high))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"<table>\\n\",\n    \"  <tr>\\n\",\n    \"    <td> <b>Best epsilon found using cross-validation: <b>  </td> \\n\",\n    \"    <td> 1.38e-18</td> \\n\",\n    \"   </tr>    \\n\",\n    \"   <tr>\\n\",\n    \"    <td> <b>Best F1 on Cross Validation Set: <b>  </td>\\n\",\n    \"     <td> 0.615385 </td> \\n\",\n    \"  </tr>\\n\",\n    \"    <tr>\\n\",\n    \"    <td> <b># anomalies found: <b>  </td>\\n\",\n    \"     <td>  117 </td> \\n\",\n    \"  </tr>\\n\",\n    \"</table>\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/C3W1A/C3W1A2/public_tests.py",
    "content": "import numpy as np\nimport random\n\ndef select_threshold_test(target):\n    p_val = np.array([i / 100 for i in range(30)])\n    y_val = np.array([1] * 5 + [0] * 25)\n    \n    best_epsilon, best_F1 = target(y_val, p_val)\n    assert np.isclose(best_epsilon, 0.04, atol=0.3 / 1000), f\"Wrong best_epsilon. Expected: {0.04} got: {best_epsilon}\"\n    assert best_F1 == 1, f\"Wrong best_F1. Expected: 1 got: {best_F1}\"\n    \n    y_val = np.array([1] * 5 + [0] * 25)\n    y_val[2] = 0 # Introduce noise\n    best_epsilon, best_F1 = target(y_val, p_val)\n    assert np.isclose(best_epsilon, 0.04, atol=0.3 / 1000), f\"Wrong best_epsilon. Expected: {0.04} got: {best_epsilon}\"\n    assert np.isclose(best_F1, 0.8888888), f\"Wrong best_F1. Expected: 0.8888888 got: {best_F1}\"\n    \n    p_val = np.array([i / 1000 for i in range(50)])\n    y_val = np.array([1] * 8 + [0] * 42)\n    y_val[5] = 0\n    index = [*range(50)]\n    random.shuffle(index)\n    p_val = p_val[index]\n    y_val = y_val[index]\n\n    best_epsilon, best_F1 = target(y_val, p_val)\n    assert np.isclose(best_epsilon, 0.007, atol=0.05 / 1000), f\"Wrong best_epsilon. Expected: {0.0070070} got: {best_epsilon}\"\n    assert np.isclose(best_F1, 0.933333333), f\"Wrong best_F1. Expected: 0.933333333 got: {best_F1}\"\n    print(\"\\033[92mAll tests passed!\")\n    \ndef estimate_gaussian_test(target):\n    np.random.seed(273)\n    \n    X = np.array([[1, 1, 1], \n                  [2, 2, 2], \n                  [3, 3, 3]]).T\n    \n    mu, var = target(X)\n    \n    assert type(mu) == np.ndarray, f\"Wrong type for mu. Expected: {np.ndarray} got: {type(mu)}\"\n    assert type(var) == np.ndarray, f\"Wrong type for mu. Expected: {np.ndarray} got: {type(var)}\"\n    \n    assert mu.shape == (X.shape[1],), f\"Wrong shape for mu. Expected: {(X.shape[1],)} got: {mu.shape}\"\n    assert type(var) == np.ndarray, f\"Wrong shape for mu. Expected: {(X.shape[1],)} got: {var.shape}\"\n    \n    assert np.allclose(mu, [1., 2., 3.]), f\"Wrong value for mu. Expected: {[1, 2, 3]} got: {mu}\"\n    assert np.allclose(var, [0., 0., 0.]), f\"Wrong value for mu. Expected: {[0, 0, 0]} got: {var}\"\n    \n    X = np.array([[1, 2, 3], \n                  [2, 4, 6], \n                  [3, 6, 9]]).T\n    \n    mu, var = target(X)\n    \n    assert type(mu) == np.ndarray, f\"Wrong type for mu. Expected: {np.ndarray} got: {type(mu)}\"\n    assert type(var) == np.ndarray, f\"Wrong type for mu. Expected: {np.ndarray} got: {type(var)}\"\n    \n    assert mu.shape == (X.shape[1],), f\"Wrong shape for mu. Expected: {(X.shape[1],)} got: {mu.shape}\"\n    assert type(var) == np.ndarray, f\"Wrong shape for mu. Expected: {(X.shape[1],)} got: {var.shape}\"\n    \n    assert np.allclose(mu, [2., 4., 6.]), f\"Wrong value for mu. Expected: {[2., 4., 6.]} got: {mu}\"\n    assert np.allclose(var, [2. / 3, 8. / 3., 18. / 3.]), f\"Wrong value for mu. Expected: {[2. / 3, 8. / 3., 18. / 3.]} got: {var}\"\n    \n    \n    m = 500\n    X = np.array([np.random.normal(0, 1, m), \n                  np.random.normal(1, 2, m), \n                  np.random.normal(3, 1.5, m)]).T\n    \n    mu, var = target(X)\n    \n    assert type(mu) == np.ndarray, f\"Wrong type for mu. Expected: {np.ndarray} got: {type(mu)}\"\n    assert type(var) == np.ndarray, f\"Wrong type for mu. Expected: {np.ndarray} got: {type(var)}\"\n    \n    assert mu.shape == (X.shape[1],), f\"Wrong shape for mu. Expected: {(X.shape[1],)} got: {mu.shape}\"\n    assert type(var) == np.ndarray, f\"Wrong shape for mu. Expected: {(X.shape[1],)} got: {var.shape}\"\n    \n    assert np.allclose(mu, [0., 1., 3.], atol=0.2), f\"Wrong value for mu. Expected: {[0, 1, 3]} got: {mu}\"\n    assert np.allclose(var, np.square([1., 2., 1.5]), atol=0.2), f\"Wrong value for mu. Expected: {np.square([1., 2., 1.5])} got: {var}\"\n    \n    print(\"\\033[92mAll tests passed!\")"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/C3W1A/C3W1A2/utils.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef load_data():\n    X = np.load(\"data/X_part1.npy\")\n    X_val = np.load(\"data/X_val_part1.npy\")\n    y_val = np.load(\"data/y_val_part1.npy\")\n    return X, X_val, y_val\n\ndef load_data_multi():\n    X = np.load(\"data/X_part2.npy\")\n    X_val = np.load(\"data/X_val_part2.npy\")\n    y_val = np.load(\"data/y_val_part2.npy\")\n    return X, X_val, y_val\n\n\ndef multivariate_gaussian(X, mu, var):\n    \"\"\"\n    Computes the probability \n    density function of the examples X under the multivariate gaussian \n    distribution with parameters mu and var. If var is a matrix, it is\n    treated as the covariance matrix. If var is a vector, it is treated\n    as the var values of the variances in each dimension (a diagonal\n    covariance matrix\n    \"\"\"\n    \n    k = len(mu)\n    \n    if var.ndim == 1:\n        var = np.diag(var)\n        \n    X = X - mu\n    p = (2* np.pi)**(-k/2) * np.linalg.det(var)**(-0.5) * \\\n        np.exp(-0.5 * np.sum(np.matmul(X, np.linalg.pinv(var)) * X, axis=1))\n    \n    return p\n        \ndef visualize_fit(X, mu, var):\n    \"\"\"\n    This visualization shows you the \n    probability density function of the Gaussian distribution. Each example\n    has a location (x1, x2) that depends on its feature values.\n    \"\"\"\n    \n    X1, X2 = np.meshgrid(np.arange(0, 35.5, 0.5), np.arange(0, 35.5, 0.5))\n    Z = multivariate_gaussian(np.stack([X1.ravel(), X2.ravel()], axis=1), mu, var)\n    Z = Z.reshape(X1.shape)\n\n    plt.plot(X[:, 0], X[:, 1], 'bx')\n\n    if np.sum(np.isinf(Z)) == 0:\n        plt.contour(X1, X2, Z, levels=10**(np.arange(-20., 1, 3)), linewidths=1)\n        \n    # Set the title\n    plt.title(\"The Gaussian contours of the distribution fit to the dataset\")\n    # Set the y-axis label\n    plt.ylabel('Throughput (mb/s)')\n    # Set the x-axis label\n    plt.xlabel('Latency (ms)')"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/Practice Quiz - Anomaly Detection/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/ea21a1bf9b251f834c72292d9d08a0f0b2cacb1a/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/Practice%20Quiz%20:%20Anomaly%20Detection/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/ea21a1bf9b251f834c72292d9d08a0f0b2cacb1a/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/Practice%20Quiz%20:%20Anomaly%20Detection/ss2.png)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/Practice Quiz - Clustering/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/10895418df442d0136c0d3d4d085351225999637/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/Practice%20Quiz:%20Clustering/ss1.png)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week1/Readme.md",
    "content": "### C3 - Week 1 Solutions\n\n\n- [Practice quiz : Clustering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/20e9e2fafcabd86aeeabdda2f79316caba6a5213/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/Practice%20Quiz:%20Clustering)\n- [Practice quiz : Anomaly Detection](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/50762882a48709806ca8cfae482eafdb7ccbc394/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/Practice%20Quiz%20:%20Anomaly%20Detection)\n  - [Programming Assignment 1](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/078956db6f34d8c9e1dda497cd613922c5146ead/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/C3W1A)\n      - [K means](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/078956db6f34d8c9e1dda497cd613922c5146ead/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/C3W1A/C3W1A1/C3_W1_KMeans_Assignment.ipynb)\n      - [Anomaly Detection](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/3f7a43ce32bc6bea2fca7bc815ad2a5883422c9b/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week1/C3W1A/C3W1A2/C3_W1_Anomaly_Detection.ipynb)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A1/C3_W2_Collaborative_RecSys_Assignment.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"Lzk7iX_CodX6\",\n    \"tags\": []\n   },\n   \"source\": [\n    \"# <img align=\\\"left\\\" src=\\\"./images/movie_camera.png\\\"     style=\\\" width:40px;  \\\" > Practice lab: Collaborative Filtering Recommender Systems\\n\",\n    \"\\n\",\n    \"In this exercise, you will implement collaborative filtering to build a recommender system for movies. \\n\",\n    \"\\n\",\n    \"# <img align=\\\"left\\\" src=\\\"./images/film_reel.png\\\"     style=\\\" width:40px;  \\\" > Outline\\n\",\n    \"- [ 1 - Notation](#1)\\n\",\n    \"- [ 2 - Recommender Systems](#2)\\n\",\n    \"- [ 3 - Movie ratings dataset](#3)\\n\",\n    \"- [ 4 - Collaborative filtering learning algorithm](#4)\\n\",\n    \"  - [ 4.1 Collaborative filtering cost function](#4.1)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"- [ 5 - Learning movie recommendations](#5)\\n\",\n    \"- [ 6 - Recommendations](#6)\\n\",\n    \"- [ 7 - Congratulations!](#7)\\n\",\n    \"\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"##  Packages <img align=\\\"left\\\" src=\\\"./images/film_strip_vertical.png\\\"     style=\\\" width:40px;   \\\" >\\n\",\n    \"We will use the now familiar NumPy and Tensorflow Packages.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow import keras\\n\",\n    \"from recsys_utils import *\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Notation\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"|General <br />  Notation  | Description| Python (if any) |\\n\",\n    \"|:-------------|:------------------------------------------------------------||\\n\",\n    \"| $r(i,j)$     | scalar; = 1  if user j rated game i  = 0  otherwise             ||\\n\",\n    \"| $y(i,j)$     | scalar; = rating given by user j on game  i    (if r(i,j) = 1 is defined) ||\\n\",\n    \"|$\\\\mathbf{w}^{(j)}$ | vector; parameters for user j ||\\n\",\n    \"|$b^{(j)}$     |  scalar; parameter for user j ||\\n\",\n    \"| $\\\\mathbf{x}^{(i)}$ |   vector; feature ratings for movie i        ||     \\n\",\n    \"| $n_u$        | number of users |num_users|\\n\",\n    \"| $n_m$        | number of movies | num_movies |\\n\",\n    \"| $n$          | number of features | num_features                    |\\n\",\n    \"| $\\\\mathbf{X}$ |  matrix of vectors $\\\\mathbf{x}^{(i)}$         | X |\\n\",\n    \"| $\\\\mathbf{W}$ |  matrix of vectors $\\\\mathbf{w}^{(j)}$         | W |\\n\",\n    \"| $\\\\mathbf{b}$ |  vector of bias parameters $b^{(j)}$ | b |\\n\",\n    \"| $\\\\mathbf{R}$ | matrix of elements $r(i,j)$                    | R |\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Recommender Systems <img align=\\\"left\\\" src=\\\"./images/film_rating.png\\\" style=\\\" width:40px;  \\\" >\\n\",\n    \"In this lab, you will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings.\\n\",\n    \"The goal of a collaborative filtering recommender system is to generate two vectors: For each user, a 'parameter vector' that embodies the movie tastes of a user. For each movie, a feature vector of the same size which embodies some description of the movie. The dot product of the two vectors plus the bias term should produce an estimate of the rating the user might give to that movie.\\n\",\n    \"\\n\",\n    \"The diagram below details how these vectors are learned.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"   <img src=\\\"./images/ColabFilterLearn.PNG\\\"  style=\\\"width:740px;height:250px;\\\" >\\n\",\n    \"</figure>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Existing ratings are provided in matrix form as shown. $Y$ contains ratings; 0.5 to 5 inclusive in 0.5 steps. 0 if the movie has not been rated. $R$ has a 1 where movies have been rated. Movies are in rows, users in columns. Each user has a parameter vector $w^{user}$ and bias. Each movie has a feature vector $x^{movie}$. These vectors are simultaneously learned by using the existing user/movie ratings as training data. One training example is shown above: $\\\\mathbf{w}^{(1)} \\\\cdot \\\\mathbf{x}^{(1)} + b^{(1)} = 4$. It is worth noting that the feature vector $x^{movie}$ must satisfy all the users while the user vector $w^{user}$ must satisfy all the movies. This is the source of the name of this approach - all the users collaborate to generate the rating set. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<figure>\\n\",\n    \"   <img src=\\\"./images/ColabFilterUse.PNG\\\"  style=\\\"width:640px;height:250px;\\\" >\\n\",\n    \"</figure>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once the feature vectors and parameters are learned, they can be used to predict how a user might rate an unrated movie. This is shown in the diagram above. The equation is an example of predicting a rating for user one on movie zero.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"In this exercise, you will implement the function `cofiCostFunc` that computes the collaborative filtering\\n\",\n    \"objective function. After implementing the objective function, you will use a TensorFlow custom training loop to learn the parameters for collaborative filtering. The first step is to detail the data set and data structures that will be used in the lab.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"6-09Hto6odYD\"\n   },\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Movie ratings dataset <img align=\\\"left\\\" src=\\\"./images/film_rating.png\\\"     style=\\\" width:40px;  \\\" >\\n\",\n    \"The data set is derived from the [MovieLens \\\"ml-latest-small\\\"](https://grouplens.org/datasets/movielens/latest/) dataset.   \\n\",\n    \"[F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. <https://doi.org/10.1145/2827872>]\\n\",\n    \"\\n\",\n    \"The original dataset has  9000 movies rated by 600 users. The dataset has been reduced in size to focus on movies from the years since 2000. This dataset consists of ratings on a scale of 0.5 to 5 in 0.5 step increments. The reduced dataset has $n_u = 443$ users, and $n_m= 4778$ movies. \\n\",\n    \"\\n\",\n    \"Below, you will load the movie dataset into the variables $Y$ and $R$.\\n\",\n    \"\\n\",\n    \"The matrix $Y$ (a  $n_m \\\\times n_u$ matrix) stores the ratings $y^{(i,j)}$. The matrix $R$ is an binary-valued indicator matrix, where $R(i,j) = 1$ if user $j$ gave a rating to movie $i$, and $R(i,j)=0$ otherwise. \\n\",\n    \"\\n\",\n    \"Throughout this part of the exercise, you will also be working with the\\n\",\n    \"matrices, $\\\\mathbf{X}$, $\\\\mathbf{W}$ and $\\\\mathbf{b}$: \\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{X} = \\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"--- (\\\\mathbf{x}^{(0)})^T --- \\\\\\\\\\n\",\n    \"--- (\\\\mathbf{x}^{(1)})^T --- \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"--- (\\\\mathbf{x}^{(n_m-1)})^T --- \\\\\\\\\\n\",\n    \"\\\\end{bmatrix} , \\\\quad\\n\",\n    \"\\\\mathbf{W} = \\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \"--- (\\\\mathbf{w}^{(0)})^T --- \\\\\\\\\\n\",\n    \"--- (\\\\mathbf{w}^{(1)})^T --- \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"--- (\\\\mathbf{w}^{(n_u-1)})^T --- \\\\\\\\\\n\",\n    \"\\\\end{bmatrix},\\\\quad\\n\",\n    \"\\\\mathbf{ b} = \\n\",\n    \"\\\\begin{bmatrix}\\n\",\n    \" b^{(0)}  \\\\\\\\\\n\",\n    \" b^{(1)} \\\\\\\\\\n\",\n    \"\\\\vdots \\\\\\\\\\n\",\n    \"b^{(n_u-1)} \\\\\\\\\\n\",\n    \"\\\\end{bmatrix}\\\\quad\\n\",\n    \"$$ \\n\",\n    \"\\n\",\n    \"The $i$-th row of $\\\\mathbf{X}$ corresponds to the\\n\",\n    \"feature vector $x^{(i)}$ for the $i$-th movie, and the $j$-th row of\\n\",\n    \"$\\\\mathbf{W}$ corresponds to one parameter vector $\\\\mathbf{w}^{(j)}$, for the\\n\",\n    \"$j$-th user. Both $x^{(i)}$ and $\\\\mathbf{w}^{(j)}$ are $n$-dimensional\\n\",\n    \"vectors. For the purposes of this exercise, you will use $n=10$, and\\n\",\n    \"therefore, $\\\\mathbf{x}^{(i)}$ and $\\\\mathbf{w}^{(j)}$ have 10 elements.\\n\",\n    \"Correspondingly, $\\\\mathbf{X}$ is a\\n\",\n    \"$n_m \\\\times 10$ matrix and $\\\\mathbf{W}$ is a $n_u \\\\times 10$ matrix.\\n\",\n    \"\\n\",\n    \"We will start by loading the movie ratings dataset to understand the structure of the data.\\n\",\n    \"We will load $Y$ and $R$ with the movie dataset.  \\n\",\n    \"We'll also load $\\\\mathbf{X}$, $\\\\mathbf{W}$, and $\\\\mathbf{b}$ with pre-computed values. These values will be learned later in the lab, but we'll use pre-computed values to develop the cost model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Y (4778, 443) R (4778, 443)\\n\",\n      \"X (4778, 10)\\n\",\n      \"W (443, 10)\\n\",\n      \"b (1, 443)\\n\",\n      \"num_features 10\\n\",\n      \"num_movies 4778\\n\",\n      \"num_users 443\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#Load data\\n\",\n    \"X, W, b, num_movies, num_features, num_users = load_precalc_params_small()\\n\",\n    \"Y, R = load_ratings_small()\\n\",\n    \"\\n\",\n    \"print(\\\"Y\\\", Y.shape, \\\"R\\\", R.shape)\\n\",\n    \"print(\\\"X\\\", X.shape)\\n\",\n    \"print(\\\"W\\\", W.shape)\\n\",\n    \"print(\\\"b\\\", b.shape)\\n\",\n    \"print(\\\"num_features\\\", num_features)\\n\",\n    \"print(\\\"num_movies\\\",   num_movies)\\n\",\n    \"print(\\\"num_users\\\",    num_users)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"id\": \"bxm1O_wbodYF\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Average rating for movie 1 : 3.400 / 5\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"#  From the matrix, we can compute statistics like average rating.\\n\",\n    \"tsmean =  np.mean(Y[0, R[0, :].astype(bool)])\\n\",\n    \"print(f\\\"Average rating for movie 1 : {tsmean:0.3f} / 5\\\" )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Collaborative filtering learning algorithm <img align=\\\"left\\\" src=\\\"./images/film_filter.png\\\"     style=\\\" width:40px;  \\\" >\\n\",\n    \"\\n\",\n    \"Now, you will begin implementing the collaborative filtering learning\\n\",\n    \"algorithm. You will start by implementing the objective function. \\n\",\n    \"\\n\",\n    \"The collaborative filtering algorithm in the setting of movie\\n\",\n    \"recommendations considers a set of $n$-dimensional parameter vectors\\n\",\n    \"$\\\\mathbf{x}^{(0)},...,\\\\mathbf{x}^{(n_m-1)}$, $\\\\mathbf{w}^{(0)},...,\\\\mathbf{w}^{(n_u-1)}$ and $b^{(0)},...,b^{(n_u-1)}$, where the\\n\",\n    \"model predicts the rating for movie $i$ by user $j$ as\\n\",\n    \"$y^{(i,j)} = \\\\mathbf{w}^{(j)}\\\\cdot \\\\mathbf{x}^{(i)} + b^{(i)}$ . Given a dataset that consists of\\n\",\n    \"a set of ratings produced by some users on some movies, you wish to\\n\",\n    \"learn the parameter vectors $\\\\mathbf{x}^{(0)},...,\\\\mathbf{x}^{(n_m-1)},\\n\",\n    \"\\\\mathbf{w}^{(0)},...,\\\\mathbf{w}^{(n_u-1)}$  and $b^{(0)},...,b^{(n_u-1)}$ that produce the best fit (minimizes\\n\",\n    \"the squared error).\\n\",\n    \"\\n\",\n    \"You will complete the code in cofiCostFunc to compute the cost\\n\",\n    \"function for collaborative filtering. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"bcqg0LJWodYH\"\n   },\n   \"source\": [\n    \"\\n\",\n    \"<a name=\\\"4.1\\\"></a>\\n\",\n    \"### 4.1 Collaborative filtering cost function\\n\",\n    \"\\n\",\n    \"The collaborative filtering cost function is given by\\n\",\n    \"$$J({\\\\mathbf{x}^{(0)},...,\\\\mathbf{x}^{(n_m-1)},\\\\mathbf{w}^{(0)},b^{(0)},...,\\\\mathbf{w}^{(n_u-1)},b^{(n_u-1)}})= \\\\frac{1}{2}\\\\sum_{(i,j):r(i,j)=1}(\\\\mathbf{w}^{(j)} \\\\cdot \\\\mathbf{x}^{(i)} + b^{(j)} - y^{(i,j)})^2\\n\",\n    \"+\\\\underbrace{\\n\",\n    \"\\\\frac{\\\\lambda}{2}\\n\",\n    \"\\\\sum_{j=0}^{n_u-1}\\\\sum_{k=0}^{n-1}(\\\\mathbf{w}^{(j)}_k)^2\\n\",\n    \"+ \\\\frac{\\\\lambda}{2}\\\\sum_{i=0}^{n_m-1}\\\\sum_{k=0}^{n-1}(\\\\mathbf{x}_k^{(i)})^2\\n\",\n    \"}_{regularization}\\n\",\n    \"\\\\tag{1}$$\\n\",\n    \"The first summation in (1) is \\\"for all $i$, $j$ where $r(i,j)$ equals $1$\\\" and could be written:\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"= \\\\frac{1}{2}\\\\sum_{j=0}^{n_u-1} \\\\sum_{i=0}^{n_m-1}r(i,j)*(\\\\mathbf{w}^{(j)} \\\\cdot \\\\mathbf{x}^{(i)} + b^{(j)} - y^{(i,j)})^2\\n\",\n    \"+\\\\text{regularization}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"You should now write cofiCostFunc (collaborative filtering cost function) to return this cost.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"**For loop Implementation:**   \\n\",\n    \"Start by implementing the cost function using for loops.\\n\",\n    \"Consider developing the cost function in two steps. First, develop the cost function without regularization. A test case that does not include regularization is provided below to test your implementation. Once that is working, add regularization and run the tests that include regularization.  Note that you should be accumulating the cost for user $j$ and movie $i$ only if $R(i,j) = 1$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# GRADED FUNCTION: cofi_cost_func\\n\",\n    \"# UNQ_C1\\n\",\n    \"\\n\",\n    \"def cofi_cost_func(X, W, b, Y, R, lambda_):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Returns the cost for the content-based filtering\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (num_movies,num_features)): matrix of item features\\n\",\n    \"      W (ndarray (num_users,num_features)) : matrix of user parameters\\n\",\n    \"      b (ndarray (1, num_users)            : vector of user parameters\\n\",\n    \"      Y (ndarray (num_movies,num_users)    : matrix of user ratings of movies\\n\",\n    \"      R (ndarray (num_movies,num_users)    : matrix, where R(i, j) = 1 if the i-th movies was rated by the j-th user\\n\",\n    \"      lambda_ (float): regularization parameter\\n\",\n    \"    Returns:\\n\",\n    \"      J (float) : Cost\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    nm, nu = Y.shape\\n\",\n    \"    J = 0\\n\",\n    \"    ### START CODE HERE ###  \\n\",\n    \"    for j in range(nu):\\n\",\n    \"        w = W[j,:]\\n\",\n    \"        b_j = b[0,j]\\n\",\n    \"        for i in range(nm):\\n\",\n    \"            x = X[i,:]\\n\",\n    \"            y = Y[i,j]\\n\",\n    \"            r = R[i,j]\\n\",\n    \"            J += r * np.square((np.dot(w,x) + b_j - y ))\\n\",\n    \"    J += (lambda_) * (np.sum(np.square(W)) + np.sum(np.square(X)))\\n\",\n    \"    J = J/2\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"\\n\",\n    \"    return J\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Public tests\\n\",\n    \"from public_tests import *\\n\",\n    \"test_cofi_cost_func(cofi_cost_func)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    You can structure the code in two for loops similar to the summation in (1).   \\n\",\n    \"    Implement the code without regularization first.   \\n\",\n    \"    Note that some of the elements in (1) are vectors. Use np.dot(). You can also use np.square().\\n\",\n    \"    Pay close attention to which elements are indexed by i and which are indexed by j. Don't forget to divide by two.\\n\",\n    \"    \\n\",\n    \"```python     \\n\",\n    \"    ### START CODE HERE ###  \\n\",\n    \"    for j in range(nu):\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        for i in range(nm):\\n\",\n    \"            \\n\",\n    \"            \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"```    \\n\",\n    \"<details>\\n\",\n    \"    <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for more hints</b></font></summary>\\n\",\n    \"        \\n\",\n    \"    Here is some more details. The code below pulls out each element from the matrix before using it. \\n\",\n    \"    One could also reference the matrix directly.  \\n\",\n    \"    This code does not contain regularization.\\n\",\n    \"    \\n\",\n    \"```python \\n\",\n    \"    nm,nu = Y.shape\\n\",\n    \"    J = 0\\n\",\n    \"    ### START CODE HERE ###  \\n\",\n    \"    for j in range(nu):\\n\",\n    \"        w = W[j,:]\\n\",\n    \"        b_j = b[0,j]\\n\",\n    \"        for i in range(nm):\\n\",\n    \"            x = \\n\",\n    \"            y = \\n\",\n    \"            r =\\n\",\n    \"            J += \\n\",\n    \"    J = J/2\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"    \\n\",\n    \"<details>\\n\",\n    \"    <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>Last Resort (full non-regularized implementation)</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python \\n\",\n    \"    nm,nu = Y.shape\\n\",\n    \"    J = 0\\n\",\n    \"    ### START CODE HERE ###  \\n\",\n    \"    for j in range(nu):\\n\",\n    \"        w = W[j,:]\\n\",\n    \"        b_j = b[0,j]\\n\",\n    \"        for i in range(nm):\\n\",\n    \"            x = X[i,:]\\n\",\n    \"            y = Y[i,j]\\n\",\n    \"            r = R[i,j]\\n\",\n    \"            J += np.square(r * (np.dot(w,x) + b_j - y ) )\\n\",\n    \"    J = J/2\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"```\\n\",\n    \"    \\n\",\n    \"<details>\\n\",\n    \"    <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>regularization</b></font></summary>\\n\",\n    \"     Regularization just squares each element of the W array and X array and them sums all the squared elements.\\n\",\n    \"     You can utilize np.square() and np.sum().\\n\",\n    \"\\n\",\n    \"<details>\\n\",\n    \"    <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b>regularization details</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python \\n\",\n    \"    J += lambda_* (np.sum(np.square(W)) + np.sum(np.square(X)))\\n\",\n    \"```\\n\",\n    \"    \\n\",\n    \"</details>\\n\",\n    \"</details>\\n\",\n    \"</details>\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cost: 13.67\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Reduce the data set size so that this runs faster\\n\",\n    \"num_users_r = 4\\n\",\n    \"num_movies_r = 5 \\n\",\n    \"num_features_r = 3\\n\",\n    \"\\n\",\n    \"X_r = X[:num_movies_r, :num_features_r]\\n\",\n    \"W_r = W[:num_users_r,  :num_features_r]\\n\",\n    \"b_r = b[0, :num_users_r].reshape(1,-1)\\n\",\n    \"Y_r = Y[:num_movies_r, :num_users_r]\\n\",\n    \"R_r = R[:num_movies_r, :num_users_r]\\n\",\n    \"\\n\",\n    \"# Evaluate cost function\\n\",\n    \"J = cofi_cost_func(X_r, W_r, b_r, Y_r, R_r, 0);\\n\",\n    \"print(f\\\"Cost: {J:0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"xGznmQ91odYL\"\n   },\n   \"source\": [\n    \"**Expected Output (lambda = 0)**:  \\n\",\n    \"$13.67$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cost (with regularization): 28.09\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Evaluate cost function with regularization \\n\",\n    \"J = cofi_cost_func(X_r, W_r, b_r, Y_r, R_r, 1.5);\\n\",\n    \"print(f\\\"Cost (with regularization): {J:0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"1xbepzUUodYP\"\n   },\n   \"source\": [\n    \"**Expected Output**:\\n\",\n    \"\\n\",\n    \"28.09\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Vectorized Implementation**\\n\",\n    \"\\n\",\n    \"It is important to create a vectorized implementation to compute $J$, since it will later be called many times during optimization. The linear algebra utilized is not the focus of this series, so the implementation is provided. If you are an expert in linear algebra, feel free to create your version without referencing the code below. \\n\",\n    \"\\n\",\n    \"Run the code below and verify that it produces the same results as the non-vectorized version.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def cofi_cost_func_v(X, W, b, Y, R, lambda_):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Returns the cost for the content-based filtering\\n\",\n    \"    Vectorized for speed. Uses tensorflow operations to be compatible with custom training loop.\\n\",\n    \"    Args:\\n\",\n    \"      X (ndarray (num_movies,num_features)): matrix of item features\\n\",\n    \"      W (ndarray (num_users,num_features)) : matrix of user parameters\\n\",\n    \"      b (ndarray (1, num_users)            : vector of user parameters\\n\",\n    \"      Y (ndarray (num_movies,num_users)    : matrix of user ratings of movies\\n\",\n    \"      R (ndarray (num_movies,num_users)    : matrix, where R(i, j) = 1 if the i-th movies was rated by the j-th user\\n\",\n    \"      lambda_ (float): regularization parameter\\n\",\n    \"    Returns:\\n\",\n    \"      J (float) : Cost\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    j = (tf.linalg.matmul(X, tf.transpose(W)) + b - Y)*R\\n\",\n    \"    J = 0.5 * tf.reduce_sum(j**2) + (lambda_/2) * (tf.reduce_sum(X**2) + tf.reduce_sum(W**2))\\n\",\n    \"    return J\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Cost: 13.67\\n\",\n      \"Cost (with regularization): 28.09\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Evaluate cost function\\n\",\n    \"J = cofi_cost_func_v(X_r, W_r, b_r, Y_r, R_r, 0);\\n\",\n    \"print(f\\\"Cost: {J:0.2f}\\\")\\n\",\n    \"\\n\",\n    \"# Evaluate cost function with regularization \\n\",\n    \"J = cofi_cost_func_v(X_r, W_r, b_r, Y_r, R_r, 1.5);\\n\",\n    \"print(f\\\"Cost (with regularization): {J:0.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"1xbepzUUodYP\"\n   },\n   \"source\": [\n    \"**Expected Output**:  \\n\",\n    \"Cost: 13.67  \\n\",\n    \"Cost (with regularization): 28.09\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"ilaeM8yWodYR\"\n   },\n   \"source\": [\n    \"<a name=\\\"5\\\"></a>\\n\",\n    \"## 5 - Learning movie recommendations <img align=\\\"left\\\" src=\\\"./images/film_man_action.png\\\" style=\\\" width:40px;  \\\" >\\n\",\n    \"------------------------------\\n\",\n    \"\\n\",\n    \"After you have finished implementing the collaborative filtering cost\\n\",\n    \"function, you can start training your algorithm to make\\n\",\n    \"movie recommendations for yourself. \\n\",\n    \"\\n\",\n    \"In the cell below, you can enter your own movie choices. The algorithm will then make recommendations for you! We have filled out some values according to our preferences, but after you have things working with our choices, you should change this to match your tastes.\\n\",\n    \"A list of all movies in the dataset is in the file [movie list](data/small_movie_list.csv).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"metadata\": {\n    \"id\": \"WJO8Jr0UodYR\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"New user ratings:\\n\",\n      \"\\n\",\n      \"Rated 5.0 for  Shrek (2001)\\n\",\n      \"Rated 5.0 for  Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)\\n\",\n      \"Rated 2.0 for  Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)\\n\",\n      \"Rated 5.0 for  Harry Potter and the Chamber of Secrets (2002)\\n\",\n      \"Rated 5.0 for  Pirates of the Caribbean: The Curse of the Black Pearl (2003)\\n\",\n      \"Rated 5.0 for  Lord of the Rings: The Return of the King, The (2003)\\n\",\n      \"Rated 3.0 for  Eternal Sunshine of the Spotless Mind (2004)\\n\",\n      \"Rated 5.0 for  Incredibles, The (2004)\\n\",\n      \"Rated 2.0 for  Persuasion (2007)\\n\",\n      \"Rated 5.0 for  Toy Story 3 (2010)\\n\",\n      \"Rated 3.0 for  Inception (2010)\\n\",\n      \"Rated 1.0 for  Louis Theroux: Law & Disorder (2008)\\n\",\n      \"Rated 1.0 for  Nothing to Declare (Rien à déclarer) (2010)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"movieList, movieList_df = load_Movie_List_pd()\\n\",\n    \"\\n\",\n    \"my_ratings = np.zeros(num_movies)          #  Initialize my ratings\\n\",\n    \"\\n\",\n    \"# Check the file small_movie_list.csv for id of each movie in our dataset\\n\",\n    \"# For example, Toy Story 3 (2010) has ID 2700, so to rate it \\\"5\\\", you can set\\n\",\n    \"my_ratings[2700] = 5 \\n\",\n    \"\\n\",\n    \"#Or suppose you did not enjoy Persuasion (2007), you can set\\n\",\n    \"my_ratings[2609] = 2;\\n\",\n    \"\\n\",\n    \"# We have selected a few movies we liked / did not like and the ratings we\\n\",\n    \"# gave are as follows:\\n\",\n    \"my_ratings[929]  = 5   # Lord of the Rings: The Return of the King, The\\n\",\n    \"my_ratings[246]  = 5   # Shrek (2001)\\n\",\n    \"my_ratings[2716] = 3   # Inception\\n\",\n    \"my_ratings[1150] = 5   # Incredibles, The (2004)\\n\",\n    \"my_ratings[382]  = 2   # Amelie (Fabuleux destin d'Amélie Poulain, Le)\\n\",\n    \"my_ratings[366]  = 5   # Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)\\n\",\n    \"my_ratings[622]  = 5   # Harry Potter and the Chamber of Secrets (2002)\\n\",\n    \"my_ratings[988]  = 3   # Eternal Sunshine of the Spotless Mind (2004)\\n\",\n    \"my_ratings[2925] = 1   # Louis Theroux: Law & Disorder (2008)\\n\",\n    \"my_ratings[2937] = 1   # Nothing to Declare (Rien à déclarer)\\n\",\n    \"my_ratings[793]  = 5   # Pirates of the Caribbean: The Curse of the Black Pearl (2003)\\n\",\n    \"my_rated = [i for i in range(len(my_ratings)) if my_ratings[i] > 0]\\n\",\n    \"\\n\",\n    \"print('\\\\nNew user ratings:\\\\n')\\n\",\n    \"for i in range(len(my_ratings)):\\n\",\n    \"    if my_ratings[i] > 0 :\\n\",\n    \"        print(f'Rated {my_ratings[i]} for  {movieList_df.loc[i,\\\"title\\\"]}');\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's add these reviews to $Y$ and $R$ and normalize the ratings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Reload ratings and add new ratings\\n\",\n    \"Y, R = load_ratings_small()\\n\",\n    \"Y    = np.c_[my_ratings, Y]\\n\",\n    \"R    = np.c_[(my_ratings != 0).astype(int), R]\\n\",\n    \"\\n\",\n    \"# Normalize the Dataset\\n\",\n    \"Ynorm, Ymean = normalizeRatings(Y, R)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's prepare to train the model. Initialize the parameters and select the Adam optimizer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"#  Useful Values\\n\",\n    \"num_movies, num_users = Y.shape\\n\",\n    \"num_features = 100\\n\",\n    \"\\n\",\n    \"# Set Initial Parameters (W, X), use tf.Variable to track these variables\\n\",\n    \"tf.random.set_seed(1234) # for consistent results\\n\",\n    \"W = tf.Variable(tf.random.normal((num_users,  num_features),dtype=tf.float64),  name='W')\\n\",\n    \"X = tf.Variable(tf.random.normal((num_movies, num_features),dtype=tf.float64),  name='X')\\n\",\n    \"b = tf.Variable(tf.random.normal((1,          num_users),   dtype=tf.float64),  name='b')\\n\",\n    \"\\n\",\n    \"# Instantiate an optimizer.\\n\",\n    \"optimizer = keras.optimizers.Adam(learning_rate=1e-1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's now train the collaborative filtering model. This will learn the parameters $\\\\mathbf{X}$, $\\\\mathbf{W}$, and $\\\\mathbf{b}$. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The operations involved in learning $w$, $b$, and $x$ simultaneously do not fall into the typical 'layers' offered in the TensorFlow neural network package.  Consequently, the flow used in Course 2: Model, Compile(), Fit(), Predict(), are not directly applicable. Instead, we can use a custom training loop.\\n\",\n    \"\\n\",\n    \"Recall from earlier labs the steps of gradient descent.\\n\",\n    \"- repeat until convergence:\\n\",\n    \"    - compute forward pass\\n\",\n    \"    - compute the derivatives of the loss relative to parameters\\n\",\n    \"    - update the parameters using the learning rate and the computed derivatives \\n\",\n    \"    \\n\",\n    \"TensorFlow has the marvelous capability of calculating the derivatives for you. This is shown below. Within the `tf.GradientTape()` section, operations on Tensorflow Variables are tracked. When `tape.gradient()` is later called, it will return the gradient of the loss relative to the tracked variables. The gradients can then be applied to the parameters using an optimizer. \\n\",\n    \"This is a very brief introduction to a useful feature of TensorFlow and other machine learning frameworks. Further information can be found by investigating \\\"custom training loops\\\" within the framework of interest.\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training loss at iteration 0: 2321191.3\\n\",\n      \"Training loss at iteration 20: 136168.7\\n\",\n      \"Training loss at iteration 40: 51863.3\\n\",\n      \"Training loss at iteration 60: 24598.8\\n\",\n      \"Training loss at iteration 80: 13630.4\\n\",\n      \"Training loss at iteration 100: 8487.6\\n\",\n      \"Training loss at iteration 120: 5807.7\\n\",\n      \"Training loss at iteration 140: 4311.6\\n\",\n      \"Training loss at iteration 160: 3435.2\\n\",\n      \"Training loss at iteration 180: 2902.1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"iterations = 200\\n\",\n    \"lambda_ = 1\\n\",\n    \"for iter in range(iterations):\\n\",\n    \"    # Use TensorFlow’s GradientTape\\n\",\n    \"    # to record the operations used to compute the cost \\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"\\n\",\n    \"        # Compute the cost (forward pass included in cost)\\n\",\n    \"        cost_value = cofi_cost_func_v(X, W, b, Ynorm, R, lambda_)\\n\",\n    \"\\n\",\n    \"    # Use the gradient tape to automatically retrieve\\n\",\n    \"    # the gradients of the trainable variables with respect to the loss\\n\",\n    \"    grads = tape.gradient( cost_value, [X,W,b] )\\n\",\n    \"\\n\",\n    \"    # Run one step of gradient descent by updating\\n\",\n    \"    # the value of the variables to minimize the loss.\\n\",\n    \"    optimizer.apply_gradients( zip(grads, [X,W,b]) )\\n\",\n    \"\\n\",\n    \"    # Log periodically.\\n\",\n    \"    if iter % 20 == 0:\\n\",\n    \"        print(f\\\"Training loss at iteration {iter}: {cost_value:0.1f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"SSzUL7eQodYS\"\n   },\n   \"source\": [\n    \"<a name=\\\"6\\\"></a>\\n\",\n    \"## 6 - Recommendations\\n\",\n    \"Below, we compute the ratings for all the movies and users and display the movies that are recommended. These are based on the movies and ratings entered as `my_ratings[]` above. To predict the rating of movie $i$ for user $j$, you compute $\\\\mathbf{w}^{(j)} \\\\cdot \\\\mathbf{x}^{(i)} + b^{(j)}$. This can be computed for all ratings using matrix multiplication.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"metadata\": {\n    \"id\": \"ns266wKtodYT\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicting rating 4.49 for movie My Sassy Girl (Yeopgijeogin geunyeo) (2001)\\n\",\n      \"Predicting rating 4.48 for movie Martin Lawrence Live: Runteldat (2002)\\n\",\n      \"Predicting rating 4.48 for movie Memento (2000)\\n\",\n      \"Predicting rating 4.47 for movie Delirium (2014)\\n\",\n      \"Predicting rating 4.47 for movie Laggies (2014)\\n\",\n      \"Predicting rating 4.47 for movie One I Love, The (2014)\\n\",\n      \"Predicting rating 4.46 for movie Particle Fever (2013)\\n\",\n      \"Predicting rating 4.45 for movie Eichmann (2007)\\n\",\n      \"Predicting rating 4.45 for movie Battle Royale 2: Requiem (Batoru rowaiaru II: Chinkonka) (2003)\\n\",\n      \"Predicting rating 4.45 for movie Into the Abyss (2011)\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"Original vs Predicted ratings:\\n\",\n      \"\\n\",\n      \"Original 5.0, Predicted 4.90 for Shrek (2001)\\n\",\n      \"Original 5.0, Predicted 4.84 for Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)\\n\",\n      \"Original 2.0, Predicted 2.13 for Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)\\n\",\n      \"Original 5.0, Predicted 4.88 for Harry Potter and the Chamber of Secrets (2002)\\n\",\n      \"Original 5.0, Predicted 4.87 for Pirates of the Caribbean: The Curse of the Black Pearl (2003)\\n\",\n      \"Original 5.0, Predicted 4.89 for Lord of the Rings: The Return of the King, The (2003)\\n\",\n      \"Original 3.0, Predicted 3.00 for Eternal Sunshine of the Spotless Mind (2004)\\n\",\n      \"Original 5.0, Predicted 4.90 for Incredibles, The (2004)\\n\",\n      \"Original 2.0, Predicted 2.11 for Persuasion (2007)\\n\",\n      \"Original 5.0, Predicted 4.80 for Toy Story 3 (2010)\\n\",\n      \"Original 3.0, Predicted 3.00 for Inception (2010)\\n\",\n      \"Original 1.0, Predicted 1.41 for Louis Theroux: Law & Disorder (2008)\\n\",\n      \"Original 1.0, Predicted 1.26 for Nothing to Declare (Rien à déclarer) (2010)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Make a prediction using trained weights and biases\\n\",\n    \"p = np.matmul(X.numpy(), np.transpose(W.numpy())) + b.numpy()\\n\",\n    \"\\n\",\n    \"#restore the mean\\n\",\n    \"pm = p + Ymean\\n\",\n    \"\\n\",\n    \"my_predictions = pm[:,0]\\n\",\n    \"\\n\",\n    \"# sort predictions\\n\",\n    \"ix = tf.argsort(my_predictions, direction='DESCENDING')\\n\",\n    \"\\n\",\n    \"for i in range(17):\\n\",\n    \"    j = ix[i]\\n\",\n    \"    if j not in my_rated:\\n\",\n    \"        print(f'Predicting rating {my_predictions[j]:0.2f} for movie {movieList[j]}')\\n\",\n    \"\\n\",\n    \"print('\\\\n\\\\nOriginal vs Predicted ratings:\\\\n')\\n\",\n    \"for i in range(len(my_ratings)):\\n\",\n    \"    if my_ratings[i] > 0:\\n\",\n    \"        print(f'Original {my_ratings[i]}, Predicted {my_predictions[i]:0.2f} for {movieList[i]}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In practice, additional information can be utilized to enhance our predictions. Above, the predicted ratings for the first few hundred movies lie in a small range. We can augment the above by selecting from those top movies, movies that have high average ratings and movies with more than 20 ratings. This section uses a [Pandas](https://pandas.pydata.org/) data frame which has many handy sorting features.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>pred</th>\\n\",\n       \"      <th>mean rating</th>\\n\",\n       \"      <th>number of ratings</th>\\n\",\n       \"      <th>title</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1743</th>\\n\",\n       \"      <td>4.030965</td>\\n\",\n       \"      <td>4.252336</td>\\n\",\n       \"      <td>107</td>\\n\",\n       \"      <td>Departed, The (2006)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2112</th>\\n\",\n       \"      <td>3.985287</td>\\n\",\n       \"      <td>4.238255</td>\\n\",\n       \"      <td>149</td>\\n\",\n       \"      <td>Dark Knight, The (2008)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>211</th>\\n\",\n       \"      <td>4.477792</td>\\n\",\n       \"      <td>4.122642</td>\\n\",\n       \"      <td>159</td>\\n\",\n       \"      <td>Memento (2000)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>929</th>\\n\",\n       \"      <td>4.887053</td>\\n\",\n       \"      <td>4.118919</td>\\n\",\n       \"      <td>185</td>\\n\",\n       \"      <td>Lord of the Rings: The Return of the King, The...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2700</th>\\n\",\n       \"      <td>4.796530</td>\\n\",\n       \"      <td>4.109091</td>\\n\",\n       \"      <td>55</td>\\n\",\n       \"      <td>Toy Story 3 (2010)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>653</th>\\n\",\n       \"      <td>4.357304</td>\\n\",\n       \"      <td>4.021277</td>\\n\",\n       \"      <td>188</td>\\n\",\n       \"      <td>Lord of the Rings: The Two Towers, The (2002)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1122</th>\\n\",\n       \"      <td>4.004469</td>\\n\",\n       \"      <td>4.006494</td>\\n\",\n       \"      <td>77</td>\\n\",\n       \"      <td>Shaun of the Dead (2004)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1841</th>\\n\",\n       \"      <td>3.980647</td>\\n\",\n       \"      <td>4.000000</td>\\n\",\n       \"      <td>61</td>\\n\",\n       \"      <td>Hot Fuzz (2007)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3083</th>\\n\",\n       \"      <td>4.084633</td>\\n\",\n       \"      <td>3.993421</td>\\n\",\n       \"      <td>76</td>\\n\",\n       \"      <td>Dark Knight Rises, The (2012)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2804</th>\\n\",\n       \"      <td>4.434171</td>\\n\",\n       \"      <td>3.989362</td>\\n\",\n       \"      <td>47</td>\\n\",\n       \"      <td>Harry Potter and the Deathly Hallows: Part 1 (...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>773</th>\\n\",\n       \"      <td>4.289679</td>\\n\",\n       \"      <td>3.960993</td>\\n\",\n       \"      <td>141</td>\\n\",\n       \"      <td>Finding Nemo (2003)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1771</th>\\n\",\n       \"      <td>4.344993</td>\\n\",\n       \"      <td>3.944444</td>\\n\",\n       \"      <td>81</td>\\n\",\n       \"      <td>Casino Royale (2006)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2649</th>\\n\",\n       \"      <td>4.133482</td>\\n\",\n       \"      <td>3.943396</td>\\n\",\n       \"      <td>53</td>\\n\",\n       \"      <td>How to Train Your Dragon (2010)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2455</th>\\n\",\n       \"      <td>4.175746</td>\\n\",\n       \"      <td>3.887931</td>\\n\",\n       \"      <td>58</td>\\n\",\n       \"      <td>Harry Potter and the Half-Blood Prince (2009)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>361</th>\\n\",\n       \"      <td>4.135291</td>\\n\",\n       \"      <td>3.871212</td>\\n\",\n       \"      <td>132</td>\\n\",\n       \"      <td>Monsters, Inc. (2001)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3014</th>\\n\",\n       \"      <td>3.967901</td>\\n\",\n       \"      <td>3.869565</td>\\n\",\n       \"      <td>69</td>\\n\",\n       \"      <td>Avengers, The (2012)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>246</th>\\n\",\n       \"      <td>4.897137</td>\\n\",\n       \"      <td>3.867647</td>\\n\",\n       \"      <td>170</td>\\n\",\n       \"      <td>Shrek (2001)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>151</th>\\n\",\n       \"      <td>3.971888</td>\\n\",\n       \"      <td>3.836364</td>\\n\",\n       \"      <td>110</td>\\n\",\n       \"      <td>Crouching Tiger, Hidden Dragon (Wo hu cang lon...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1150</th>\\n\",\n       \"      <td>4.898892</td>\\n\",\n       \"      <td>3.836000</td>\\n\",\n       \"      <td>125</td>\\n\",\n       \"      <td>Incredibles, The (2004)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>793</th>\\n\",\n       \"      <td>4.874935</td>\\n\",\n       \"      <td>3.778523</td>\\n\",\n       \"      <td>149</td>\\n\",\n       \"      <td>Pirates of the Caribbean: The Curse of the Bla...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>366</th>\\n\",\n       \"      <td>4.843375</td>\\n\",\n       \"      <td>3.761682</td>\\n\",\n       \"      <td>107</td>\\n\",\n       \"      <td>Harry Potter and the Sorcerer's Stone (a.k.a. ...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>754</th>\\n\",\n       \"      <td>4.021774</td>\\n\",\n       \"      <td>3.723684</td>\\n\",\n       \"      <td>76</td>\\n\",\n       \"      <td>X2: X-Men United (2003)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>79</th>\\n\",\n       \"      <td>4.242984</td>\\n\",\n       \"      <td>3.699248</td>\\n\",\n       \"      <td>133</td>\\n\",\n       \"      <td>X-Men (2000)</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>622</th>\\n\",\n       \"      <td>4.878342</td>\\n\",\n       \"      <td>3.598039</td>\\n\",\n       \"      <td>102</td>\\n\",\n       \"      <td>Harry Potter and the Chamber of Secrets (2002)</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"          pred  mean rating  number of ratings  \\\\\\n\",\n       \"1743  4.030965     4.252336                107   \\n\",\n       \"2112  3.985287     4.238255                149   \\n\",\n       \"211   4.477792     4.122642                159   \\n\",\n       \"929   4.887053     4.118919                185   \\n\",\n       \"2700  4.796530     4.109091                 55   \\n\",\n       \"653   4.357304     4.021277                188   \\n\",\n       \"1122  4.004469     4.006494                 77   \\n\",\n       \"1841  3.980647     4.000000                 61   \\n\",\n       \"3083  4.084633     3.993421                 76   \\n\",\n       \"2804  4.434171     3.989362                 47   \\n\",\n       \"773   4.289679     3.960993                141   \\n\",\n       \"1771  4.344993     3.944444                 81   \\n\",\n       \"2649  4.133482     3.943396                 53   \\n\",\n       \"2455  4.175746     3.887931                 58   \\n\",\n       \"361   4.135291     3.871212                132   \\n\",\n       \"3014  3.967901     3.869565                 69   \\n\",\n       \"246   4.897137     3.867647                170   \\n\",\n       \"151   3.971888     3.836364                110   \\n\",\n       \"1150  4.898892     3.836000                125   \\n\",\n       \"793   4.874935     3.778523                149   \\n\",\n       \"366   4.843375     3.761682                107   \\n\",\n       \"754   4.021774     3.723684                 76   \\n\",\n       \"79    4.242984     3.699248                133   \\n\",\n       \"622   4.878342     3.598039                102   \\n\",\n       \"\\n\",\n       \"                                                  title  \\n\",\n       \"1743                               Departed, The (2006)  \\n\",\n       \"2112                            Dark Knight, The (2008)  \\n\",\n       \"211                                      Memento (2000)  \\n\",\n       \"929   Lord of the Rings: The Return of the King, The...  \\n\",\n       \"2700                                 Toy Story 3 (2010)  \\n\",\n       \"653       Lord of the Rings: The Two Towers, The (2002)  \\n\",\n       \"1122                           Shaun of the Dead (2004)  \\n\",\n       \"1841                                    Hot Fuzz (2007)  \\n\",\n       \"3083                      Dark Knight Rises, The (2012)  \\n\",\n       \"2804  Harry Potter and the Deathly Hallows: Part 1 (...  \\n\",\n       \"773                                 Finding Nemo (2003)  \\n\",\n       \"1771                               Casino Royale (2006)  \\n\",\n       \"2649                    How to Train Your Dragon (2010)  \\n\",\n       \"2455      Harry Potter and the Half-Blood Prince (2009)  \\n\",\n       \"361                               Monsters, Inc. (2001)  \\n\",\n       \"3014                               Avengers, The (2012)  \\n\",\n       \"246                                        Shrek (2001)  \\n\",\n       \"151   Crouching Tiger, Hidden Dragon (Wo hu cang lon...  \\n\",\n       \"1150                            Incredibles, The (2004)  \\n\",\n       \"793   Pirates of the Caribbean: The Curse of the Bla...  \\n\",\n       \"366   Harry Potter and the Sorcerer's Stone (a.k.a. ...  \\n\",\n       \"754                             X2: X-Men United (2003)  \\n\",\n       \"79                                         X-Men (2000)  \\n\",\n       \"622      Harry Potter and the Chamber of Secrets (2002)  \"\n      ]\n     },\n     \"execution_count\": 51,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"filter=(movieList_df[\\\"number of ratings\\\"] > 20)\\n\",\n    \"movieList_df[\\\"pred\\\"] = my_predictions\\n\",\n    \"movieList_df = movieList_df.reindex(columns=[\\\"pred\\\", \\\"mean rating\\\", \\\"number of ratings\\\", \\\"title\\\"])\\n\",\n    \"movieList_df.loc[ix[:300]].loc[filter].sort_values(\\\"mean rating\\\", ascending=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"7\\\"></a>\\n\",\n    \"## 7 - Congratulations! <img align=\\\"left\\\" src=\\\"./images/film_award.png\\\"     style=\\\" width:40px;  \\\" >\\n\",\n    \"You have implemented a useful recommender system!\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A1/data/small_movie_list.csv",
    "content": ",mean rating,number of ratings,title\n0,3.4,5,\"Yards, The (2000)\"\n1,3.25,6,Next Friday (2000)\n2,2.0,4,Supernova (2000)\n3,2.0,4,Down to You (2000)\n4,2.6724137931034484,29,Scream 3 (2000)\n5,4.22093023255814,43,\"Boondock Saints, The (2000)\"\n6,1.0,1,Gun Shy (2000)\n7,3.0625,32,\"Beach, The (2000)\"\n8,2.3,5,Snow Day (2000)\n9,3.1666666666666665,3,\"Tigger Movie, The (2000)\"\n10,3.7142857142857144,21,Boiler Room (2000)\n11,2.6666666666666665,3,Hanging Up (2000)\n12,3.5641025641025643,39,Pitch Black (2000)\n13,3.5238095238095237,42,\"Whole Nine Yards, The (2000)\"\n14,5.0,1,Black Tar Heroin: The Dark End of the Street (2000)\n15,2.4615384615384617,13,Reindeer Games (2000)\n16,3.659090909090909,22,Wonder Boys (2000)\n17,4.0,1,Chain of Fools (2000)\n18,1.3333333333333333,3,Drowning Mona (2000)\n19,2.1666666666666665,3,\"Next Best Thing, The (2000)\"\n20,3.1666666666666665,3,What Planet Are You From? (2000)\n21,4.5,1,\"Closer You Get, The (2000)\"\n22,2.6904761904761907,21,Mission to Mars (2000)\n23,3.5285714285714285,70,Erin Brockovich (2000)\n24,2.9285714285714284,35,Final Destination (2000)\n25,3.0833333333333335,18,Romeo Must Die (2000)\n26,3.0,2,Here on Earth (2000)\n27,2.25,2,Whatever It Takes (2000)\n28,3.6666666666666665,75,High Fidelity (2000)\n29,3.1153846153846154,13,\"Road to El Dorado, The (2000)\"\n30,2.75,10,\"Skulls, The (2000)\"\n31,3.4166666666666665,36,Frequency (2000)\n32,2.25,6,Ready to Rumble (2000)\n33,3.5625,8,Return to Me (2000)\n34,3.0714285714285716,7,Rules of Engagement (2000)\n35,3.0,1,Joe Gould's Secret (2000)\n36,3.611111111111111,9,Me Myself I (2000)\n37,3.3,25,28 Days (2000)\n38,3.788135593220339,59,American Psycho (2000)\n39,3.4523809523809526,21,Keeping the Faith (2000)\n40,3.3333333333333335,3,Where the Money Is (2000)\n41,3.25,2,\"Filth and the Fury, The (2000)\"\n42,2.4,5,Gossip (2000)\n43,3.0,3,Love and Basketball (2000)\n44,3.4242424242424243,33,U-571 (2000)\n45,2.5,3,\"Crow: Salvation, The (2000)\"\n46,1.625,12,\"Flintstones in Viva Rock Vegas, The (2000)\"\n47,3.5,7,Where the Heart Is (2000)\n48,2.875,8,\"Big Kahuna, The (2000)\"\n49,5.0,1,Bossa Nova (2000)\n50,3.0,2,Time Code (2000)\n51,3.9382352941176473,170,Gladiator (2000)\n52,3.5,2,Up at the Villa (2000)\n53,1.6578947368421053,19,Battlefield Earth (2000)\n54,3.25,6,Center Stage (2000)\n55,1.0,1,Screwed (2000)\n56,2.0,2,Whipped (2000)\n57,3.0,3,Hamlet (2000)\n58,3.107142857142857,14,Dinosaur (2000)\n59,3.3,10,Loser (2000)\n60,3.0853658536585367,41,Road Trip (2000)\n61,3.5416666666666665,12,Small Time Crooks (2000)\n62,2.7142857142857144,77,Mission: Impossible II (2000)\n63,3.1511627906976742,43,Shanghai Noon (2000)\n64,2.176470588235294,17,Big Momma's House (2000)\n65,3.221311475409836,61,Gone in 60 Seconds (2000)\n66,3.0,2,Love's Labour's Lost (2000)\n67,2.0,3,Boys and Girls (2000)\n68,2.6578947368421053,19,Shaft (2000)\n69,3.413793103448276,29,Titan A.E. (2000)\n70,3.551282051282051,78,Chicken Run (2000)\n71,3.0869565217391304,46,\"Me, Myself & Irene (2000)\"\n72,3.448529411764706,68,\"Patriot, The (2000)\"\n73,2.2222222222222223,9,\"Adventures of Rocky and Bullwinkle, The (2000)\"\n74,3.1704545454545454,44,\"Perfect Storm, The (2000)\"\n75,4.0,1,\"Golden Bowl, The (2000)\"\n76,2.590909090909091,11,\"Kid, The (2000)\"\n77,2.92,50,Scary Movie (2000)\n78,4.0,2,Groove (2000)\n79,3.699248120300752,133,X-Men (2000)\n80,2.5,2,Chuck & Buck (2000)\n81,2.6666666666666665,3,\"In Crowd, The (2000)\"\n82,3.2,25,What Lies Beneath (2000)\n83,1.5833333333333333,6,Pokémon the Movie 2000 (2000)\n84,2.28125,16,Nutty Professor II: The Klumps (2000)\n85,2.409090909090909,11,Autumn in New York (2000)\n86,2.7413793103448274,29,Coyote Ugly (2000)\n87,2.2948717948717947,39,Hollow Man (2000)\n88,2.891304347826087,23,Space Cowboys (2000)\n89,1.5,2,Psycho Beach Party (2000)\n90,3.909090909090909,11,Saving Grace (2000)\n91,5.0,1,I'm the One That I Want (2000)\n92,3.3846153846153846,13,\"Tao of Steve, The (2000)\"\n93,4.0,1,Bless the Child (2000)\n94,3.5,5,Cecil B. DeMented (2000)\n95,3.75,2,\"Eyes of Tammy Faye, The (2000)\"\n96,2.6842105263157894,19,\"Replacements, The (2000)\"\n97,3.75,2,About Adam (2000)\n98,3.0444444444444443,45,\"Cell, The (2000)\"\n99,3.25,4,\"Original Kings of Comedy, The (2000)\"\n100,2.0,2,\"Art of War, The (2000)\"\n101,2.6911764705882355,34,Bring It On (2000)\n102,1.5,3,\"Crew, The (2000)\"\n103,4.0,1,Skipped Parts (2000)\n104,2.25,6,Highlander: Endgame (Highlander IV) (2000)\n105,3.3421052631578947,19,Nurse Betty (2000)\n106,1.5,1,\"Watcher, The (2000)\"\n107,3.125,8,\"Way of the Gun, The (2000)\"\n108,3.8674698795180724,83,Almost Famous (2000)\n109,2.5,2,Bait (2000)\n110,1.0,1,Circus (2000)\n111,4.0,1,Crime and Punishment in Suburbia (2000)\n112,3.25,2,Duets (2000)\n113,2.5,2,Under Suspicion (2000)\n114,1.5,3,Urban Legends: Final Cut (2000)\n115,2.0,4,Woman on Top (2000)\n116,3.975,20,Dancer in the Dark (2000)\n117,3.688679245283019,53,Best in Show (2000)\n118,2.5,2,Beautiful (2000)\n119,4.0,1,\"Broken Hearts Club, The (2000)\"\n120,3.6666666666666665,6,Girlfight (2000)\n121,3.7439024390243905,41,Remember the Titans (2000)\n122,2.9375,8,Bamboozled (2000)\n123,3.0,1,Digimon: The Movie (2000)\n124,2.5,5,Get Carter (2000)\n125,3.4175824175824174,91,Meet the Parents (2000)\n126,3.921875,96,Requiem for a Dream (2000)\n127,3.5625,8,Tigerland (2000)\n128,5.0,1,Two Family House (2000)\n129,3.0714285714285716,7,\"Contender, The (2000)\"\n130,2.125,4,Dr. T and the Women (2000)\n131,1.5833333333333333,6,\"Ladies Man, The (2000)\"\n132,3.8513513513513513,37,Billy Elliot (2000)\n133,3.074074074074074,27,Bedazzled (2000)\n134,3.4423076923076925,26,Pay It Forward (2000)\n135,1.125,4,Book of Shadows: Blair Witch 2 (2000)\n136,3.0,1,\"Little Vampire, The (2000)\"\n137,2.7222222222222223,72,Charlie's Angels (2000)\n138,3.066666666666667,15,\"Legend of Bagger Vance, The (2000)\"\n139,2.5,24,Little Nicky (2000)\n140,3.392857142857143,14,Men of Honor (2000)\n141,2.3461538461538463,13,Red Planet (2000)\n142,4.166666666666667,9,You Can Count on Me (2000)\n143,2.8,20,\"6th Day, The (2000)\"\n144,2.4166666666666665,6,Bounce (2000)\n145,3.0454545454545454,33,How the Grinch Stole Christmas (a.k.a. The Grinch) (2000)\n146,2.25,4,Rugrats in Paris: The Movie (2000)\n147,2.7777777777777777,9,102 Dalmatians (2000)\n148,3.7,5,Malèna (2000)\n149,3.0,6,Quills (2000)\n150,3.4794520547945207,73,Unbreakable (2000)\n151,3.8363636363636364,110,\"Crouching Tiger, Hidden Dragon (Wo hu cang long) (2000)\"\n152,1.8333333333333333,12,Dungeons & Dragons (2000)\n153,3.0416666666666665,12,Proof of Life (2000)\n154,2.607142857142857,14,Vertical Limit (2000)\n155,4.155913978494624,93,Snatch (2000)\n156,3.521276595744681,47,Chocolat (2000)\n157,2.727272727272727,33,\"Dude, Where's My Car? (2000)\"\n158,3.7162162162162162,37,\"Emperor's New Groove, The (2000)\"\n159,3.5357142857142856,14,Pollock (2000)\n160,3.138888888888889,54,What Women Want (2000)\n161,3.7142857142857144,28,Finding Forrester (2000)\n162,2.923076923076923,13,\"Gift, The (2000)\"\n163,4.3,5,Before Night Falls (2000)\n164,3.7,100,Cast Away (2000)\n165,3.130434782608696,23,\"Family Man, The (2000)\"\n166,2.1666666666666665,3,\"House of Mirth, The (2000)\"\n167,3.0546875,64,Miss Congeniality (2000)\n168,3.8085106382978724,94,\"O Brother, Where Art Thou? (2000)\"\n169,3.875,12,State and Main (2000)\n170,1.8,5,Dracula 2000 (2000)\n171,2.5,2,All the Pretty Horses (2000)\n172,4.0,1,\"Everlasting Piece, An (2000)\"\n173,3.923076923076923,13,Thirteen Days (2000)\n174,3.9,70,Traffic (2000)\n175,2.8333333333333335,3,\"Claim, The (2000)\"\n176,3.1363636363636362,11,Shadow of the Vampire (2000)\n177,3.5625,8,Antitrust (2001)\n178,2.5,2,Double Take (2001)\n179,2.84375,16,Save the Last Dance (2001)\n180,4.0,2,Panic (2000)\n181,3.2857142857142856,7,\"Pledge, The (2001)\"\n182,2.7142857142857144,7,Sugar & Spice (2001)\n183,2.909090909090909,22,\"Wedding Planner, The (2001)\"\n184,4.5,2,\"With a Friend Like Harry... (Harry, un ami qui vous veut du bien) (2000)\"\n185,2.5,4,Head Over Heels (2001)\n186,4.25,2,Left Behind: The Movie (2000)\n187,3.5,1,Valentine (2001)\n188,4.214285714285714,14,In the Mood For Love (Fa yeung nin wa) (2000)\n189,3.0,3,\"Million Dollar Hotel, The (2001)\"\n190,4.0,2,Nico and Dani (Krámpack) (2000)\n191,2.9358974358974357,39,Hannibal (2001)\n192,2.772727272727273,11,Saving Silverman (Evil Woman) (2001)\n193,4.25,2,Vatel (2000)\n194,2.4166666666666665,12,Down to Earth (2001)\n195,4.0,2,Recess: School's Out (2001)\n196,3.3125,8,Sweet November (2001)\n197,2.0,1,Company Man (2000)\n198,4.5,1,\"Price of Milk, The (2000)\"\n199,2.0833333333333335,6,Monkeybone (2001)\n200,2.7142857142857144,7,3000 Miles to Graceland (2001)\n201,4.125,4,\"Widow of St. Pierre, The (Veuve de Saint-Pierre, La) (2000)\"\n202,3.347826086956522,23,\"Mexican, The (2001)\"\n203,3.5,2,\"Caveman's Valentine, The (2001)\"\n204,2.5,2,Series 7: The Contenders (2001)\n205,3.0,10,15 Minutes (2001)\n206,2.75,4,Get Over It (2001)\n207,4.0,1,Long Night's Journey Into Day (2000)\n208,3.5125,40,Enemy at the Gates (2001)\n209,2.75,2,Exit Wounds (2001)\n210,4.0,7,\"Dish, The (2001)\"\n211,4.122641509433962,159,Memento (2000)\n212,2.15,10,Heartbreakers (2001)\n213,2.125,4,Say It Isn't So (2001)\n214,3.5,5,Someone Like You (2001)\n215,2.9464285714285716,28,Spy Kids (2001)\n216,2.0,6,Tomcats (2001)\n217,3.2,5,\"Tailor of Panama, The (2001)\"\n218,3.9347826086956523,23,Amores Perros (Love's a Bitch) (2000)\n219,3.5,1,Keep the River on Your Right: A Modern Cannibal Tale (2000)\n220,4.0,1,\"Gleaners & I, The (Les glaneurs et la glaneuse) (2000)\"\n221,3.1470588235294117,17,Along Came a Spider (2001)\n222,3.8137254901960786,51,Blow (2001)\n223,2.0,1,Just Visiting (2001)\n224,2.1,5,Pokémon 3: The Movie (2001)\n225,2.0,1,Beautiful Creatures (2000)\n226,4.0,1,Brigham City (2001)\n227,3.623076923076923,65,Bridget Jones's Diary (2001)\n228,2.380952380952381,21,Joe Dirt (2001)\n229,1.75,6,Josie and the Pussycats (2001)\n230,2.0,1,Chopper (2000)\n231,3.25,2,\"Circle, The (Dayereh) (2000)\"\n232,2.1923076923076925,13,Crocodile Dundee in Los Angeles (2001)\n233,2.2,10,Freddy Got Fingered (2001)\n234,3.6666666666666665,3,\"Center of the World, The (2001)\"\n235,3.5,4,\"Luzhin Defence, The (2000)\"\n236,2.0,1,\"Visit, The (2000)\"\n237,3.0,5,Driven (2001)\n238,3.4,5,One Night at McCool's (2001)\n239,2.5,2,Town & Country (2001)\n240,3.088888888888889,45,\"Mummy Returns, The (2001)\"\n241,4.5,2,Under the Sand (2000)\n242,3.341463414634146,41,\"Knight's Tale, A (2001)\"\n243,4.0,3,Bread and Roses (2000)\n244,3.6,5,Startup.com (2001)\n245,1.625,4,Angel Eyes (2001)\n246,3.8676470588235294,170,Shrek (2001)\n247,4.5,1,\"Fast Food, Fast Women (2000)\"\n248,3.590909090909091,55,Moulin Rouge (2001)\n249,2.9651162790697674,43,Pearl Harbor (2001)\n250,3.5,2,\"Man Who Cried, The (2000)\"\n251,4.6,5,Yi Yi (2000)\n252,2.1363636363636362,11,\"Animal, The (2001)\"\n253,4.5,1,Big Eden (2000)\n254,3.037037037037037,27,Evolution (2001)\n255,3.1029411764705883,34,Swordfish (2001)\n256,4.0,3,\"Anniversary Party, The (2001)\"\n257,3.8333333333333335,3,Divided We Fall (Musíme si pomáhat) (2000)\n258,3.3421052631578947,19,Atlantis: The Lost Empire (2001)\n259,2.9,40,Lara Croft: Tomb Raider (2001)\n260,2.3636363636363638,11,Dr. Dolittle 2 (2001)\n261,3.066666666666667,45,\"Fast and the Furious, The (2001)\"\n262,3.3392857142857144,56,A.I. Artificial Intelligence (2001)\n263,0.5,1,Baby Boy (2001)\n264,3.4,5,Crazy/Beautiful (2001)\n265,3.1666666666666665,3,Pootie Tang (2001)\n266,3.7142857142857144,14,Sexy Beast (2000)\n267,3.857142857142857,7,\"Princess and the Warrior, The (Krieger und die Kaiserin, Der) (2000)\"\n268,3.375,4,\"Closet, The (Placard, Le) (2001)\"\n269,3.5,5,\"Crimson Rivers, The (Rivières pourpres, Les) (2000)\"\n270,3.0,1,Lumumba (2000)\n271,2.8181818181818183,11,Cats & Dogs (2001)\n272,3.7777777777777777,9,Kiss of the Dragon (2001)\n273,2.6538461538461537,26,Scary Movie 2 (2001)\n274,3.625,4,Lost and Delirious (2001)\n275,3.5,2,Rape Me (Baise-moi) (2000)\n276,3.3548387096774195,31,Final Fantasy: The Spirits Within (2001)\n277,3.15625,64,Legally Blonde (2001)\n278,3.55,20,\"Score, The (2001)\"\n279,2.0,1,Adanggaman (2000)\n280,3.642857142857143,7,Bully (2001)\n281,3.5,1,Jump Tomorrow (2001)\n282,2.6666666666666665,6,Made (2001)\n283,3.5,1,Michael Jordan to the Max (2000)\n284,2.8472222222222223,36,Jurassic Park III (2001)\n285,2.5833333333333335,18,America's Sweethearts (2001)\n286,3.75,4,Brother (2000)\n287,3.5,34,Ghost World (2001)\n288,4.181818181818182,11,Hedwig and the Angry Inch (2000)\n289,2.7448979591836733,49,Planet of the Apes (2001)\n290,3.6666666666666665,3,Bread and Tulips (Pane e tulipani) (2000)\n291,1.5,1,Greenfingers (2000)\n292,3.642857142857143,7,Wet Hot American Summer (2001)\n293,2.75,2,Original Sin (2001)\n294,3.037037037037037,27,\"Princess Diaries, The (2001)\"\n295,3.370967741935484,31,Rush Hour 2 (2001)\n296,3.066666666666667,45,American Pie 2 (2001)\n297,2.625,8,Osmosis Jones (2001)\n298,3.6272727272727274,55,\"Others, The (2001)\"\n299,2.125,4,American Outlaws (2001)\n300,3.0,1,All Over the Guy (2001)\n301,1.8333333333333333,3,\"Deep End, The (2001)\"\n302,3.625,4,Session 9 (2001)\n303,3.125,4,Captain Corelli's Mandolin (2001)\n304,3.0555555555555554,18,Rat Race (2001)\n305,1.8888888888888888,9,Bubble Boy (2001)\n306,2.5,3,\"Curse of the Jade Scorpion, The (2001)\"\n307,3.161764705882353,34,Jay and Silent Bob Strike Back (2001)\n308,2.6,5,Ghosts of Mars (2001)\n309,2.6666666666666665,3,Summer Catch (2001)\n310,4.0,4,Happy Accidents (2000)\n311,3.5,1,Maybe Baby (2000)\n312,3.7142857142857144,7,Together (Tillsammans) (2000)\n313,3.25,2,Tortilla Soup (2001)\n314,2.5,7,Jeepers Creepers (2001)\n315,3.625,4,O (2001)\n316,1.5,2,\"Musketeer, The (2001)\"\n317,2.5,3,Rock Star (2001)\n318,4.0,1,Two Can Play That Game (2001)\n319,4.333333333333333,3,L.I.E. (2001)\n320,4.0,1,\"Our Lady of the Assassins (Virgen de los sicarios, La) (2000)\"\n321,3.0,1,Into the Arms of Strangers: Stories of the Kindertransport (2000)\n322,2.75,2,\"Glass House, The (2001)\"\n323,2.6666666666666665,3,Hardball (2001)\n324,3.5,1,Dinner Rush (2000)\n325,3.0,4,Haiku Tunnel (2001)\n326,2.1666666666666665,6,Big Trouble (2002)\n327,0.5,1,Glitter (2001)\n328,3.7941176470588234,51,Training Day (2001)\n329,2.5,1,\"American Astronaut, The (2001)\"\n330,2.0,2,Liam (2000)\n331,2.5,2,Sidewalks of New York (2001)\n332,3.5,1,\"Endurance: Shackleton's Legendary Antarctic Expedition, The (2000)\"\n333,2.5,5,Don't Say a Word (2001)\n334,2.9166666666666665,6,Hearts in Atlantis (2001)\n335,3.509259259259259,54,Zoolander (2001)\n336,2.0,1,Extreme Days (2001)\n337,3.1,5,Joy Ride (2001)\n338,2.5,3,Max Keeble's Big Move (2001)\n339,3.14,25,Serendipity (2001)\n340,3.0,1,\"Swamp, The (Ciénaga, La) (2001)\"\n341,2.6666666666666665,6,Bandits (2001)\n342,1.3333333333333333,3,Corky Romano (2001)\n343,3.0,2,Fat Girl (À ma soeur!) (2001)\n344,3.843137254901961,51,Mulholland Drive (2001)\n345,3.3333333333333335,3,My First Mister (2001)\n346,1.6666666666666667,3,Bones (2001)\n347,3.65,20,From Hell (2001)\n348,2.8125,8,\"Last Castle, The (2001)\"\n349,3.1,5,Riding in Cars with Boys (2001)\n350,3.5,1,Focus (2001)\n351,3.6842105263157894,19,Waking Life (2001)\n352,3.484848484848485,33,K-PAX (2001)\n353,2.4583333333333335,12,Thirteen Ghosts (a.k.a. Thir13en Ghosts) (2001)\n354,3.5,1,Better Than Sex (2000)\n355,3.981651376146789,109,Donnie Darko (2001)\n356,3.0,1,High Heels and Low Lifes (2001)\n357,3.888888888888889,9,Life as a House (2001)\n358,3.735294117647059,17,\"Man Who Wasn't There, The (2001)\"\n359,4.0,1,\"Town is Quiet, The (Ville est tranquille, La) (2000)\"\n360,2.0,1,Domestic Disturbance (2001)\n361,3.871212121212121,132,\"Monsters, Inc. (2001)\"\n362,2.75,14,\"One, The (2001)\"\n363,3.875,4,Tape (2001)\n364,3.4285714285714284,7,Heist (2001)\n365,3.032258064516129,31,Shallow Hal (2001)\n366,3.7616822429906542,107,Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001)\n367,2.625,4,Novocaine (2001)\n368,2.1875,8,Black Knight (2001)\n369,3.0,5,Out Cold (2001)\n370,3.4814814814814814,27,Spy Game (2001)\n371,3.35,10,\"Devil's Backbone, The (Espinazo del diablo, El) (2001)\"\n372,3.0,10,In the Bedroom (2001)\n373,2.0,1,Everybody's Famous! (Iedereen beroemd!) (2000)\n374,3.25,20,Behind Enemy Lines (2001)\n375,3.25,2,\"Affair of the Necklace, The (2001)\"\n376,2.5,1,Pornstar: The Legend of Ron Jeremy (2001)\n377,2.0,1,Texas Rangers (2001)\n378,3.8445378151260505,119,Ocean's Eleven (2001)\n379,3.5,1,Baran (2001)\n380,3.5,2,\"Business of Strangers, The (2001)\"\n381,4.1923076923076925,13,No Man's Land (2001)\n382,4.183333333333334,120,\"Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)\"\n383,2.5714285714285716,14,Not Another Teen Movie (2001)\n384,3.4204545454545454,44,Vanilla Sky (2001)\n385,3.875,4,Iris (2001)\n386,3.5,2,Kandahar (Safar e Ghandehar) (2001)\n387,3.8333333333333335,6,Lantana (2001)\n388,3.6838235294117645,68,\"Royal Tenenbaums, The (2001)\"\n389,2.9444444444444446,9,How High (2001)\n390,3.2,5,Jimmy Neutron: Boy Genius (2001)\n391,3.3333333333333335,3,Joe Somebody (2001)\n392,3.2058823529411766,17,Kate & Leopold (2001)\n393,4.106060606060606,198,\"Lord of the Rings: The Fellowship of the Ring, The (2001)\"\n394,3.5,10,\"Majestic, The (2001)\"\n395,4.0,123,\"Beautiful Mind, A (2001)\"\n396,3.0,8,Ali (2001)\n397,3.81,50,Black Hawk Down (2001)\n398,3.75,2,Charlotte Gray (2001)\n399,3.7037037037037037,27,Gosford Park (2001)\n400,3.764705882352941,17,I Am Sam (2001)\n401,3.3055555555555554,18,Monster's Ball (2001)\n402,3.0,6,\"Shipping News, The (2001)\"\n403,2.6333333333333333,15,Orange County (2002)\n404,3.8214285714285716,14,\"Brotherhood of the Wolf (Pacte des loups, Le) (2001)\"\n405,2.0,1,What Time Is It There? (Ni neibian jidian) (2001)\n406,3.0,3,Impostor (2002)\n407,3.2222222222222223,9,Kung Pow: Enter the Fist (2002)\n408,2.3333333333333335,6,Snow Dogs (2002)\n409,3.0,2,Italian for Beginners (Italiensk for begyndere) (2000)\n410,3.828125,32,The Count of Monte Cristo (2002)\n411,3.076923076923077,13,\"Mothman Prophecies, The (2002)\"\n412,3.5,14,\"Walk to Remember, A (2002)\"\n413,4.0,1,Beijing Bicycle (Shiqi sui de dan che) (2001)\n414,3.25,2,Escaflowne: The Movie (Escaflowne) (2000)\n415,4.0,1,Maelström (2000)\n416,3.9615384615384617,13,Metropolis (2001)\n417,3.8333333333333335,3,\"Son's Room, The (Stanza del figlio, La) (2001)\"\n418,3.1666666666666665,3,Storytelling (2001)\n419,3.6666666666666665,3,Waydowntown (2000)\n420,2.6,5,Slackers (2002)\n421,2.8,5,Birthday Girl (2001)\n422,3.5,2,Rare Birds (2001)\n423,3.0,5,Big Fat Liar (2002)\n424,2.7777777777777777,9,Collateral Damage (2002)\n425,1.7857142857142858,7,Rollerball (2002)\n426,4.5,1,\"Scotland, Pa. (2001)\"\n427,2.0714285714285716,7,Crossroads (2002)\n428,3.1,5,Hart's War (2002)\n429,2.909090909090909,11,John Q (2002)\n430,4.0,1,Return to Never Land (2002)\n431,3.6041666666666665,24,Super Troopers (2001)\n432,3.0,1,Last Orders (2001)\n433,3.5,3,Dragonfly (2002)\n434,2.3636363636363638,11,Queen of the Damned (2002)\n435,3.25,2,How to Kill Your Neighbor's Dog (2000)\n436,3.0,2,Mean Machine (2001)\n437,3.8333333333333335,12,Monsoon Wedding (2001)\n438,2.5,2,Wendigo (2001)\n439,3.8333333333333335,3,Scratch (2001)\n440,3.111111111111111,9,Vampire Hunter D: Bloodlust (Banpaia hantâ D) (2000)\n441,3.0,18,40 Days and 40 Nights (2002)\n442,3.3333333333333335,18,We Were Soldiers (2002)\n443,2.5,2,All About the Benjamins (2002)\n444,2.8636363636363638,22,\"Time Machine, The (2002)\"\n445,3.6882352941176473,85,Ice Age (2002)\n446,3.0833333333333335,30,Resident Evil (2002)\n447,2.4,10,Showtime (2002)\n448,3.0,1,Harrison's Flowers (2000)\n449,3.375,4,Kissing Jessica Stein (2001)\n450,4.25,2,Promises (2001)\n451,3.9523809523809526,21,And Your Mother Too (Y tu mamá también) (2001)\n452,3.2058823529411766,34,Blade II (2002)\n453,2.375,4,Sorority Boys (2002)\n454,2.75,2,Stolen Summer (2002)\n455,2.0,2,George Washington (2000)\n456,2.25,4,Clockstoppers (2002)\n457,3.3181818181818183,11,Death to Smoochy (2002)\n458,3.0,37,Panic Room (2002)\n459,3.2916666666666665,12,\"Rookie, The (2002)\"\n460,2.5,1,No Such Thing (2001)\n461,4.0,1,\"Piano Teacher, The (La pianiste) (2001)\"\n462,3.0,1,Time Out (L'emploi du temps) (2001)\n463,3.25,2,High Crimes (2002)\n464,2.909090909090909,22,National Lampoon's Van Wilder (2002)\n465,2.5,1,Crush (2001)\n466,4.0,1,Lucky Break (2001)\n467,3.3333333333333335,9,Changing Lanes (2002)\n468,3.8636363636363638,11,Frailty (2001)\n469,2.9,10,\"Sweetest Thing, The (2002)\"\n470,2.5,2,\"Cat's Meow, The (2002)\"\n471,3.5,5,Human Nature (2001)\n472,3.2666666666666666,60,My Big Fat Greek Wedding (2002)\n473,2.857142857142857,7,Murder by Numbers (2002)\n474,2.2954545454545454,22,The Scorpion King (2002)\n475,3.142857142857143,7,Enigma (2001)\n476,3.5,6,Nine Queens (Nueve reinas) (2000)\n477,2.0,1,\"Triumph of Love, The (2001)\"\n478,4.0,1,World Traveler (2001)\n479,1.1875,8,Jason X (2002)\n480,2.75,2,Life or Something Like It (2002)\n481,3.7857142857142856,7,Dogtown and Z-Boyz (2001)\n482,5.0,1,Rain (2001)\n483,3.35,10,\"Salton Sea, The (2002)\"\n484,2.5,2,Deuces Wild (2002)\n485,4.0,1,Hollywood Ending (2002)\n486,3.540983606557377,122,Spider-Man (2002)\n487,2.5,9,\"New Guy, The (2002)\"\n488,3.5,7,Unfaithful (2002)\n489,4.0,1,\"Lady and the Duke, The (Anglaise et le duc, L') (2001)\"\n490,3.715909090909091,44,About a Boy (2002)\n491,3.157608695652174,92,Star Wars: Episode II - Attack of the Clones (2002)\n492,4.25,6,\"Believer, The (2001)\"\n493,3.85,10,\"Importance of Being Earnest, The (2002)\"\n494,4.0,3,Enough (2002)\n495,3.303030303030303,33,Insomnia (2002)\n496,3.5,2,Spirit: Stallion of the Cimarron (2002)\n497,3.0,1,CQ (2001)\n498,4.0,3,Thirteen Conversations About One Thing (a.k.a. 13 Conversations) (2001)\n499,2.9705882352941178,17,\"Sum of All Fears, The (2002)\"\n500,1.8333333333333333,9,Undercover Brother (2002)\n501,2.875,4,Bad Company (2002)\n502,3.125,4,Divine Secrets of the Ya-Ya Sisterhood (2002)\n503,5.0,1,Cherish (2002)\n504,3.8333333333333335,3,\"Fast Runner, The (Atanarjuat) (2001)\"\n505,3.8169642857142856,112,\"Bourne Identity, The (2002)\"\n506,2.8529411764705883,17,Scooby-Doo (2002)\n507,2.875,8,Windtalkers (2002)\n508,3.5,6,\"Dangerous Lives of Altar Boys, The (2002)\"\n509,4.0,1,\"Emperor's New Clothes, The (2001)\"\n510,3.0,3,Gangster No. 1 (2000)\n511,3.0,1,Harvard Man (2001)\n512,4.25,2,Dark Blue World (Tmavomodrý svet) (2001)\n513,1.5,2,Juwanna Mann (2002)\n514,3.810344827586207,29,Lilo & Stitch (2002)\n515,3.6375,120,Minority Report (2002)\n516,3.642857142857143,7,Rabbit-Proof Fence (2002)\n517,3.5,2,Sunshine State (2002)\n518,2.0,1,Hey Arnold! The Movie (2002)\n519,2.909090909090909,22,Mr. Deeds (2002)\n520,3.2,5,Lovely & Amazing (2001)\n521,3.0,5,Pumpkin (2002)\n522,2.5,1,Like Mike (2002)\n523,2.959016393442623,61,Men in Black II (a.k.a. MIIB) (a.k.a. MIB 2) (2002)\n524,3.6666666666666665,3,\"Powerpuff Girls, The (2002)\"\n525,2.5,1,Me Without You (2001)\n526,2.5,2,\"Crocodile Hunter: Collision Course, The (2002)\"\n527,2.7222222222222223,18,Reign of Fire (2002)\n528,3.520408163265306,49,Road to Perdition (2002)\n529,3.0,1,All About Lily Chou-Chou (Riri Shushu no subete) (2001)\n530,4.0,4,My Wife is an Actress (Ma Femme est une Actrice) (2001)\n531,2.0,3,Halloween: Resurrection (Halloween 8) (2002)\n532,3.875,4,Sex and Lucia (Lucía y el sexo) (2001)\n533,2.75,8,Eight Legged Freaks (2002)\n534,3.5,6,K-19: The Widowmaker (2002)\n535,2.6,5,Stuart Little 2 (2002)\n536,2.8461538461538463,65,Austin Powers in Goldmember (2002)\n537,3.8333333333333335,3,\"Kid Stays in the Picture, The (2002)\"\n538,3.3333333333333335,3,Tadpole (2002)\n539,2.5,2,Who Is Cletis Tout? (2001)\n540,1.8571428571428572,7,\"Master of Disguise, The (2002)\"\n541,3.1666666666666665,63,Signs (2002)\n542,4.0,1,\"Last Kiss, The (Ultimo bacio, L') (2001)\"\n543,2.2142857142857144,7,Spy Kids 2: The Island of Lost Dreams (2002)\n544,3.2083333333333335,12,\"Good Girl, The (2002)\"\n545,3.3333333333333335,3,Blood Work (2002)\n546,2.7708333333333335,24,xXx (2002)\n547,3.5714285714285716,7,24 Hour Party People (2002)\n548,3.25,2,Secret Ballot (Raye makhfi) (2001)\n549,5.0,1,Martin Lawrence Live: Runteldat (2002)\n550,3.5,1,Songs From the Second Floor (Sånger från andra våningen) (2000)\n551,2.3333333333333335,3,\"Adventures of Pluto Nash, The (2002)\"\n552,2.6875,8,Blue Crush (2002)\n553,3.5,1,Mostly Martha (Bella Martha) (2001)\n554,3.5,4,Possession (2002)\n555,3.473684210526316,19,One Hour Photo (2002)\n556,3.25,2,Serving Sara (2002)\n557,2.142857142857143,7,Simone (S1m0ne) (2002)\n558,2.75,2,Undisputed (2002)\n559,4.0,1,Amy's O (a.k.a. Amy's Orgasm) (2001)\n560,5.0,1,Satin Rouge (2002)\n561,1.5,3,FearDotCom (a.k.a. Fear.com) (a.k.a. Fear Dot Com) (2002)\n562,2.5,1,Snipes (2001)\n563,2.8333333333333335,3,City by the Sea (2002)\n564,2.0,2,Swimfan (2002)\n565,2.7142857142857144,7,Barbershop (2002)\n566,2.2857142857142856,7,Stealing Harvard (2002)\n567,3.519230769230769,26,\"Transporter, The (2002)\"\n568,2.0,1,Alias Betty (Betty Fisher et autres histoires) (2001)\n569,3.6666666666666665,12,Igby Goes Down (2002)\n570,5.0,1,\"Son of the Bride (Hijo de la novia, El) (2001)\"\n571,3.8636363636363638,11,\"Das Experiment (Experiment, The) (2001)\"\n572,2.5,2,Ballistic: Ecks vs. Sever (2002)\n573,2.0,3,\"Banger Sisters, The (2002)\"\n574,2.75,2,\"Four Feathers, The (2002)\"\n575,2.0,1,Trapped (2002)\n576,3.8333333333333335,3,8 Women (2002)\n577,4.5,1,\"His Secret Life (a.k.a. Ignorant Fairies, The) (Fate ignoranti, Le) (2001)\"\n578,3.5,1,Invincible (2001)\n579,3.9,25,Secretary (2002)\n580,4.155172413793103,87,Spirited Away (Sen to Chihiro no kamikakushi) (2001)\n581,4.0,2,\"Trials of Henry Kissinger, The (2002)\"\n582,3.0535714285714284,28,Sweet Home Alabama (2002)\n583,2.5,10,\"Tuxedo, The (2002)\"\n584,3.5,3,Moonlight Mile (2002)\n585,3.5,5,Wasabi (2001)\n586,2.0,1,Jonah: A VeggieTales Movie (2002)\n587,3.435483870967742,31,Red Dragon (2002)\n588,3.6,5,Bloody Sunday (2002)\n589,3.75,2,Heaven (2002)\n590,3.0,1,\"Man from Elysian Fields, The (2001)\"\n591,2.5,2,Welcome to Collinwood (2002)\n592,3.5,1,Below (2002)\n593,1.5,1,Brown Sugar (2002)\n594,2.5,5,Knockaround Guys (2002)\n595,3.6,5,\"Rules of Attraction, The (2002)\"\n596,3.25,2,Tuck Everlasting (2002)\n597,2.5,4,White Oleander (2002)\n598,3.7758620689655173,58,Bowling for Columbine (2002)\n599,3.0,2,Comedian (2002)\n600,0.5,2,Pokemon 4 Ever (a.k.a. Pokémon 4: The Movie) (2002)\n601,3.621212121212121,33,Punch-Drunk Love (2002)\n602,1.0,1,Swept Away (2002)\n603,2.6666666666666665,3,Formula 51 (2001)\n604,3.202127659574468,47,\"Ring, The (2002)\"\n605,3.1666666666666665,3,Auto Focus (2002)\n606,3.25,2,\"Grey Zone, The (2001)\"\n607,3.0,1,Naqoyqatsi (2002)\n608,4.25,2,Real Women Have Curves (2002)\n609,2.75,2,Tully (2000)\n610,2.5,9,Ghost Ship (2002)\n611,3.5,17,Jackass: The Movie (2002)\n612,4.5,1,Paid in Full (2002)\n613,2.5,1,\"Truth About Charlie, The (2002)\"\n614,3.6666666666666665,3,All or Nothing (2002)\n615,3.78125,16,Frida (2002)\n616,3.6666666666666665,6,Roger Dodger (2002)\n617,2.5,7,I Spy (2002)\n618,2.3,5,\"Santa Clause 2, The (2002)\"\n619,3.375,4,Femme Fatale (2002)\n620,3.292682926829268,41,8 Mile (2002)\n621,3.8636363636363638,11,Far from Heaven (2002)\n622,3.5980392156862746,102,Harry Potter and the Chamber of Secrets (2002)\n623,4.5,1,Ararat (2002)\n624,4.5,1,\"Crime of Father Amaro, The (Crimen del padre Amaro, El) (2002)\"\n625,3.5,3,Standing in the Shadows of Motown (2002)\n626,4.0,1,Men with Brooms (2002)\n627,4.666666666666667,3,Dog Soldiers (2002)\n628,2.925925925925926,27,Die Another Day (2002)\n629,3.875,4,The Emperor's Club (2002)\n630,2.4,5,Friday After Next (2002)\n631,2.5,1,Personal Velocity (2002)\n632,3.9,5,\"Quiet American, The (2002)\"\n633,4.035714285714286,14,Talk to Her (Hable con Ella) (2002)\n634,1.5,4,Eight Crazy Nights (Adam Sandler's Eight Crazy Nights) (2002)\n635,1.75,2,Extreme Ops (2002)\n636,3.075,20,Solaris (2002)\n637,3.5625,8,Treasure Planet (2002)\n638,1.5,1,They (2002)\n639,4.5,2,Elling (2001)\n640,2.5625,8,Analyze That (2002)\n641,4.5,1,Empire (2002)\n642,3.9456521739130435,46,Adaptation (2002)\n643,3.875,44,Equilibrium (2002)\n644,3.3,5,Visitor Q (Bizita Q) (2001)\n645,2.9166666666666665,6,Drumline (2002)\n646,2.8333333333333335,6,\"Hot Chick, The (2002)\"\n647,2.6052631578947367,19,Maid in Manhattan (2002)\n648,3.1052631578947367,19,Star Trek: Nemesis (2002)\n649,3.173076923076923,26,About Schmidt (2002)\n650,3.0,1,Evelyn (2002)\n651,4.25,2,Intact (Intacto) (2001)\n652,3.25,4,Morvern Callar (2002)\n653,4.0212765957446805,188,\"Lord of the Rings: The Two Towers, The (2002)\"\n654,4.0,1,Devils on the Doorstep (Guizi lai le) (2000)\n655,3.840909090909091,22,25th Hour (2002)\n656,3.5555555555555554,9,Antwone Fisher (2002)\n657,3.518181818181818,55,Gangs of New York (2002)\n658,3.263157894736842,19,Two Weeks Notice (2002)\n659,3.2777777777777777,9,Narc (2002)\n660,3.9217391304347826,115,Catch Me If You Can (2002)\n661,3.5,1,Pinocchio (2002)\n662,3.7244897959183674,49,Chicago (2002)\n663,3.7,15,\"Hours, The (2002)\"\n664,2.9,5,Max (2002)\n665,4.0,2,Nicholas Nickleby (2002)\n666,4.108695652173913,46,\"Pianist, The (2002)\"\n667,2.25,2,Heavy Metal 2000 (2000)\n668,3.3333333333333335,3,Love Liza (2002)\n669,3.6,15,Confessions of a Dangerous Mind (2002)\n670,3.0,3,Blue Collar Comedy Tour: The Movie (2003)\n671,2.75,8,Just Married (2003)\n672,3.0,1,\"City of Lost Souls, The (Hyôryuu-gai) (2000)\"\n673,2.5,4,\"Guy Thing, A (2003)\"\n674,1.75,2,Kangaroo Jack (2003)\n675,2.5,5,National Security (2003)\n676,4.1466666666666665,75,City of God (Cidade de Deus) (2002)\n677,2.125,4,Darkness Falls (2003)\n678,3.8333333333333335,3,Amen. (2002)\n679,3.0,1,Blind Spot: Hitler's Secretary (Im toten Winkel - Hitlers Sekretärin) (2002)\n680,2.0,1,Biker Boyz (2003)\n681,3.05,10,Final Destination 2 (2003)\n682,3.3529411764705883,17,\"Recruit, The (2003)\"\n683,3.0,2,\"Guru, The (2002)\"\n684,3.0833333333333335,6,Lost in La Mancha (2002)\n685,3.3333333333333335,3,May (2002)\n686,4.0,1,Ordinary Decent Criminal (2000)\n687,3.2758620689655173,29,How to Lose a Guy in 10 Days (2003)\n688,3.210526315789474,19,Shanghai Knights (2003)\n689,2.5,32,Daredevil (2003)\n690,3.0,1,\"Jungle Book 2, The (2003)\"\n691,4.25,2,All the Real Girls (2003)\n692,3.1666666666666665,3,Gerry (2002)\n693,4.5,1,He Loves Me... He Loves Me Not (À la folie... pas du tout) (2002)\n694,3.0,3,Dark Blue (2003)\n695,3.0,1,Gods and Generals (2003)\n696,3.8125,8,\"Life of David Gale, The (2003)\"\n697,3.5128205128205128,39,Old School (2003)\n698,5.0,1,Open Hearts (Elsker dig for evigt) (2002)\n699,3.8333333333333335,3,Poolhall Junkies (2002)\n700,4.0,2,Stone Reader (2002)\n701,3.5,3,Cradle 2 the Grave (2003)\n702,3.3333333333333335,3,Spider (2002)\n703,3.5,1,Late Marriage (Hatuna Meuheret) (2001)\n704,3.0,1,Volcano High (Whasango) (2001)\n705,1.9166666666666667,6,Bringing Down the House (2003)\n706,2.642857142857143,7,Tears of the Sun (2003)\n707,3.888888888888889,9,Irreversible (Irréversible) (2002)\n708,3.0,3,Laurel Canyon (2002)\n709,3.75,2,Nowhere in Africa (Nirgendwo in Afrika) (2001)\n710,3.0,2,\"Safety of Objects, The (2001)\"\n711,3.3,40,Bend It Like Beckham (2002)\n712,3.0,3,\"Hunted, The (2003)\"\n713,2.5,4,Willard (2003)\n714,3.0,1,Prozac Nation (2001)\n715,3.25,2,Spun (2001)\n716,2.375,4,Boat Trip (2003)\n717,2.642857142857143,7,Dreamcatcher (2003)\n718,2.0,1,Piglet's Big Movie (2003)\n719,2.375,4,View from the Top (2003)\n720,3.75,2,Basic (2003)\n721,2.375,8,\"Core, The (2003)\"\n722,2.25,2,Head of State (2003)\n723,3.0625,8,What a Girl Wants (2003)\n724,2.0,1,Assassination Tango (2002)\n725,3.75,2,Raising Victor Vargas (2002)\n726,4.25,2,Stevie (2002)\n727,4.0,1,\"Good Thief, The (2002)\"\n728,2.25,2,\"Man Apart, A (2003)\"\n729,3.140625,32,Phone Booth (2002)\n730,3.9,20,Cowboy Bebop: The Movie (Cowboy Bebop: Tengoku no Tobira) (2001)\n731,4.0,1,Levity (2003)\n732,3.875,4,\"Man Without a Past, The (Mies vailla menneisyyttä) (2002)\"\n733,3.0588235294117645,34,Anger Management (2003)\n734,3.875,4,Better Luck Tomorrow (2002)\n735,3.5,1,Ghosts of the Abyss (2003)\n736,2.7142857142857144,7,House of 1000 Corpses (2003)\n737,3.5,3,Lilya 4-Ever (Lilja 4-ever) (2002)\n738,2.409090909090909,11,Bulletproof Monk (2003)\n739,2.0,1,Chasing Papi (a.k.a. Papi Chulo) (2003)\n740,3.75,14,\"Mighty Wind, A (2003)\"\n741,3.425,20,Holes (2003)\n742,2.6666666666666665,3,Malibu's Most Wanted (2003)\n743,3.5,9,\"Winged Migration (Peuple migrateur, Le) (2001)\"\n744,4.5,3,Flickering Lights (Blinkende lygter) (2000)\n745,4.166666666666667,3,I Am Trying to Break Your Heart (2002)\n746,2.8333333333333335,6,Confidence (2003)\n747,3.6818181818181817,22,Identity (2003)\n748,2.5,2,It Runs in the Family (2003)\n749,3.5,3,\"Decade Under the Influence, A (2003)\"\n750,3.0,1,Manic (2001)\n751,3.0,2,People I Know (2002)\n752,3.923076923076923,13,Spellbound (2002)\n753,3.0,4,\"Lizzie McGuire Movie, The (2003)\"\n754,3.723684210526316,76,X2: X-Men United (2003)\n755,3.5,3,Blue Car (2002)\n756,3.8333333333333335,3,\"Dancer Upstairs, The (2002)\"\n757,3.0,1,Marooned in Iraq (Gomgashtei dar Aragh) (2002)\n758,3.5,4,Owning Mahowny (2003)\n759,2.3333333333333335,6,Daddy Day Care (2003)\n760,3.875,4,\"Man on the Train (Homme du train, L') (2002)\"\n761,3.25,4,\"Shape of Things, The (2003)\"\n762,2.0,1,\"Trip, The (2002)\"\n763,3.5,1,101 Reykjavik (101 Reykjavík) (2000)\n764,3.3541666666666665,96,\"Matrix Reloaded, The (2003)\"\n765,2.9,10,Down with Love (2003)\n766,4.0,4,Cinemania (2002)\n767,4.0,5,\"Spanish Apartment, The (L'auberge espagnole) (2002)\"\n768,0.5,1,Pokémon Heroes (2003)\n769,3.316901408450704,71,Bruce Almighty (2003)\n770,2.1666666666666665,3,\"In-Laws, The (2003)\"\n771,4.0,4,Gigantic (A Tale of Two Johns) (2002)\n772,3.5,2,Respiro (2002)\n773,3.9609929078014185,141,Finding Nemo (2003)\n774,3.6186440677966103,59,\"Italian Job, The (2003)\"\n775,3.0,2,Wrong Turn (2003)\n776,3.9285714285714284,7,Capturing the Friedmans (2003)\n777,3.0,1,Together (Han ni Zai Yiki) (2002)\n778,2.6052631578947367,19,\"2 Fast 2 Furious (Fast and the Furious 2, The) (2003)\"\n779,3.75,14,Whale Rider (2002)\n780,3.5,1,Murder on a Sunday Morning (Un coupable idéal) (2001)\n781,1.9545454545454546,11,Dumb and Dumberer: When Harry Met Lloyd (2003)\n782,0.8333333333333334,3,From Justin to Kelly (2003)\n783,2.9285714285714284,7,Hollywood Homicide (2003)\n784,2.0,1,Alex and Emma (2003)\n785,3.9741379310344827,58,28 Days Later (2002)\n786,2.3703703703703702,27,Charlie's Angels: Full Throttle (2003)\n787,3.5,1,Fulltime Killer (Chuen jik sat sau) (2001)\n788,2.5606060606060606,33,Hulk (2003)\n789,2.409090909090909,11,\"Legally Blonde 2: Red, White & Blonde (2003)\"\n790,3.6666666666666665,6,Sinbad: Legend of the Seven Seas (2003)\n791,3.0444444444444443,45,Terminator 3: Rise of the Machines (2003)\n792,3.6,5,Swimming Pool (2003)\n793,3.778523489932886,149,Pirates of the Caribbean: The Curse of the Black Pearl (2003)\n794,2.625,32,\"League of Extraordinary Gentlemen, The (a.k.a. LXG) (2003)\"\n795,3.5,1,\"Cuckoo, The (Kukushka) (2002)\"\n796,4.5,1,I Capture the Castle (2003)\n797,2.25,2,Northfork (2003)\n798,2.8823529411764706,17,Bad Boys II (2003)\n799,3.5,3,How to Deal (2003)\n800,2.9615384615384617,13,Johnny English (2003)\n801,4.0,1,\"Anarchist Cookbook, The (2002)\"\n802,3.625,12,Dirty Pretty Things (2002)\n803,4.0,1,\"Embalmer, The (Imbalsamatore, L') (2002)\"\n804,1.25,2,Garage Days (2002)\n805,2.5,1,Masked & Anonymous (2003)\n806,2.6470588235294117,17,Lara Croft Tomb Raider: The Cradle of Life (2003)\n807,3.6944444444444446,18,Seabiscuit (2003)\n808,2.0,3,Spy Kids 3-D: Game Over (2003)\n809,3.5,3,Buffalo Soldiers (2001)\n810,3.25,2,Camp (2003)\n811,4.0,1,\"Mondays in the Sun (Lunes al sol, Los) (2002)\"\n812,3.5,2,Scorched (2003)\n813,3.09375,16,American Wedding (American Pie 3) (2003)\n814,1.5,1,Gigli (2003)\n815,4.0,1,And Now... Ladies and Gentlemen... (2002)\n816,4.333333333333333,6,\"Magdalene Sisters, The (2002)\"\n817,4.0,1,\"Secret Lives of Dentists, The (2002)\"\n818,3.2083333333333335,24,Freaky Friday (2003)\n819,3.3214285714285716,14,S.W.A.T. (2003)\n820,3.5,1,\"Divorce, Le (2003)\"\n821,3.0,1,\"Princess Blade, The (Shura Yukihime) (2001)\"\n822,3.5,1,Step Into Liquid (2002)\n823,2.3333333333333335,9,Freddy vs. Jason (2003)\n824,2.75,2,Grind (2003)\n825,3.9444444444444446,9,Open Range (2003)\n826,3.823529411764706,17,Shaolin Soccer (Siu lam juk kau) (2001)\n827,2.9166666666666665,6,Uptown Girls (2003)\n828,3.8333333333333335,18,American Splendor (2003)\n829,1.9,5,Agent Cody Banks (2003)\n830,2.25,2,Comic Book Villains (2002)\n831,4.0,1,Revolution OS (2001)\n832,2.7,5,\"Medallion, The (2003)\"\n833,2.0,4,My Boss's Daughter (2003)\n834,3.5,1,Autumn Spring (Babí léto) (2001)\n835,4.0,1,\"Battle of Shaker Heights, The (2003)\"\n836,2.5,1,Dust (2001)\n837,2.0,1,Stoked: The Rise and Fall of Gator (2002)\n838,1.875,4,Jeepers Creepers 2 (2003)\n839,2.5,1,Bollywood/Hollywood (2002)\n840,3.0,1,Once Upon a Time in the Midlands (2002)\n841,2.3,5,Dickie Roberts: Former Child Star (2003)\n842,2.5,1,Party Monster (2003)\n843,3.5,1,Taking Sides (2001)\n844,2.875,8,Cabin Fever (2002)\n845,3.7205882352941178,34,Matchstick Men (2003)\n846,3.2142857142857144,14,Once Upon a Time in Mexico (2003)\n847,3.625,4,Dummy (2002)\n848,4.033783783783784,74,Lost in Translation (2003)\n849,3.8,5,Millennium Actress (Sennen joyû) (2001)\n850,2.8333333333333335,3,Anything Else (2003)\n851,2.5,1,Cold Creek Manor (2003)\n852,3.5,1,\"Fighting Temptations, The (2003)\"\n853,3.5,12,Secondhand Lions (2003)\n854,3.5,33,Underworld (2003)\n855,3.3214285714285716,14,Bubba Ho-tep (2002)\n856,3.0,2,In This World (2002)\n857,2.4285714285714284,7,Duplex (2003)\n858,3.1470588235294117,17,\"Rundown, The (2003)\"\n859,3.2777777777777777,9,Under the Tuscan Sun (2003)\n860,4.0,1,Luther (2003)\n861,3.0,1,Mambo Italiano (2003)\n862,3.5,2,My Life Without Me (2003)\n863,3.0,1,To Be and to Have (Être et avoir) (2002)\n864,3.7045454545454546,22,\"Triplets of Belleville, The (Les triplettes de Belleville) (2003)\"\n865,4.0,1,Life and Debt (2001)\n866,4.166666666666667,3,Lagaan: Once Upon a Time in India (2001)\n867,3.5,3,Avalon (2001)\n868,4.0,6,Ginger Snaps (2000)\n869,3.5,2,Out of Time (2003)\n870,3.4015151515151514,66,School of Rock (2003)\n871,3.5833333333333335,12,\"Station Agent, The (2003)\"\n872,3.4285714285714284,7,Wonderland (2003)\n873,3.8333333333333335,3,Bus 174 (Ônibus 174) (2002)\n874,3.7115384615384617,52,Mystic River (2003)\n875,1.125,4,\"House of the Dead, The (2003)\"\n876,3.175,20,Intolerable Cruelty (2003)\n877,3.9618320610687023,131,Kill Bill: Vol. 1 (2003)\n878,3.4411764705882355,17,Runaway Jury (2003)\n879,1.8571428571428572,7,\"Texas Chainsaw Massacre, The (2003)\"\n880,3.0714285714285716,7,Pieces of April (2003)\n881,2.5,1,Returner (Ritaanaa) (2002)\n882,3.0,1,Sylvia (2003)\n883,3.0,2,Veronica Guerin (2003)\n884,2.8333333333333335,3,In the Cut (2003)\n885,3.5,1,Beyond Borders (2003)\n886,4.1,5,Radio (2003)\n887,2.275,20,Scary Movie 3 (2003)\n888,3.0,2,Brother Bear (2003)\n889,3.5555555555555554,9,Elephant (2003)\n890,3.3333333333333335,3,Sweet Sixteen (2002)\n891,4.0,2,Interstate 60 (2002)\n892,3.25,2,\"Eye, The (Gin gwai) (Jian gui) (2002)\"\n893,4.0,1,\"Human Stain, The (2003)\"\n894,3.5,7,Shattered Glass (2003)\n895,3.151898734177215,79,\"Matrix Revolutions, The (2003)\"\n896,4.0,1,\"Revolution Will Not Be Televised, The (a.k.a. Chavez: Inside the Coup) (2003)\"\n897,3.6923076923076925,39,Elf (2003)\n898,4.0,1,Billabong Odyssey (2003)\n899,3.788135593220339,59,Love Actually (2003)\n900,4.25,2,My Architect: A Son's Journey (2003)\n901,2.75,2,Looney Tunes: Back in Action (2003)\n902,3.638888888888889,36,Master and Commander: The Far Side of the World (2003)\n903,4.0,1,Tupac: Resurrection (2003)\n904,3.5,1,\"Big Empty, The (2003)\"\n905,3.6666666666666665,3,\"Missing, The (2003)\"\n906,2.1,5,\"Cat in the Hat, The (2003)\"\n907,3.111111111111111,9,Gothika (2003)\n908,3.3,25,21 Grams (2003)\n909,4.0,3,\"Barbarian Invasions, The (Les invasions barbares) (2003)\"\n910,3.3653846153846154,26,Bad Santa (2003)\n911,2.5,10,\"Haunted Mansion, The (2003)\"\n912,2.0,3,Timeline (2003)\n913,3.5,1,OT: Our Town (2002)\n914,4.25,2,Devil's Playground (2002)\n915,3.5,1,Journeys with George (2002)\n916,3.933333333333333,30,Battle Royale (Batoru rowaiaru) (2000)\n917,4.0,1,Things You Can Tell Just by Looking at Her (2000)\n918,3.9210526315789473,38,Hero (Ying xiong) (2002)\n919,5.0,1,Rivers and Tides (2001)\n920,3.55,10,\"Cooler, The (2003)\"\n921,3.857142857142857,7,In America (2002)\n922,4.0,1,My Flesh and Blood (2003)\n923,3.0,3,Honey (2003)\n924,3.903225806451613,62,\"Last Samurai, The (2003)\"\n925,3.8333333333333335,69,Big Fish (2003)\n926,3.6470588235294117,17,Something's Gotta Give (2003)\n927,2.8,10,Stuck on You (2003)\n928,3.375,12,Girl with a Pearl Earring (2003)\n929,4.118918918918919,185,\"Lord of the Rings: The Return of the King, The (2003)\"\n930,2.8,10,Mona Lisa Smile (2003)\n931,2.625,8,Calendar Girls (2003)\n932,4.3076923076923075,13,\"Fog of War: Eleven Lessons from the Life of Robert S. McNamara, The (2003)\"\n933,3.7777777777777777,9,House of Sand and Fog (2003)\n934,3.5294117647058822,17,Monster (2003)\n935,1.9375,8,Cheaper by the Dozen (2003)\n936,3.2916666666666665,12,Cold Mountain (2003)\n937,2.911764705882353,17,Paycheck (2003)\n938,3.0833333333333335,6,Peter Pan (2003)\n939,3.0,1,\"Company, The (2003)\"\n940,3.0,1,Japanese Story (2003)\n941,3.9375,8,Chasing Liberty (2004)\n942,3.5,2,Aileen: Life and Death of a Serial Killer (2003)\n943,3.0416666666666665,24,Along Came Polly (2004)\n944,2.25,2,Torque (2004)\n945,3.5,1,Crimson Gold (Talaye sorgh) (2003)\n946,3.5,1,Osama (2003)\n947,3.0,1,Beyond Re-Animator (2003)\n948,3.4166666666666665,6,Ichi the Killer (Koroshiya 1) (2001)\n949,3.5,1,\"Suriyothai (a.k.a. Legend of Suriyothai, The) (2001)\"\n950,3.630434782608696,46,The Butterfly Effect (2004)\n951,2.8125,8,Win a Date with Tad Hamilton! (2004)\n952,4.0,8,Touching the Void (2003)\n953,2.0,4,\"Big Bounce, The (2004)\"\n954,2.75,4,\"Perfect Score, The (2004)\"\n955,2.125,4,You Got Served (2004)\n956,3.5,1,Latter Days (2003)\n957,2.1666666666666665,3,Barbershop 2: Back in Business (2004)\n958,2.0,1,Catch That Kid (2004)\n959,3.35,10,Miracle (2004)\n960,4.0,1,An Amazing Couple (2002)\n961,3.730769230769231,13,\"Dreamers, The (2003)\"\n962,4.0,1,\"Lost Skeleton of Cadavra, The (2002)\"\n963,1.0,1,\"Hip Hop Witch, Da (2000)\"\n964,3.611111111111111,9,Thirteen (2003)\n965,3.5531914893617023,47,50 First Dates (2004)\n966,3.25,4,Welcome to Mooseport (2004)\n967,3.6666666666666665,3,Kitchen Stories (Salmer fra kjøkkenet) (2003)\n968,3.6666666666666665,3,Monsieur Ibrahim (Monsieur Ibrahim et les fleurs du Coran) (2003)\n969,3.0,1,\"Herod's Law (Ley de Herodes, La) (2000)\"\n970,2.0,2,Against the Ropes (2004)\n971,2.375,4,Confessions of a Teenage Drama Queen (2004)\n972,3.1333333333333333,15,EuroTrip (2004)\n973,2.6153846153846154,13,\"Passion of the Christ, The (2004)\"\n974,2.0833333333333335,6,Club Dread (2004)\n975,3.4,5,Dirty Dancing: Havana Nights (2004)\n976,2.75,2,Twisted (2004)\n977,3.8043478260869565,23,\"Good bye, Lenin! (2003)\"\n978,3.3181818181818183,11,Hidalgo (2004)\n979,3.2857142857142856,21,Starsky & Hutch (2004)\n980,3.5,1,\"Reckoning, The (2004)\"\n981,4.0,1,Agent Cody Banks 2: Destination London (2004)\n982,3.125,20,\"Girl Next Door, The (2004)\"\n983,3.3,15,Secret Window (2004)\n984,3.5,3,Spartan (2004)\n985,3.0,1,Broken Wings (Knafayim Shvurot) (2002)\n986,4.0,2,Wilbur Wants to Kill Himself (2002)\n987,3.90625,16,Dawn of the Dead (2004)\n988,4.1603053435114505,131,Eternal Sunshine of the Spotless Mind (2004)\n989,3.25,4,Taking Lives (2004)\n990,4.0,1,Intermission (2003)\n991,2.9375,8,Jersey Girl (2004)\n992,2.5714285714285716,14,\"Ladykillers, The (2004)\"\n993,3.5,1,Never Die Alone (2004)\n994,2.25,4,Scooby-Doo 2: Monsters Unleashed (2004)\n995,4.025,20,Dogville (2003)\n996,3.0,1,Ned Kelly (2003)\n997,3.3780487804878048,41,Hellboy (2004)\n998,3.0714285714285716,7,\"Prince & Me, The (2004)\"\n999,3.0,6,Walking Tall (2004)\n1000,3.5,1,\"United States of Leland, The (2003)\"\n1001,3.5,6,The Alamo (2004)\n1002,3.357142857142857,7,Ella Enchanted (2004)\n1003,3.142857142857143,7,\"Whole Ten Yards, The (2004)\"\n1004,4.25,2,I'm Not Scared (Io non ho paura) (2003)\n1005,1.5,1,Prey for Rock & Roll (2003)\n1006,3.868181818181818,110,Kill Bill: Vol. 2 (2004)\n1007,3.5,14,\"Punisher, The (2004)\"\n1008,3.25,2,Paper Clips (2004)\n1009,3.5,1,This So-Called Disaster (2003)\n1010,3.1904761904761907,21,13 Going on 30 (2004)\n1011,3.6285714285714286,35,Man on Fire (2004)\n1012,2.75,4,Envy (2004)\n1013,2.5,2,Godsend (2004)\n1014,2.75,4,Laws of Attraction (2004)\n1015,3.7564102564102564,39,Mean Girls (2004)\n1016,2.7083333333333335,24,Van Helsing (2004)\n1017,3.0,1,\"Mudge Boy, The (2003)\"\n1018,3.5,1,Breakin' All the Rules (2004)\n1019,3.4270833333333335,48,Troy (2004)\n1020,4.5,1,Carandiru (2003)\n1021,3.5833333333333335,6,Coffee and Cigarettes (2003)\n1022,2.5,1,Eye See You (D-Tox) (2002)\n1023,3.25,4,100 Girls (2000)\n1024,4.5,1,Wit (2001)\n1025,3.0,2,Rose Red (2002)\n1026,3.75,2,Versus (2000)\n1027,4.75,2,\"Best of Youth, The (La meglio gioventù) (2003)\"\n1028,4.0,2,\"Bang, Bang, You're Dead (2002)\"\n1029,4.0,2,\"11'09\"\"01 - September 11 (2002)\"\n1030,3.75,2,Children of Dune (2003)\n1031,4.375,4,Dune (2000)\n1032,4.5,1,Lammbock (2001)\n1033,3.0,3,Tremors 3: Back to Perfection (2001)\n1034,3.5,1,Notorious C.H.O. (2002)\n1035,3.6666666666666665,3,Dark Days (2000)\n1036,4.5,1,Ken Park (2002)\n1037,4.333333333333333,9,Infernal Affairs (Mou gaan dou) (2002)\n1038,3.25,4,\"Tale of Two Sisters, A (Janghwa, Hongryeon) (2003)\"\n1039,4.0,4,\"Weather Underground, The (2002)\"\n1040,4.25,10,\"Spring, Summer, Fall, Winter... and Spring (Bom yeoreum gaeul gyeoul geurigo bom) (2003)\"\n1041,4.0,1,Dark Water (Honogurai mizu no soko kara) (2002)\n1042,2.0,2,\"Seducing Doctor Lewis (Grande séduction, La) (2003)\"\n1043,3.5,1,Pursuit of Happiness (2001)\n1044,4.25,2,Dolls (2002)\n1045,3.5760869565217392,92,Shrek 2 (2004)\n1046,3.05,50,\"Day After Tomorrow, The (2004)\"\n1047,2.75,4,Raising Helen (2004)\n1048,3.75,2,Soul Plane (2004)\n1049,3.375,4,Baadasssss! (How to Get the Man's Foot Outta Your Ass) (2003)\n1050,3.892857142857143,14,Saved! (2004)\n1051,3.913978494623656,93,Harry Potter and the Prisoner of Azkaban (2004)\n1052,2.6666666666666665,3,Mindhunters (2004)\n1053,3.9583333333333335,12,\"Blind Swordsman: Zatoichi, The (Zatôichi) (2003)\"\n1054,3.3043478260869565,23,\"Chronicles of Riddick, The (2004)\"\n1055,2.3,5,Garfield: The Movie (2004)\n1056,2.25,12,\"Stepford Wives, The (2004)\"\n1057,2.5,1,\"Hunting of the President, The (2004)\"\n1058,3.324324324324324,37,Napoleon Dynamite (2004)\n1059,1.0,1,Hope Springs (2003)\n1060,3.51,50,Super Size Me (2004)\n1061,2.5,1,Animal Factory (2000)\n1062,2.25,2,Fear X (2003)\n1063,2.5,3,Around the World in 80 Days (2004)\n1064,3.551282051282051,39,Dodgeball: A True Underdog Story (2004)\n1065,3.3191489361702127,47,\"Terminal, The (2004)\"\n1066,3.75,2,Dear Frankie (2004)\n1067,2.8333333333333335,6,White Chicks (2004)\n1068,2.5,1,\"Door in the Floor, The (2004)\"\n1069,3.5657894736842106,38,\"Notebook, The (2004)\"\n1070,3.5,1,Two Brothers (Deux frères) (2004)\n1071,4.0,1,De-Lovely (2004)\n1072,3.0,1,\"Happenstance (Battement d'ailes du papillon, Le) (2001)\"\n1073,4.0,1,Comandante (2003)\n1074,3.25,2,Undead (2003)\n1075,3.5,1,Mayor of the Sunset Strip (2003)\n1076,4.0,1,Killing Me Softly (2002)\n1077,3.25,2,Taxi 3 (2003)\n1078,3.9,5,Tokyo Godfathers (2003)\n1079,3.4864864864864864,37,Fahrenheit 9/11 (2004)\n1080,0.5,1,Secret Society (2002)\n1081,3.8037974683544302,79,Spider-Man 2 (2004)\n1082,3.7,15,Before Sunset (2004)\n1083,2.9615384615384617,13,King Arthur (2004)\n1084,3.7719298245614037,57,Anchorman: The Legend of Ron Burgundy (2004)\n1085,3.2777777777777777,9,\"Cinderella Story, A (2004)\"\n1086,3.4918032786885247,61,\"I, Robot (2004)\"\n1087,3.9375,8,\"Maria Full of Grace (Maria, Llena eres de gracia) (2004)\"\n1088,3.7866666666666666,75,\"Bourne Supremacy, The (2004)\"\n1089,1.3333333333333333,9,Catwoman (2004)\n1090,3.5,1,A Home at the End of the World (2004)\n1091,4.0,2,To End All Wars (2001)\n1092,3.5,1,Unprecedented: The 2000 Presidential Election (2002)\n1093,3.0416666666666665,12,\"Manchurian Candidate, The (2004)\"\n1094,2.75,2,Thunderbirds (2004)\n1095,3.1923076923076925,26,\"Village, The (2004)\"\n1096,3.7083333333333335,48,Garden State (2004)\n1097,4.5,1,Musa the Warrior (Musa) (2001)\n1098,3.7613636363636362,44,Collateral (2004)\n1099,2.1666666666666665,3,Little Black Book (2004)\n1100,4.5,1,Code 46 (2003)\n1101,3.4558823529411766,34,Harold and Kumar Go to White Castle (2004)\n1102,2.5,7,\"Princess Diaries 2: Royal Engagement, The (2004)\"\n1103,3.25,2,Danny Deckchair (2003)\n1104,2.823529411764706,17,AVP: Alien vs. Predator (2004)\n1105,4.0,2,We Don't Live Here Anymore (2004)\n1106,2.0,7,Without a Paddle (2004)\n1107,2.5,3,Exorcist: The Beginning (2004)\n1108,1.75,2,Anacondas: The Hunt for the Blood Orchid (2004)\n1109,3.5,1,Suspect Zero (2004)\n1110,3.25,2,Warriors of Heaven and Earth (Tian di ying xiong) (2003)\n1111,3.5,3,Vanity Fair (2004)\n1112,2.3333333333333335,3,Paparazzi (2004)\n1113,3.3333333333333335,3,Wicker Park (2004)\n1114,2.857142857142857,7,Cellular (2004)\n1115,2.9285714285714284,14,Resident Evil: Apocalypse (2004)\n1116,2.6666666666666665,3,Mr. 3000 (2004)\n1117,2.638888888888889,18,Sky Captain and the World of Tomorrow (2004)\n1118,3.111111111111111,9,Wimbledon (2004)\n1119,3.3333333333333335,6,First Daughter (2004)\n1120,2.5,5,\"Forgotten, The (2004)\"\n1121,3.7962962962962963,27,\"Motorcycle Diaries, The (Diarios de motocicleta) (2004)\"\n1122,4.0064935064935066,77,Shaun of the Dead (2004)\n1123,2.3461538461538463,13,Shark Tale (2004)\n1124,3.6666666666666665,6,Ladder 49 (2004)\n1125,3.4523809523809526,21,I Heart Huckabees (2004)\n1126,5.0,1,Raise Your Voice (2004)\n1127,1.75,2,Taxi (2004)\n1128,3.7941176470588234,17,Primer (2004)\n1129,3.5,1,Stage Beauty (2004)\n1130,3.0,3,Shall We Dance? (2004)\n1131,3.46875,32,Team America: World Police (2004)\n1132,3.0,1,Eulogy (2004)\n1133,4.25,2,P.S. (2004)\n1134,3.5625,8,Friday Night Lights (2004)\n1135,4.0,1,Tarnation (2003)\n1136,3.6666666666666665,3,\"Final Cut, The (2004)\"\n1137,1.75,2,Being Julia (2004)\n1138,2.75,2,Surviving Christmas (2004)\n1139,2.3,10,\"Grudge, The (2004)\"\n1140,2.3333333333333335,3,Alfie (2004)\n1141,3.7435897435897436,39,Sideways (2004)\n1142,3.986842105263158,38,The Machinist (2004)\n1143,3.0,4,Vera Drake (2004)\n1144,3.5,1,Falling Angels (2003)\n1145,4.0,1,Lightning in a Bottle (2004)\n1146,3.0,1,Undertow (2004)\n1147,3.1818181818181817,33,Saw (2004)\n1148,3.6842105263157894,19,Ray (2004)\n1149,2.5,3,Birth (2004)\n1150,3.836,125,\"Incredibles, The (2004)\"\n1151,3.0,1,Callas Forever (2002)\n1152,3.1363636363636362,11,\"Polar Express, The (2004)\"\n1153,3.125,8,Kinsey (2004)\n1154,1.75,4,Seed of Chucky (Child's Play 5) (2004)\n1155,3.5833333333333335,6,After the Sunset (2004)\n1156,3.025,20,Bridget Jones: The Edge of Reason (2004)\n1157,3.609375,32,Finding Neverland (2004)\n1158,3.263157894736842,38,National Treasure (2004)\n1159,3.75,8,Bad Education (La mala educación) (2004)\n1160,3.0,2,\"SpongeBob SquarePants Movie, The (2004)\"\n1161,2.2,10,Alexander (2004)\n1162,2.5,2,Christmas with the Kranks (2004)\n1163,4.0,1,Guerrilla: The Taking of Patty Hearst (2004)\n1164,3.71875,16,Closer (2004)\n1165,3.0,3,I Am David (2003)\n1166,3.52,25,House of Flying Daggers (Shi mian mai fu) (2004)\n1167,3.2906976744186047,43,Ocean's Twelve (2004)\n1168,2.857142857142857,14,Blade: Trinity (2004)\n1169,3.0,1,Bush's Brain (2004)\n1170,4.1,5,Love Me If You Dare (Jeux d'enfants) (2003)\n1171,4.333333333333333,3,Control Room (2004)\n1172,4.0,1,Dark Portals: The Chronicles of Vidocq  (Vidocq) (2001)\n1173,4.0,1,In July (Im Juli) (2000)\n1174,3.875,4,Taxi 2 (2000)\n1175,4.0,1,If These Walls Could Talk 2 (2000)\n1176,2.75,2,\"10th Kingdom, The (2000)\"\n1177,4.071428571428571,7,2046 (2004)\n1178,1.5,1,Bartleby (2001)\n1179,3.5,3,Batman Beyond: Return of the Joker (2000)\n1180,5.0,1,\"Nine Lives of Tomas Katz, The (2000)\"\n1181,4.5,1,Monday (2000)\n1182,4.0,1,Paradise Lost 2: Revelations (2000)\n1183,4.0,3,Asterix & Obelix: Mission Cleopatra (Astérix & Obélix: Mission Cléopâtre) (2002)\n1184,4.0,1,Daria: Is It Fall Yet? (2000)\n1185,4.5,1,Late Night Shopping (2001)\n1186,5.0,1,61* (2001)\n1187,4.25,4,Joint Security Area (Gongdong gyeongbi guyeok JSA) (2000)\n1188,3.3333333333333335,3,Ripley's Game (2002)\n1189,3.75,2,Jalla! Jalla! (2000)\n1190,2.0,1,Teknolust (2002)\n1191,2.5,1,\"Accidental Spy, The (Dak miu mai shing) (2001)\"\n1192,1.5,3,Darkness (2002)\n1193,3.25,2,Blood: The Last Vampire (2000)\n1194,2.5,1,Blueberry (2004)\n1195,2.5,1,American Psycho II: All American Girl (2002)\n1196,3.0,3,Ali G Indahouse (2002)\n1197,4.5,1,Dead or Alive 2: Tôbôsha (2000)\n1198,3.75,2,Cube 2: Hypercube (2002)\n1199,3.0,1,Pulse (Kairo) (2001)\n1200,3.0,1,Dog Days (Hundstage) (2001)\n1201,5.0,1,My Sassy Girl (Yeopgijeogin geunyeo) (2001)\n1202,3.5,1,Nothing (2003)\n1203,2.5,1,Undertaking Betty (Plots with a View) (2002)\n1204,1.5,1,Dead or Alive: Final (2002)\n1205,3.75,2,Fubar (2002)\n1206,4.333333333333333,3,\"Happiness of the Katakuris, The (Katakuri-ke no kôfuku) (2001)\"\n1207,3.5,2,Dead End (2003)\n1208,3.8,5,Sympathy for Mr. Vengeance (Boksuneun naui geot) (2002)\n1209,0.5,1,Jesus Christ Vampire Hunter (2001)\n1210,3.125,4,Suicide Club (Jisatsu saakuru) (2001)\n1211,3.8157894736842106,19,Battlestar Galactica (2003)\n1212,2.75,2,\"Sound of Thunder, A (2005)\"\n1213,4.5,2,\"Lion King 1½, The (2004)\"\n1214,4.5,1,Oasis (2002)\n1215,3.5,1,Remember Me (Ricordati di me) (2003)\n1216,3.7,20,\"Animatrix, The (2003)\"\n1217,3.25,2,\"Brown Bunny, The (2003)\"\n1218,3.0,1,Ju-on: The Curse (2000)\n1219,3.75,4,11:14 (2003)\n1220,2.0,1,Tremors 4: The Legend Begins (2004)\n1221,1.75,2,Bring It On Again (2004)\n1222,2.5,1,\"Crimson Rivers 2: Angels of the Apocalypse (Rivières pourpres II - Les anges de l'apocalypse, Les) (2004)\"\n1223,3.5,1,And Starring Pancho Villa as Himself (2003)\n1224,3.0,1,Nicotina (2003)\n1225,5.0,1,Battle Royale 2: Requiem (Batoru rowaiaru II: Chinkonka) (2003)\n1226,1.5,1,In Hell (2003)\n1227,3.16,25,Lemony Snicket's A Series of Unfortunate Events (2004)\n1228,4.0,1,Helen of Troy (2003)\n1229,4.5,1,\"Green Butchers, The (Grønne slagtere, De) (2003)\"\n1230,3.6666666666666665,6,\"Very Long Engagement, A (Un long dimanche de fiançailles) (2004)\"\n1231,4.166666666666667,3,Last Life in the Universe (Ruang rak noi nid mahasan) (2003)\n1232,2.1666666666666665,3,Ghost in the Shell 2: Innocence (a.k.a. Innocence) (Inosensu) (2004)\n1233,3.75,8,\"Cat Returns, The (Neko no ongaeshi) (2002)\"\n1234,3.8333333333333335,6,\"Twilight Samurai, The (Tasogare Seibei) (2002)\"\n1235,4.5,1,\"Facing Windows (Finestra di fronte, La) (2003)\"\n1236,3.0,1,Ginger Snaps: Unleashed (2004)\n1237,5.0,1,'Salem's Lot (2004)\n1238,2.5,1,Comic Book: The Movie (2004)\n1239,3.75,2,Intimate Strangers (Confidences trop intimes) (2004)\n1240,3.0,1,Down to the Bone (2004)\n1241,2.5833333333333335,6,Ju-on: The Grudge (2002)\n1242,4.089743589743589,39,Old Boy (2003)\n1243,4.5,1,Red Lights (Feux rouges) (2004)\n1244,3.0,1,Ginger Snaps Back: The Beginning (2004)\n1245,1.5,1,One Missed Call (Chakushin ari) (2003)\n1246,3.0,8,\"Jacket, The (2005)\"\n1247,3.5,4,Millions (2004)\n1248,1.5,2,Starship Troopers 2: Hero of the Federation (2004)\n1249,4.0,12,Ong-Bak: The Thai Warrior (Ong Bak) (2003)\n1250,3.0,1,Infernal Affairs 2 (Mou gaan dou II) (2003)\n1251,4.0,8,\"Sea Inside, The (Mar adentro) (2004)\"\n1252,3.3636363636363638,11,Spanglish (2004)\n1253,3.909090909090909,11,\"Chorus, The (Choristes, Les) (2004)\"\n1254,2.5,1,Saints and Soldiers (2003)\n1255,4.0,2,\"Story of the Weeping Camel, The (Geschichte vom weinenden Kamel, Die) (2003)\"\n1256,3.25,6,\"Interpreter, The (2005)\"\n1257,2.7,5,Open Water (2003)\n1258,4.0,1,Touch of Pink (2004)\n1259,3.5,1,Slasher (2004)\n1260,2.5,1,\"Bobby Jones, Stroke of Genius (2004)\"\n1261,3.8157894736842106,19,Layer Cake (2004)\n1262,4.333333333333333,3,\"Return, The (Vozvrashcheniye) (2003)\"\n1263,2.875,4,Flight of the Phoenix (2004)\n1264,3.75,2,Mean Creek (2004)\n1265,2.3,5,\"Ring Two, The (2005)\"\n1266,3.9,10,\"Corporation, The (2003)\"\n1267,3.75,2,\"Yes Men, The (2003)\"\n1268,2.75,2,Azumi (2003)\n1269,3.5,1,In My Father's Den (2004)\n1270,4.5,1,Tae Guk Gi: The Brotherhood of War (Taegukgi hwinalrimyeo) (2004)\n1271,3.1666666666666665,3,Metallica: Some Kind of Monster (2004)\n1272,3.875,4,Born into Brothels (2004)\n1273,4.125,4,DiG! (2004)\n1274,4.0,4,Riding Giants (2004)\n1275,2.75,2,What the #$*! Do We Know!? (a.k.a. What the Bleep Do We Know!?) (2004)\n1276,3.6136363636363638,22,\"Scanner Darkly, A (2006)\"\n1277,3.5,1,Casshern (2004)\n1278,3.3,5,Outfoxed: Rupert Murdoch's War on Journalism (2004)\n1279,3.8461538461538463,52,Million Dollar Baby (2004)\n1280,4.75,2,Gozu (Gokudô kyôfu dai-gekijô: Gozu) (2003)\n1281,3.7916666666666665,48,Hotel Rwanda (2004)\n1282,3.0,61,Charlie and the Chocolate Factory (2005)\n1283,4.5,3,3-Iron (Bin-jip) (2004)\n1284,3.4864864864864864,37,\"Life Aquatic with Steve Zissou, The (2004)\"\n1285,3.5285714285714285,35,\"Aviator, The (2004)\"\n1286,3.1666666666666665,12,\"Phantom of the Opera, The (2004)\"\n1287,2.0,1,Beyond the Sea (2004)\n1288,3.8333333333333335,3,\"Woodsman, The (2004)\"\n1289,3.4545454545454546,11,In Good Company (2004)\n1290,3.106060606060606,33,Meet the Fockers (2004)\n1291,3.5,2,\"Assassination of Richard Nixon, The (2004)\"\n1292,3.1666666666666665,3,\"Love Song for Bobby Long, A (2004)\"\n1293,3.75,4,\"Merchant of Venice, The (2004)\"\n1294,1.5,2,Fat Albert (2004)\n1295,3.0,1,\"Keys to the House, The (Chiavi di casa, Le) (2004)\"\n1296,2.5,8,White Noise (2005)\n1297,3.1666666666666665,3,\"Upside of Anger, The (2005)\"\n1298,3.5,2,Stander (2003)\n1299,3.0,2,Imaginary Heroes (2004)\n1300,2.5,1,Ruby & Quentin (Tais-toi!) (2003)\n1301,3.0,1,\"Life and Death of Peter Sellers, The (2004)\"\n1302,3.8333333333333335,3,Appleseed (Appurushîdo) (2004)\n1303,2.05,10,Elektra (2005)\n1304,2.0,1,Racing Stripes (2005)\n1305,3.0714285714285716,7,Coach Carter (2005)\n1306,4.7,5,Memories of Murder (Salinui chueok) (2003)\n1307,3.875,20,\"Downfall (Untergang, Der) (2004)\"\n1308,3.1875,8,Assault on Precinct 13 (2005)\n1309,0.5,1,Are We There Yet? (2005)\n1310,0.5,1,Alone in the Dark (2005)\n1311,2.3333333333333335,6,Hide and Seek (2005)\n1312,2.0,2,Boogeyman (2005)\n1313,3.1363636363636362,11,\"Wedding Date, The (2005)\"\n1314,4.5,1,Rory O'Shea Was Here (Inside I'm Dancing) (2004)\n1315,4.25,2,Nobody Knows (Dare mo shiranai) (2004)\n1316,2.25,2,Employee of the Month (2004)\n1317,4.0,1,Purple Butterfly (Zi hudie) (2003)\n1318,4.075,40,Howl's Moving Castle (Hauru no ugoku shiro) (2004)\n1319,3.0,2,Steamboy (Suchîmubôi) (2004)\n1320,3.1666666666666665,45,Hitch (2005)\n1321,0.5,1,Uncle Nino (2003)\n1322,3.25,2,Bride & Prejudice (2004)\n1323,3.4714285714285715,35,Constantine (2005)\n1324,1.0,1,Son of the Mask (2005)\n1325,3.1666666666666665,3,Because of Winn-Dixie (2005)\n1326,4.0,1,Turtles Can Fly (Lakposhtha hâm parvaz mikonand) (2004)\n1327,3.25,4,Night Watch (Nochnoy dozor) (2004)\n1328,3.0,1,Man of the House (2005)\n1329,3.5789473684210527,19,Kung Fu Hustle (Gong fu) (2004)\n1330,4.0,1,Zelary (2003)\n1331,3.5,1,Control (Kontroll) (2003)\n1332,2.6666666666666665,3,Tyler Perry's Diary of a Mad Black Woman (2005)\n1333,2.1666666666666665,6,Cursed (2005)\n1334,2.7142857142857144,7,\"Pacifier, The (2005)\"\n1335,2.4375,8,Be Cool (2005)\n1336,3.6666666666666665,3,Gunner Palace (2004)\n1337,3.05,10,Hostage (2005)\n1338,3.026315789473684,19,Robots (2005)\n1339,2.6666666666666665,3,Cube Zero (2004)\n1340,3.5,1,Stealing Rembrandt (Rembrandt) (2003)\n1341,3.375,4,Ice Princess (2005)\n1342,3.1,5,Melinda and Melinda (2004)\n1343,3.5,1,Milk and Honey (2003)\n1344,2.25,8,Miss Congeniality 2: Armed and Fabulous (2005)\n1345,2.3333333333333335,3,Guess Who (2005)\n1346,2.8333333333333335,3,D.E.B.S. (2004)\n1347,4.0,1,\"League of Ordinary Gentlemen, A (2004)\"\n1348,3.5,1,Incident at Loch Ness (2004)\n1349,2.5,1,800 Bullets (800 Balas) (2002)\n1350,4.75,2,\"Wild Parrots of Telegraph Hill, The (2003)\"\n1351,3.125,4,\"Ballad of Jack and Rose, The (2005)\"\n1352,3.857142857142857,84,Sin City (2005)\n1353,2.0,1,Beauty Shop (2005)\n1354,3.0714285714285716,7,Sahara (2005)\n1355,2.9375,8,Fever Pitch (2005)\n1356,3.5,1,Eros (2004)\n1357,3.5,1,Not on the Lips (Pas sur la bouche) (2003)\n1358,2.5,1,Tanguy (2001)\n1359,1.0,1,National Lampoon's Lady Killers (National Lampoon's Gold Diggers) (2003)\n1360,3.0,1,Ringu 0: Bâsudei (2000)\n1361,4.25,2,Brothers (Brødre) (2004)\n1362,3.0,1,Carrie (2002)\n1363,3.4204545454545454,44,\"Hitchhiker's Guide to the Galaxy, The (2005)\"\n1364,2.25,6,\"Amityville Horror, The (2005)\"\n1365,4.0,2,Mutant Aliens (2001)\n1366,3.5,1,Before the Fall (NaPolA - Elite für den Führer) (2004)\n1367,1.5,1,State Property 2 (2005)\n1368,5.0,1,Palindromes (2004)\n1369,3.0,6,\"Lot Like Love, A (2005)\"\n1370,2.5,1,King's Ransom (2005)\n1371,4.0,6,Enron: The Smartest Guys in the Room (2005)\n1372,2.0,5,xXx: State of the Union (2005)\n1373,3.5,17,Kingdom of Heaven (2005)\n1374,1.8333333333333333,6,House of Wax (2005)\n1375,3.89,50,Crash (2004)\n1376,2.0,2,Mysterious Skin (2004)\n1377,4.5,1,\"Common Thread, A (a.k.a. Sequins) (Brodeuses) (2004)\"\n1378,3.6666666666666665,3,Dear Wendy (2005)\n1379,3.5,1,Los Angeles Plays Itself (2003)\n1380,3.4444444444444446,9,Unleashed (Danny the Dog) (2005)\n1381,3.4294871794871793,78,Star Wars: Episode III - Revenge of the Sith (2005)\n1382,2.642857142857143,7,Kicking & Screaming (2005)\n1383,2.5,4,Monster-in-Law (2005)\n1384,3.0,1,\"Snow Walker, The (2003)\"\n1385,3.375,40,Madagascar (2005)\n1386,3.3333333333333335,3,Mad Hot Ballroom (2005)\n1387,2.5,1,Dominion: Prequel to the Exorcist (2005)\n1388,3.111111111111111,9,\"Longest Yard, The (2005)\"\n1389,5.0,1,Saving Face (2004)\n1390,4.088235294117647,17,Cinderella Man (2005)\n1391,3.0714285714285716,7,\"Sisterhood of the Traveling Pants, The (2005)\"\n1392,3.0,7,Lords of Dogtown (2005)\n1393,3.5,3,Rock School (2005)\n1394,3.2796610169491527,59,Mr. & Mrs. Smith (2005)\n1395,1.75,2,\"Adventures of Sharkboy and Lavagirl 3-D, The (2005)\"\n1396,3.75,2,High Tension (Haute tension) (Switchblade Romance) (2003)\n1397,3.1666666666666665,3,It's All Gone Pete Tong (2004)\n1398,3.8620689655172415,116,Batman Begins (2005)\n1399,3.0,1,Godzilla: Final Wars (Gojira: Fainaru uôzu) (2004)\n1400,3.0,2,\"Perfect Man, The (2005)\"\n1401,2.5,1,Saint Ralph (2004)\n1402,3.1666666666666665,3,Herbie: Fully Loaded (2005)\n1403,3.125,4,Land of the Dead (2005)\n1404,2.269230769230769,13,Bewitched (2005)\n1405,3.5,1,Rize (2005)\n1406,3.75,4,Me and You and Everyone We Know (2005)\n1407,4.5,1,\"Perfect Crime, The (Crimen Ferpecto) (Ferpect Crime) (2004)\"\n1408,3.3333333333333335,3,3 Extremes (Three... Extremes) (Saam gaang yi) (2004)\n1409,4.0,5,\"Edukators, The (Die Fetten Jahre sind vorbei) (2004)\"\n1410,3.15,50,War of the Worlds (2005)\n1411,3.5555555555555554,18,\"March of the Penguins (Marche de l'empereur, La) (2005)\"\n1412,3.0,1,Rebound (2005)\n1413,2.0,3,Dark Water (2005)\n1414,3.0,2,\"Beat That My Heart Skipped, The (battre mon coeur s'est arrêté, De) (2005)\"\n1415,2.763888888888889,36,Fantastic Four (2005)\n1416,3.8333333333333335,3,Murderball (2005)\n1417,3.5086206896551726,58,Wedding Crashers (2005)\n1418,3.5,1,Happy Endings (2005)\n1419,3.75,4,Hustle & Flow (2005)\n1420,5.0,1,\"Calcium Kid, The (2004)\"\n1421,3.3548387096774195,31,\"Island, The (2005)\"\n1422,3.0,3,Bad News Bears (2005)\n1423,3.7142857142857144,7,\"Devil's Rejects, The (2005)\"\n1424,3.0,2,Last Days (2005)\n1425,3.0,1,November (2004)\n1426,2.9166666666666665,6,Sky High (2005)\n1427,2.5,5,Stealth (2005)\n1428,2.0,4,Must Love Dogs (2005)\n1429,3.35,10,\"Aristocrats, The (2005)\"\n1430,3.0,1,\"Order, The (2001)\"\n1431,3.94,50,Serenity (2005)\n1432,3.4761904761904763,21,Broken Flowers (2005)\n1433,2.0833333333333335,12,\"Dukes of Hazzard, The (2005)\"\n1434,2.0,3,The Chumscrubber (2005)\n1435,3.6666666666666665,3,Junebug (2005)\n1436,2.8333333333333335,3,Deuce Bigalow: European Gigolo (2005)\n1437,3.0,4,\"Skeleton Key, The (2005)\"\n1438,3.5714285714285716,7,Four Brothers (2005)\n1439,3.1666666666666665,3,The Great Raid (2005)\n1440,3.0,1,Pretty Persuasion (2005)\n1441,3.7,5,Grizzly Man (2005)\n1442,2.5,1,Pusher II: With Blood on My Hands (2004)\n1443,3.5,1,Duma (2005)\n1444,3.5472972972972974,74,\"40-Year-Old Virgin, The (2005)\"\n1445,3.388888888888889,9,Red Eye (2005)\n1446,2.5,2,Hidden (a.k.a. Cache) (Caché) (2005)\n1447,1.5,1,Valiant (2005)\n1448,2.625,20,\"Brothers Grimm, The (2005)\"\n1449,4.0,1,\"Baxter, The (2005)\"\n1450,2.6666666666666665,3,\"Cave, The (2005)\"\n1451,3.4615384615384617,13,\"Constant Gardener, The (2005)\"\n1452,3.21875,16,Transporter 2 (2005)\n1453,2.6875,8,Just Like Heaven (2005)\n1454,3.5714285714285716,7,Proof (2005)\n1455,3.7142857142857144,35,Lord of War (2005)\n1456,2.25,2,Cry_Wolf (a.k.a. Cry Wolf) (2005)\n1457,3.7777777777777777,9,Everything Is Illuminated (2005)\n1458,3.3333333333333335,3,Thumbsucker (2005)\n1459,3.7083333333333335,12,Family Guy Presents Stewie Griffin: The Untold Story (2005)\n1460,3.5,1,New Police Story (Xin jing cha gu shi) (2004)\n1461,3.5,4,Why We Fight (2005)\n1462,2.15,10,Doom (2005)\n1463,2.875,8,Domino (2005)\n1464,3.269230769230769,13,Waiting... (2005)\n1465,2.761904761904762,21,Aeon Flux (2005)\n1466,3.0,1,\"Unfinished Life, An (2005)\"\n1467,2.5,1,\"Man, The (2005)\"\n1468,3.5,1,Survive Style 5+ (2004)\n1469,2.9166666666666665,6,\"Exorcism of Emily Rose, The (2005)\"\n1470,2.8636363636363638,11,Flightplan (2005)\n1471,3.534090909090909,44,Corpse Bride (2005)\n1472,3.7222222222222223,9,Green Street Hooligans (a.k.a. Hooligans) (2005)\n1473,3.5,27,\"History of Violence, A (2005)\"\n1474,3.5,3,Oliver Twist (2005)\n1475,3.3,5,\"Greatest Game Ever Played, The (2005)\"\n1476,3.869565217391304,23,Capote (2005)\n1477,3.55,10,Final Fantasy VII: Advent Children (2004)\n1478,2.0,1,Roll Bounce (2005)\n1479,2.5,2,Into the Blue (2005)\n1480,3.611111111111111,9,MirrorMask (2005)\n1481,3.6333333333333333,30,Wallace & Gromit in The Curse of the Were-Rabbit (2005)\n1482,4.071428571428571,35,Kiss Kiss Bang Bang (2005)\n1483,4.166666666666667,3,\"Bittersweet Life, A (Dalkomhan insaeng) (2005)\"\n1484,4.0,1,Darwin's Nightmare (2004)\n1485,2.5,1,Beowulf & Grendel (2005)\n1486,4.625,4,No Direction Home: Bob Dylan (2005)\n1487,4.25,2,Goal! The Dream Begins (Goal!) (2005)\n1488,3.125,4,In Her Shoes (2005)\n1489,3.4444444444444446,9,\"Squid and the Whale, The (2005)\"\n1490,3.5,1,Two for the Money (2005)\n1491,3.5588235294117645,34,Brokeback Mountain (2005)\n1492,3.5,4,Elizabethtown (2005)\n1493,3.5,4,North Country (2005)\n1494,3.775,20,\"Good Night, and Good Luck. (2005)\"\n1495,2.5,1,Dreamer: Inspired by a True Story (2005)\n1496,3.5625,8,\"Proposition, The (2005)\"\n1497,2.1,5,\"Fog, The (2005)\"\n1498,3.0,7,Shopgirl (2005)\n1499,3.0,4,Stay (2005)\n1500,2.9,5,\"Legend of Zorro, The (2005)\"\n1501,3.388888888888889,9,\"Weather Man, The (2005)\"\n1502,2.8076923076923075,13,Saw II (2005)\n1503,2.6,5,Prime (2005)\n1504,4.5,1,Don't Move (Non ti muovere) (2004)\n1505,2.142857142857143,7,American Pie Presents: Band Camp (American Pie 4: Band Camp) (2005)\n1506,2.75,2,\"Great Yokai War, The (Yôkai daisensô) (2005)\"\n1507,4.125,4,Manderlay (2005)\n1508,3.357142857142857,7,Revolver (2005)\n1509,3.676470588235294,17,Jarhead (2005)\n1510,2.2857142857142856,7,Chicken Little (2005)\n1511,4.75,2,Dead Man's Shoes (2004)\n1512,3.7,5,Joyeux Noël (Merry Christmas) (2005)\n1513,2.9166666666666665,6,Just Friends (2005)\n1514,3.53125,16,Syriana (2005)\n1515,2.0,1,One-Way Ticket to Mombasa (Menolippu Mombasaan) (2002)\n1516,3.25,4,Derailed (2005)\n1517,3.0,1,Creep (2004)\n1518,3.5555555555555554,27,Pride & Prejudice (2005)\n1519,3.25,4,Wolf Creek (2005)\n1520,3.5454545454545454,11,\"Descent, The (2005)\"\n1521,3.816901408450704,71,Harry Potter and the Goblet of Fire (2005)\n1522,3.7564102564102564,39,Walk the Line (2005)\n1523,3.15,10,Rent (2005)\n1524,3.375,4,Zathura (2005)\n1525,3.5,2,C.R.A.Z.Y. (2005)\n1526,2.0,2,Sarah Silverman: Jesus Is Magic (2005)\n1527,3.875,4,Breakfast on Pluto (2005)\n1528,3.6666666666666665,3,\"Ice Harvest, The (2005)\"\n1529,3.3333333333333335,3,\"Yours, Mine and Ours (2005)\"\n1530,3.5,1,\"Libertine, The (2004)\"\n1531,3.4411764705882355,17,Match Point (2005)\n1532,4.0,2,Paradise Now (2005)\n1533,3.443548387096774,62,\"Chronicles of Narnia: The Lion, the Witch and the Wardrobe, The (2005)\"\n1534,3.4,40,King Kong (2005)\n1535,3.5833333333333335,12,Memoirs of a Geisha (2005)\n1536,2.875,8,\"Family Stone, The (2005)\"\n1537,2.75,2,Havoc (2005)\n1538,3.2142857142857144,7,\"Matador, The (2005)\"\n1539,4.5,1,Wal-Mart: The High Cost of Low Price (2005)\n1540,2.5,1,Mozart and the Whale (2005)\n1541,4.0,5,\"Three Burials of Melquiades Estrada, The (2006)\"\n1542,3.8095238095238093,21,Munich (2005)\n1543,3.388888888888889,9,\"Producers, The (2005)\"\n1544,3.5,6,Transamerica (2005)\n1545,2.8333333333333335,3,Rumor Has It... (2005)\n1546,1.875,4,Cheaper by the Dozen 2 (2005)\n1547,2.1818181818181817,11,Fun with Dick and Jane (2005)\n1548,2.75,4,\"Ringer, The (2005)\"\n1549,3.0,3,Casanova (2005)\n1550,2.0,2,Mrs. Henderson Presents (2005)\n1551,3.2,5,\"New World, The (2005)\"\n1552,4.5,1,Voices of a Distant Star (Hoshi no koe) (2003)\n1553,3.5,1,\"Boys of Baraka, The (2005)\"\n1554,4.0,5,Lady Vengeance (Sympathy for Lady Vengeance) (Chinjeolhan geumjassi) (2005)\n1555,2.5,1,Grand Theft Parsons (2003)\n1556,3.8,5,District 13 (Banlieue 13) (2004)\n1557,2.8636363636363638,11,Hostel (2005)\n1558,2.5714285714285716,7,Grandma's Boy (2006)\n1559,3.6,5,Tristan & Isolde (2006)\n1560,3.75,2,Glory Road (2006)\n1561,3.5,3,Last Holiday (2006)\n1562,3.25,4,Hoodwinked! (2005)\n1563,3.09375,16,Underworld: Evolution (2006)\n1564,3.5,1,Looking for Comedy in the Muslim World (2005)\n1565,3.5,1,Water (2005)\n1566,4.625,4,Sophie Scholl: The Final Days (Sophie Scholl - Die letzten Tage) (2005)\n1567,3.7857142857142856,7,\"World's Fastest Indian, The (2005)\"\n1568,1.0,1,Bandidas (2006)\n1569,3.3333333333333335,3,Tristram Shandy: A Cock and Bull Story (2005)\n1570,3.5,1,Helter Skelter (2004)\n1571,2.75,2,Annapolis (2006)\n1572,2.0,1,Big Momma's House 2 (2006)\n1573,3.1666666666666665,6,Nanny McPhee (2005)\n1574,2.8333333333333335,9,Final Destination 3 (2006)\n1575,4.5,1,Something New (2006)\n1576,3.1666666666666665,3,Block Party (a.k.a. Dave Chappelle's Block Party) (2005)\n1577,3.5,1,Imagine Me & You (2005)\n1578,2.857142857142857,7,\"Pink Panther, The (2006)\"\n1579,3.5,2,Curious George (2006)\n1580,2.75,8,Firewall (2006)\n1581,1.3,5,When a Stranger Calls (2006)\n1582,3.0,1,London (2005)\n1583,2.0,1,Freedomland (2006)\n1584,4.0,1,Winter Passing (2005)\n1585,3.0,4,Eight Below (2006)\n1586,0.8333333333333334,3,Date Movie (2006)\n1587,3.3,5,Running Scared (2006)\n1588,1.9230769230769231,13,Ultraviolet (2006)\n1589,3.0,1,Just My Luck (2006)\n1590,1.75,2,Pulse (2006)\n1591,3.0,9,16 Blocks (2006)\n1592,3.25,6,Failure to Launch (2006)\n1593,2.75,2,Ultimate Avengers (2006)\n1594,3.217391304347826,23,Ice Age 2: The Meltdown (2006)\n1595,4.0,1,Ask the Dust (2006)\n1596,3.885,100,V for Vendetta (2006)\n1597,3.75,6,She's the Man (2006)\n1598,4.027777777777778,36,Thank You for Smoking (2006)\n1599,3.0,1,Find Me Guilty (2006)\n1600,3.8,40,Inside Man (2006)\n1601,3.25,4,Tsotsi (2005)\n1602,2.0,2,Aquamarine (2006)\n1603,3.5,1,Lights in the Dusk (Laitakaupungin valot) (2006)\n1604,3.0833333333333335,6,\"Hills Have Eyes, The (2006)\"\n1605,1.25,2,\"Shaggy Dog, The (2006)\"\n1606,3.5,1,Unknown White Male (2005)\n1607,4.117647058823529,34,\"Lives of Others, The (Das leben der Anderen) (2006)\"\n1608,3.75,2,Take the Lead (2006)\n1609,4.5,1,\"Devil and Daniel Johnston, The (2005)\"\n1610,3.8552631578947367,38,Lucky Number Slevin (2006)\n1611,3.8125,8,Volver (2006)\n1612,3.8,5,Akeelah and the Bee (2006)\n1613,2.5,1,Brainstorm (2001)\n1614,1.3333333333333333,3,Stay Alive (2006)\n1615,2.1666666666666665,3,Basic Instinct 2 (2006)\n1616,3.875,12,Brick (2005)\n1617,3.0,1,\"Dead Hate the Living!, The (2000)\"\n1618,4.5,1,Evil Aliens (2005)\n1619,3.5,6,This Film Is Not Yet Rated (2006)\n1620,3.0,5,Slither (2006)\n1621,2.772727272727273,11,\"Benchwarmers, The (2006)\"\n1622,2.5,1,Renaissance (2006)\n1623,5.0,1,Go for Zucker! (Alles auf Zucker!) (2004)\n1624,3.25,4,Friends with Money (2006)\n1625,4.75,2,Reefer Madness: The Movie Musical (2005)\n1626,4.25,2,Candy (2006)\n1627,4.0,1,\"Child, The (L'enfant) (2005)\"\n1628,5.0,1,9/11 (2002)\n1629,2.25,12,Scary Movie 4 (2006)\n1630,3.5,12,Hard Candy (2005)\n1631,3.6666666666666665,6,\"Prairie Home Companion, A (2006)\"\n1632,2.75,2,\"Sentinel, The (2006)\"\n1633,2.5,1,\"Wild, The (2006)\"\n1634,2.9583333333333335,12,Silent Hill (2006)\n1635,3.0,1,American Dreamz (2006)\n1636,3.75,4,Kinky Boots (2005)\n1637,3.0,1,\"Protector, The (a.k.a. Warrior King) (Tom yum goong) (2005)\"\n1638,3.5892857142857144,28,Mission: Impossible III (2006)\n1639,2.0,3,RV (2006)\n1640,4.142857142857143,7,United 93 (2006)\n1641,2.5833333333333335,6,Stick It (2006)\n1642,1.5,2,\"American Haunting, An (2005)\"\n1643,3.25,2,Down in the Valley (2005)\n1644,3.4285714285714284,14,Over the Hedge (2006)\n1645,3.3333333333333335,6,Art School Confidential (2006)\n1646,2.875,4,Poseidon (2006)\n1647,3.122448979591837,49,\"Da Vinci Code, The (2006)\"\n1648,3.355769230769231,52,X-Men: The Last Stand (2006)\n1649,2.590909090909091,11,\"Break-Up, The (2006)\"\n1650,5.0,1,Peaceful Warrior (2006)\n1651,3.3780487804878048,41,Cars (2006)\n1652,3.0,2,\"Notorious Bettie Page, The (2005)\"\n1653,2.5,1,Game 6 (2005)\n1654,2.0,1,On a Clear Day (2005)\n1655,2.3,5,\"Omen, The (2006)\"\n1656,2.4642857142857144,14,Nacho Libre (2006)\n1657,3.1,10,\"Lake House, The (2006)\"\n1658,2.9782608695652173,23,Click (2006)\n1659,3.5588235294117645,34,\"Devil Wears Prada, The (2006)\"\n1660,3.5069444444444446,72,Pirates of the Caribbean: Dead Man's Chest (2006)\n1661,2.1666666666666665,6,\"You, Me and Dupree (2006)\"\n1662,3.8846153846153846,13,Clerks II (2006)\n1663,2.8333333333333335,9,Lady in the Water (2006)\n1664,1.5833333333333333,6,My Super Ex-Girlfriend (2006)\n1665,3.1923076923076925,13,Marie Antoinette (2006)\n1666,3.7,5,Who Killed the Electric Car? (2006)\n1667,3.576923076923077,13,\"Inconvenient Truth, An (2006)\"\n1668,2.0,3,High School Musical (2006)\n1669,4.5,1,I Am a Sex Addict (2005)\n1670,2.0,1,Stoned (2005)\n1671,3.125,8,Jet Li's Fearless (Huo Yuan Jia) (2006)\n1672,2.090909090909091,11,\"Fast and the Furious: Tokyo Drift, The (Fast and the Furious 3, The) (2006)\"\n1673,2.25,2,Garfield: A Tail of Two Kitties (2006)\n1674,4.0,2,Metal: A Headbanger's Journey (2005)\n1675,3.08,25,Superman Returns (2006)\n1676,3.5,1,\"Road to Guantanamo, The (2006)\"\n1677,3.5,3,Edmond (2005)\n1678,2.5,1,\"OH in Ohio, The (2006)\"\n1679,3.883116883116883,77,Little Miss Sunshine (2006)\n1680,3.5,31,Babel (2006)\n1681,2.5,1,Strangers with Candy (2005)\n1682,4.0,3,Wordplay (2006)\n1683,1.0,1,Little Man (2006)\n1684,3.111111111111111,9,Monster House (2006)\n1685,2.642857142857143,14,Snakes on a Plane (2006)\n1686,3.375,4,Scoop (2006)\n1687,3.25,28,Talladega Nights: The Ballad of Ricky Bobby (2006)\n1688,3.239130434782609,23,Night at the Museum (2006)\n1689,2.6666666666666665,6,World Trade Center (2006)\n1690,3.6931818181818183,44,Stranger than Fiction (2006)\n1691,2.75,10,Miami Vice (2006)\n1692,3.7934782608695654,46,\"Pursuit of Happyness, The (2006)\"\n1693,2.7222222222222223,9,John Tucker Must Die (2006)\n1694,3.25,2,\"Ant Bully, The (2006)\"\n1695,3.4285714285714284,14,Crank (2006)\n1696,4.0,1,\"Secret Life of Words, The (2005)\"\n1697,3.5,1,Chaos (2005)\n1698,2.8333333333333335,3,\"Night Listener, The (2006)\"\n1699,3.5,4,Step Up (2006)\n1700,2.5,1,Zoom (2006)\n1701,4.25,2,Mind Game (2004)\n1702,4.0,5,Half Nelson (2006)\n1703,3.5,1,\"Moustache, La (2005)\"\n1704,3.0,3,Tideland (2005)\n1705,4.166666666666667,3,Adam's Apples (Adams æbler) (2005)\n1706,2.0,1,Material Girls (2006)\n1707,2.9285714285714284,14,Accepted (2006)\n1708,3.7790697674418605,43,\"Illusionist, The (2006)\"\n1709,3.730769230769231,13,The Queen (2006)\n1710,3.5,6,Beerfest (2006)\n1711,3.0,7,Invincible (2006)\n1712,4.0,1,Idlewild (2006)\n1713,3.5,3,Angel-A (2005)\n1714,2.5,1,\"Puffy Chair, The (2006)\"\n1715,1.0,4,\"Wicker Man, The (2006)\"\n1716,3.75,4,\"Wind That Shakes the Barley, The (2006)\"\n1717,3.0,1,Severance (2006)\n1718,3.2,5,Hollywoodland (2006)\n1719,3.0,1,\"Covenant, The (2006)\"\n1720,3.8333333333333335,3,\"Last Kiss, The (2006)\"\n1721,2.5,1,SherryBaby (2006)\n1722,3.7222222222222223,18,Idiocracy (2006)\n1723,4.071428571428571,7,Jesus Camp (2006)\n1724,4.0,1,\"Bow, The (Hwal) (2005)\"\n1725,4.0,1,\"Tiger and the Snow, The (La tigre e la neve) (2005)\"\n1726,3.5,14,\"Fountain, The (2006)\"\n1727,3.8636363636363638,11,\"Science of Sleep, The (La science des rêves) (2006)\"\n1728,3.25,6,\"Black Dahlia, The (2006)\"\n1729,3.0,1,\"Woods, The (2006)\"\n1730,3.0,2,Gridiron Gang (2006)\n1731,3.5,1,Land of Plenty (Angst and Alienation in America) (2004)\n1732,4.0,1,\"Bridge, The (2006)\"\n1733,3.6052631578947367,19,Apocalypto (2006)\n1734,2.0,1,Flyboys (2006)\n1735,3.5714285714285716,7,Jackass Number Two (2006)\n1736,3.0,3,All the King's Men (2006)\n1737,4.0,2,Conversations with Other Women (2005)\n1738,3.3461538461538463,65,Borat: Cultural Learnings of America for Make Benefit Glorious Nation of Kazakhstan (2006)\n1739,3.814814814814815,81,\"Pan's Labyrinth (Laberinto del fauno, El) (2006)\"\n1740,3.0,4,\"Guardian, The (2006)\"\n1741,3.0,2,Open Season (2006)\n1742,2.5,1,School for Scoundrels (2006)\n1743,4.252336448598131,107,\"Departed, The (2006)\"\n1744,2.5,2,\"Texas Chainsaw Massacre: The Beginning, The (2006)\"\n1745,2.8333333333333335,6,Employee of the Month (2006)\n1746,2.5,1,Running With Scissors (2006)\n1747,2.5,3,Man of the Year (2006)\n1748,2.75,2,\"Marine, The (2006)\"\n1749,3.75,2,Infamous (2006)\n1750,2.75,2,Feast (2005)\n1751,3.875,4,Little Children (2006)\n1752,4.5,2,Deliver Us from Evil (2006)\n1753,3.975,20,\"Last King of Scotland, The (2006)\"\n1754,4.0,1,\"U.S. vs. John Lennon, The (2006)\"\n1755,4.0,3,Shortbus (2006)\n1756,3.9453125,64,Children of Men (2006)\n1757,4.0055555555555555,90,\"Prestige, The (2006)\"\n1758,3.4545454545454546,11,Flags of Our Fathers (2006)\n1759,4.5,1,13 Tzameti (2005)\n1760,3.0454545454545454,11,Saw III (2006)\n1761,3.25,2,Catch a Fire (2006)\n1762,3.25,2,Death of a President (2006)\n1763,3.5,7,Flushed Away (2006)\n1764,3.25,8,Perfume: The Story of a Murderer (2006)\n1765,2.75,2,\"Santa Clause 3: The Escape Clause, The (2006)\"\n1766,3.6666666666666665,3,\"Good Year, A (2006)\"\n1767,3.0,3,Shut Up & Sing (2006)\n1768,2.375,4,For Your Consideration (2006)\n1769,3.0,1,Fuck (2005)\n1770,4.0,1,Shooting Dogs (a.k.a. Beyond the Gates) (2005)\n1771,3.9444444444444446,81,Casino Royale (2006)\n1772,2.6818181818181817,11,Happy Feet (2006)\n1773,1.3333333333333333,3,Let's Go to Prison (2006)\n1774,3.3,20,Déjà Vu (Deja Vu) (2006)\n1775,3.8333333333333335,3,Bobby (2006)\n1776,2.6666666666666665,3,10 Items or Less (2006)\n1777,3.2083333333333335,24,\"Holiday, The (2006)\"\n1778,3.0,1,Harsh Times (2006)\n1779,3.8333333333333335,3,Fast Food Nation (2006)\n1780,3.4444444444444446,9,Tenacious D in The Pick of Destiny (2006)\n1781,3.5,2,\"Nativity Story, The (2006)\"\n1782,3.7641509433962264,53,Blood Diamond (2006)\n1783,3.3333333333333335,3,Charlotte's Web (2006)\n1784,2.3333333333333335,12,Eragon (2006)\n1785,3.3214285714285716,14,Rocky Balboa (2006)\n1786,3.0,1,Fur: An Imaginary Portrait of Diane Arbus (2006)\n1787,3.5,1,Another Gay Movie (2006)\n1788,4.0,4,\"Painted Veil, The (2006)\"\n1789,3.75,2,We Are Marshall (2006)\n1790,3.142857142857143,7,\"Good Shepherd, The (2006)\"\n1791,3.1,5,Dreamgirls (2006)\n1792,3.75,4,Freedom Writers (2007)\n1793,4.0,1,When the Levees Broke: A Requiem in Four Acts (2006)\n1794,3.6666666666666665,6,Inland Empire (2006)\n1795,3.0,2,\"History Boys, The (2006)\"\n1796,3.75,4,Notes on a Scandal (2006)\n1797,2.0,2,DOA: Dead or Alive (2006)\n1798,2.5,3,Curse of the Golden Flower (Man cheng jin dai huang jin jia) (2006)\n1799,3.0,1,\"Good German, The (2006)\"\n1800,3.7058823529411766,17,Letters from Iwo Jima (2006)\n1801,2.0,1,Black Christmas (2006)\n1802,2.5,2,Stomp the Yard (2007)\n1803,3.75,2,Miss Potter (2006)\n1804,1.1666666666666667,3,American Pie Presents The Naked Mile (American Pie 5: The Naked Mile) (2006)\n1805,3.5,2,Venus (2006)\n1806,2.0,1,Primeval (2007)\n1807,3.125,8,Alpha Dog (2007)\n1808,2.5,2,\"Hitcher, The (2007)\"\n1809,3.5,1,After the Wedding (Efter brylluppet) (2006)\n1810,2.7777777777777777,9,Bridge to Terabithia (2007)\n1811,4.0,1,Beer League (2006)\n1812,4.5,1,Dead Meat (2004)\n1813,4.0,2,49 Up (2005)\n1814,3.8333333333333335,6,Waitress (2007)\n1815,2.25,2,Catch and Release (2006)\n1816,3.1,10,Smokin' Aces (2006)\n1817,2.0,2,Blood and Chocolate (2007)\n1818,1.125,4,Epic Movie (2007)\n1819,2.5,2,\"Messengers, The (2007)\"\n1820,3.0,3,Because I Said So (2007)\n1821,3.5,3,Hannibal Rising (2007)\n1822,1.0,1,Norbit (2007)\n1823,4.5,2,\"Boss of It All, The (Direktøren for det hele) (2006)\"\n1824,4.5,2,Cocaine Cowboys (2006)\n1825,3.8680555555555554,72,Ratatouille (2007)\n1826,2.9375,8,\"Paris, I Love You (Paris, je t'aime) (2006)\"\n1827,2.25,2,\"Astronaut Farmer, The (2007)\"\n1828,3.5,1,\"Wake Up, Ron Burgundy (2004)\"\n1829,1.0,2,It's a Boy Girl Thing (2006)\n1830,5.0,1,\"Ugly Duckling and Me!, The (2006)\"\n1831,3.0,4,Unknown (2006)\n1832,2.25,12,Ghost Rider (2007)\n1833,3.5625,8,Breach (2007)\n1834,2.0,1,Tyler Perry's Daddy's Little Girls (2007)\n1835,3.1,10,Music and Lyrics (2007)\n1836,2.9285714285714284,14,\"Number 23, The (2007)\"\n1837,3.0,2,Reno 911!: Miami (2007)\n1838,3.5,4,Black Snake Moan (2006)\n1839,4.5,1,My Father and My Son (Babam ve oglum) (2005)\n1840,3.5,1,Factory Girl (2006)\n1841,4.0,61,Hot Fuzz (2007)\n1842,4.5,1,Golden Door (Nuovomondo) (2006)\n1843,2.8214285714285716,14,Next (2007)\n1844,3.0,1,Amazing Grace (2006)\n1845,3.25,2,2001 Maniacs (2005)\n1846,3.710526315789474,38,Zodiac (2007)\n1847,2.5,1,Pusher III: I'm the Angel of Death (2005)\n1848,2.3,5,Wild Hogs (2007)\n1849,3.68125,80,300 (2007)\n1850,2.0,1,\"Abandoned, The (2006)\"\n1851,3.5,1,Starter for 10 (2006)\n1852,3.0,2,\"Last Mimzy, The (2007)\"\n1853,4.25,2,Priceless (Hors de prix) (2006)\n1854,4.0,7,\"Host, The (Gwoemul) (2006)\"\n1855,2.9166666666666665,6,Becoming Jane (2007)\n1856,3.6666666666666665,3,\"Namesake, The (2006)\"\n1857,2.5,1,I Think I Love My Wife (2007)\n1858,3.5,3,Premonition (2007)\n1859,3.25,2,Dead Silence (2007)\n1860,4.1,5,Reign Over Me (2007)\n1861,2.5,1,Pride (2007)\n1862,3.86,25,Shooter (2007)\n1863,2.5,2,\"Hills Have Eyes II, The (2007)\"\n1864,3.0833333333333335,6,TMNT (Teenage Mutant Ninja Turtles) (2007)\n1865,2.5,1,Black Book (Zwartboek) (2006)\n1866,3.25,6,\"Lookout, The (2007)\"\n1867,3.088235294117647,17,Blades of Glory (2007)\n1868,1.5,1,Are We Done Yet? (2007)\n1869,3.425925925925926,27,Grindhouse (2007)\n1870,2.5,3,\"Reaping, The (2007)\"\n1871,3.5454545454545454,11,Meet the Robinsons (2007)\n1872,3.5,1,American Hardcore (2006)\n1873,3.642857142857143,14,Sunshine (2007)\n1874,3.5,2,\"Hoax, The (2007)\"\n1875,3.15,10,Disturbia (2007)\n1876,3.0,1,Aqua Teen Hunger Force Colon Movie Film for Theaters (2007)\n1877,3.25,2,\"Vie en Rose, La (Môme, La) (2007)\"\n1878,3.6666666666666665,12,Fracture (2007)\n1879,3.75,2,Vacancy (2007)\n1880,4.0,2,In the Land of Women (2007)\n1881,2.875,4,Mr. Bean's Holiday (2007)\n1882,2.3333333333333335,3,\"Invisible, The (2007)\"\n1883,2.0,1,Kickin It Old Skool (2007)\n1884,3.0,44,Spider-Man 3 (2007)\n1885,3.0,1,Lucky You (2007)\n1886,1.5,2,It's a Very Merry Muppet Christmas Movie (2002)\n1887,4.0,1,Sharkwater (2006)\n1888,4.0,1,\"Ex, The (2007)\"\n1889,3.7777777777777777,9,Paprika (Papurika) (2006)\n1890,2.5,2,Day Watch (Dnevnoy dozor) (2006)\n1891,4.0,8,This Is England (2006)\n1892,3.625,4,Away from Her (2006)\n1893,3.6538461538461537,52,Knocked Up (2007)\n1894,3.5,15,Hairspray (2007)\n1895,3.5952380952380953,21,28 Weeks Later (2007)\n1896,3.875,4,Jonestown: The Life and Death of Peoples Temple (2006)\n1897,3.0238095238095237,21,Shrek the Third (2007)\n1898,3.9444444444444446,9,Once (2006)\n1899,3.4375,56,Pirates of the Caribbean: At World's End (2007)\n1900,2.875,4,Bug (2007)\n1901,4.0,7,Mr. Brooks (2007)\n1902,3.5,2,\"Librarian: Return to King Solomon's Mines, The (2006)\"\n1903,3.75,2,\"Librarian: Quest for the Spear, The (2004)\"\n1904,2.0,1,Fay Grim (2006)\n1905,3.0,2,\"I'm a Cyborg, But That's OK (Saibogujiman kwenchana) (2006)\"\n1906,5.0,1,\"Breed, The (2006)\"\n1907,3.25,4,Cashback (2006)\n1908,3.484848484848485,33,Ocean's Thirteen (2007)\n1909,2.0,1,Them (Ils) (2006)\n1910,2.0,4,Hostel: Part II (2007)\n1911,4.0,1,Paranoid Park (2007)\n1912,3.5,7,Surf's Up (2007)\n1913,2.575,20,Fantastic Four: Rise of the Silver Surfer (2007)\n1914,2.8333333333333335,3,Nancy Drew (2007)\n1915,3.2857142857142856,7,Fido (2006)\n1916,3.1346153846153846,26,Death Proof (2007)\n1917,3.3333333333333335,3,Rescue Dawn (2006)\n1918,4.0,2,\"TV Set, The (2006)\"\n1919,5.0,1,\"Valet, The (La doublure) (2006)\"\n1920,2.0,1,Bring It On: All or Nothing (2006)\n1921,4.5,1,\"Power of Nightmares, The: The Rise of the Politics of Fear (2004)\"\n1922,3.7142857142857144,14,Sicko (2007)\n1923,3.5,1,\"Mighty Heart, A (2007)\"\n1924,3.16,25,1408 (2007)\n1925,3.25,4,Death at a Funeral (2007)\n1926,3.40625,32,Live Free or Die Hard (2007)\n1927,2.3333333333333335,3,License to Wed (2007)\n1928,2.5384615384615383,13,Evan Almighty (2007)\n1929,3.3461538461538463,39,Transformers (2007)\n1930,3.8620689655172415,58,Harry Potter and the Order of the Phoenix (2007)\n1931,3.4545454545454546,11,I Now Pronounce You Chuck and Larry (2007)\n1932,3.25,2,First Snow (2006)\n1933,3.75,2,Manufactured Landscapes (2006)\n1934,4.045454545454546,11,Across the Universe (2007)\n1935,3.142857142857143,7,Hot Rod (2007)\n1936,3.6029411764705883,34,Stardust (2007)\n1937,3.619565217391304,46,\"Simpsons Movie, The (2007)\"\n1938,0.5,1,I Know Who Killed Me (2007)\n1939,3.25,2,No Reservations (2007)\n1940,3.5,5,Charlie Bartlett (2007)\n1941,3.697530864197531,81,\"Bourne Ultimatum, The (2007)\"\n1942,4.0,1,China Blue (2005)\n1943,3.8333333333333335,3,Tell No One (Ne le dis à personne) (2006)\n1944,3.8636363636363638,55,Superbad (2007)\n1945,3.5,1,\"Brice Man, The (Brice de Nice) (2005)\"\n1946,2.7,5,Rush Hour 3 (2007)\n1947,3.0,1,\"Last Legion, The (2007)\"\n1948,2.8333333333333335,3,Balls of Fury (2007)\n1949,2.5,2,Sydney White (2007)\n1950,3.8125,8,\"Kingdom, The (2007)\"\n1951,3.25,2,Rocket Science (2007)\n1952,0.5,1,Daddy Day Camp (2007)\n1953,3.0,2,\"Invasion, The (2007)\"\n1954,3.1666666666666665,3,\"Nanny Diaries, The (2007)\"\n1955,3.25,4,Halloween (2007)\n1956,3.5,1,Death Sentence (2007)\n1957,3.5,1,2 Days in Paris (2007)\n1958,3.9166666666666665,12,\"King of Kong, The (2007)\"\n1959,2.0,1,Taxi 4 (2007)\n1960,4.0,1,Behind the Mask: The Rise of Leslie Vernon (2006)\n1961,0.5,1,\"Brothers Solomon, The (2007)\"\n1962,2.5,1,\"Nines, The (2007)\"\n1963,3.7954545454545454,22,Planet Terror (2007)\n1964,4.06,25,3:10 to Yuma (2007)\n1965,3.4583333333333335,12,Shoot 'Em Up (2007)\n1966,4.5,1,\"Ten, The (2007)\"\n1967,3.5384615384615383,13,Atonement (2007)\n1968,1.0,1,Electroma (2006)\n1969,3.5,1,Requiem (2006)\n1970,4.0,3,\"4 Months, 3 Weeks and 2 Days (4 luni, 3 saptamâni si 2 zile) (2007)\"\n1971,4.0,1,No End in Sight (2007)\n1972,3.25,2,\"Brave One, The (2007)\"\n1973,3.5,3,In the Valley of Elah (2007)\n1974,3.0,1,December Boys (2007)\n1975,4.0,1,Shanghai Kiss (2007)\n1976,2.6666666666666665,3,\"Hunting Party, The (2007)\"\n1977,4.026315789473684,19,Eastern Promises (2007)\n1978,4.0,1,\"Unreasonable Man, An (2006)\"\n1979,4.625,4,Tekkonkinkreet (Tekkon kinkurîto) (2006)\n1980,3.0,1,Love and Other Disasters (2006)\n1981,2.5,1,Interview (2007)\n1982,3.0,1,Cashback (2004)\n1983,2.95,10,Resident Evil: Extinction (2007)\n1984,2.6666666666666665,3,Mr. Woodcock (2007)\n1985,3.25,4,Good Luck Chuck (2007)\n1986,3.902439024390244,41,Into the Wild (2007)\n1987,2.6666666666666665,3,\"Game Plan, The (2007)\"\n1988,3.75,4,\"Lust, Caution (Se, jie) (2007)\"\n1989,1.0,1,\"Seeker: The Dark Is Rising, The (2007)\"\n1990,2.5,8,\"Heartbreak Kid, The (2007)\"\n1991,3.388888888888889,9,Dan in Real Life (2007)\n1992,3.4285714285714284,21,\"Darjeeling Limited, The (2007)\"\n1993,3.75,2,We Own the Night (2007)\n1994,2.75,2,Elizabeth: The Golden Age (2007)\n1995,3.911764705882353,17,Michael Clayton (2007)\n1996,4.25,2,Sleuth (2007)\n1997,3.5625,8,Lars and the Real Girl (2007)\n1998,3.1818181818181817,11,30 Days of Night (2007)\n1999,3.45,20,Gone Baby Gone (2007)\n2000,0.75,2,\"Comebacks, The (2007)\"\n2001,2.5,1,Weirdsville (2007)\n2002,4.1,10,\"Assassination of Jesse James by the Coward Robert Ford, The (2007)\"\n2003,4.5,1,10th & Wolf (2006)\n2004,4.181818181818182,11,Persepolis (2007)\n2005,4.166666666666667,3,Control (2007)\n2006,3.5,2,The Jane Austen Book Club (2007)\n2007,4.0,1,\"Last Winter, The (2006)\"\n2008,2.3333333333333335,3,Black Sheep (2006)\n2009,3.5,1,\"Edge of Heaven, The (Auf der anderen Seite) (2007)\"\n2010,3.2,5,Saw IV (2007)\n2011,3.25,2,For the Bible Tells Me So (2007)\n2012,4.5,1,My Kid Could Paint That (2007)\n2013,4.3,10,Elite Squad (Tropa de Elite) (2007)\n2014,3.0,1,King of California (2007)\n2015,3.9054054054054053,37,American Gangster (2007)\n2016,3.15,10,Bee Movie (2007)\n2017,3.0,1,Before the Devil Knows You're Dead (2007)\n2018,4.1,5,\"Diving Bell and the Butterfly, The (Scaphandre et le papillon, Le) (2007)\"\n2019,3.8984375,64,No Country for Old Men (2007)\n2020,3.4,10,Be Kind Rewind (2008)\n2021,3.5,1,Itty Bitty Titty Committee (2007)\n2022,3.5,6,August Rush (2007)\n2023,4.1875,8,\"Man from Earth, The (2007)\"\n2024,4.0,2,Lions For Lambs (2007)\n2025,2.5625,8,Beowulf (2007)\n2026,3.0,2,Southland Tales (2006)\n2027,2.75,2,\"Evening with Kevin Smith 2: Evening Harder, An (2006)\"\n2028,4.5,1,I Served the King of England (Obsluhoval jsem anglického krále) (2006)\n2029,3.4285714285714284,14,\"Mist, The (2007)\"\n2030,3.676470588235294,17,Enchanted (2007)\n2031,3.2857142857142856,7,Hitman (2007)\n2032,4.0,1,Awake (2007)\n2033,3.111111111111111,18,\"Golden Compass, The (2007)\"\n2034,3.4838709677419355,62,I Am Legend (2007)\n2035,1.5,3,Alvin and the Chipmunks (2007)\n2036,3.9,10,Futurama: Bender's Big Score (2007)\n2037,3.0,1,Margot at the Wedding (2007)\n2038,3.5,2,I'm Not There (2007)\n2039,3.5,1,\"Savages, The (2007)\"\n2040,3.0,2,Wrong Turn 2: Dead End (2007)\n2041,3.875,8,\"Orphanage, The (Orfanato, El) (2007)\"\n2042,3.769230769230769,65,Juno (2007)\n2043,3.0,2,\"Maxed Out: Hard Times, Easy Credit and the Era of Predatory Lenders (2006)\"\n2044,4.0,1,My Blueberry Nights (2007)\n2045,3.25,4,Helvetica (2007)\n2046,3.230769230769231,13,\"Bucket List, The (2007)\"\n2047,3.3333333333333335,3,\"Kite Runner, The (2007)\"\n2048,3.0,1,\"Deaths of Ian Stone, The (2007)\"\n2049,4.0,6,Wristcutters: A Love Story (2006)\n2050,3.716666666666667,30,Sweeney Todd: The Demon Barber of Fleet Street (2007)\n2051,3.0416666666666665,24,National Treasure: Book of Secrets (2007)\n2052,4.142857142857143,28,There Will Be Blood (2007)\n2053,3.375,8,Charlie Wilson's War (2007)\n2054,2.5833333333333335,6,AVPR: Aliens vs. Predator - Requiem (2007)\n2055,3.3,5,Walk Hard: The Dewey Cox Story (2007)\n2056,3.0,1,As You Like It (2006)\n2057,4.0,1,Drained (O cheiro do Ralo) (2006)\n2058,4.0,1,Dedication (2007)\n2059,4.0,1,\"Water Horse: Legend of the Deep, The (2007)\"\n2060,3.8,5,Battlestar Galactica: Razor (2007)\n2061,3.090909090909091,11,P.S. I Love You (2007)\n2062,3.3,20,27 Dresses (2008)\n2063,4.0,1,Cassandra's Dream (2007)\n2064,4.5,2,Like Stars on Earth (Taare Zameen Par) (2007)\n2065,3.5,3,\"Band's Visit, The (Bikur Ha-Tizmoret) (2007)\"\n2066,4.0625,8,[REC] (2007)\n2067,0.5,1,In the Name of the King: A Dungeon Siege Tale (2008)\n2068,3.3518518518518516,27,Cloverfield (2008)\n2069,4.0,1,Hatchet (2006)\n2070,4.0,1,Cat Soup (Nekojiru-so) (2001)\n2071,4.1,10,\"Girl Who Leapt Through Time, The (Toki o kakeru shôjo) (2006)\"\n2072,3.5,1,First Sunday (2008)\n2073,3.0,1,Untraceable (2008)\n2074,3.2777777777777777,9,Rambo (Rambo 4) (2008)\n2075,2.5,3,Meet the Spartans (2008)\n2076,3.25,2,Strange Wilderness (2008)\n2077,3.5,1,\"Signal, The (2007)\"\n2078,3.8055555555555554,18,Hellboy II: The Golden Army (2008)\n2079,4.158536585365853,41,In Bruges (2008)\n2080,4.5,1,Rise of the Footsoldier (2007)\n2081,2.0,1,Teeth (2007)\n2082,2.3333333333333335,3,Fool's Gold (2008)\n2083,3.0,18,Jumper (2008)\n2084,3.4285714285714284,14,\"Definitely, Maybe (2008)\"\n2085,3.0,1,\"Air I Breathe, The (2007)\"\n2086,3.4166666666666665,6,Vantage Point (2008)\n2087,3.4,5,\"Spiderwick Chronicles, The (2008)\"\n2088,3.25,2,Step Up 2 the Streets (2008)\n2089,3.7857142857142856,7,\"Other Boleyn Girl, The (2008)\"\n2090,2.9,10,Semi-Pro (2008)\n2091,3.0,7,Run Fatboy Run (2007)\n2092,4.0,1,Taxi to the Dark Side (2007)\n2093,1.0,1,Descent (2007)\n2094,4.5,1,College Road Trip (2008)\n2095,2.7058823529411766,17,\"10,000 BC (2008)\"\n2096,3.6153846153846154,13,\"Bank Job, The (2008)\"\n2097,4.0,1,Doomsday (2008)\n2098,3.4375,8,Horton Hears a Who! (2008)\n2099,4.375,4,Funny Games U.S. (2007)\n2100,3.3333333333333335,3,\"Counterfeiters, The (Die Fälscher) (2007)\"\n2101,2.5,1,Mongol (2007)\n2102,4.5,1,War Dance (2007)\n2103,2.125,4,\"Love Guru, The (2008)\"\n2104,2.8333333333333335,3,Diary of the Dead (2007)\n2105,3.5,4,Penelope (2006)\n2106,4.0,1,City of Men (Cidade dos Homens) (2007)\n2107,3.75,2,Zeitgeist: The Movie (2007)\n2108,2.75,2,Justice League: The New Frontier (2008) \n2109,4.166666666666667,3,Heima (2007)\n2110,3.0,1,Snow Angels (2007)\n2111,3.5,2,\"Class, The (Klass) (2007)\"\n2112,4.238255033557047,149,\"Dark Knight, The (2008)\"\n2113,3.5,1,Never Back Down (2008)\n2114,3.5,2,Drillbit Taylor (2008)\n2115,2.5,1,Youth Without Youth (2007)\n2116,3.75,18,21 (2008)\n2117,2.75,2,Smart People (2008)\n2118,2.5,1,\"Shepherd: Border Patrol, The (2008)\"\n2119,2.375,4,Leatherheads (2008)\n2120,4.5,1,Assembly (Ji jie hao) (2007) \n2121,3.5,2,Zebraman (2004)\n2122,3.25,2,Stop-Loss (2008)\n2123,3.5,1,Shine a Light (2008)\n2124,4.0,1,Inside (À l'intérieur) (2007)\n2125,3.0,2,Nim's Island (2008)\n2126,3.5,1,\"Ruins, The (2008)\"\n2127,3.769230769230769,39,Forgetting Sarah Marshall (2008)\n2128,2.0,3,Superhero Movie (2008)\n2129,2.5,1,Street Kings (2008)\n2130,4.5,1,\"Visitor, The (2007)\"\n2131,3.3076923076923075,13,Harold & Kumar Escape from Guantanamo Bay (2008)\n2132,4.0,1,99 francs (2007)\n2133,3.1666666666666665,3,Speed Racer (2008)\n2134,3.8333333333333335,3,\"Forbidden Kingdom, The (2008)\"\n2135,3.5,1,Happy-Go-Lucky (2008)\n2136,3.8,5,Religulous (2008)\n2137,2.0,2,Outpost (2008)\n2138,3.5,1,Are You Scared? (2006)\n2139,4.666666666666667,3,Son of Rambow (2007)\n2140,4.25,2,Super High Me (2007)\n2141,3.5,1,Outsourced (2006)\n2142,3.1363636363636362,11,Baby Mama (2008)\n2143,1.0,1,Expelled: No Intelligence Allowed (2008)\n2144,0.75,2,Prom Night (2008)\n2145,3.824468085106383,94,Iron Man (2008)\n2146,2.875,4,Made of Honor (2008)\n2147,3.0,1,Redbelt (2008)\n2148,3.619047619047619,42,Taken (2008)\n2149,3.9545454545454546,11,\"Fall, The (2006)\"\n2150,2.75,8,What Happens in Vegas... (2008)\n2151,2.75,2,American Pie Presents Beta House (American Pie 6: Beta House) (2007)\n2152,3.5,1,Bella (2006)\n2153,3.5416666666666665,12,\"Chronicles of Narnia: Prince Caspian, The (2008)\"\n2154,4.0,1,Shelter (2007)\n2155,3.75,2,\"Girl Next Door, The (2007)\"\n2156,2.8333333333333335,39,Indiana Jones and the Kingdom of the Crystal Skull (2008)\n2157,4.0,1,Nina's Heavenly Delights (2006)\n2158,2.409090909090909,11,Sex and the City (2008)\n2159,3.1666666666666665,3,\"Strangers, The (2008)\"\n2160,3.5,1,\"Bigger, Stronger, Faster* (2008)\"\n2161,3.0,1,All the Boys Love Mandy Lane (2006)\n2162,3.4444444444444446,54,Kung Fu Panda (2008)\n2163,3.5,1,Recount (2008)\n2164,5.0,1,Ex Drummer (2007)\n2165,2.8666666666666667,15,You Don't Mess with the Zohan (2008)\n2166,3.5,1,Stuck (2007)\n2167,4.0,1,Chaos Theory (2007)\n2168,4.0,1,Boy A (2007)\n2169,4.5,1,Spiral (2007)\n2170,3.0,5,\"Happening, The (2008)\"\n2171,3.1714285714285713,35,\"Incredible Hulk, The (2008)\"\n2172,3.5,1,\"Children of Huang Shi, The (2008)\"\n2173,4.0576923076923075,104,WALL·E (2008)\n2174,3.159090909090909,22,Wanted (2008)\n2175,3.0344827586206895,29,Hancock (2008)\n2176,3.46875,16,Get Smart (2008)\n2177,2.25,2,Young People Fucking (a.k.a. YPF) (2007)\n2178,2.5,1,St. Trinian's (2007)\n2179,3.5,6,Futurama: The Beast with a Billion Backs (2008)\n2180,3.0,2,Kit Kittredge: An American Girl (2008)\n2181,3.75,2,Gonzo: The Life and Work of Dr. Hunter S. Thompson (2008)\n2182,4.0,3,\"Wackness, The (2008)\"\n2183,4.0,1,Strange Circus (Kimyô na sâkasu) (2005)\n2184,4.5,3,Encounters at the End of the World (2008)\n2185,0.5,1,Zombie Strippers! (2008)\n2186,4.0,1,Battle for Haditha (2007)\n2187,2.9642857142857144,14,Mamma Mia! (2008)\n2188,4.5,2,Welcome to the Sticks (Bienvenue chez les Ch'tis) (2008)\n2189,3.5,1,Rogue (2007)\n2190,2.5,5,Journey to the Center of the Earth (2008)\n2191,3.5,2,Meet Dave (2008)\n2192,2.25,2,\"Machine Girl, The (Kataude mashin gâru) (2008)\"\n2193,3.5,1,Shrooms (2007)\n2194,2.5,1,Transsiberian (2008)\n2195,2.5,1,Stargate: Continuum (2008)\n2196,3.988372093023256,43,Watchmen (2009)\n2197,2.5,1,Shotgun Stories (2007)\n2198,5.0,1,Watching the Detectives (2007)\n2199,3.5,2,Felon (2008)\n2200,3.5535714285714284,28,Step Brothers (2008)\n2201,2.875,4,\"X-Files: I Want to Believe, The (2008)\"\n2202,3.6875,8,Man on Wire (2008)\n2203,4.0,1,Hogfather (Terry Pratchett's Hogfather) (2006)\n2204,4.0,1,Pathology (2008)\n2205,3.0,1,\"Tracey Fragments, The (2007)\"\n2206,3.5,2,\"Zone, The (La Zona) (2007)\"\n2207,3.0,1,\"Edge of Love, The (2008)\"\n2208,2.642857142857143,7,\"Mummy: Tomb of the Dragon Emperor, The (2008)\"\n2209,2.875,4,\"Midnight Meat Train, The (2008)\"\n2210,4.5,1,Frozen River (2008)\n2211,3.09375,16,Vicky Cristina Barcelona (2008)\n2212,3.5,3,Batman: Gotham Knight (2008)\n2213,2.0,1,\"Walker, The (2007)\"\n2214,3.6774193548387095,31,Pineapple Express (2008)\n2215,4.0,3,Red Cliff (Chi bi) (2008)\n2216,2.5,1,\"Sisterhood of the Traveling Pants 2, The (2008)\"\n2217,2.0,1,Hell Ride (2008)\n2218,2.0,1,High School Musical 2 (2007)\n2219,3.5,33,Tropic Thunder (2008)\n2220,2.357142857142857,7,Star Wars: The Clone Wars (2008)\n2221,4.0,1,Henry Poole is Here (2008)\n2222,3.5,1,Mutant Chronicles (2008)\n2223,3.0,5,Waltz with Bashir (Vals im Bashir) (2008)\n2224,3.911764705882353,17,Let the Right One In (Låt den rätte komma in) (2008)\n2225,2.0,1,Hamlet 2 (2008)\n2226,3.2222222222222223,9,Death Race (2008)\n2227,2.5,10,\"House Bunny, The (2008)\"\n2228,2.75,2,\"Rocker, The (2008)\"\n2229,4.0,1,I.O.U.S.A. (a.k.a. IOUSA) (2008)\n2230,1.5,1,Mirrors (2008)\n2231,3.3333333333333335,3,Sukiyaki Western Django (2008)\n2232,3.5,1,Somers Town (2008)\n2233,3.4871794871794872,39,Burn After Reading (2008)\n2234,0.8333333333333334,3,Disaster Movie (2008)\n2235,2.3333333333333335,3,Babylon A.D. (2008)\n2236,3.375,4,Traitor (2008)\n2237,4.0,1,\"Onion Movie, The (2008)\"\n2238,2.5,2,\"Spirit, The (2008)\"\n2239,4.0,2,John Adams (2008)\n2240,3.25,2,Bangkok Dangerous (2008)\n2241,2.0,1,Sunflower (Xiang ri kui) (2005)\n2242,3.0,1,Altered (2006)\n2243,3.0,1,Righteous Kill (2008)\n2244,3.0,2,Lakeview Terrace (2008)\n2245,2.0,2,Ghost Town (2008)\n2246,0.5,1,\"Crow, The: Wicked Prayer (2005)\"\n2247,3.0,1,Appaloosa (2008)\n2248,3.0,1,Dead Fury (2008)\n2249,3.0625,8,Eagle Eye (2008)\n2250,2.8333333333333335,6,How to Lose Friends & Alienate People (2008)\n2251,3.35,10,Nick and Norah's Infinite Playlist (2008)\n2252,3.7,5,Gomorrah (Gomorra) (2008)\n2253,3.5,4,\"Duchess, The (2008)\"\n2254,3.5,1,Alone in the Dark II (2008)\n2255,4.277777777777778,9,FLCL (2000)\n2256,2.75,2,Rachel Getting Married (2008)\n2257,3.772727272727273,11,Body of Lies (2008)\n2258,2.8333333333333335,3,City of Ember (2008)\n2259,2.1666666666666665,3,Max Payne (2008)\n2260,3.5238095238095237,21,Zack and Miri Make a Porno (2008)\n2261,2.5,1,W. (2008)\n2262,4.0,3,My Best Friend's Girl (2008)\n2263,3.125,4,\"Synecdoche, New York (2008)\"\n2264,4.0,1,\"Secret Life of Bees, The (2008)\"\n2265,2.0,1,\"American Carol, An (2008)\"\n2266,3.5714285714285716,7,\"Wave, The (Welle, Die) (2008)\"\n2267,2.5,1,\"Angus, Thongs and Perfect Snogging (2008)\"\n2268,3.0,2,Pride and Glory (2008)\n2269,3.0,1,\"Express, The (2008)\"\n2270,4.0,1,Babylon 5: The Legend of the Rangers: To Live and Die in Starlight (2002)\n2271,4.0,1,Babylon 5: The Lost Tales - Voices in the Dark (2007)\n2272,3.769230769230769,13,RocknRolla (2008)\n2273,3.4444444444444446,9,Futurama: Bender's Game (2008)\n2274,2.25,2,Tin Man (2007)\n2275,3.6923076923076925,13,Madagascar: Escape 2 Africa (2008)\n2276,3.5,1,Blindness (2008)\n2277,3.4375,8,Changeling (2008)\n2278,3.888888888888889,9,\"Road, The (2009)\"\n2279,3.8098591549295775,71,Slumdog Millionaire (2008)\n2280,3.4571428571428573,35,Quantum of Solace (2008)\n2281,3.625,20,Role Models (2008)\n2282,3.5,1,Tokyo! (2008)\n2283,3.25,2,JCVD (2008)\n2284,4.0,2,Crows Zero (Kurôzu zero) (2007)\n2285,2.0,1,Krabat (2008)\n2286,1.0,1,Camp Rock (2008)\n2287,3.5,4,Farscape: The Peacekeeper Wars (2004)\n2288,3.1666666666666665,3,Saw V (2008)\n2289,3.625,4,Sex Drive (2008)\n2290,4.0,1,The Island (2006)\n2291,2.125,4,Beverly Hills Chihuahua (2008)\n2292,2.75,2,\"Class, The (Entre les murs) (2008)\"\n2293,4.0,1,Splinter (2008)\n2294,3.2,5,Australia (2008)\n2295,3.388888888888889,18,Bolt (2008)\n2296,3.5454545454545454,11,Milk (2008)\n2297,2.409090909090909,22,Twilight (2008)\n2298,3.5,1,\"Children, The (2008)\"\n2299,2.6666666666666665,6,Transporter 3 (2008)\n2300,2.8333333333333335,3,Four Christmases (2008)\n2301,3.4166666666666665,6,\"Boy in the Striped Pajamas, The (Boy in the Striped Pyjamas, The) (2008)\"\n2302,1.0,1,Fireproof (2008)\n2303,3.5,2,Igor (2008)\n2304,3.3333333333333335,3,Dinotopia (2002)\n2305,4.75,2,Hunger (2008)\n2306,3.0,6,Punisher: War Zone (2008)\n2307,2.5,2,Shrek the Halls (2007)\n2308,3.5,1,\"Pervert's Guide to Cinema, The (2006)\"\n2309,3.7,5,Wallace and Gromit in 'A Matter of Loaf and Death' (2008)\n2310,2.7857142857142856,7,\"Day the Earth Stood Still, The (2008)\"\n2311,5.0,1,Che: Part One (2008)\n2312,5.0,1,Che: Part Two (2008)\n2313,3.357142857142857,7,Doubt (2008)\n2314,3.9456521739130435,46,Gran Torino (2008)\n2315,3.9444444444444446,9,Frost/Nixon (2008)\n2316,3.2142857142857144,7,\"Reader, The (2008)\"\n2317,4.0,1,Sword of the Stranger (Sutorejia: Mukô hadan) (2007)\n2318,4.136363636363637,11,Seven Pounds (2008)\n2319,3.735294117647059,34,\"Wrestler, The (2008)\"\n2320,3.5945945945945947,37,\"Curious Case of Benjamin Button, The (2008)\"\n2321,3.6176470588235294,34,Yes Man (2008)\n2322,3.409090909090909,11,Valkyrie (2008)\n2323,3.5,3,5 Centimeters per Second (Byôsoku 5 senchimêtoru) (2007)\n2324,3.0,2,War of the Worlds (2005)\n2325,3.5,1,Ben X (2007)\n2326,3.3636363636363638,11,Bedtime Stories (2008)\n2327,3.5,3,Choke (2008)\n2328,3.7,5,Revolutionary Road (2008)\n2329,3.875,4,Dear Zachary: A Letter to a Son About His Father (2008)\n2330,3.0,1,Wild Child (2008)\n2331,3.9285714285714284,7,Defiance (2008)\n2332,4.5,1,Zeitgeist: Addendum (2008)\n2333,3.142857142857143,14,Marley & Me (2008)\n2334,4.0,11,Ponyo (Gake no ue no Ponyo) (2008)\n2335,3.0,1,Earthsea (Legend of Earthsea) (2004)\n2336,3.95,10,Ip Man (2008)\n2337,3.0,1,\"Tale of Despereaux, The (2008)\"\n2338,3.5,4,Bride Wars (2009)\n2339,2.5,1,\"Gamers, The: Dorkness Rising (2008)\"\n2340,4.0,1,Mesrine: Killer Instinct (L'instinct de mort) (2008)\n2341,2.0,1,My Bloody Valentine 3-D (2009)\n2342,4.0,1,Battle in Seattle (2007)\n2343,4.25,4,\"Timecrimes (Cronocrímenes, Los) (2007)\"\n2344,2.0,1,Fire and Ice (2008)\n2345,3.642857142857143,7,Underworld: Rise of the Lycans (2009)\n2346,3.5,5,Inkheart (2008)\n2347,2.7142857142857144,7,Paul Blart: Mall Cop (2009)\n2348,3.0,1,Notorious (2009)\n2349,3.5,1,\"Uninvited, The (2009)\"\n2350,3.3333333333333335,3,Outlander (2008)\n2351,4.0,1,Eden Lake (2008)\n2352,3.7,35,Coraline (2009)\n2353,3.0,2,Push (2009)\n2354,3.0,2,\"International, The (2009)\"\n2355,2.8181818181818183,11,He's Just Not That Into You (2009)\n2356,3.5,1,Dead Like Me: Life After Death (2009)\n2357,3.5,6,Futurama: Into the Wild Green Yonder (2009)\n2358,3.5,1,Frontière(s) (2007)\n2359,4.0,1,\"11th Hour, The (2007)\"\n2360,2.5,1,Afro Samurai: Resurrection (2009)\n2361,4.1,5,Departures (Okuribito) (2008)\n2362,3.0,1,My Name Is Bruce (2007)\n2363,3.5,5,Funny People (2009)\n2364,4.25,2,Berlin Calling (2008)\n2365,4.0,1,Nuremberg (2000)\n2366,3.5,4,Away We Go (2009)\n2367,4.0,2,\"Divo, Il (2008)\"\n2368,3.0,2,Friday the 13th (2009)\n2369,3.8,5,\"Good, the Bad, the Weird, The (Joheunnom nabbeunnom isanghannom) (2008)\"\n2370,2.75,2,\"Pink Panther 2, The (2009)\"\n2371,3.9166666666666665,24,Dr. Horrible's Sing-Along Blog (2008)\n2372,4.25,2,\"Cottage, The (2008)\"\n2373,3.5294117647058822,17,\"I Love You, Man (2009)\"\n2374,3.5,1,Dance of the Dead (2008)\n2375,2.5,2,\"Haunting in Connecticut, The (2009)\"\n2376,2.5,1,Duplicity (2009)\n2377,2.8333333333333335,9,Knowing (2009)\n2378,3.94,25,\"Girl with the Dragon Tattoo, The (Män som hatar kvinnor) (2009)\"\n2379,3.375,4,Sunshine Cleaning (2008)\n2380,2.875,4,Kung Fu Panda: Secrets of the Furious Five (2008)\n2381,2.0,1,Echelon Conspiracy (2009)\n2382,3.25,8,Monsters vs. Aliens (2009)\n2383,4.0,1,\"Baader Meinhof Komplex, Der (2008)\"\n2384,2.75,2,Big Stan (2007)\n2385,5.0,1,Strictly Sexual (2008)\n2386,3.75,2,Anvil! The Story of Anvil (2008)\n2387,2.625,4,Observe and Report (2009)\n2388,3.3333333333333335,15,Adventureland (2009)\n2389,3.5,4,Confessions of a Shopaholic (2009)\n2390,0.5,1,The Butterfly Effect 3: Revelations (2009)\n2391,3.25,10,\"Fast & Furious (Fast and the Furious 4, The) (2009)\"\n2392,4.3,5,In the Loop (2009)\n2393,3.4,5,Pirate Radio (2009)\n2394,3.55,10,17 Again (2009)\n2395,4.136363636363637,88,Inglourious Basterds (2009)\n2396,4.0,3,State of Play (2009)\n2397,4.25,2,\"Damned United, The (2009)\"\n2398,2.9375,8,Crank: High Voltage (2009)\n2399,3.96875,32,Moon (2009)\n2400,4.25,2,\"Young Victoria, The (2009)\"\n2401,2.8653846153846154,26,X-Men Origins: Wolverine (2009)\n2402,4.0,1,Sin Nombre (2009)\n2403,3.864406779661017,59,Star Trek (2009)\n2404,3.5,1,\"Great Buck Howard, The (2008)\"\n2405,4.0,3,Red Cliff Part II (Chi Bi Xia: Jue Zhan Tian Xia) (2009)\n2406,3.5,1,Earth (2007)\n2407,4.0,1,Stanley Kubrick: A Life in Pictures (2001)\n2408,2.0,1,Crossing Over (2009)\n2409,3.264705882352941,17,Angels & Demons (2009)\n2410,2.0,1,Balls Out: Gary the Tennis Coach (2009)\n2411,2.0,1,Powder Blue (2009)\n2412,2.875,8,Fanboys (2009)\n2413,3.25,18,Terminator Salvation (2009)\n2414,3.1,10,Night at the Museum: Battle of the Smithsonian (2009)\n2415,3.5,1,Were the World Mine (2008)\n2416,4.333333333333333,3,\"Brothers Bloom, The (2008)\"\n2417,4.0,1,I Do: How to Get Married and Stay Single (Prête-moi ta main) (2006)\n2418,3.5,2,\"Soloist, The (2009)\"\n2419,3.75,6,Drag Me to Hell (2009)\n2420,4.004761904761905,105,Up (2009)\n2421,2.75,2,Fullmetal Alchemist the Movie: Conqueror of Shamballa (Gekijô-ban hagane no renkinjutsushi: Shanbara wo yuku mono) (2005)\n2422,3.5833333333333335,6,Fired Up (2009)\n2423,1.5,1,In the Electric Mist (2009)\n2424,3.6315789473684212,76,\"Hangover, The (2009)\"\n2425,2.5,1,Killshot (2008)\n2426,3.8,5,Antichrist (2009)\n2427,4.0,1,Sweeney Todd (2006)\n2428,5.0,1,Boy Eats Girl (2005)\n2429,3.5,1,Special (2006)\n2430,4.25,4,Dead Snow (Død snø) (2009)\n2431,3.2142857142857144,7,Land of the Lost (2009)\n2432,4.0,1,Imagine That (2009)\n2433,3.1666666666666665,6,\"Taking of Pelham 1 2 3, The (2009)\"\n2434,4.5,1,\"Stoning of Soraya M., The (2008)\"\n2435,3.5,16,\"Proposal, The (2009)\"\n2436,2.7857142857142856,7,Year One (2009)\n2437,5.0,1,Garfield's Pet Force (2009)\n2438,4.0588235294117645,34,\"Hurt Locker, The (2008)\"\n2439,3.5,1,Breakfast with Scot (2007)\n2440,2.75,2,\"Limits of Control, The (2009)\"\n2441,2.425,20,Transformers: Revenge of the Fallen (2009)\n2442,4.5,2,Home (2009)\n2443,3.0,2,Whatever Works (2009)\n2444,2.5,3,Ghosts of Girlfriends Past (2009)\n2445,3.0555555555555554,9,Public Enemies (2009)\n2446,2.607142857142857,14,Ice Age: Dawn of the Dinosaurs (2009)\n2447,3.5,1,Prison Break: The Final Break (2009)\n2448,1.0,1,Daria: Is It College Yet? (2002)\n2449,4.0,1,My Sister's Keeper (2009)\n2450,4.5,1,Hood of Horror (2006)\n2451,3.5,1,Watchmen: Tales of the Black Freighter (2009)\n2452,3.6666666666666665,42,(500) Days of Summer (2009)\n2453,3.65,10,Brüno (Bruno) (2009)\n2454,3.75,2,\"Librarian, The: The Curse of the Judas Chalice (2008)\"\n2455,3.8879310344827585,58,Harry Potter and the Half-Blood Prince (2009)\n2456,5.0,1,Eichmann (2007)\n2457,2.5,1,Open Water 2: Adrift (2006)\n2458,3.8125,16,\"Imaginarium of Doctor Parnassus, The (2009)\"\n2459,3.5,1,9to5: Days in Porn (a.k.a. 9 to 5: Days in Porn) (2008)\n2460,2.5,1,Humpday (2009)\n2461,1.5,1,Polytechnique (2009)\n2462,3.0,4,Orphan (2009)\n2463,3.6666666666666665,12,\"Ugly Truth, The (2009)\"\n2464,3.5,1,\"Collector, The (2009)\"\n2465,4.0,1,\"Perfect Getaway, A (2009)\"\n2466,3.776923076923077,65,District 9 (2009)\n2467,2.9583333333333335,12,Julie & Julia (2009)\n2468,3.5,1,Obsessed (2009)\n2469,3.5,2,Race to Witch Mountain (2009)\n2470,3.5,1,Hannah Montana: The Movie (2009)\n2471,2.625,8,G.I. Joe: The Rise of Cobra (2009)\n2472,3.0,3,12 Rounds (2009)\n2473,5.0,1,Max Manus (2008)\n2474,3.6666666666666665,3,Lost in Austen (2008)\n2475,3.8333333333333335,3,Evangelion: 1.0 You Are (Not) Alone (Evangerion shin gekijôban: Jo) (2007)\n2476,2.0,1,\"Deal, The (2008)\"\n2477,3.625,4,\"Goods: Live Hard, Sell Hard, The (2009)\"\n2478,3.9,5,\"Time Traveler's Wife, The (2009)\"\n2479,4.0,1,I Can't Think Straight (2007)\n2480,2.8333333333333335,3,Miss March (2009)\n2481,2.5,1,\"I Love You, Beth Cooper (2009)\"\n2482,2.75,2,Paper Heart (2009)\n2483,3.5,1,G-Force (2009)\n2484,4.5,1,Tetro (2009)\n2485,3.8333333333333335,3,Bronson (2009)\n2486,3.75,2,It Might Get Loud (2008)\n2487,2.0,1,My Life in Ruins (2009)\n2488,3.0,3,Taking Woodstock (2009)\n2489,3.0,3,Halloween II (2009)\n2490,3.857142857142857,7,\"Secret in Their Eyes, The (El secreto de sus ojos) (2009)\"\n2491,3.642857142857143,14,9 (2009)\n2492,3.6,5,Frequently Asked Questions About Time Travel (2009)\n2493,4.5,1,\"White Ribbon, The (Das weiße Band) (2009)\"\n2494,3.5,1,Green Lantern: First Flight (2009)\n2495,4.5,2,\"Most Hated Family in America, The (2007)\"\n2496,3.4,5,Pandorum (2009)\n2497,3.388888888888889,9,\"Men Who Stare at Goats, The (2009)\"\n2498,3.0,1,\"Hunt For Gollum, The (2009)\"\n2499,1.0,2,Jennifer's Body (2009)\n2500,3.6666666666666665,3,\"Informant!, The (2009)\"\n2501,2.0,2,Extract (2009)\n2502,2.6666666666666665,3,\"Final Destination, The (Final Destination 4) (Final Destination in 3-D, The) (2009)\"\n2503,3.4285714285714284,7,Gamer (2009)\n2504,3.5294117647058822,17,Cloudy with a Chance of Meatballs (2009)\n2505,5.0,1,Tyler Perry's I Can Do Bad All by Myself (2009)\n2506,3.6875,8,\"Food, Inc. (2008)\"\n2507,4.0,3,Thirst (Bakjwi) (2009)\n2508,3.5,1,Bright Star (2009)\n2509,3.0,1,Blood Creek (a.k.a. Town Creek) (2009)\n2510,3.4,5,Paranormal Activity (2009)\n2511,3.0,2,World's Greatest Dad (2009)\n2512,1.0,1,Still Walking (Aruitemo aruitemo) (2008)\n2513,4.25,4,\"Cove, The (2009)\"\n2514,3.227272727272727,11,\"Serious Man, A (2009)\"\n2515,4.0,1,City Island (2009)\n2516,3.5,1,Ink (2009)\n2517,2.5,1,Metropia (2009)\n2518,3.5,1,\"Haunted World of El Superbeasto, The (2009)\"\n2519,2.5,1,Trick 'r Treat (2007)\n2520,3.1,5,Whip It (2009)\n2521,2.3333333333333335,6,\"Invention of Lying, The (2009)\"\n2522,3.0357142857142856,14,Surrogates (2009)\n2523,3.8773584905660377,53,Zombieland (2009)\n2524,1.0,1,Assassination of a High School President (2008)\n2525,3.8333333333333335,6,\"Education, An (2009)\"\n2526,4.0,1,Coco Before Chanel (Coco avant Chanel) (2009)\n2527,3.5,1,Burma VJ: Reporting from a Closed Country (Burma VJ: Reporter i et lukket land) (2008)\n2528,3.3333333333333335,3,Couples Retreat (2009)\n2529,2.5,2,I Sell the Dead (2008)\n2530,2.6666666666666665,12,Where the Wild Things Are (2009)\n2531,3.5,4,\"New York, I Love You (2009)\"\n2532,3.0625,8,Law Abiding Citizen (2009)\n2533,3.0,1,\"Misfortunates, The (De helaasheid der dingen) (2009)\"\n2534,4.2,10,Mary and Max (2009)\n2535,3.5,1,Spread (2009)\n2536,2.5,1,\"Tournament, The (2009)\"\n2537,3.71875,32,Up in the Air (2009)\n2538,3.5,3,Saw VI (2009)\n2539,5.0,1,Love Exposure (Ai No Mukidashi) (2008)\n2540,2.0,1,Cirque du Freak: The Vampire's Assistant (2009)\n2541,3.375,4,\"Boondock Saints II: All Saints Day, The (2009)\"\n2542,3.5,2,Black Dynamite (2009)\n2543,4.5,1,Welcome to Dongmakgol (2005)\n2544,2.25,2,Gentlemen Broncos (2009)\n2545,4.083333333333333,18,Fantastic Mr. Fox (2009)\n2546,3.0,2,\"Christmas Carol, A (2009)\"\n2547,4.25,2,Battlestar Galactica: The Plan (2009)\n2548,3.5,1,\"Private Lives of Pippa Lee, The (2009)\"\n2549,4.375,4,Partly Cloudy (2009)\n2550,2.619047619047619,21,2012 (2009)\n2551,3.5,3,Precious (2009)\n2552,2.25,2,Bad Lieutenant: Port of Call New Orleans (2009)\n2553,2.6666666666666665,9,\"Twilight Saga: New Moon, The (2009)\"\n2554,0.5,1,Derailed (2002)\n2555,3.5,1,\"Messenger, The (2009)\"\n2556,4.166666666666667,3,Ninja Assassin (2009)\n2557,4.5,1,Cell 211 (Celda 211) (2009)\n2558,3.0,1,Teenage Mutant Ninja Turtles: Turtles Forever (2009)\n2559,3.0,1,Merry Madagascar (2009)\n2560,3.5,5,Brothers (2009)\n2561,3.5,1,Garage (2007)\n2562,3.6774193548387095,31,\"Blind Side, The  (2009)\"\n2563,2.0,1,Shrink (2009)\n2564,0.5,1,Old Dogs (2009)\n2565,3.0,3,Planet 51 (2009)\n2566,4.0,1,Earthlings (2006)\n2567,3.8333333333333335,3,\"Single Man, A (2009)\"\n2568,3.1666666666666665,3,\"Lovely Bones, The (2009)\"\n2569,3.857142857142857,7,Invictus (2009)\n2570,3.75,12,\"Princess and the Frog, The (2009)\"\n2571,3.0,1,Did You Hear About the Morgans? (2009)\n2572,4.0,1,Alice (2009)\n2573,3.6030927835051547,97,Avatar (2009)\n2574,2.75,4,It's Complicated (2009)\n2575,3.853448275862069,58,Sherlock Holmes (2009)\n2576,4.1,5,Crazy Heart (2009)\n2577,2.6666666666666665,3,Alvin and the Chipmunks: The Squeakquel (2009)\n2578,2.1666666666666665,3,American Pie Presents: The Book of Love (American Pie 7: The Book of Love) (2009)\n2579,3.75,2,Pontypool (2008)\n2580,3.0,5,Youth in Revolt (2009)\n2581,3.4166666666666665,6,Daybreakers (2010)\n2582,3.5,2,Hachiko: A Dog's Story (a.k.a. Hachi: A Dog's Tale) (2009)\n2583,2.4,5,Leap Year (2010)\n2584,3.28125,16,\"Book of Eli, The (2010)\"\n2585,4.2,5,\"Girl Who Kicked the Hornet's Nest, The (Luftslottet som sprängdes) (2009)\"\n2586,4.25,4,\"Prophet, A (Un Prophète) (2009)\"\n2587,1.5,1,Staten Island (2009)\n2588,2.5,1,\"Maiden Heist, The (2009)\"\n2589,2.0,1,Blood: The Last Vampire (2009)\n2590,3.5,1,Bart Got a Room (2008)\n2591,3.0,1,Robin-B-Hood (Bo bui gai wak) (2006)\n2592,3.0,1,\"Concert, Le (2009)\"\n2593,3.0,1,Ninja (2009)\n2594,4.0,1,Asterix at the Olympic Games (Astérix aux jeux olympiques) (2008)\n2595,3.0,1,\"Chaser, The (Chugyeogja) (2008)\"\n2596,1.5,1,\"Dennis the Menace Christmas, A (2007)\"\n2597,4.0,1,Undisputed II: Last Man Standing (2006)\n2598,4.75,2,3 Idiots (2009)\n2599,2.8333333333333335,3,Legion (2010)\n2600,1.0,2,Stan Helsing (2009)\n2601,2.5,4,When in Rome (2010)\n2602,4.0,2,Triangle (2009)\n2603,1.5,2,I Love You Phillip Morris (2009)\n2604,4.375,4,Temple Grandin (2010)\n2605,4.0,1,\"House of the Devil, The (2009)\"\n2606,2.6666666666666665,3,Valentine's Day (2010)\n2607,2.25,2,\"Wolfman, The (2010)\"\n2608,4.022388059701493,67,Shutter Island (2010)\n2609,3.3333333333333335,3,Persuasion (2007)\n2610,4.25,4,\"Girl Who Played with Fire, The (Flickan som lekte med elden) (2009)\"\n2611,2.357142857142857,7,Percy Jackson & the Olympians: The Lightning Thief (2010)\n2612,2.5,5,Cop Out (2010)\n2613,3.7857142857142856,7,\"Ghost Writer, The (2010)\"\n2614,4.0,2,\"Secret of Kells, The (2009)\"\n2615,2.0,1,\"Spy Next Door, The (2010)\"\n2616,4.0,1,Agora (2009)\n2617,1.0,1,Motherhood (2009)\n2618,3.75,2,District 13: Ultimatum (Banlieue 13 - Ultimatum) (2009)\n2619,4.0,1,\"Yes Men Fix the World, The (2009)\"\n2620,4.0,1,Mike Bassett: England Manager (2001)\n2621,3.375,4,\"Crazies, The (2010)\"\n2622,3.5,1,Dear John (2010)\n2623,3.0,1,Last Train Home (2009)\n2624,2.1666666666666665,3,Tooth Fairy (2010)\n2625,3.6666666666666665,3,[REC]² (2009)\n2626,3.6666666666666665,3,\"Room, The (2003)\"\n2627,2.875,28,Alice in Wonderland (2010)\n2628,3.75,2,\"Town Called Panic, A (Panique au village) (2009)\"\n2629,3.5,9,Green Zone (2010)\n2630,2.5,2,From Paris with Love (2010)\n2631,3.0,5,Dorian Gray (2009)\n2632,2.3333333333333335,3,Greenberg (2010)\n2633,3.2222222222222223,9,She's Out of My League (2010)\n2634,4.0,2,Harry Brown (2009)\n2635,4.0,1,Remember Me (2010)\n2636,3.0,1,\"Burrowers, The (2008)\"\n2637,3.25,2,Frozen (2010)\n2638,2.5,1,Little Ashes (2008)\n2639,1.5,1,Our Family Wedding (2010)\n2640,2.75,6,\"Bounty Hunter, The (2010)\"\n2641,3.1666666666666665,3,Leaves of Grass (2009)\n2642,2.0,1,Women in Trouble (2009)\n2643,3.5,6,Repo Men (2010)\n2644,0.5,1,Case 39 (2009)\n2645,4.0,1,Oceans (Océans) (2009)\n2646,3.5,2,\"Slammin' Salmon, The (2009)\"\n2647,3.3,15,Hot Tub Time Machine (2010)\n2648,5.0,1,Mother (Madeo) (2009)\n2649,3.943396226415094,53,How to Train Your Dragon (2010)\n2650,4.5,1,\"Bone Man, The (Der Knochenmann) (2009)\"\n2651,3.75,2,Micmacs (Micmacs à tire-larigot) (2009)\n2652,2.3076923076923075,13,Clash of the Titans (2010)\n2653,3.6511627906976742,43,Kick-Ass (2010)\n2654,3.3181818181818183,11,Date Night (2010)\n2655,4.5,1,\"Emperor's New Groove 2: Kronk's New Groove, The (2005)\"\n2656,3.5,1,Steam of Life (Miesten vuoro) (2010)\n2657,3.5,1,Mortadelo & Filemon: The Big Adventure (La gran aventura de Mortadelo y Filemón) (2003)\n2658,3.0,1,American Drug War: The Last White Hope (2007)\n2659,3.5,1,\"Runaways, The (2010)\"\n2660,4.0,1,Wild China (2008)\n2661,3.25,2,Death at a Funeral (2010)\n2662,2.5,1,Valhalla Rising (2009)\n2663,3.5,1,Diary of a Wimpy Kid (2010)\n2664,3.5,1,\"Union: The Business Behind Getting High, The (2007)\"\n2665,3.5,1,Disgrace (2008)\n2666,3.0,3,\"Losers, The (2010)\"\n2667,3.5,1,\"Last Song, The (2010)\"\n2668,2.5,1,Cyrus (2010)\n2669,0.5,1,\"Human Centipede, The (First Sequence) (2009)\"\n2670,4.038461538461538,13,Exit Through the Gift Shop (2010)\n2671,3.5106382978723403,47,Iron Man 2 (2010)\n2672,3.0,1,MacGruber (2010)\n2673,2.5,1,\"Sky Crawlers, The (Sukai kurora) (2008)\"\n2674,3.5,1,Cargo (2009)\n2675,1.25,2,\"Nightmare on Elm Street, A (2010)\"\n2676,4.0,5,Four Lions (2010)\n2677,2.0,1,St Trinian's 2: The Legend of Fritton's Gold (2009)\n2678,3.1666666666666665,6,Robin Hood (2010)\n2679,3.5,1,Lovers & Leavers (Kuutamolla) (2002)\n2680,4.0,1,Merantau (2009)\n2681,4.0,1,Stingray Sam (2009)\n2682,3.5,1,Cemetery Junction (2010)\n2683,2.8,5,Blue Valentine (2010)\n2684,2.25,2,Killers (2010)\n2685,3.5,4,Buried (2010)\n2686,2.5,1,Shake Hands with the Devil (2007)\n2687,2.8846153846153846,13,Prince of Persia: The Sands of Time (2010)\n2688,3.5,1,Please Give (2010)\n2689,4.5,1,Baarìa (2009)\n2690,3.5,1,Ricky Gervais Live: Animals (2003)\n2691,1.0,2,Sex and the City 2 (2010)\n2692,3.55,10,Get Him to the Greek (2010)\n2693,3.0,1,Unthinkable (2010)\n2694,3.0,1,\"Back-up Plan, The (2010)\"\n2695,3.5,3,Splice (2009)\n2696,2.8333333333333335,3,Letters to Juliet (2010)\n2697,3.8,5,Exam (2009)\n2698,1.875,4,Jonah Hex (2010)\n2699,3.1470588235294117,17,\"A-Team, The (2010)\"\n2700,4.109090909090909,55,Toy Story 3 (2010)\n2701,3.0,1,Ricky Gervais Live 3: Fame (2007)\n2702,3.4375,8,Winter's Bone (2010)\n2703,3.5,1,Barking Dogs Never Bite (Flandersui gae) (2000)\n2704,3.5714285714285716,7,Shrek Forever After (a.k.a. Shrek: The Final Chapter) (2010)\n2705,3.75,2,TiMER (2009)\n2706,4.0,1,Best Worst Movie (2009)\n2707,2.45,10,\"Twilight Saga: Eclipse, The (2010)\"\n2708,5.0,2,Enter the Void (2009)\n2709,1.9166666666666667,6,\"Last Airbender, The (2010)\"\n2710,3.0,1,Endgame (2009)\n2711,4.5,1,Empire of Dreams: The Story of the 'Star Wars' Trilogy (2004)\n2712,4.0,8,South Park: Imaginationland (2008)\n2713,3.3,10,Predators (2010)\n2714,4.0,1,When You're Strange (2009)\n2715,3.6842105263157894,38,Despicable Me (2010)\n2716,4.066433566433567,143,Inception (2010)\n2717,3.45,10,Grown Ups (2010)\n2718,3.45,10,\"Sorcerer's Apprentice, The (2010)\"\n2719,3.4166666666666665,6,Knight and Day (2010)\n2720,2.5,8,\"Karate Kid, The (2010)\"\n2721,2.9166666666666665,6,\"Kids Are All Right, The (2010)\"\n2722,2.25,2,\"Serbian Film, A (Srpski film) (2010)\"\n2723,2.5,1,Cherrybomb (2009)\n2724,3.6666666666666665,3,Batman: Under the Red Hood (2010)\n2725,2.966666666666667,15,Salt (2010)\n2726,3.0,1,\"No. 1 Ladies' Detective Agency, The (2008)\"\n2727,4.142857142857143,7,Mr. Nobody (2009)\n2728,3.2857142857142856,7,Dinner for Schmucks (2010)\n2729,4.0,1,Deadly Outlaw: Rekka (a.k.a. Violent Fire) (Jitsuroku Andô Noboru kyôdô-den: Rekka) (2002)\n2730,3.0,1,Hellsinki (Rööperi) (2009)\n2731,3.0,5,Ip Man 2 (2010)\n2732,2.5,1,Ramona and Beezus (2010)\n2733,2.5,1,\"Rebound, The (2009)\"\n2734,3.261904761904762,21,\"Other Guys, The (2010)\"\n2735,3.5,1,\"Two Escobars, The (2010)\"\n2736,4.0,1,Paper Man (2009)\n2737,3.0588235294117645,17,\"Expendables, The (2010)\"\n2738,3.7045454545454546,44,Scott Pilgrim vs. the World (2010)\n2739,3.5,2,I Killed My Mother (J'ai tué ma mère) (2009)\n2740,2.5,1,Heartless (2009)\n2741,2.1666666666666665,3,Piranha (Piranha 3D) (2010)\n2742,2.5,1,\"Extraordinary Adventures of Adèle Blanc-Sec, The (2010)\"\n2743,5.0,1,Get Low (2009)\n2744,3.5,1,\"Joneses, The (2009)\"\n2745,4.0,1,\"Last Exorcism, The (2010)\"\n2746,5.0,1,Sisters (Syostry) (2001)\n2747,2.625,4,\"American, The (2010)\"\n2748,4.166666666666667,3,Jackass 2.5 (2007)\n2749,2.5,1,\"Horde, The (La Horde) (2009)\"\n2750,2.6666666666666665,6,\"Switch, The (2010)\"\n2751,3.25,12,Machete (2010)\n2752,3.1666666666666665,3,Going the Distance (2010)\n2753,3.142857142857143,7,Resident Evil: Afterlife (2010)\n2754,2.5,1,Princess (Prinsessa) (2010)\n2755,3.8859649122807016,57,\"Social Network, The (2010)\"\n2756,3.9545454545454546,22,\"Town, The (2010)\"\n2757,3.7962962962962963,27,Easy A (2010)\n2758,3.5,2,Eat Pray Love (2010)\n2759,3.5,1,Howl (2010)\n2760,4.0,1,I'm Still Here (2010)\n2761,4.0,1,\"Patrik Age 1.5 (Patrik 1,5) (2008)\"\n2762,3.8333333333333335,3,Flipped (2010)\n2763,3.1666666666666665,3,Wall Street: Money Never Sleeps (2010)\n2764,4.0,2,Legend of the Guardians: The Owls of Ga'Hoole (2010)\n2765,3.1875,8,It's Kind of a Funny Story (2010)\n2766,3.25,2,Middle Men (2009)\n2767,3.857142857142857,7,Let Me In (2010)\n2768,3.5,1,Sintel (2010)\n2769,2.5,1,Secretariat (2010)\n2770,3.25,2,Devil (2010)\n2771,3.5,1,You Again (2010)\n2772,3.0,6,Life as We Know It (2010)\n2773,3.6666666666666665,3,Catfish (2010)\n2774,3.5,1,You Will Meet a Tall Dark Stranger (2010)\n2775,3.25,2,Stone (2010)\n2776,4.291666666666667,12,Inside Job (2010)\n2777,3.5,1,Monsters (2010)\n2778,4.0,3,Never Let Me Go (2010)\n2779,3.0,3,\"Illusionist, The (L'illusionniste) (2010)\"\n2780,4.5,1,Luck by Chance (2009)\n2781,3.75,2,Rubber (2010)\n2782,3.9166666666666665,6,Jackass 3D (2010)\n2783,3.1666666666666665,3,Restrepo (2010)\n2784,4.0,1,Waiting for 'Superman' (2010)\n2785,3.5,20,Red (2010)\n2786,3.5,1,In a Better World (Hævnen) (2010)\n2787,2.5,1,Heartbreaker (L'Arnacoeur) (2010)\n2788,3.7,5,Paranormal Activity 2 (2010)\n2789,3.0,2,Heartbeats (Les amours imaginaires) (2010)\n2790,4.0,1,Hereafter (2010)\n2791,4.0,1,Undisputed III: Redemption (2010)\n2792,4.5,1,Saw VII 3D - The Final Chapter (2010)\n2793,3.1,10,Due Date (2010)\n2794,3.8333333333333335,18,127 Hours (2010)\n2795,3.6,20,Megamind (2010)\n2796,3.630952380952381,42,Black Swan (2010)\n2797,3.357142857142857,7,Unstoppable (2010)\n2798,2.6666666666666665,6,Morning Glory (2010)\n2799,4.0,1,Certified Copy (Copie conforme) (2010)\n2800,3.857142857142857,7,\"Next Three Days, The (2010)\"\n2801,3.0,1,Somewhere (2010)\n2802,4.25,2,Biutiful (2010)\n2803,4.0,1,\"First Beautiful Thing, The (La prima cosa bella) (2010)\"\n2804,3.9893617021276597,47,Harry Potter and the Deathly Hallows: Part 1 (2010)\n2805,4.043103448275862,58,\"King's Speech, The (2010)\"\n2806,3.9166666666666665,24,Tangled (2010)\n2807,4.0,2,\"Art of the Steal, The (2009)\"\n2808,3.760869565217391,23,\"Fighter, The (2010)\"\n2809,3.0,1,\"Romantics, The (2010)\"\n2810,4.25,2,\"Loved Ones, The (2009)\"\n2811,4.0,2,Casino Jack (2010)\n2812,3.0,1,Vincent Wants to Sea (Vincent will meer) (2010)\n2813,1.5,1,London Boulevard (2010)\n2814,0.5,1,Skyline (2010)\n2815,3.5,1,Beastly (2011)\n2816,2.7857142857142856,7,Love and Other Drugs (2010)\n2817,3.857142857142857,7,\"Chronicles of Narnia: The Voyage of the Dawn Treader, The (2010)\"\n2818,2.7142857142857144,7,\"Tourist, The (2010)\"\n2819,4.25,2,Rare Exports: A Christmas Tale (Rare Exports) (2010)\n2820,4.5,1,Hatchet II (2010)\n2821,3.75,2,All Good Things (2010)\n2822,3.75,28,True Grit (2010)\n2823,3.236842105263158,19,Tron: Legacy (2010)\n2824,2.6666666666666665,3,How Do You Know (2010)\n2825,4.0,1,Barney's Version (2010)\n2826,2.5,1,\"Company Men, The (2010)\"\n2827,3.875,4,I Saw the Devil (Akmareul boatda) (2010)\n2828,0.5,1,Trash Humpers (2009)\n2829,5.0,1,Faster (2010)\n2830,3.5,1,Little Big Soldier (Da bing xiao jiang) (2010)\n2831,3.5,1,Rabbit Hole (2010)\n2832,1.8333333333333333,6,Little Fockers (2010)\n2833,1.8333333333333333,6,Gulliver's Travels (2010)\n2834,4.5,1,Sweetgrass (2009)\n2835,2.5,1,Burlesque (2010)\n2836,3.5,4,\"Secret World of Arrietty, The (Kari-gurashi no Arietti) (2010)\"\n2837,3.9166666666666665,18,Tucker & Dale vs Evil (2010)\n2838,1.1666666666666667,3,Yogi Bear (2010)\n2839,2.0,1,Made in Dagenham (2010)\n2840,3.0,11,\"Green Hornet, The (2011)\"\n2841,4.0,1,\"Way Back, The (2010)\"\n2842,3.5,1,\"Warrior's Way, The (2010)\"\n2843,3.5,1,Season of the Witch (2011)\n2844,0.5,1,Amer (2009)\n2845,3.0,9,Cowboys & Aliens (2011)\n2846,2.5,1,Anything for Her (Pour elle) (2008)\n2847,4.1,5,Day & Night (2010)\n2848,4.25,2,Marwencol (2010)\n2849,2.5,2,\"Dilemma, The (2011)\"\n2850,4.0,1,\"Trip, The (2010)\"\n2851,3.95,30,Limitless (2011)\n2852,4.5,1,Happy People: A Year in the Taiga (2010)\n2853,3.5,3,Evangelion: 2.0 You Can (Not) Advance (Evangerion shin gekijôban: Ha) (2009)\n2854,3.75,2,I Spit on Your Grave (2010)\n2855,4.0,1,Even the Rain (También la lluvia) (2010)\n2856,5.0,1,Zeitgeist: Moving Forward (2011)\n2857,2.9285714285714284,14,No Strings Attached (2011)\n2858,3.6,10,\"Lincoln Lawyer, The (2011)\"\n2859,4.25,2,All-Star Superman (2011)\n2860,3.3333333333333335,6,Unknown (2011)\n2861,2.25,2,Cedar Rapids (2011)\n2862,3.0,1,Gnomeo & Juliet (2011)\n2863,4.0,1,Burke and Hare (2010)\n2864,4.0,1,Castaway on the Moon (Kimssi pyoryugi) (2009)\n2865,3.2,15,Paul (2011)\n2866,4.5,1,Brother 2 (Brat 2) (2000)\n2867,2.5,2,Emma (2009)\n2868,3.75,2,Drive Angry (2011)\n2869,3.6176470588235294,17,Rango (2011)\n2870,2.5,1,Take Me Home Tonight (2011)\n2871,4.0,1,Confessions (Kokuhaku) (2010)\n2872,3.1666666666666665,21,\"Adjustment Bureau, The (2011)\"\n2873,3.375,4,\"Mechanic, The (2011)\"\n2874,3.125,4,Hall Pass (2011)\n2875,2.0,1,\"Eagle, The (2011)\"\n2876,3.1,5,I Am Number Four (2011)\n2877,2.375,4,Battle: Los Angeles (2011)\n2878,3.8333333333333335,3,Summer Wars (Samâ wôzu) (2009)\n2879,4.0,1,\"Sunset Limited, The (2011)\"\n2880,3.5,1,Mars Needs Moms (2011)\n2881,5.0,1,Scooby-Doo! Curse of the Lake Monster (2010)\n2882,4.357142857142857,7,Elite Squad: The Enemy Within (Tropa de Elite 2 - O Inimigo Agora É Outro) (2010)\n2883,3.5,3,Mesrine: Public Enemy #1 (L'ennemi public n°1) (2008)\n2884,3.2142857142857144,7,Just Go with It (2011)\n2885,3.0,1,Cave of Forgotten Dreams (2010)\n2886,3.5,2,Red Riding Hood (2011)\n2887,1.0,1,\"Big Mommas: Like Father, Like Son (2011)\"\n2888,2.9,5,Super (2010)\n2889,3.75,2,\"Troll Hunter, The (Trolljegeren) (2010)\"\n2890,3.6029411764705883,34,Source Code (2011)\n2891,3.0,1,Jane Eyre (2011)\n2892,3.125,12,Sucker Punch (2011)\n2893,4.5,3,BURN-E (2008)\n2894,3.75,6,Senna (2010)\n2895,3.5833333333333335,6,Family Guy Presents: It's a Trap (2010)\n2896,2.6666666666666665,3,Insidious (2010)\n2897,2.375,4,Hobo with a Shotgun (2011)\n2898,3.75,6,Win Win (2011)\n2899,2.75,2,Room in Rome (Habitación en Roma) (2010)\n2900,4.0,1,Boy (2010)\n2901,3.5,1,Diary of a Wimpy Kid: Rodrick Rules (2011)\n2902,2.0,1,Henry's Crime (2010)\n2903,2.75,2,Hop (2011)\n2904,4.0,1,Playing the Victim (Izobrazhaya zhertvu) (2006)\n2905,0.5,1,Films to Keep You Awake: The Christmas Tale (Películas para no dormir: Cuento de navidad) (2005)\n2906,4.0,4,13 Assassins (Jûsan-nin no shikaku) (2010)\n2907,3.3181818181818183,11,Hanna (2011)\n2908,3.5,1,Into Eternity (2010)\n2909,4.0,3,American: The Bill Hicks Story (2009)\n2910,3.0,6,Arthur (2011)\n2911,2.6666666666666665,3,Scream 4 (2011)\n2912,3.125,8,Rio (2011)\n2913,3.0714285714285716,7,Melancholia (2011)\n2914,3.514705882352941,34,Thor (2011)\n2915,4.055555555555555,9,Louis C.K.: Hilarious (2010)\n2916,3.2857142857142856,7,Louis C.K.: Chewed Up (2008)\n2917,2.0,1,Atlas Shrugged: Part 1 (2011)\n2918,4.0,7,Louis C.K.: Shameless (2007)\n2919,4.5,1,Mildred Pierce (2011)\n2920,4.3,5,Voices from the List (2004)\n2921,3.375,4,Water for Elephants (2011)\n2922,2.5,1,African Cats (2011)\n2923,4.0,2,Kill the Irishman (2011)\n2924,3.05,10,\"Fast Five (Fast and the Furious 5, The) (2011)\"\n2925,5.0,1,Louis Theroux: Law & Disorder (2008)\n2926,5.0,1,Idiots and Angels (2008)\n2927,4.4,5,Incendies (2010)\n2928,3.5,1,Soul Surfer (2011)\n2929,3.25,2,Something Borrowed (2011)\n2930,3.761904761904762,21,Bridesmaids (2011)\n2931,3.0,3,Priest (2011)\n2932,3.2291666666666665,24,Pirates of the Caribbean: On Stranger Tides (2011)\n2933,3.56,25,Midnight in Paris (2011)\n2934,4.5,3,The Man from Nowhere (2010)\n2935,4.0,2,\"Tree of Life, The (2011)\"\n2936,3.2666666666666666,15,\"Hangover Part II, The (2011)\"\n2937,4.0,1,Nothing to Declare (Rien à déclarer) (2010)\n2938,2.0,1,Across the Hall (2009)\n2939,2.0,1,\"Roommate, The (2011)\"\n2940,3.5,6,Attack the Block (2011)\n2941,3.5,2,The Way (2010)\n2942,3.0,1,Let the Bullets Fly (2010)\n2943,3.323529411764706,17,Kung Fu Panda 2 (2011)\n2944,3.7906976744186047,43,X-Men: First Class (2011)\n2945,4.166666666666667,3,Submarine (2010)\n2946,4.0,1,American Grindhouse (2010)\n2947,2.6666666666666665,3,Everything Must Go (2010)\n2948,3.625,4,Beginners (2010)\n2949,3.5952380952380953,21,Super 8 (2011)\n2950,2.35,10,Green Lantern (2011)\n2951,2.5,2,Elektra Luxx (2010)\n2952,2.7,5,Mr. Popper's Penguins (2011)\n2953,2.888888888888889,9,Bad Teacher (2011)\n2954,2.3636363636363638,11,Transformers: Dark of the Moon (2011)\n2955,2.75,2,Larry Crowne (2011)\n2956,3.111111111111111,9,Your Highness (2011)\n2957,4.5,1,Too Big to Fail (2011)\n2958,2.75,2,Takers (2010)\n2959,5.0,1,My Life as McDull (Mak dau goo si) (2001)\n2960,2.75,4,Zookeeper (2011)\n2961,3.4375,24,Horrible Bosses (2011)\n2962,3.1666666666666665,3,Cars 2 (2011)\n2963,3.0,1,Between the Folds (2008)\n2964,4.0,1,Delhi Belly (2011)\n2965,3.0,1,Upside Down: The Creation Records Story (2010)\n2966,3.0,1,Monte Carlo (2011)\n2967,3.91,50,Harry Potter and the Deathly Hallows: Part 2 (2011)\n2968,3.765625,32,Drive (2011)\n2969,3.546875,32,Captain America: The First Avenger (2011)\n2970,3.9838709677419355,31,\"Crazy, Stupid, Love. (2011)\"\n2971,3.5,1,One Day (2011)\n2972,3.625,4,\"Guard, The (2011)\"\n2973,3.0,1,Winnie the Pooh (2011)\n2974,3.5,2,\"Woman, The (2011)\"\n2975,4.0,1,Mike's New Car (2002)\n2976,1.875,4,\"Smurfs, The (2011)\"\n2977,3.05,20,Friends with Benefits (2011)\n2978,5.0,1,Paper Birds (Pájaros de papel) (2010)\n2979,3.0,1,Blitz (2011)\n2980,2.5,1,\"Yellow Sea, The (a.k.a. The Murderer) (Hwanghae) (2010)\"\n2981,3.25,2,Our Idiot Brother (2011)\n2982,3.5,2,Death Race 2 (2010)\n2983,3.462962962962963,27,Rise of the Planet of the Apes (2011)\n2984,3.5,2,Terri (2011)\n2985,2.85,10,\"Change-Up, The (2011)\"\n2986,3.8333333333333335,21,\"Help, The (2011)\"\n2987,2.6666666666666665,6,30 Minutes or Less (2011)\n2988,3.0,1,My Afternoons with Margueritte (La tête en friche) (2010)\n2989,4.5,1,Final Destination 5 (2011)\n2990,3.25,2,\"Very Harold & Kumar 3D Christmas, A (2011)\"\n2991,3.0,2,Don't Be Afraid of the Dark (2010)\n2992,3.25,2,Fright Night (2011)\n2993,4.5,1,Another Earth (2011)\n2994,4.0,1,Hesher (2010)\n2995,4.0,1,Stake Land (2010)\n2996,2.5,1,\"Debt, The (2011)\"\n2997,2.5,6,Colombiana (2011)\n2998,4.5,1,Bill Cunningham New York (2011)\n2999,3.0,4,\"Skin I Live In, The (La piel que habito) (2011)\"\n3000,1.75,2,Conan the Barbarian (2011)\n3001,3.5,1,Walled In (2009)\n3002,3.5,1,Birdemic: Shock and Terror (2010)\n3003,3.6666666666666665,3,\"Inbetweeners Movie, The (2011)\"\n3004,3.0,1,Red State (2011)\n3005,0.5,1,Pearl Jam Twenty (2011)\n3006,1.5,1,I Don't Know How She Does It (2011)\n3007,1.5,1,Shark Night 3D (2011)\n3008,3.7083333333333335,12,Contagion (2011)\n3009,3.8846153846153846,26,Moneyball (2011)\n3010,4.0,1,Neds (2010)\n3011,4.5,2,Cold Fish (Tsumetai nettaigyo) (2010)\n3012,3.5,1,Phineas and Ferb the Movie: Across the 2nd Dimension (2011)\n3013,4.0,1,Northanger Abbey (2007)\n3014,3.869565217391304,69,\"Avengers, The (2012)\"\n3015,3.8125,8,Tinker Tailor Soldier Spy (2011)\n3016,4.333333333333333,3,\"Separation, A (Jodaeiye Nader az Simin) (2011)\"\n3017,3.0,2,\"Dangerous Method, A (2011)\"\n3018,3.727272727272727,11,Warrior (2011)\n3019,3.2222222222222223,9,\"Ides of March, The (2011)\"\n3020,4.5,1,Kill List (2011)\n3021,2.8333333333333335,3,Killer Elite (2011)\n3022,3.5,1,Bellflower (2011)\n3023,3.6315789473684212,19,50/50 (2011)\n3024,3.5,1,Generation P (2011)\n3025,4.045454545454546,11,The Artist (2011)\n3026,3.0,1,BlinkyTM (2011)\n3027,3.75,2,Take Shelter (2011)\n3028,3.0,6,Real Steel (2011)\n3029,2.75,2,Footloose (2011)\n3030,3.5,4,\"Thing, The (2011)\"\n3031,4.0,1,Beautiful Boy (2010)\n3032,3.5,1,Tyrannosaur (2011)\n3033,3.75,2,Martha Marcy May Marlene (2011)\n3034,3.5,4,We Need to Talk About Kevin (2011)\n3035,2.0,1,Behind Enemy Lines II: Axis of Evil (2006)\n3036,3.1666666666666665,3,\"Three Musketeers, The (2011)\"\n3037,3.392857142857143,14,In Time (2011)\n3038,3.0,1,Margaret (2011)\n3039,4.0,4,Carnage (2011)\n3040,3.7857142857142856,7,Margin Call (2011)\n3041,3.5,2,Paranormal Activity 3 (2011)\n3042,3.0,2,Puncture (2011)\n3043,3.0,6,Johnny English Reborn (2011)\n3044,3.5,1,Abduction (2011)\n3045,3.5,1,This Must Be the Place (2011)\n3046,3.75,4,Shame (2011)\n3047,4.5,1,What's Your Number? (2011)\n3048,3.6,5,Headhunters (Hodejegerne) (2011)\n3049,3.6666666666666665,3,Batman: Year One (2011)\n3050,3.5,1,Miss Representation (2011)\n3051,3.5625,8,Puss in Boots (2011)\n3052,3.5,4,Tower Heist (2011)\n3053,2.5,1,J. Edgar (2011)\n3054,1.5,1,\"Double, The (2011)\"\n3055,3.5,12,\"Adventures of Tintin, The (2011)\"\n3056,4.0,1,Starsuckers (2009)\n3057,3.0,1,Tomboy (2011)\n3058,4.0,1,George Harrison: Living in the Material World (2011)\n3059,3.75,18,Hugo (2011)\n3060,2.6666666666666665,3,Immortals (2011)\n3061,2.3,5,Jack and Jill (2011)\n3062,5.0,1,Into the Abyss (2011)\n3063,4.0,11,\"Descendants, The (2011)\"\n3064,2.5,2,Like Crazy (2011)\n3065,2.6,10,\"Muppets, The (2011)\"\n3066,2.2142857142857144,7,\"Twilight Saga: Breaking Dawn - Part 1, The (2011)\"\n3067,3.0,3,War Horse (2011)\n3068,3.5833333333333335,6,\"Rum Diary, The (2011)\"\n3069,4.0,1,Lifted (2006)\n3070,3.5,1,Hipsters (Stilyagi) (2008)\n3071,2.5,1,Another Cinderella Story (2008)\n3072,1.5,1,Bunraku (2010)\n3073,2.9,5,\"Sitter, The (2011)\"\n3074,2.75,2,Extremely Loud and Incredibly Close (2011)\n3075,4.0,1,Play the Game (2009)\n3076,5.0,1,Asterix and the Vikings (Astérix et les Vikings) (2006)\n3077,5.0,1,Happy Feet Two (2011)\n3078,0.5,1,Arthur Christmas (2011)\n3079,3.0,2,Violet & Daisy (2011)\n3080,1.5,1,Bullet to the Head (2012)\n3081,3.5,10,\"Expendables 2, The (2012)\"\n3082,3.435185185185185,54,The Hunger Games (2012)\n3083,3.9934210526315788,76,\"Dark Knight Rises, The (2012)\"\n3084,3.138888888888889,18,\"Bourne Legacy, The (2012)\"\n3085,3.763157894736842,38,Sherlock Holmes: A Game of Shadows (2011)\n3086,2.5,1,Coriolanus (2011)\n3087,2.25,2,Young Adult (2011)\n3088,2.0,3,New Year's Eve (2011)\n3089,3.659090909090909,22,Mission: Impossible - Ghost Protocol (2011)\n3090,3.3,5,We Bought a Zoo (2011)\n3091,3.488095238095238,42,\"Girl with the Dragon Tattoo, The (2011)\"\n3092,2.5,2,\"Darkest Hour, The (2011)\"\n3093,1.0,2,Alvin and the Chipmunks: Chipwrecked (2011)\n3094,1.5,1,Salvation Boulevard (2011)\n3095,3.6666666666666665,3,Friends with Kids (2011)\n3096,4.5,1,Girl Walks Into a Bar (2011)\n3097,3.0,2,Contraband (2012)\n3098,3.5,1,Being Elmo: A Puppeteer's Journey (2011)\n3099,1.5,1,Joyful Noise (2012)\n3100,3.0,1,\"Iron Lady, The (2011)\"\n3101,2.5,1,Albatross (2011)\n3102,4.5,1,\"Revenant, The (2009)\"\n3103,3.8,5,Underworld: Awakening (2012)\n3104,2.9166666666666665,6,\"Grey, The (2012)\"\n3105,3.1666666666666665,3,Man on a Ledge (2012)\n3106,2.5,1,Sacrifice (Zhao shi gu er) (2010)\n3107,2.8333333333333335,3,Haywire (2011)\n3108,3.0,1,Contact High (2009)\n3109,3.25,2,\"Whistleblower, The (2010)\"\n3110,3.5,1,Einstein and Eddington (2008)\n3111,2.0,1,Apollo 18 (2011)\n3112,2.0,1,Seeking Justice (2011)\n3113,1.5,1,Red Tails (2012)\n3114,4.5,1,\"Flowers of War, The (Jin líng shí san chai) (2011)\"\n3115,4.108108108108108,37,Intouchables (2011)\n3116,2.0,2,One for the Money (2012)\n3117,2.75,2,\"Innkeepers, The (2011)\"\n3118,3.1666666666666665,3,Grave Encounters (2011)\n3119,3.375,8,Chronicle (2012)\n3120,3.75,2,\"Woman in Black, The (2012)\"\n3121,1.0,1,Woman in Love (Rubbeldiekatz) (2011)\n3122,3.0,2,\"Art of Getting By, The (2011)\"\n3123,4.25,2,All Watched Over by Machines of Loving Grace (2011)\n3124,5.0,1,Dylan Moran: Monster (2004)\n3125,3.375,4,Safe House (2012)\n3126,2.6666666666666665,3,\"Vow, The (2012)\"\n3127,4.3,10,Louis C.K.: Live at the Beacon Theater (2011)\n3128,4.5,2,Monsieur Lazhar (2011)\n3129,2.5,1,\"For a Good Time, Call... (2012)\"\n3130,2.5,1,Janie Jones (2010)\n3131,0.5,1,Journey 2: The Mysterious Island (2012)\n3132,4.25,2,Perfect Sense (2011)\n3133,2.0,1,Rollo and the Woods Sprite (Rölli ja metsänhenki) (2001)\n3134,1.6,5,Ghost Rider: Spirit of Vengeance (2012)\n3135,3.5,1,Prayers for Bobby (2009)\n3136,4.25,2,\"Very Potter Musical, A (2009)\"\n3137,5.0,1,\"Very Potter Sequel, A (2010)\"\n3138,5.0,1,Miss Nobody (2010)\n3139,3.5,1,\"Women on the 6th Floor, The (Les Femmes du 6ème Étage) (2010)\"\n3140,1.5,1,Mega Shark vs. Crocosaurus (2010)\n3141,3.0,1,Red Hill (2010)\n3142,1.5,1,Gone (2012)\n3143,2.0625,8,Project X (2012)\n3144,3.1,5,Dr. Seuss' The Lorax (2012)\n3145,3.75,4,\"Big Year, The (2011)\"\n3146,3.75,2,Act of Valor (2012)\n3147,2.6363636363636362,11,This Means War (2012)\n3148,3.090909090909091,11,John Carter (2012)\n3149,3.25,4,Goon (2011)\n3150,2.5,1,\"Ledge, The (2011)\"\n3151,3.8653846153846154,26,21 Jump Street (2012)\n3152,4.0,1,\"Jeff, Who Lives at Home (2012)\"\n3153,3.1666666666666665,3,Lockout (2012)\n3154,2.5,1,\"Snowtown (Snowtown Murders, The) (2011)\"\n3155,3.0,1,Space Battleship Yamato (2010)\n3156,4.1,5,Jiro Dreams of Sushi (2011)\n3157,2.25,2,Damsels in Distress (2011)\n3158,1.5,1,Salmon Fishing in the Yemen (2011)\n3159,2.625,4,Wrath of the Titans (2012)\n3160,4.0,1,Detachment (2011)\n3161,3.75,2,Iron Sky (2012)\n3162,3.5,1,Absentia (2011)\n3163,2.625,4,American Reunion (American Pie 4) (2012)\n3164,4.333333333333333,9,The Raid: Redemption (2011)\n3165,4.0227272727272725,22,\"Cabin in the Woods, The (2012)\"\n3166,4.5,1,God Bless America (2011)\n3167,1.25,2,\"Three Stooges, The (2012)\"\n3168,2.0,1,\"Raven, The (2012)\"\n3169,4.5,1,North & South (2004)\n3170,1.5,1,\"Big Bang, The (2011)\"\n3171,3.0,4,Mirror Mirror (2012)\n3172,2.3333333333333335,3,Battleship (2012)\n3173,3.875,4,\"Best Exotic Marigold Hotel, The (2011)\"\n3174,2.0,1,Comic-Con Episode IV: A Fan's Hope (2011)\n3175,3.5,2,Bully (2011)\n3176,4.0,2,Hysteria (2011)\n3177,3.0,1,Dante's Inferno: An Animated Epic (2010)\n3178,2.5,4,\"Five-Year Engagement, The (2012)\"\n3179,2.5,2,Think Like a Man (2012)\n3180,4.0,1,\"Lucky One, The (2012)\"\n3181,3.5,2,Safe (2012)\n3182,2.5,6,Dark Shadows (2012)\n3183,2.5,1,96 Minutes (2011) \n3184,3.6666666666666665,3,\"Decoy Bride, The (2011)\"\n3185,4.0,1,Rocket Singh: Salesman of the Year (2009)\n3186,3.5588235294117645,17,\"Dictator, The (2012)\"\n3187,4.0,1,Walking with Monsters (2005)\n3188,3.2777777777777777,18,Men in Black III (M.III.B.) (M.I.B.³) (2012)\n3189,2.8333333333333335,6,Snow White and the Huntsman (2012)\n3190,3.5,1,Sound of My Voice (2011)\n3191,5.0,1,Eva (2011)\n3192,3.3333333333333335,3,\"Pirates! Band of Misfits, The (2012)\"\n3193,3.2962962962962963,27,Prometheus (2012)\n3194,3.5,1,\"Pact, The (2012)\"\n3195,3.5,2,Bernie (2011)\n3196,2.5,1,Inhale (2010)\n3197,3.5,1,Take This Waltz (2011)\n3198,3.0,5,Wanderlust (2012)\n3199,3.7758620689655173,29,Moonrise Kingdom (2012)\n3200,3.8333333333333335,3,Get the Gringo (2012)\n3201,4.0,1,Superman/Doomsday (2007) \n3202,2.8333333333333335,3,\"Thousand Words, A (2012)\"\n3203,3.75,6,Safety Not Guaranteed (2012)\n3204,3.388888888888889,9,Madagascar 3: Europe's Most Wanted (2012)\n3205,4.0,1,Your Sister's Sister (2011)\n3206,5.0,1,Superman/Batman: Public Enemies (2009)\n3207,3.466666666666667,30,Brave (2012)\n3208,5.0,1,Front of the Class (2008)\n3209,3.0,1,What to Expect When You're Expecting (2012)\n3210,3.0,1,To Rome with Love (2012)\n3211,2.75,6,Abraham Lincoln: Vampire Hunter (2012)\n3212,3.5,1,First Position (2011)\n3213,2.75,4,Rock of Ages (2012)\n3214,4.166666666666667,3,Seeking a Friend for the End of the World (2012)\n3215,5.0,1,Presto (2008)\n3216,4.5,1,Jack-Jack Attack (2005)\n3217,3.8333333333333335,3,One Man Band (2005)\n3218,3.2857142857142856,21,Ted (2012)\n3219,2.7,5,Magic Mike (2012)\n3220,3.5,1,Cleanskin (2012)\n3221,3.25,30,\"Amazing Spider-Man, The (2012)\"\n3222,2.9166666666666665,6,Ice Age 4: Continental Drift (2012)\n3223,3.357142857142857,7,Beasts of the Southern Wild (2012)\n3224,2.75,2,Savages (2012)\n3225,3.0,1,\"Firm, The (2009)\"\n3226,4.5,1,Spirit Camp (2009)\n3227,4.5,1,Some Guy Who Kills People (2011)\n3228,2.0,1,Treasure Island (2012)\n3229,3.0,9,\"Watch, The (2012)\"\n3230,2.5,1,2 Days in New York (2012)\n3231,4.5,1,Killer Joe (2011)\n3232,0.5,1,Anaconda: The Offspring (2008)\n3233,3.9,5,For the Birds (2000)\n3234,3.5,4,Ruby Sparks (2012)\n3235,3.1875,8,Total Recall (2012)\n3236,4.0,1,\"Angels' Share, The (2012)\"\n3237,3.5,1,\"Immature, The (Immaturi) (2011)\"\n3238,3.6666666666666665,3,Sidewalls (Medianeras) (2011)\n3239,3.81,50,Skyfall (2012)\n3240,3.4444444444444446,9,\"Campaign, The (2012)\"\n3241,2.0,1,Brake (2012)\n3242,1.0,1,Hope Springs (2012)\n3243,2.0,1,\"Queen of Versailles, The (2012)\"\n3244,3.5,2,ParaNorman (2012)\n3245,3.0,1,Diary of a Wimpy Kid: Dog Days (2012)\n3246,4.5,1,Broken (2012)\n3247,4.0,1,6 Days to Air: The Making of South Park (2011)\n3248,3.2,5,Premium Rush (2012)\n3249,5.0,1,\"Odd Life of Timothy Green, The (2012)\"\n3250,3.625,4,Lawless (2012)\n3251,2.1666666666666665,3,Piranha 3DD (a.k.a. Piranha DD) (2012)\n3252,4.25,4,Searching for Sugar Man (2012)\n3253,3.0,1,Prime Suspect 6: The Last Witness (2003)\n3254,2.5,1,Conception (2011)\n3255,4.0,1,Paradise Lost 3: Purgatory (2011)\n3256,4.0,1,\"Words, The (2012)\"\n3257,3.625,20,Pitch Perfect (2012)\n3258,3.8333333333333335,3,Samsara (2011)\n3259,3.640625,32,Looper (2012)\n3260,2.5,3,That's My Boy (2012)\n3261,3.0,3,Robot & Frank (2012)\n3262,3.75,2,Resident Evil: Retribution (2012)\n3263,1.5,1,Lola Versus (2012)\n3264,3.9166666666666665,6,\"Master, The (2012)\"\n3265,3.7142857142857144,14,Dredd (2012)\n3266,3.9166666666666665,6,End of Watch (2012)\n3267,2.75,2,V/H/S (2012)\n3268,3.825,20,\"Perks of Being a Wallflower, The (2012)\"\n3269,4.3,10,\"Hunt, The (Jagten) (2012)\"\n3270,5.0,1,Holy Motors (2012)\n3271,3.2857142857142856,7,Taken 2 (2012)\n3272,4.0,1,House at the End of the Street (2012)\n3273,5.0,1,My Left Eye Sees Ghosts (Ngo joh aan gin diy gwai) (2002)\n3274,3.5,1,Love Lasts Three Years (L'amour dure trois ans) (2011)\n3275,1.5,1,\"Tall Man, The (2012)\"\n3276,3.5,1,LOL (2012)\n3277,0.5,1,Rust and Bone (De rouille et d'os) (2012)\n3278,3.5,2,Marley (2012)\n3279,2.5,2,Frankenweenie (2012)\n3280,4.0,1,Sinister (2012)\n3281,3.409090909090909,11,Hotel Transylvania (2012)\n3282,3.0,1,Side by Side (2012)\n3283,3.982142857142857,28,Argo (2012)\n3284,3.466666666666667,15,Seven Psychopaths (2012)\n3285,3.0,1,Liberal Arts (2012)\n3286,1.25,2,Catch .44 (2011)\n3287,3.0,2,[REC]³ 3 Génesis (2012)\n3288,2.5,1,Asterix & Obelix: God Save Britannia (Astérix et Obélix: Au service de Sa Majesté) (2012)\n3289,3.0,1,Paranormal Activity 4 (2012)\n3290,1.5,1,Alex Cross (2012)\n3291,3.736842105263158,19,Cloud Atlas (2012)\n3292,4.0,1,'Hellboy': The Seeds of Creation (2004)\n3293,3.0,1,Silent Hill: Revelation 3D (2012)\n3294,2.5714285714285716,7,Here Comes the Boom (2012)\n3295,2.0,1,Mental (2012)\n3296,3.125,4,Killing Them Softly (2012)\n3297,4.75,2,\"Imposter, The (2012)\"\n3298,4.0,1,\"Sessions, The (Surrogate, The) (2012)\"\n3299,4.0,1,Smashed (2012)\n3300,3.75,22,Wreck-It Ralph (2012)\n3301,3.716666666666667,30,Silver Linings Playbook (2012)\n3302,3.5,9,Flight (2012)\n3303,3.0,1,Anna Karenina (2012)\n3304,3.629032258064516,31,Life of Pi (2012)\n3305,2.5,1,\"Man with the Iron Fists, The (2012)\"\n3306,2.5,1,\"Bay, The (2012)\"\n3307,3.5,1,Himizu (2011)\n3308,4.0,2,Jackass 3.5 (2011)\n3309,3.0,2,Indie Game: The Movie (2012)\n3310,3.9285714285714284,7,\"Batman: The Dark Knight Returns, Part 1 (2012)\"\n3311,4.5,4,Lincoln (2012)\n3312,1.5,1,Nature Calls (2012)\n3313,1.5,1,Vamps (2012)\n3314,1.875,4,\"Twilight Saga: Breaking Dawn - Part 2, The (2012)\"\n3315,3.5,1,10 Years (2011)\n3316,2.25,2,Red Dawn (2012)\n3317,3.7916666666666665,12,Rise of the Guardians (2012)\n3318,4.0,2,\"Fantastic Fear of Everything, A (2012)\"\n3319,1.5,1,Deadfall (2012)\n3320,4.0,1,Byzantium (2012)\n3321,4.375,8,Paperman (2012)\n3322,3.5,5,Hitchcock (2012)\n3323,4.5,1,From Up on Poppy Hill (Kokuriko-zaka kara) (2011)\n3324,4.5,1,Redline (2009)\n3325,2.5,1,\"Liar's Autobiography: The Untrue Story of Monty Python's Graham Chapman, A (2012)\"\n3326,3.8125,40,\"Hobbit: An Unexpected Journey, The (2012)\"\n3327,2.0,1,Hyde Park on Hudson (2012)\n3328,1.5,1,How to Make Love to a Woman (2010)\n3329,4.107142857142857,14,Zero Dark Thirty (2012)\n3330,1.5,1,Fire with Fire (2012)\n3331,3.5,14,Warm Bodies (2013)\n3332,4.0,1,Wrong (2012)\n3333,3.0,1,Playing for Keeps (2012)\n3334,2.5,3,\"Guilt Trip, The (2012)\"\n3335,3.4166666666666665,12,Jack Reacher (2012)\n3336,3.943661971830986,71,Django Unchained (2012)\n3337,3.125,8,This Is 40 (2012)\n3338,3.6666666666666665,3,\"Impossible, The (Imposible, Lo) (2012)\"\n3339,3.5,7,\"Misérables, Les (2012)\"\n3340,2.0,1,Parental Guidance (2012)\n3341,4.0,2,John Dies at the End (2012)\n3342,2.25,2,\"Misérables, Les (2000)\"\n3343,3.0,1,Promised Land (2012)\n3344,5.0,1,English Vinglish (2012)\n3345,3.0,1,Fish Story (Fisshu sutôrî) (2009)\n3346,1.5,1,Texas Chainsaw 3D (2013)\n3347,3.3333333333333335,3,Gangster Squad (2013)\n3348,3.5,1,\"Iceman, The (2012)\"\n3349,4.25,2,It's Such a Beautiful Day (2012)\n3350,3.875,8,\"Batman: The Dark Knight Returns, Part 2 (2013)\"\n3351,3.0,1,Everything or Nothing: The Untold Story of 007 (2012)\n3352,4.0,1,Codependent Lesbian Space Alien Seeks Same (2011)\n3353,3.25,2,\"Last Stand, The (2013)\"\n3354,3.1666666666666665,3,Upstream Color (2013)\n3355,3.0,1,Shadow Dancer (2012)\n3356,4.0,1,Human Planet (2011)\n3357,3.5,1,Comme un chef (2012)\n3358,3.5,3,Movie 43 (2013)\n3359,3.5,1,\"Pervert's Guide to Ideology, The (2012)\"\n3360,4.5,1,Sightseers (2012)\n3361,2.9,5,Hansel & Gretel: Witch Hunters (2013)\n3362,4.5,1,Jim Jefferies: Fully Functional (EPIX) (2012)\n3363,1.5,1,Why Stop Now (2012)\n3364,4.0,1,Tabu (2012)\n3365,3.0,1,Upside Down (2012)\n3366,3.0,1,\"Liability, The (2012)\"\n3367,2.5,1,Stand Up Guys (2012)\n3368,3.9166666666666665,6,Side Effects (2013)\n3369,2.875,4,Identity Thief (2013)\n3370,3.5,1,\"ABCs of Death, The (2012)\"\n3371,2.0,1,Beautiful Creatures (2013)\n3372,2.0833333333333335,6,\"Good Day to Die Hard, A (2013)\"\n3373,3.625,4,21 and Over (2013)\n3374,4.0,1,Safe Haven (2013)\n3375,4.5,2,Frozen Planet (2011)\n3376,5.0,1,\"Act of Killing, The (2012)\"\n3377,4.0,1,Universal Soldier: Day of Reckoning (2012)\n3378,4.0,2,Escape from Planet Earth (2013)\n3379,3.5,3,Before Midnight (2013)\n3380,3.5,1,Snitch (2013)\n3381,3.0,2,Dark Skies (2013)\n3382,3.5,1,Oh Boy (A Coffee in Berlin) (2012)\n3383,4.75,2,Journey to the West: Conquering the Demons (Daai wa sai you chi Chui mo chun kei) (2013)\n3384,2.2,5,Jack the Giant Slayer (2013)\n3385,2.5,1,Wadjda (2012)\n3386,2.5,1,\"Unintentional Kidnapping of Mrs. Elfriede Ott, The (Die Unabsichtliche Entführung der Frau Elfriede Ott) (2010)\"\n3387,3.0,3,G.I. Joe: Retaliation (2013)\n3388,3.75,2,Stoker (2013)\n3389,3.0833333333333335,6,Oz the Great and Powerful (2013)\n3390,3.7777777777777777,9,\"Croods, The (2013)\"\n3391,2.8333333333333335,3,\"Incredible Burt Wonderstone, The (2013)\"\n3392,1.5,1,\"Call, The (2013)\"\n3393,2.875,8,Olympus Has Fallen (2013)\n3394,1.5,2,\"First Time, The (2012)\"\n3395,3.5,5,\"Place Beyond the Pines, The (2012)\"\n3396,2.0,1,\"Brass Teapot, The (2012)\"\n3397,2.0,1,Phil Spector (2013)\n3398,2.5,4,\"Host, The (2013)\"\n3399,3.0,4,Admission (2013)\n3400,3.25,2,Evil Dead (2013)\n3401,3.75,2,Trance (2013)\n3402,4.25,2,\"Perfect Plan, A (Plan parfait, Un) (2012)\"\n3403,3.3,20,Oblivion (2013)\n3404,1.5,1,Dark Tide (2012)\n3405,4.0,1,42 (2013)\n3406,4.0,4,Wolf Children (Okami kodomo no ame to yuki) (2012)\n3407,1.5,1,Disconnect (2012)\n3408,3.0,1,\"Invincible Iron Man, The (2007)\"\n3409,3.2,5,Pain & Gain (2013)\n3410,3.0,1,Hulk Vs. (2009)\n3411,4.0,1,Resolution (2012)\n3412,3.0,1,Grabbers (2012)\n3413,5.0,1,Justice League: Doom (2012) \n3414,2.0,1,\"Grandmaster, The (Yi dai zong shi) (2013)\"\n3415,3.2142857142857144,14,This Is the End (2013)\n3416,3.5625,32,Iron Man 3 (2013)\n3417,2.0,1,\"English Teacher, The (2013)\"\n3418,4.333333333333333,3,Mud (2012)\n3419,2.0,1,Pawn (2013)\n3420,2.75,2,Syrup (2013)\n3421,3.375,20,\"Great Gatsby, The (2013)\"\n3422,3.685185185185185,27,Star Trek Into Darkness (2013)\n3423,3.55,10,\"Internship, The (2013)\"\n3424,3.0,1,Darkon (2006)\n3425,3.75,2,Only God Forgives (2013)\n3426,3.375,8,\"Hangover Part III, The (2013)\"\n3427,3.357142857142857,7,\"Fast & Furious 6 (Fast and the Furious 6, The) (2013)\"\n3428,3.0,2,Epic (2013)\n3429,4.0,1,Tie Xi Qu: West of the Tracks (Tiexi qu) (2003)\n3430,4.0,1,Down Terrace (2009)\n3431,2.5,1,Frances Ha (2012)\n3432,2.5,1,\"Lords of Salem, The (2012)\"\n3433,3.0,1,Behind the Candelabra (2013)\n3434,3.5,1,As I Was Moving Ahead Occasionally I Saw Brief Glimpses of Beauty (2000)\n3435,3.0,1,With Great Power: The Stan Lee Story (2012)\n3436,1.0,2,After Earth (2013)\n3437,3.409090909090909,22,Now You See Me (2013)\n3438,4.5,1,Inhuman Resources (Redd Inc.) (2012)\n3439,3.125,4,\"Way, Way Back, The (2013)\"\n3440,3.0,1,Much Ado About Nothing (2012)\n3441,3.2954545454545454,22,Man of Steel (2013)\n3442,3.5,2,\"Kings of Summer, The (2013)\"\n3443,3.25,4,\"Purge, The (2013)\"\n3444,3.0,1,Rapture-Palooza (2013)\n3445,4.25,2,20 Feet from Stardom (Twenty Feet from Stardom) (2013)\n3446,2.0,2,\"Bling Ring, The (2013)\"\n3447,3.875,16,Monsters University (2013)\n3448,1.0,1,Schlussmacher (2013)\n3449,2.0,1,Fullmetal Alchemist: The Sacred Star of Milos (2011)\n3450,3.5,1,Maniac (2012)\n3451,2.5,1,Not Suitable for Children (2012)\n3452,3.607142857142857,14,Pacific Rim (2013)\n3453,3.0,1,LEGO Batman: The Movie - DC Heroes Unite (2013)\n3454,4.0,5,\"Best Offer, The (Migliore offerta, La) (2013)\"\n3455,2.5,1,Adam and Eve (National Lampoon's Adam & Eve) (2005)\n3456,3.026315789473684,19,World War Z (2013)\n3457,3.3125,16,Elysium (2013)\n3458,3.5789473684210527,19,Despicable Me 2 (2013)\n3459,2.9166666666666665,6,White House Down (2013)\n3460,3.4166666666666665,18,\"World's End, The (2013)\"\n3461,2.0,1,Redemption (Hummingbird) (2013)\n3462,3.4166666666666665,6,\"Heat, The (2013)\"\n3463,2.7857142857142856,7,\"Lone Ranger, The (2013)\"\n3464,1.0,1,Passion (2012)\n3465,4.5,1,V/H/S/2 (2013)\n3466,1.5,1,\"Knot, The (2012)\"\n3467,3.25,2,The Spectacular Now (2013)\n3468,3.5,2,\"Lifeguard, The (2013)\"\n3469,1.875,4,Sharknado (2013)\n3470,5.0,1,Craig Ferguson: I'm Here To Help (2013)\n3471,4.0,1,Stuck in Love (2012)\n3472,4.0,2,Fruitvale Station (2013)\n3473,1.8333333333333333,3,R.I.P.D. (2013)\n3474,4.5,1,\"Field in England, A (2013)\"\n3475,4.214285714285714,7,\"Conjuring, The (2013)\"\n3476,2.5,1,Turbo (2013)\n3477,3.375,16,\"Wolverine, The (2013)\"\n3478,2.3333333333333335,3,Drinking Buddies (2013)\n3479,3.2,5,Red 2 (2013)\n3480,2.25,2,Coffee Town (2013)\n3481,2.0,1,Revenge for Jolly! (2012)\n3482,4.0,3,2 Guns (2013)\n3483,3.375,4,Blue Jasmine (2013)\n3484,3.5,3,\"Great Beauty, The (Grande Bellezza, La) (2013)\"\n3485,4.125,4,Louis C.K.: Oh My God (2013)\n3486,1.75,2,Percy Jackson: Sea of Monsters (2013)\n3487,1.875,4,\"Smurfs 2, The (2013)\"\n3488,4.0,1,Alan Partridge: Alpha Papa (2013)\n3489,3.25,2,Man of Tai Chi (2013)\n3490,3.0,1,Batman: Mystery of the Batwoman (2003)\n3491,3.738095238095238,21,We're the Millers (2013)\n3492,3.4,5,Grown Ups 2 (2013)\n3493,3.4545454545454546,11,Kick-Ass 2 (2013)\n3494,3.3,5,Riddick (2013)\n3495,3.0,2,Planes (2013)\n3496,3.0,2,Blackfish (2013)\n3497,4.0,3,\"Wind Rises, The (Kaze tachinu) (2013)\"\n3498,2.5,3,Jobs (2013)\n3499,1.5,1,Lee Daniels' The Butler (2013)\n3500,4.0,2,In a World... (2013)\n3501,3.933333333333333,15,About Time (2013)\n3502,3.25,2,Justice League: Crisis on Two Earths (2010)\n3503,3.75,2,You're Next (2011)\n3504,0.5,1,Maria Bamford: The Special Special Special! (2012)\n3505,1.0,1,Getaway (2013)\n3506,3.578125,32,Gravity (2013)\n3507,2.6666666666666665,3,What If (2013)\n3508,4.0,2,\"History of Future Folk, The (2012)\"\n3509,4.15625,16,Prisoners (2013)\n3510,3.1666666666666665,3,Austenland (2013)\n3511,3.0,2,Insidious: Chapter 2 (2013)\n3512,3.8181818181818183,11,Rush (2013)\n3513,3.5,3,\"Family, The (2013)\"\n3514,3.75,2,Short Term 12 (2013)\n3515,2.5,4,\"To Do List, The (2013)\"\n3516,1.0,1,Inescapable (2012)\n3517,4.125,4,Nebraska (2013)\n3518,3.0,2,Enough Said (2013)\n3519,3.125,8,Don Jon (2013)\n3520,3.5,1,Mood Indigo (L'écume des jours) (2013)\n3521,3.75,2,\"Century of the Self, The (2002)\"\n3522,3.5,2,Crystal Fairy & the Magical Cactus and 2012 (2013)\n3523,1.0,1,Bad Milo (Bad Milo!) (2013)\n3524,2.25,2,Runner Runner (2013)\n3525,3.25,2,Blue Is the Warmest Color (La vie d'Adèle) (2013)\n3526,2.25,2,Cloudy with a Chance of Meatballs 2 (2013)\n3527,4.0476190476190474,21,Captain Phillips (2013)\n3528,4.0,2,Machete Kills (Machete 2) (2013)\n3529,4.5,2,Filth (2013)\n3530,4.0,4,Escape Plan (2013)\n3531,2.5,2,Carrie (2013)\n3532,4.5,1,UnHung Hero (2013)\n3533,1.5,1,\"Counselor, The (2013)\"\n3534,3.0,1,Escape From Tomorrow (2013)\n3535,4.0,1,\"Double, The (2013)\"\n3536,3.625,16,12 Years a Slave (2013)\n3537,3.625,4,All Is Lost (2013)\n3538,3.4375,16,Ender's Game (2013)\n3539,3.25,2,Jackass Presents: Bad Grandpa (2013)\n3540,3.3095238095238093,21,Thor: The Dark World (2013)\n3541,3.9705882352941178,17,Dallas Buyers Club (2013)\n3542,3.5,1,\"Selfish Giant, The (2013)\"\n3543,2.0,2,Last Vegas (2013)\n3544,3.25,2,Philomena (2013)\n3545,3.0,1,\"Book Thief, The (2013)\"\n3546,3.6346153846153846,26,The Hunger Games: Catching Fire (2013)\n3547,3.58,25,\"Hobbit: The Desolation of Smaug, The (2013)\"\n3548,2.642857142857143,7,47 Ronin (2013)\n3549,2.875,4,Delivery Man (2013)\n3550,1.5,1,Charlie Countryman (2013)\n3551,4.5,1,Red Flag (2012)\n3552,4.571428571428571,7,\"Day of the Doctor, The (2013)\"\n3553,3.0,1,Guilty of Romance (Koi no tsumi) (2011) \n3554,3.6206896551724137,29,Frozen (2013)\n3555,3.642857142857143,7,Inside Llewyn Davis (2013)\n3556,3.9166666666666665,54,\"Wolf of Wall Street, The (2013)\"\n3557,3.0,1,Homefront (2013)\n3558,3.75,2,Mandela: Long Walk to Freedom (2013)\n3559,2.5,3,Evangelion: 3.0 You Can (Not) Redo (2012)\n3560,1.5,1,All is Bright (2013)\n3561,4.0,1,Tim's Vermeer (2013)\n3562,3.25,16,American Hustle (2013)\n3563,4.0,20,\"Secret Life of Walter Mitty, The (2013)\"\n3564,3.92,25,Her (2013)\n3565,3.0,1,RoboGeisha (Robo-geisha) (2009)\n3566,3.0,2,Lone Survivor (2013)\n3567,3.6875,8,Saving Mr. Banks (2013)\n3568,3.0,2,Oldboy (2013)\n3569,2.5,1,Dampfnudelblues (2013)\n3570,3.3636363636363638,11,Anchorman 2: The Legend Continues (2013)\n3571,3.4285714285714284,21,Snowpiercer (2013)\n3572,3.0,1,Haunter (2013)\n3573,4.0,1,Wrong Cops (2013)\n3574,1.5,1,\"Muppet Christmas: Letters to Santa, A (2008)\"\n3575,4.5,1,Ninja: Shadow of a Tear (2013)\n3576,2.75,2,\"Fuck You, Goethe (Fack Ju Göhte) (2013)\"\n3577,4.5,1,High School (2010)\n3578,2.5,2,Grudge Match (2013)\n3579,3.0,1,Highlander: The Search for Vengeance (2007)\n3580,5.0,1,Only Lovers Left Alive (2013)\n3581,2.0,1,Bad Karma (2012)\n3582,5.0,1,Hunting Elephants (2013)\n3583,4.0,2,Dragon Ball Z: Battle of Gods (2013)\n3584,2.0,1,Freezer (2014)\n3585,2.25,2,We Are What We Are (2013)\n3586,5.0,1,Chinese Puzzle (Casse-tête chinois) (2013)\n3587,3.1666666666666665,6,Ride Along (2014)\n3588,2.0,2,Jack Ryan: Shadow Recruit (2014)\n3589,3.0,18,Divergent (2014)\n3590,3.5,1,Hotel Chevalier (Part 1 of 'The Darjeeling Limited') (2007)\n3591,4.5,1,Ernest & Célestine (Ernest et Célestine) (2012)\n3592,2.0,1,Drift (2013)\n3593,2.25,2,\"I, Frankenstein (2014)\"\n3594,1.5,1,Better Living Through Chemistry (2014)\n3595,4.25,2,Nymphomaniac: Volume I (2013)\n3596,3.5,7,Enemy (2013)\n3597,5.0,1,Wonder Woman (2009)\n3598,2.6666666666666665,6,\"Monuments Men, The (2014)\"\n3599,3.870967741935484,31,The Lego Movie (2014)\n3600,2.3333333333333335,6,RoboCop (2014)\n3601,3.3333333333333335,3,\"Art of the Steal, The (2013)\"\n3602,4.5,1,Nymphomaniac: Volume II (2013)\n3603,2.25,2,Knights of Badassdom (2013)\n3604,4.5,1,Venus in Fur (La Vénus à la fourrure) (2013)\n3605,2.0,1,Date and Switch (2014)\n3606,3.0,3,\"Zero Theorem, The (2013)\"\n3607,1.5,1,Winter's Tale (2014)\n3608,5.0,1,On the Other Side of the Tracks (De l'autre côté du périph) (2012)\n3609,3.0,1,GLOW: The Story of the Gorgeous Ladies of Wrestling (2012)\n3610,2.0,1,Cold Comes the Night (2013)\n3611,4.0,1,Chouchou (2003)\n3612,2.5,1,Someone Marry Barry (2014)\n3613,2.25,2,About Last Night (2014)\n3614,3.7788461538461537,52,\"Grand Budapest Hotel, The (2014)\"\n3615,3.5,1,\"Oversimplification of Her Beauty, An (2012)\"\n3616,2.0,1,Bring It On: Fight to the Finish (2009)\n3617,2.625,4,That Awkward Moment (2014)\n3618,3.993150684931507,73,Interstellar (2014)\n3619,1.0,2,3 Days to Kill (2014)\n3620,2.0,2,Welcome to the Jungle (2013)\n3621,3.25,4,Non-Stop (2014)\n3622,4.0,1,Wrinkles (Arrugas) (2011)\n3623,5.0,1,\"Garden of Words, The (Koto no ha no niwa) (2013)\"\n3624,2.125,4,300: Rise of an Empire (2014)\n3625,5.0,1,Particle Fever (2013)\n3626,3.5,1,\"Bag Man, The (2014)\"\n3627,3.25,4,Mr. Peabody & Sherman (2014)\n3628,2.75,2,Under the Skin (2013)\n3629,3.75,2,Need for Speed (2014)\n3630,3.0,2,Barefoot (2014)\n3631,1.75,2,Veronica Mars (2014)\n3632,2.0,3,Bad Words (2013)\n3633,0.5,1,Son of God (2014)\n3634,3.0,1,Puss in Boots: The Three Diablos (2012)\n3635,4.25,2,Why Don't You Play In Hell? (Jigoku de naze warui) (2013)\n3636,4.5,1,Ocho apellidos vascos (2014)\n3637,3.7419354838709675,31,Captain America: The Winter Soldier (2014)\n3638,2.8,5,Noah (2014)\n3639,4.333333333333333,3,\"Nut Job, The (2014)\"\n3640,3.5,1,13 Sins (2014)\n3641,3.3333333333333335,3,Muppets Most Wanted (2014)\n3642,4.5,1,Me and you (io e te) (2012)\n3643,4.5,1,Free to Play (2014)\n3644,4.0,1,\"Unknown Known, The (2013)\"\n3645,3.9285714285714284,7,The Raid 2: Berandal (2014)\n3646,2.6363636363636362,11,The Amazing Spider-Man 2 (2014)\n3647,4.0,1,Calvary (2014)\n3648,4.5,1,Oculus (2013)\n3649,0.5,1,God's Not Dead (2014)\n3650,2.5,1,Cold in July (2014)\n3651,2.0,2,Rio 2 (2014)\n3652,3.75,2,\"Honest Liar, An (2014)\"\n3653,4.0,1,Fading Gigolo (2013)\n3654,2.2857142857142856,7,Transcendence (2014)\n3655,4.0,1,Hatchet III (2013)\n3656,2.9166666666666665,6,\"Other Woman, The (2014)\"\n3657,0.5,1,\"Haunted House 2, A (2014)\"\n3658,2.5,1,Mulan II (2004)\n3659,1.5,1,Brick Mansions (2014)\n3660,3.8333333333333335,3,Locke (2013)\n3661,3.2916666666666665,12,Neighbors (2014)\n3662,4.5,1,Alpha and Omega 3: The Great Wolf Games (2014)\n3663,1.5,1,Mom's Night Out (2014)\n3664,2.8214285714285716,14,Lucy (2014)\n3665,3.8333333333333335,30,X-Men: Days of Future Past (2014)\n3666,2.6153846153846154,13,Godzilla (2014)\n3667,2.5,1,Walk of Shame (2014)\n3668,4.0,1,Blue Ruin (2013)\n3669,2.75,2,Chef (2014)\n3670,3.5,1,Afflicted (2013)\n3671,3.8125,8,Blended (2014)\n3672,4.0,3,Begin Again (2013)\n3673,2.75,16,Maleficent (2014)\n3674,2.5,2,Zombeavers (2014)\n3675,1.5,1,At Middleton (2013)\n3676,3.5,1,\"Dance of Reality, The (Danza de la realidad, La) (2013)\"\n3677,2.9583333333333335,12,A Million Ways to Die in the West (2014)\n3678,3.977272727272727,44,Edge of Tomorrow (2014)\n3679,3.6470588235294117,17,Mission: Impossible - Rogue Nation (2015)\n3680,0.5,1,Midnight Chronicles (2009)\n3681,3.0,1,Million Dollar Arm (2014)\n3682,3.0,1,G.B.F. (2013)\n3683,3.5,1,Jimi: All Is by My Side (2013)\n3684,3.5,1,Bad Asses (Bad Ass 2) (2014)\n3685,4.5,1,Lilting (2014)\n3686,3.388888888888889,9,The Fault in Our Stars (2014)\n3687,3.8333333333333335,3,Tangled Ever After (2012)\n3688,3.5,1,Maps to the Stars (2014)\n3689,3.6842105263157894,19,22 Jump Street (2014)\n3690,3.2777777777777777,9,\"Equalizer, The (2014)\"\n3691,3.7666666666666666,15,How to Train Your Dragon 2 (2014)\n3692,3.3461538461538463,26,Birdman: Or (The Unexpected Virtue of Ignorance) (2014)\n3693,3.823529411764706,17,Boyhood (2014)\n3694,1.5,1,Think Like a Man Too (2014)\n3695,2.5,1,Jersey Boys (2014)\n3696,4.5,1,\"Internet's Own Boy: The Story of Aaron Swartz, The (2014)\"\n3697,1.875,4,Transformers: Age of Extinction (2014)\n3698,4.333333333333333,3,Frank (2014)\n3699,2.0,1,They Came Together (2014)\n3700,3.5,1,Honey (Miele) (2013)\n3701,2.25,2,Planes: Fire & Rescue (2014)\n3702,2.0,2,Tammy (2014)\n3703,5.0,1,Colourful (Karafuru) (2010)\n3704,4.5,3,\"Babadook, The (2014)\"\n3705,4.0394736842105265,38,Whiplash (2014)\n3706,3.7162162162162162,37,Gone Girl (2014)\n3707,4.0,1,\"Angriest Man in Brooklyn, The (2014)\"\n3708,3.433333333333333,15,Dawn of the Planet of the Apes (2014)\n3709,3.0,1,Deliver Us from Evil (2014)\n3710,2.0,1,And So It Goes (2014)\n3711,2.6,5,Sex Tape (2014)\n3712,3.8333333333333335,3,I Origins (2014)\n3713,3.3,5,\"Purge: Anarchy, The (2014)\"\n3714,4.0508474576271185,59,Guardians of the Galaxy (2014)\n3715,4.5,1,\"Signal, The (2014)\"\n3716,3.5,3,The Expendables 3 (2014)\n3717,2.0,4,Hercules (2014)\n3718,4.5,1,A Most Wanted Man (2014)\n3719,3.5,1,Life After Beth (2014)\n3720,3.0,1,Felony (2013)\n3721,3.5,1,Get on Up (2014)\n3722,3.0,1,Magic in the Moonlight (2014)\n3723,4.25,2,Housebound (2014)\n3724,4.5,1,The Hundred-Foot Journey (2014)\n3725,3.0,1,Batman: Assault on Arkham (2014)\n3726,4.0,1,White Frog (2012)\n3727,3.5,1,\"Den, The (2013)\"\n3728,2.0,2,Jupiter Ascending (2015)\n3729,2.75,4,Teenage Mutant Ninja Turtles (2014)\n3730,1.5,1,I'll Follow You Down (2013)\n3731,2.3,5,\"Giver, The (2014)\"\n3732,4.0,1,\"Pretty One, The (2013)\"\n3733,1.5,1,Revenge of the Green Dragons (2014)\n3734,3.1,5,Let's Be Cops (2014)\n3735,3.5,2,\"Inbetweeners 2, The (2014)\"\n3736,4.0,1,\"Sacrament, The (2013)\"\n3737,3.25,4,Sin City: A Dame to Kill For (2014)\n3738,2.0,1,If I Stay (2014)\n3739,3.5,1,\"Two Days, One Night (Deux jours, une nuit) (2014)\"\n3740,4.0,2,Coherence (2013)\n3741,3.0,1,\"As Above, So Below (2014)\"\n3742,5.0,1,\"One I Love, The (2014)\"\n3743,2.5,1,Headshot (2011)\n3744,4.0,1,\"Guest, The (2014)\"\n3745,4.0,2,Pride (2014)\n3746,3.5,1,Honeymoon (2014)\n3747,2.8333333333333335,3,The Drop (2014)\n3748,4.0,1,\"20,000 Days on Earth (2014)\"\n3749,3.3333333333333335,3,The Skeleton Twins (2014)\n3750,2.5,1,Beautiful Losers (2008)\n3751,2.9375,16,\"Maze Runner, The (2014)\"\n3752,3.5,1,Camp X-Ray (2014)\n3753,3.0,1,\"Walk Among the Tombstones, A (2014)\"\n3754,5.0,1,Laggies (2014)\n3755,1.5,1,Cesar Chavez (2014)\n3756,1.5,1,Who Am I (Kein System Ist Sicher) (2014)\n3757,1.0,1,\"Tale of Princess Kaguya, The (Kaguyahime no monogatari) (2013)\"\n3758,2.75,2,This Is Where I Leave You (2014)\n3759,3.8,15,American Sniper (2014)\n3760,3.5,1,Tusk (2014)\n3761,4.25,2,Hector and the Search for Happiness (2014)\n3762,3.0,1,Horns (2014)\n3763,3.0,2,Annabelle (2014)\n3764,2.8333333333333335,3,Two Night Stand (2014)\n3765,3.5,4,Dracula Untold (2014)\n3766,2.1666666666666665,3,Stretch (2014)\n3767,3.0,1,Autómata (Automata) (2014)\n3768,2.0,1,\"Captive, The (2014)\"\n3769,3.9,10,Predestination (2014)\n3770,2.0,1,Justin and the Knights of Valour (2013)\n3771,3.5,1,Ward 13 (2003)\n3772,4.833333333333333,3,What We Do in the Shadows (2014)\n3773,3.8448275862068964,29,John Wick (2014)\n3774,2.0,1,Plastic (2014)\n3775,2.5,1,\"Judge, The (2014)\"\n3776,3.0,1,\"Culture High, The (2014)\"\n3777,3.769230769230769,13,Fury (2014)\n3778,3.0,1,\"Salvation, The (2014)\"\n3779,3.25,2,St. Vincent (2014)\n3780,3.5,1,\"Rewrite, The (2014)\"\n3781,4.166666666666667,18,Nightcrawler (2014)\n3782,3.8536585365853657,41,Big Hero 6 (2014)\n3783,4.0,2,The Book of Life (2014)\n3784,3.5,1,\"Love, Rosie (2014)\"\n3785,3.8333333333333335,3,Time Lapse (2014)\n3786,3.9107142857142856,28,Ex Machina (2015)\n3787,3.0,1,Mr Hublot (2013)\n3788,4.5,1,Copenhagen (2014)\n3789,3.6666666666666665,3,\"Simpsons: The Longest Daycare, The (2012)\"\n3790,4.0,1,Generation War (2013)\n3791,2.0,1,Leviathan (2014)\n3792,2.5,1,Reign of Assassins (2010)\n3793,1.5,1,Zulu (2013)\n3794,4.5,1,Tangerines (2013)\n3795,3.0,1,Life Partners (2014)\n3796,1.5,1,Drive Hard (2014)\n3797,3.0,1,New Kids Nitro (2011)\n3798,3.0,1,Stalingrad (2013)\n3799,4.0,2,Dead Snow 2: Red vs. Dead (2014) \n3800,4.0,1,You Are the Apple of My Eye (2011)\n3801,3.5,1,DeadHeads (2011)\n3802,4.02,50,The Imitation Game (2014)\n3803,3.6666666666666665,6,Inherent Vice (2014)\n3804,4.5,1,Rudderless (2014)\n3805,3.869565217391304,23,The Hunger Games: Mockingjay - Part 1 (2014)\n3806,2.5,1,Sex Ed (2014)\n3807,2.75,2,Exodus: Gods and Kings (2014)\n3808,4.25,10,Wild Tales (2014)\n3809,2.3333333333333335,3,Dumb and Dumber To (2014)\n3810,1.5,1,The Longest Week (2014)\n3811,2.5,1,Miss Meadows (2014)\n3812,3.0,2,Too Many Cooks (2014)\n3813,3.5,1,Painted Skin (2008)\n3814,3.7058823529411766,17,The Theory of Everything (2014)\n3815,4.5,3,Doctor Who: The Time of the Doctor (2013)\n3816,4.0,1,Virunga (2014)\n3817,3.5,1,The Madagascar Penguins in a Christmas Caper (2005)\n3818,3.0,1,Song of the Sea (2014)\n3819,3.5,3,In the Heart of the Sea (2015)\n3820,3.0,1,Hello Ladies: The Movie (2014)\n3821,3.260869565217391,23,Jurassic World (2015)\n3822,5.0,1,Watermark (2014)\n3823,3.875,4,Citizenfour (2014)\n3824,3.75,2,Asterix: The Land of the Gods (Astérix: Le domaine des dieux) (2014)\n3825,2.5,1,Hit by Lightning (2014)\n3826,3.6363636363636362,11,Horrible Bosses 2 (2014)\n3827,4.5,1,Dragonheart 2: A New Beginning (2000)\n3828,3.7,5,Penguins of Madagascar (2014)\n3829,4.0,1,'71 (2014)\n3830,3.0,1,The Rabbi's Cat (Le chat du rabbin) (2011)\n3831,3.8,5,Still Alice (2014)\n3832,4.0,4,Paddington (2014)\n3833,1.8333333333333333,6,Maze Runner: Scorch Trials (2015)\n3834,3.5,1,Ice Age: A Mammoth Christmas (2011)\n3835,3.5,1,The Voices (2014)\n3836,2.5,1,The Green Prince (2014)\n3837,1.5,1,Dying of the Light (2014)\n3838,5.0,1,Hellbenders (2012)\n3839,2.0,1,By the Gun (2014)\n3840,3.0,1,Kill the Messenger (2014)\n3841,1.5,1,Bring It On: In It To Win It (2007)\n3842,3.5,2,The Mule (2014)\n3843,3.4166666666666665,18,The Hobbit: The Battle of the Five Armies (2014)\n3844,2.5,2,Selma (2014)\n3845,2.5,3,Unbroken (2014)\n3846,4.0,1,Black Sea (2015)\n3847,3.5,1,Good Copy Bad Copy (2007)\n3848,2.0,1,Playing It Cool (2014)\n3849,5.0,1,National Lampoon's Bag Boy (2007)\n3850,4.0,1,Closer to the Moon (2013)\n3851,3.5,2,\"Girl Walks Home Alone at Night, A (2014)\"\n3852,4.25,2,Dave Chappelle: For What it's Worth (2004)\n3853,5.0,1,Scooby-Doo! Abracadabra-Doo (2010)\n3854,4.0,1,Mommy (2014)\n3855,2.9,5,Wild (2014)\n3856,3.0,2,Top Five (2014)\n3857,4.5,1,Bill Burr: I'm Sorry You Feel That Way (2014)\n3858,3.75,2,Big Eyes (2014)\n3859,2.8,5,Into the Woods (2014)\n3860,2.0,1,\"Men, Women & Children (2014)\"\n3861,3.4583333333333335,12,The Interview (2014)\n3862,3.986111111111111,36,Kingsman: The Secret Service (2015)\n3863,4.5,1,Bill Burr: You People Are All the Same (2012)\n3864,3.5,6,Night at the Museum: Secret of the Tomb (2014)\n3865,3.5,1,Paradox (2010)\n3866,2.5,1,The Punisher: Dirty Laundry (2012)\n3867,2.25,2,Seventh Son (2014)\n3868,3.5,1,Corner Gas: The Movie (2014)\n3869,2.0,1,A Merry Friggin' Christmas (2014)\n3870,5.0,1,Into the Forest of Fireflies' Light (2011)\n3871,5.0,1,PK (2014)\n3872,3.25,10,Chappie (2015)\n3873,4.333333333333333,3,The Salt of the Earth (2014)\n3874,4.5,1,The Fool (2014)\n3875,2.7,5,Taken 3 (2015)\n3876,2.5,2,Blackhat (2015)\n3877,2.5,1,Son of a Gun (2014)\n3878,2.5,9,Terminator Genisys (2015)\n3879,4.0,1,John Mulaney: New In Town (2012)\n3880,4.5,1,Patton Oswalt: My Weakness Is Strong (2009)\n3881,3.0,1,Man on High Heels (2014)\n3882,4.0,1,Space Buddies (2009)\n3883,2.0,1,Houdini (2014)\n3884,2.5,1,101 Dalmatians II: Patch's London Adventure (2003)\n3885,2.5,1,The Hungover Games (2014)\n3886,3.5,1,The Duke of Burgundy (2014)\n3887,4.0,1,Red Army (2014)\n3888,4.125,4,It Follows (2014)\n3889,3.0,1,The Town that Dreaded Sundown (2014)\n3890,4.5,1,Bill Burr: Let It Go (2010)\n3891,4.5,1,Bill Burr: Why Do I Do This? (2008)\n3892,4.5,1,Killer Movie (2008)\n3893,3.5,1,Sebastian Maniscalco: What's Wrong with People? (2012)\n3894,5.0,1,Stuart Little 3: Call of the Wild (2005)\n3895,5.0,1,Guy X (2005)\n3896,0.5,1,Tooth Fairy 2 (2012)\n3897,2.5,1,The Diary of Anne Frank (2009)\n3898,1.5,1,Wicked Blood (2014)\n3899,3.8191489361702127,47,Mad Max: Fury Road (2015)\n3900,2.5,2,Insidious: Chapter 3 (2015)\n3901,3.8536585365853657,41,Star Wars: Episode VII - The Force Awakens (2015)\n3902,0.5,1,Ben-hur (2016)\n3903,3.4166666666666665,6,Warcraft (2016)\n3904,3.5185185185185186,27,Avengers: Age of Ultron (2015)\n3905,3.7857142857142856,7,Pirates of the Caribbean: Dead Men Tell No Tales (2017)\n3906,2.5,6,Justice League (2017)\n3907,3.7222222222222223,27,Ant-Man (2015)\n3908,2.1,5,Fantastic Four (2015)\n3909,3.8333333333333335,54,Deadpool (2016)\n3910,3.727272727272727,11,Black Panther (2017)\n3911,4.0,13,Avengers: Infinity War - Part I (2018)\n3912,4.025,20,Thor: Ragnarok (2017)\n3913,3.925925925925926,27,Guardians of the Galaxy 2 (2017)\n3914,3.6136363636363638,22,Captain America: Civil War (2016)\n3915,3.7045454545454546,22,Doctor Strange (2016)\n3916,3.0714285714285716,14,X-Men: Apocalypse (2016)\n3917,4.15625,16,Untitled Spider-Man Reboot (2017)\n3918,2.0,1,Elsa & Fred (2014)\n3919,4.5,1,Jim Jefferies: I Swear to God (2009)\n3920,1.0,1,In the Name of the King III (2014)\n3921,2.75,2,Cake (2014)\n3922,4.5,1,Kevin Smith: Too Fat For 40 (2010)\n3923,5.0,1,\"Snowflake, the White Gorilla (2011)\"\n3924,5.0,1,Delirium (2014)\n3925,2.0,1,The Gambler (2014)\n3926,3.0,1,Mortdecai (2015)\n3927,1.0,3,Fifty Shades of Grey (2015)\n3928,3.75,2,Halloweentown High (2004)\n3929,1.5,1,American Heist (2015)\n3930,4.75,2,The Pacific (2010)\n3931,3.0,1,Strange Magic (2015)\n3932,3.25,6,The DUFF (2015)\n3933,4.0,1,\"Daddy, I'm A Zombie (2012)\"\n3934,5.0,1,The Fox and the Hound 2 (2006)\n3935,3.75,4,Project Almanac (2015)\n3936,4.375,4,Louis C.K.: Live at The Comedy Store (2015)\n3937,3.875,4,Brooklyn (2015)\n3938,4.0,1,The End of the Tour (2015)\n3939,4.5,1,Experimenter (2015)\n3940,4.0,1,Mistress America (2015)\n3941,2.0,1,Zipper (2015)\n3942,3.5,1,A Walk in the Woods (2015)\n3943,3.25,2,True Story (2015)\n3944,3.0,1,Kurt Cobain: Montage of Heck (2015)\n3945,4.25,2,Going Clear: Scientology and the Prison of Belief (2015)\n3946,3.5,1,\"What Happened, Miss Simone? (2015)\"\n3947,3.5,1,A Story of Children and Film (2013)\n3948,4.0,1,\"Story of Film: An Odyssey, The (2011)\"\n3949,4.0,1,Eden (2014)\n3950,2.0,1,The D Train (2015)\n3951,3.5833333333333335,6,Dope (2015)\n3952,3.75,6,Me and Earl and the Dying Girl (2015)\n3953,3.75,2,The Overnight (2015)\n3954,4.5,1,The Stanford Prison Experiment (2015)\n3955,4.0,2,A Pigeon Sat on a Branch Reflecting on Existence (2014)\n3956,1.5,1,The Loft (2014)\n3957,1.0,1,Vice (2015)\n3958,4.0,1,Family Guy Presents: Blue Harvest (2007)\n3959,4.5,1,Kevin Hart: I'm a Grown Little Man (2009)\n3960,4.0,1,Jim Norton: American Degenerate (2013)\n3961,4.5,1,Jim Jefferies: BARE (2014)\n3962,3.75,18,The Hateful Eight (2015)\n3963,4.0,1,Patton Oswalt: Tragedy Plus Comedy Equals Time (2014)\n3964,3.0,1,Wild Card (2015)\n3965,2.0,2,Paper Towns (2015)\n3966,3.8,5,The Wedding Ringer (2015)\n3967,3.5,1,Wyrmwood (2015)\n3968,1.0,1,The Boy Next Door (2015)\n3969,4.0,1,Boy Meets Girl (2015)\n3970,3.9,5,Victoria (2015)\n3971,2.5,1,The Dark Valley (2014)\n3972,3.5,1,I'm Here (2010)\n3973,3.5,1,The Last Five Years (2014)\n3974,3.5,1,Crimson Peak (2015)\n3975,4.0,1,Dragonheart 3: The Sorcerer's Curse (2015)\n3976,4.0,1,The Forgotten Space (2010)\n3977,4.0,1,Cloudburst (2011)\n3978,5.0,1,Tom Segura: Completely Normal (2014)\n3979,4.0,2,Stitch! The Movie (2003)\n3980,2.875,4,Hot Tub Time Machine 2 (2015)\n3981,4.5,1,Johnny Express (2014)\n3982,2.0,1,Northmen - A Viking Saga (2014)\n3983,0.5,1,Superfast! (2015)\n3984,4.0,1,Reality (2014)\n3985,1.5,1,Julia (2014)\n3986,3.25,10,Focus (2015)\n3987,2.75,2,Marvel One-Shot: Item 47 (2012)\n3988,3.0,2,The Second Best Exotic Marigold Hotel (2015)\n3989,5.0,1,George Carlin: It's Bad for Ya! (2008)\n3990,1.0,1,Tracers (2015)\n3991,3.5,1,\"McFarland, USA (2015)\"\n3992,2.25,2,Unfinished Business (2015)\n3993,2.5,1,Ghost in the Shell Arise - Border 1: Ghost Pain (2013)\n3994,2.1666666666666665,3,Run All Night (2015)\n3995,3.0,1,Digging Up the Marrow (2014)\n3996,3.5,1,Clown (2014)\n3997,4.0,1,Cinderella (2015)\n3998,2.0,1,Kidnapping Mr. Heineken (2015)\n3999,3.75,2,The Cobbler (2015)\n4000,4.0,1,Ruby Red (2013)\n4001,2.5,3,Pan (2015)\n4002,4.0,1,While We're Young (2014)\n4003,2.8333333333333335,6,Insurgent (2015)\n4004,4.0,3,The Amazing Screw-On Head (2006)\n4005,3.25,8,Home (2015)\n4006,2.375,4,Midnight Special (2015)\n4007,4.0,1,\"Gunman, The (2015)\"\n4008,2.75,6,Furious 7 (2015)\n4009,3.0,1,The Final Girls (2015)\n4010,4.5,1,Spring (2015)\n4011,4.0,1,Power/Rangers (2015)\n4012,5.0,1,George Carlin: Life Is Worth Losing (2005)\n4013,4.5,1,Legend No. 17 (2013)\n4014,3.125,8,Get Hard (2015)\n4015,4.5,1,That Sugar Film (2014)\n4016,5.0,1,Saving Santa (2013)\n4017,5.0,1,Tom and Jerry: A Nutcracker Tale (2007)\n4018,5.0,1,What Men Talk About (2010)\n4019,3.0,1,Kill Me Three Times (2014)\n4020,2.0,1,Poker Night (2014)\n4021,3.0,2,Shaun the Sheep Movie (2015)\n4022,3.0,3,Last Knights (2015)\n4023,5.0,1,The Jinx: The Life and Deaths of Robert Durst (2015)\n4024,3.0,1,Batman vs. Robin (2015)\n4025,3.0,1,Libre et assoupi (2014)\n4026,4.0,1,Woman in Gold (2015)\n4027,3.75,2,Iliza Shlesinger: Freezing Hot (2015)\n4028,4.5,1,The Road Within (2014)\n4029,2.5,7,Tomorrowland (2015)\n4030,5.0,1,Buzzard (2015)\n4031,2.75,4,Paul Blart: Mall Cop 2 (2015)\n4032,5.0,1,Seve (2014)\n4033,1.0,1,Breathe (2014)\n4034,3.25,2,Da Sweet Blood of Jesus (2014)\n4035,1.75,2,The Longest Ride (2015)\n4036,3.5,1,Girltrash: All Night Long (2014)\n4037,1.0,1,Sword of Vengeance (2014)\n4038,2.0,1,Lovesick (2014)\n4039,4.5,1,Danny Collins (2015)\n4040,3.5,1,The Even Stevens Movie (2003)\n4041,1.5,1,Kite (2014)\n4042,3.75,2,Man Up (2015)\n4043,1.875,4,San Andreas (2015)\n4044,3.0,2,Welcome to Me (2014)\n4045,3.0,1,Comedy Central Roast of James Franco (2013)\n4046,4.0,1,We Could Be King (2014)\n4047,1.5,1,Hitman: Agent 47 (2015)\n4048,3.5,1,B/W (2015)\n4049,2.0,1,Ricki and the Flash (2015)\n4050,2.0,1,Partisan (2015)\n4051,1.5,1,Infini (2015)\n4052,3.4444444444444446,9,Pitch Perfect 2 (2015)\n4053,1.0,1,Just Before I Go (2014)\n4054,4.0,1,Carol (2015)\n4055,4.0,7,The Lobster (2015)\n4056,3.0,1,Güeros (2014)\n4057,2.0,1,Maggie (2015)\n4058,1.5,1,Hot Pursuit (2015)\n4059,4.25,2,Slow West (2015)\n4060,3.5,1,The Green Inferno (2014)\n4061,4.5,1,Barely Lethal (2015)\n4062,5.0,1,What Love Is (2007)\n4063,2.5,1,5 to 7 (2014)\n4064,5.0,1,My Love (2006)\n4065,5.0,1,Radio Day (2008)\n4066,4.0,48,The Martian (2015)\n4067,1.5,1,Return to Sender (2015)\n4068,3.7142857142857144,7,Kung Fury (2015)\n4069,4.5,1,Elections Day (2007)\n4070,3.5,4,Youth (2015)\n4071,0.5,1,Survivor (2015)\n4072,3.0,1,Hot Girls Wanted (2015)\n4073,4.5,1,Phir Hera Pheri (2006)\n4074,3.6875,16,Spy (2015)\n4075,3.2,10,Trainwreck (2015)\n4076,3.0,1,Turtle Power: The Definitive History of the Teenage Mutant Ninja Turtles (2014)\n4077,0.5,1,Aloha (2015)\n4078,2.0,1,Dragon Blade (2015)\n4079,2.0,1,Entourage (2015)\n4080,5.0,1,Bitter Lake (2015)\n4081,2.0,1,No Way Jose (2015)\n4082,5.0,1,Ghost Graduation (2012)\n4083,3.813953488372093,43,Inside Out (2015)\n4084,3.5,1,The Wolfpack (2015)\n4085,2.5,1,Trevor Noah: African American (2013)\n4086,4.333333333333333,3,Love & Mercy (2014)\n4087,3.7,10,The Hunger Games: Mockingjay - Part 2 (2015)\n4088,2.5833333333333335,6,Pixels (2015)\n4089,3.6785714285714284,14,Fantastic Beasts and Where to Find Them (2016)\n4090,4.0,1,The Hairdresser (2010)\n4091,3.0,1,Mr. Holmes (2015)\n4092,3.0714285714285716,7,The Secret Life of Pets (2016)\n4093,3.25,2,Ghost in the Shell: Stand Alone Complex - The Laughing Man (2005)\n4094,2.75,2,Self/less (2015)\n4095,3.3333333333333335,3,The Last Witch Hunter (2015)\n4096,3.5,1,Krampus (2015)\n4097,2.9166666666666665,12,Suicide Squad (2016)\n4098,2.375,4,Independence Day: Resurgence (2016)\n4099,3.466666666666667,15,Star Trek Beyond (2016)\n4100,4.5,1,Golmaal (2006)\n4101,3.5,1,Bruce Lee: A Warrior's Journey (2000)\n4102,2.7777777777777777,9,Ted 2 (2015)\n4103,3.0,1,Absolutely Anything (2015)\n4104,3.1666666666666665,15,Minions (2015)\n4105,3.0,4,The Good Dinosaur (2015)\n4106,2.5,3,Black Mass (2015)\n4107,3.0588235294117645,17,Spectre (2015)\n4108,1.0,2,Sharknado 3: Oh Hell No! (2015)\n4109,5.0,1,Scooby-Doo! and the Samurai Sword (2009)\n4110,5.0,1,Scooby-Doo! and the Loch Ness Monster (2004)\n4111,5.0,1,Big Top Scooby-Doo! (2012)\n4112,1.5,1,Gabriel Iglesias: Hot and Fluffy (2007)\n4113,3.75,2,Ghost in the Shell 2.0 (2008)\n4114,3.5,1,Kevin Hart: Laugh at My Pain (2011)\n4115,5.0,1,Tom and Jerry: Shiver Me Whiskers (2006)\n4116,2.0,1,Jeff Dunham: All Over the Map (2014)\n4117,4.5,1,The FP (2012)\n4118,5.0,1,Kung Fu Panda: Secrets of the Masters (2011)\n4119,3.75,6,Steve Jobs (2015)\n4120,2.5,1,Macbeth (2015)\n4121,3.4166666666666665,6,Vacation (2015)\n4122,3.0,2,Creep (2014)\n4123,1.5,1,The Face of an Angel (2015)\n4124,1.0,1,Wild Horses (2015)\n4125,2.25,2,Search Party (2014)\n4126,2.0,1,The Squeeze (2015)\n4127,1.5,1,Careful What You Wish For (2015)\n4128,1.5,1,Robot Overlords (2014)\n4129,2.5,1,Bad Asses on the Bayou (2015)\n4130,5.0,1,The Eye: Infinity (2005)\n4131,2.5,1,Kiss me Kismet (2006)\n4132,3.5,1,Da geht noch was! (2013)\n4133,1.5,1,The Lovers (2015)\n4134,2.34375,16,Batman v Superman: Dawn of Justice (2016)\n4135,2.0,1,God Loves Caviar (2012)\n4136,3.5,4,Amy (2015)\n4137,2.0,1,That Demon Within (2014)\n4138,4.0,1,Magic Mike XXL (2015)\n4139,3.6363636363636362,11,The Jungle Book (2016)\n4140,4.0,1,Dragon Ball Z: Resurrection of F (2015)\n4141,3.4722222222222223,18,The Man from U.N.C.L.E. (2015)\n4142,0.5,1,Sorrow (2015)\n4143,4.0,2,7 Days in Hell (2015)\n4144,3.0,2,The Walk (2015)\n4145,3.3,5,13 Hours (2016)\n4146,3.5,1,There Will Come a Day (2013)\n4147,1.5,1,The Opposite Sex (2014)\n4148,1.5,1,The Gallows (2015)\n4149,5.0,1,Tokyo Tribe (2014)\n4150,3.0,1,Feast (2014)\n4151,0.5,1,Joe Dirt 2: Beautiful Loser (2015)\n4152,5.0,1,Nasu: Summer in Andalusia (2003)\n4153,2.0,1,Dark Places (2015)\n4154,2.5,1,Afro Samurai (2007)\n4155,4.5,1,Massu Engira Maasilamani (2015)\n4156,3.903225806451613,31,The Revenant (2015)\n4157,3.0,1,Irrational Man (2015)\n4158,3.0,1,Exte: Hair Extensions (2007)\n4159,5.0,1,Ooops! Noah is Gone... (2015)\n4160,2.8333333333333335,6,Southpaw (2015)\n4161,3.5454545454545454,11,Sicario (2015)\n4162,3.0,1,Goodnight Mommy (Ich seh ich seh) (2014)\n4163,1.25,2,10 Cent Pistol (2015)\n4164,3.5,1,Before We Go (2014)\n4165,3.25,2,Anomalisa (2015)\n4166,1.5,1,Colonia (2016)\n4167,2.5,1,Ghost in the Shell Arise - Border 2: Ghost Whispers (2013)\n4168,2.25,2,How to Make Love Like an Englishman (2014)\n4169,2.0,1,Always Watching: A Marble Hornets Story (2015)\n4170,3.6538461538461537,13,The Intern (2015)\n4171,4.5,1,Love (2015)\n4172,3.5625,8,Room (2015)\n4173,2.0,1,The Runner (2015)\n4174,3.25,4,The Gift (2015)\n4175,4.0,1,The Witch (2015)\n4176,4.0,1,Men & Chicken (2015)\n4177,3.0,1,The Escort (2015)\n4178,4.0,4,Doctor Who: The Waters of Mars (2009)\n4179,4.5,1,\"Family Guy Presents: Something, Something, Something, Dark Side (2009)\"\n4180,3.5,1,\"Visit, The (2015)\"\n4181,1.5,1,Secret in Their Eyes (2015)\n4182,4.0,1,Jeff Ross Roasts Criminals: Live at Brazos County Jail (2015)\n4183,5.0,1,Battle For Sevastopol (2015)\n4184,2.6666666666666665,6,American Ultra (2015)\n4185,4.214285714285714,7,Straight Outta Compton (2015)\n4186,3.5,2,Cop Car (2015)\n4187,3.5,1,The Lost Room (2006)\n4188,4.5,1,Tangerine (2015)\n4189,2.0,1,Every Secret Thing (2014)\n4190,3.2,5,Joy (2015)\n4191,3.25,2,Victor Frankenstein (2015)\n4192,2.5,1,Guardians (2016)\n4193,3.75,2,Scouts Guide to the Zombie Apocalypse (2015)\n4194,2.5,1,Suffragette (2015)\n4195,4.0,1,Fort Tilden (2014)\n4196,3.5,3,Turbo Kid (2015)\n4197,3.5,1,The Unauthorized Saved by the Bell Story (2014)\n4198,0.5,1,War Room (2015)\n4199,3.5,2,Legend (2015)\n4200,5.0,1,Deathgasm (2015)\n4201,3.75,2,The Danish Girl (2015)\n4202,3.5,1,Cooties (2015)\n4203,4.0,1,Steve Jobs: The Man in the Machine (2015)\n4204,3.3333333333333335,3,Green Room (2015)\n4205,3.0,2,Beasts of No Nation (2015)\n4206,5.0,1,Bloodsucking Bastards (2015)\n4207,0.5,1,Saving Christmas (2014)\n4208,3.0,1,Iron Man & Hulk: Heroes United (2013)\n4209,1.5,1,Knock Knock (2015)\n4210,4.75,2,The Blue Planet (2001)\n4211,4.5,1,Cornered! (2009)\n4212,3.125,4,Demolition (2016)\n4213,3.0,1,Cigarette Burns (2005)\n4214,2.75,2,Our Brand Is Crisis (2015)\n4215,3.0,2,High Rise (2015)\n4216,4.25,4,The Night Before (2015)\n4217,2.0,1,Into the Forest (2015)\n4218,5.0,1,The Editor (2015)\n4219,3.0833333333333335,6,Everest (2015)\n4220,4.0,2,The Brand New Testament (2015)\n4221,4.157894736842105,19,Spotlight (2015)\n4222,2.5,1,Pawn Sacrifice (2015)\n4223,3.75,2,Hardcore Henry (2015)\n4224,2.0,3,Burnt (2015)\n4225,4.5,1,Ryuzo and the Seven Henchmen (2015)\n4226,3.0,1,If I Were a Rich Man (2002)\n4227,4.0,1,Last Shift (2014)\n4228,1.0,1,\"F*ck You, Goethe 2 (2015)\"\n4229,4.5,1,Garam Masala (2005)\n4230,2.5,1,Life Eternal (2015)\n4231,2.875,4,Hotel Transylvania 2 (2015)\n4232,1.5,1,Anti-Social (2015)\n4233,5.0,1,Jump In! (2007)\n4234,2.25,2,The Little Prince (2015)\n4235,1.5,1,Narcopolis (2014)\n4236,3.0,2,Ashby (2015)\n4237,3.3076923076923075,13,Wonder Woman (2017)\n4238,2.5,1,The Circle (2016)\n4239,3.75,2,Silence (2016)\n4240,3.4375,8,Bridge of Spies (2015)\n4241,4.0,1,The Great Hypnotist (2014)\n4242,1.5,1,Into the Grizzly Maze (2015)\n4243,5.0,1,Human (2015)\n4244,4.0,1,Chasuke's Journey (2015)\n4245,5.0,1,L.A. Slasher (2015)\n4246,2.8125,8,\"Hail, Caesar! (2016)\"\n4247,4.5,1,How To Change The World (2015)\n4248,2.8333333333333335,3,Er ist wieder da (2015)\n4249,4.0,1,Just Eat It: A Food Waste Story (2014)\n4250,3.0,1,Bros Before Hos (2013)\n4251,2.0,1,Slow Learners (2015)\n4252,0.5,1,Unforgiven (2013)\n4253,4.5,1,\"Sex, Drugs & Taxation (2013)\"\n4254,2.0,1,Sky High (2003)\n4255,4.0,1,Confessions of a Dangerous Mind (2002)\n4256,2.0,2,Goosebumps (2015)\n4257,1.5,1,The Perfect Guy (2015)\n4258,2.0,1,Rock the Kasbah (2015)\n4259,4.5,1,Freaks of Nature (2015)\n4260,4.5,2,Bone Tomahawk (2015)\n4261,2.5,1,Extraordinary Tales (2015)\n4262,3.0,1,The Dressmaker (2015)\n4263,3.5,1,Nowitzki: The Perfect Shot (2014)\n4264,4.0,1,Trumbo (2015)\n4265,3.5,1,Our Lips Are Sealed (2000)\n4266,2.5,3,Concussion (2015)\n4267,1.5,1,\"Peanuts Movie, The (2015)\"\n4268,3.0,1,Blue Mountain State: The Rise of Thadland (2015)\n4269,4.0,1,The Boy and the Beast (2015)\n4270,3.75,8,Creed (2015)\n4271,5.0,1,Dragons: Gift of the Night Fury (2011)\n4272,4.0,1,Twinsters (2015)\n4273,5.0,1,Cosmic Scrat-tastrophe (2015)\n4274,2.0,1,Solace (2015)\n4275,2.0,1,Lost in the Sun (2015)\n4276,4.0,1,Eros (2004)\n4277,3.0,1,Those Happy Days (2006)\n4278,4.0,1,What Men Still Talk About (2011)\n4279,3.75,2,Doctor Who: Last Christmas (2014)\n4280,4.166666666666667,3,\"Doctor Who: The Doctor, the Widow and the Wardrobe (2011)\"\n4281,4.75,2,Doctor Who: A Christmas Carol (2010)\n4282,3.0,4,Doctor Who: Planet of the Dead (2009)\n4283,2.875,4,Doctor Who: The Next Doctor (2008)\n4284,4.5,3,Doctor Who: Voyage Of The Damned (2007)\n4285,4.0,4,Doctor Who: The Runaway Bride (2007)\n4286,5.0,1,A Perfect Day (2015)\n4287,3.5,1,Hitchcock/Truffaut (2015)\n4288,2.5,2,The 5th Wave (2016)\n4289,2.5,1,A Very Murray Christmas (2015)\n4290,3.75,2,Chi-Raq (2015)\n4291,3.0,1,Truth (2015)\n4292,3.0,1,Just Jim (2015)\n4293,3.9615384615384617,26,\"Big Short, The (2015)\"\n4294,4.5,1,Applesauce (2015)\n4295,3.25,2,The Ridiculous 6 (2015)\n4296,3.5,1,John Mulaney: The Comeback Kid (2015)\n4297,4.5,2,Saw (2003)\n4298,2.0,1,North Pole: Open For Christmas (2015)\n4299,2.0,1,Mojave (2015)\n4300,4.0,1,Wizards of Waverly Place: The Movie (2009)\n4301,4.5,2,World of Tomorrow (2015)\n4302,2.5,3,Zoolander 2 (2016)\n4303,3.125,4,How to Be Single (2016)\n4304,4.0,1,Blue Exorcist: The Movie (2012)\n4305,3.5,1,He Never Died (2015)\n4306,4.0,1,Parasyte: Part 1 (2014)\n4307,4.0,1,Parasyte: Part 2 (2015)\n4308,5.0,1,Lumberjack Man (2015)\n4309,2.6666666666666665,6,Daddy's Home (2015)\n4310,2.5,7,Sisters (2015)\n4311,1.5,1,'Tis the Season for Love (2015)\n4312,3.375,8,Kung Fu Panda 3 (2016)\n4313,5.0,1,Spellbound (2011)\n4314,5.0,1,Unicorn City (2012)\n4315,2.5,1,Standoff (2016)\n4316,2.0,1,Swelter (2014)\n4317,4.5,1,Pride and Prejudice and Zombies (2016)\n4318,2.0,1,Garm Wars: The Last Druid (2014)\n4319,2.0,1,The Devil's Candy (2015)\n4320,3.0,2,Close Range (2015)\n4321,3.85,10,Sherlock: The Abominable Bride (2016)\n4322,4.25,2,Doctor Who: The Husbands of River Song (2015)\n4323,2.5,1,Moonwalkers (2015)\n4324,4.5,1,Tomorrow (2015)\n4325,4.0,1,Anacleto: Agente secreto (2015)\n4326,3.0,1,Wiener-Dog (2016)\n4327,2.0,3,Ride Along 2 (2016)\n4328,2.0,1,Maggie's Plan (2015)\n4329,2.8333333333333335,3,Eddie the Eagle (2016)\n4330,1.5,1,Exposed (2016)\n4331,2.5,1,Stonewall (2015)\n4332,2.0,1,Frankenstein (2015)\n4333,1.5,1,Welcome to Happiness (2015)\n4334,0.5,1,Risen (2016)\n4335,4.0,1,The Survivalist (2015)\n4336,2.875,4,Dirty Grandpa (2016)\n4337,2.5,1,Requiem for the American Dream (2015)\n4338,4.0,2,Death Note Rewrite: Genshisuru Kami (2007)\n4339,3.0,1,The Finest Hours (2016)\n4340,3.5,1,Ghost in the Shell: Solid State Society (2006)\n4341,2.0,2,Grease Live (2016)\n4342,0.5,1,Gods of Egypt (2016)\n4343,3.5,1,Embrace of the Serpent (2016)\n4344,1.5,1,Race (2016)\n4345,3.6785714285714284,14,10 Cloverfield Lane (2016)\n4346,2.0,2,London Has Fallen (2016)\n4347,3.890625,32,Zootopia (2016)\n4348,3.3333333333333335,3,Whiskey Tango Foxtrot (2016)\n4349,2.5,1,Desierto (2016)\n4350,3.125,4,The Brothers Grimsby (2016)\n4351,3.5,1,The Wait (2015)\n4352,3.5,1,War and Peace (2016)\n4353,3.5,1,Southbound (2016)\n4354,4.0,1,Ip Man 3 (2015)\n4355,1.5,1,Santa's Little Helper (2015)\n4356,5.0,1,Who Killed Chea Vichea? (2010)\n4357,4.375,4,Hunt for the Wilderpeople (2016)\n4358,3.5,1,Rabbits (2002)\n4359,3.0,1,Genius Party (2007)\n4360,4.0,1,Tears for Sale (2008)\n4361,2.0,1,Dad's Army (2016)\n4362,4.0,1,The Barkley Marathons: The Race That Eats Its Young (2015)\n4363,4.0,1,Merci Patron ! (2016)\n4364,3.0,2,The Neon Demon (2016)\n4365,4.0,1,Fraktus (2012)\n4366,2.8333333333333335,3,Eye in the Sky (2016)\n4367,2.0,1,Camino (2016)\n4368,3.5,3,Mr. Right (2016)\n4369,4.0,1,Florence Foster Jenkins (2016)\n4370,2.0,1,My Big Fat Greek Wedding 2 (2016)\n4371,3.5,1,Neon Bull (2015)\n4372,3.25,2,Get a Job (2016)\n4373,2.5,2,Keanu (2016)\n4374,2.0,1,Me Him Her (2015)\n4375,5.0,1,Ice Age: The Great Egg-Scapade (2016)\n4376,4.166666666666667,3,Everybody Wants Some (2016)\n4377,3.8333333333333335,3,Sing Street (2016)\n4378,2.5,1,Zoom (2015)\n4379,1.0,3,The Huntsman Winter's War (2016)\n4380,2.6,5,Neighbors 2: Sorority Rising (2016)\n4381,2.5,1,The Trust (2016)\n4382,2.6666666666666665,3,Hush (2016)\n4383,4.0,1,Jimmy Carr: Telling Jokes (2009)\n4384,2.5,1,Jimmy Carr: Making People Laugh (2010)\n4385,3.0,1,The Man Who Knew Infinity (2016)\n4386,2.0,1,Despite the Falling Snow (2016)\n4387,2.25,2,Money Monster (2016)\n4388,2.5,1,Barbershop: The Next Cut (2016)\n4389,3.576923076923077,13,Finding Dory (2016)\n4390,2.6666666666666665,3,The Boss (2016)\n4391,2.5,1,The Angry Birds Movie (2016)\n4392,4.0,1,Bakuman (2015)\n4393,1.5,1,I Am Wrath (2016)\n4394,1.0,1,Precious Cargo (2016)\n4395,3.8333333333333335,6,Snowden (2016)\n4396,1.5,1,Ratchet & Clank (2016)\n4397,1.5,1,Kicking Off (2016)\n4398,5.0,1,SORI: Voice from the Heart (2016)\n4399,3.5,1,Gintama: The Final Chapter - Be Forever Yorozuya (2013)\n4400,3.8333333333333335,15,The Nice Guys (2016)\n4401,1.5,1,Kindergarten Cop 2 (2016)\n4402,2.5,1,The Crew (2016)\n4403,2.5,1,The Shallows (2016)\n4404,2.6666666666666665,3,The Handmaiden (2016)\n4405,2.0,4,Alice Through the Looking Glass (2016)\n4406,2.5,1,The BFG (2016)\n4407,3.5,1,My Scientology Movie (2016)\n4408,3.642857142857143,7,Sausage Party (2016)\n4409,5.0,1,All Yours (2016)\n4410,2.5,2,Kill Command (2016)\n4411,4.5,5,Captain Fantastic (2016)\n4412,3.6666666666666665,3,Toni Erdmann (2016)\n4413,3.0,1,The Wailing (2016)\n4414,2.5,1,The Meddler (2016)\n4415,3.6363636363636362,11,Now You See Me 2 (2016)\n4416,4.5,1,Ali Wong: Baby Cobra (2016)\n4417,3.0,1,Café Society (2016)\n4418,3.25,2,Swiss Army Man (2016)\n4419,3.5,2,The Do-Over (2016)\n4420,2.0,1,Teenage Mutant Ninja Turtles: Out of the Shadows (2016)\n4421,3.75,2,The Fundamentals of Caring (2016)\n4422,2.75,2,Popstar: Never Stop Never Stopping (2016)\n4423,4.0,1,A Midsummer Night's Dream (2016)\n4424,4.214285714285714,7,Planet Earth (2006)\n4425,2.5,1,Bo Burnham: Make Happy (2016)\n4426,2.25,2,The Conjuring 2 (2016)\n4427,4.5,1,Pelé: Birth of a Legend (2016)\n4428,3.125,8,Ghostbusters (2016)\n4429,3.625,4,Central Intelligence (2016)\n4430,4.5,1,O.J.: Made in America (2016)\n4431,3.0,1,Genius (2016)\n4432,2.7222222222222223,9,Jason Bourne (2016)\n4433,1.5,1,The Maid's Room (2014)\n4434,3.0833333333333335,6,The Legend of Tarzan (2016)\n4435,2.75,2,The Purge: Election Year (2016)\n4436,3.3333333333333335,3,Mike & Dave Need Wedding Dates (2016)\n4437,1.0,1,Ice Age: Collision Course (2016)\n4438,3.0,2,Lights Out (2016)\n4439,3.0,1,Pete's Dragon (2016)\n4440,5.0,1,Indignation (2016)\n4441,2.5,1,Goat (2016)\n4442,3.5,1,Marauders (2016)\n4443,4.5,2,Piper (2016)\n4444,2.0,1,The Adderall Diaries (2015)\n4445,3.0,1,Hazard (2005)\n4446,4.5,1,The Red Turtle (2016)\n4447,0.5,1,Satanic (2016)\n4448,2.625,4,Nerve (2016)\n4449,1.0,1,Hellevator (2004)\n4450,3.1666666666666665,6,Sully (2016)\n4451,4.5,1,Jim Jefferies: Freedumb (2016)\n4452,3.75,2,The Infiltrator (2016)\n4453,3.625,8,War Dogs (2016)\n4454,1.0,1,Vigilante Diaries (2016)\n4455,3.0,1,Batman: The Killing Joke (2016)\n4456,3.8333333333333335,3,Bad Moms (2016)\n4457,3.5625,8,Hell or High Water (2016)\n4458,3.5,1,Kingsglaive: Final Fantasy XV (2016)\n4459,3.3,5,Don't Breathe (2016)\n4460,1.0,1,Body (2015)\n4461,1.5,1,Sharknado 4: The 4th Awakens (2016)\n4462,3.25,2,The Edge of Seventeen (2016)\n4463,4.0,1,Elle (2016)\n4464,3.5,1,Train to Busan (2016)\n4465,5.0,1,Tom Segura: Mostly Stories (2016)\n4466,3.5833333333333335,6,The Magnificent Seven (2016)\n4467,4.0,3,Masterminds (2016)\n4468,4.0,6,Kubo and the Two Strings (2016)\n4469,1.0,1,Bridget Jones's Baby (2016)\n4470,3.5,1,Deepwater Horizon (2016)\n4471,3.0,3,Miss Peregrine's Home for Peculiar Children (2016)\n4472,4.5,1,The Girl on the Train (2016)\n4473,3.0,6,The Accountant (2016)\n4474,2.5,1,Imperium (2016)\n4475,4.0,1,Kizumonogatari Part 1: Tekketsu (2016)\n4476,3.0,1,Steins;Gate the Movie: The Burden of Déjà vu (2013)\n4477,4.0,1,Shin Godzilla (2016)\n4478,4.0,3,Your Name. (2016)\n4479,4.0,1,Comedy Central Roast of David Hasselhoff (2010)\n4480,4.0,1,DC Super Hero Girls: Hero of the Year (2016)\n4481,3.9375,8,Hacksaw Ridge (2016)\n4482,3.0,1,David Cross: Making America Great Again (2016)\n4483,2.5,1,Over the Garden Wall (2013)\n4484,3.5,1,Blair Witch (2016)\n4485,3.5,1,31 (2016)\n4486,4.5,1,ARQ (2016)\n4487,3.980769230769231,26,Arrival (2016)\n4488,4.0,2,Storks (2016)\n4489,4.5,1,Maximum Ride (2016)\n4490,3.5,1,Endless Poetry (2016)\n4491,5.0,1,The Girl with All the Gifts (2016)\n4492,1.5,1,All Roads Lead to Rome (2016)\n4493,3.5,1,Amanda Knox (2016)\n4494,2.5,1,Dirty 30 (2016)\n4495,2.0,1,Gimme Danger (2016)\n4496,4.0,1,Go Figure (2005)\n4497,2.0,1,Anything for Love (2016)\n4498,1.5,1,Night Guards (2016)\n4499,3.388888888888889,9,La La Land (2016)\n4500,4.5,2,13th (2016)\n4501,2.5,1,London Town (2016)\n4502,2.875,4,Inferno (2016)\n4503,3.75,2,Keeping Up with the Joneses (2016)\n4504,2.0,1,Wild Oats (2016)\n4505,1.5,1,The Rocky Horror Picture Show: Let's Do the Time Warp Again (2016)\n4506,2.0,1,Jack Reacher: Never Go Back (2016)\n4507,4.0,1,Joe Rogan: Triggered (2016)\n4508,2.5,1,Ethel & Ernest (2016)\n4509,2.0,1,Flowers for Algernon (2000)\n4510,2.9,5,Manchester by the Sea (2016)\n4511,4.5,1,Lion (2016)\n4512,1.0,1,The Thinning (2016)\n4513,3.8333333333333335,3,While You Were Fighting: A Thor Mockumentary (2016)\n4514,0.5,1,Bad Santa 2 (2016)\n4515,4.5,1,Ice Guardians (2016)\n4516,4.0,1,Risk (2016)\n4517,3.25,2,The True Memoirs of an International Assassin (2016)\n4518,5.0,1,Alesha Popovich and Tugarin the Dragon (2004)\n4519,4.0,2,HyperNormalisation (2016)\n4520,3.5,1,The African Doctor (2016)\n4521,4.75,2,Whiplash (2013)\n4522,4.0,1,Sapphire Blue (2014)\n4523,4.0,2,A Silent Voice (2016)\n4524,3.45,10,Moana (2016)\n4525,3.5,3,Office Christmas Party (2016)\n4526,1.0,1,The Space Between Us (2016)\n4527,3.925925925925926,27,Rogue One: A Star Wars Story (2016)\n4528,3.3333333333333335,6,Split (2017)\n4529,3.0,1,Underworld: Blood Wars (2016)\n4530,4.5,2,Miss Sloane (2016)\n4531,3.6666666666666665,6,Passengers (2016)\n4532,3.8,10,Hidden Figures (2016)\n4533,3.0,1,Fences (2016)\n4534,4.25,2,The Founder (2016)\n4535,3.125,4,Why Him? (2016)\n4536,3.75,4,Sing (2016)\n4537,5.0,1,I Am Not Your Negro (2017)\n4538,2.5,2,Assassin's Creed (2016)\n4539,4.5,1,A Dog's Purpose (2017)\n4540,4.5,1,Fist Fight (2017)\n4541,2.5,1,Shakespeare Behind Bars (2005)\n4542,3.5,1,A Street Cat Named Bob (2016)\n4543,3.7142857142857144,7,The Lego Batman Movie (2017)\n4544,3.5,1,The Good Boy (2016)\n4545,4.5,1,\"Dana Carvey: Straight White Male, 60 (2016)\"\n4546,4.0,1,Marvel One-Shot: Agent Carter (2013)\n4547,4.0,1,Kizumonogatari II: Passionate Blood (2016)\n4548,4.0,1,Joe Rogan: Live (2006)\n4549,3.0,1,Jim Gaffigan: Cinco (2017)\n4550,4.5,1,Kizumonogatari III: Cold Blood (2017)\n4551,4.142857142857143,7,John Wick: Chapter Two (2017)\n4552,3.6333333333333333,15,Get Out (2017)\n4553,4.28,25,Logan (2017)\n4554,3.125,4,Kong: Skull Island (2017)\n4555,3.75,4,T2: Trainspotting (2017)\n4556,4.0,4,The Big Sick (2017)\n4557,2.5,1,100 Streets (2016)\n4558,3.5,2,Beauty and the Beast (2017)\n4559,3.8,5,The Boss Baby (2017)\n4560,2.0,1,Mercury Plains (2016)\n4561,3.75,4,Call Me by Your Name (2017)\n4562,3.0,1,Mudbound (2017)\n4563,3.75,2,Ghost in the Shell (2017)\n4564,4.0,1,Bill Burr: Walk Your Way Out (2017)\n4565,1.0,1,Fifty Shades Darker (2017)\n4566,4.5,1,Neal Brennan: 3 Mics (2017)\n4567,4.5,1,Lemonade (2016)\n4568,2.5,1,American Fable (2017)\n4569,1.5,1,The Void (2016)\n4570,1.0,1,Buster's Mal Heart (2017)\n4571,3.0,1,Power Rangers (2017)\n4572,2.8,5,Alien: Covenant (2017)\n4573,3.5,1,Free Fire (2017)\n4574,1.0,1,Species III (2004)\n4575,2.0,1,Ultimate Avengers 2 (2006)\n4576,4.0,1,Dave Chappelle: The Age of Spin (2017)\n4577,4.0,1,CHiPS (2017)\n4578,3.0,1,Table 19 (2017)\n4579,4.0,1,Dave Chappelle: Deep in the Heart of Texas (2017)\n4580,4.125,4,Gifted (2017)\n4581,4.285714285714286,7,Band of Brothers (2001)\n4582,3.0,3,Baywatch (2017)\n4583,4.0,1,Snatched (2017)\n4584,3.5,1,The Mummy (2017)\n4585,4.0,1,Life-Size (2000)\n4586,2.3333333333333335,3,The Fate of the Furious (2017)\n4587,2.0,1,Sandy Wexler (2017)\n4588,4.0,1,Betting on Zero (2016)\n4589,3.0,1,Win It All (2017)\n4590,3.5,1,Captain Underpants: The First Epic Movie (2017)\n4591,3.5,1,It Comes at Night (2017)\n4592,3.0,1,Cars 3 (2017)\n4593,4.0,1,Mini's First Time (2006)\n4594,4.2,5,Planet Earth II (2016)\n4595,4.0,1,The Hero (2017)\n4596,4.0,1,\"Nobody Speak: Hulk Hogan, Gawker and Trials of a Free Press (2017)\"\n4597,3.0,1,Robin Williams: Live on Broadway (2002)\n4598,3.5,1,The Death of Louis XIV (2016)\n4599,2.5,1,Munna bhai M.B.B.S. (2003)\n4600,3.0,1,The Beguiled (2017)\n4601,4.333333333333333,9,Baby Driver (2017)\n4602,3.75,2,Okja (2017)\n4603,4.0,1,Embassy (2013)\n4604,3.25,2,Rough Night (2017)\n4605,4.5,1,Mystère à la Tour Eiffel (2015)\n4606,3.0,1,Stefan Zweig: Farewell to Europe (2016)\n4607,2.5,1,The Prime Gig (2000)\n4608,2.0,1,Late Night with Conan O'Brien: The Best of Triumph the Insult Comic Dog (2004)\n4609,3.0,1,Get Me Roger Stone (2017)\n4610,2.25,2,Despicable Me 3 (2017)\n4611,5.0,1,Tickling Giants (2017)\n4612,5.0,1,A Detective Story (2003)\n4613,3.5,1,Final Flight of the Osiris (2003)\n4614,3.0,1,Kid's Story (2003)\n4615,2.875,4,War for the Planet of the Apes (2017)\n4616,4.25,2,The Square (2017)\n4617,4.0,1,The Meyerowitz Stories (2017)\n4618,4.0,1,War Machine (2017)\n4619,4.5,1,Tokyo Idols (2017)\n4620,3.5,1,Vir Das: Abroad Understanding (2017)\n4621,2.5,1,\"Norm Macdonald: Hitler's Dog, Gossip & Trickery (2017)\"\n4622,2.875,4,Valerian and the City of a Thousand Planets (2017)\n4623,0.5,1,The Gracefield Incident (2015)\n4624,4.5,1,Shadow World (2016)\n4625,2.0,1,Tiger Raid (2016)\n4626,4.25,2,Seven Sisters (2017)\n4627,3.0,1,Atomic Blonde (2017)\n4628,5.0,1,Empties (2007)\n4629,4.0,1,Goon: Last of the Enforcers (2017)\n4630,4.75,4,Black Mirror: White Christmas (2014)\n4631,3.423076923076923,13,Dunkirk (2017)\n4632,2.5,1,The Putin Interviews (2017)\n4633,4.0,1,Unedited Footage of a Bear (2014)\n4634,3.5,1,\"Oh, Hello: On Broadway (2017)\"\n4635,3.0,1,Good Time (2017)\n4636,3.5,1,The House (2017)\n4637,4.5,1,Logan Lucky (2017)\n4638,3.5,1,The Dark Tower (2017)\n4639,4.0,1,Annabelle: Creation (2017)\n4640,3.3333333333333335,9,It (2017)\n4641,0.5,1,The Emoji Movie (2017)\n4642,1.6666666666666667,3,Death Note (2017)\n4643,3.4,5,Wind River (2017)\n4644,1.5,1,Rory Scovel Tries Stand-Up for the First Time (2017)\n4645,3.5,1,Shot Caller (2017)\n4646,2.6666666666666665,3,The Hitman's Bodyguard (2017)\n4647,2.5,1,Rick and Morty: State of Georgia Vs. Denver Fenton Allen (2016)\n4648,3.0,1,A German Life (2016)\n4649,3.0,1,Self-criticism of a Bourgeois Dog (2017)\n4650,3.5,1,LEGO DC Super Hero Girls: Brain Drain (2017)\n4651,3.0625,8,Kingsman: The Golden Circle (2017)\n4652,4.0,1,Ari Shaffir: Double Negative (2017)\n4653,3.8055555555555554,18,Blade Runner 2049 (2017)\n4654,2.0,1,The Nut Job 2: Nutty by Nature (2017)\n4655,4.5,1,Bliss (2012)\n4656,3.5,1,Alles Inklusive (2014)\n4657,3.25,2,Mother! (2017)\n4658,4.0,1,Icarus (2017)\n4659,1.0,1,Cage Dive (2017)\n4660,4.5,2,American Made (2017)\n4661,4.0,1,Little Boxes (2017)\n4662,2.0,1,Geostorm (2017)\n4663,4.5,1,Maz Jobrani: Immigrant (2017)\n4664,3.0,1,Sword Art Online The Movie: Ordinal Scale (2017)\n4665,4.75,8,\"Three Billboards Outside Ebbing, Missouri (2017)\"\n4666,3.3333333333333335,3,Lady Bird (2017)\n4667,2.8333333333333335,3,Murder on the Orient Express (2017)\n4668,3.5384615384615383,13,Coco (2017)\n4669,4.5,1,\"The Night Is Short, Walk on Girl (2017)\"\n4670,3.3333333333333335,3,\"I, Tonya (2017)\"\n4671,2.0,1,\"Fireworks, Should We See It from the Side or the Bottom? (2017)\"\n4672,3.5,1,Adventures in Plymptoons! (2011)\n4673,3.5,1,Gaga: Five Foot Two (2017)\n4674,4.0,1,Dave Chappelle: Killin' Them Softly (2000)\n4675,3.5,1,Front Cover (2016)\n4676,4.25,2,Paddington 2 (2017)\n4677,3.5,1,2048: Nowhere to Run (2017)\n4678,4.0,1,The Death of Stalin (2017)\n4679,5.0,1,Loving Vincent (2017)\n4680,5.0,1,Blue Planet II (2017)\n4681,3.0,2,Christina P: Mother Inferior (2017)\n4682,3.6666666666666665,6,Jumanji: Welcome to the Jungle (2017)\n4683,4.0,1,Dane Cook: Troublemaker (2014)\n4684,1.0,1,Mayhem (2017)\n4685,4.0,1,Emerald Green (2016)\n4686,3.0,1,Wonder Wheel (2017)\n4687,1.5,1,Creep 2 (2017)\n4688,4.0,1,LBJ (2017)\n4689,1.5,1,\"Roman J. Israel, Esq. (2017)\"\n4690,3.8333333333333335,3,Darkest Hour (2017)\n4691,3.125,12,Star Wars: The Last Jedi (2017)\n4692,3.5,1,A Bad Moms Christmas (2017)\n4693,3.6875,8,The Shape of Water (2017)\n4694,4.0,1,Molly's Game (2017)\n4695,4.0,3,Wonder (2017)\n4696,4.0,1,Daddy's Home 2 (2017)\n4697,4.5,1,Jim & Andy: The Great Beyond (2017)\n4698,3.75,2,The Disaster Artist (2017)\n4699,4.25,2,The Post (2017)\n4700,4.5,1,Die Frauen von Ravensbrück (2005)\n4701,3.0,5,The Greatest Showman (2017)\n4702,1.5,1,Ferdinand (2017)\n4703,3.75,2,Jack Whitehall: At Large (2017)\n4704,1.5,1,Lynne Koplitz: Hormonal Beast (2017)\n4705,3.5,1,Phantom Thread (2017)\n4706,4.5,1,Too Funny to Fail: The Life and Death of The Dana Carvey Show (2017)\n4707,2.5,1,Craig Ferguson: Tickle Fight (2017)\n4708,4.0,1,The Second Renaissance Part II (2003)\n4709,3.8333333333333335,3,Annihilation (2018)\n4710,2.0,1,A Christmas Story Live! (2017)\n4711,4.5,1,Pixel Perfect (2004)\n4712,2.5,1,Judd Apatow: The Return (2017)\n4713,3.5,1,The Purple Sea (2009)\n4714,2.875,4,Bright (2017)\n4715,1.5,1,The Commuter (2018)\n4716,3.5,1,Dave Chappelle: Equanimity (2017)\n4717,4.0,1,Quest (2017)\n4718,3.5,1,Dave Chappelle: The Bird Revelation (2017)\n4719,3.5,1,Insidious: The Last Key (2018)\n4720,4.0,1,Game Night (2018)\n4721,3.5,1,Maze Runner: The Death Cure (2018)\n4722,3.5,5,Isle of Dogs (2018)\n4723,1.5,1,The Clapper (2018)\n4724,4.5,1,Tom Segura: Disgraceful (2018)\n4725,3.5,2,When We First Met (2018)\n4726,2.0,1,Battle Planet (2008)\n4727,2.25,2,The Cloverfield Paradox (2018)\n4728,4.0,1,Making a Murderer (2015)\n4729,3.5,1,Elsa & Fred (2005)\n4730,2.5,4,Tomb Raider (2018)\n4731,0.5,1,Fullmetal Alchemist 2018 (2017)\n4732,4.0,1,First Reformed (2017)\n4733,3.25,2,Fred Armisen: Standup for Drummers (2018)\n4734,4.0,1,Death Wish (2018)\n4735,3.0,1,A Wrinkle in Time (2018)\n4736,4.0,1,\"Love, Simon (2018)\"\n4737,2.75,4,A Quiet Place (2018)\n4738,4.5,1,Alpha (2018)\n4739,2.0,1,I Kill Giants (2018)\n4740,4.75,2,Sherlock - A Study in Pink (2010)\n4741,3.0,1,\"Game Over, Man! (2018)\"\n4742,3.0,1,Blockers (2018)\n4743,2.75,2,Pacific Rim: Uprising (2018)\n4744,3.0,1,Rampage (2018)\n4745,3.25,2,Jurassic World: Fallen Kingdom (2018)\n4746,3.0,4,Incredibles 2 (2018)\n4747,3.875,12,Deadpool 2 (2018)\n4748,3.9,5,Solo: A Star Wars Story (2018)\n4749,5.0,1,Won't You Be My Neighbor? (2018)\n4750,4.5,1,Sorry to Bother You (2018)\n4751,3.6666666666666665,3,Ant-Man and the Wasp (2018)\n4752,3.5,1,Dogman (2018)\n4753,4.5,1,Mamma Mia: Here We Go Again! (2018)\n4754,4.0,1,Tag (2018)\n4755,4.5,1,The Man Who Killed Don Quixote (2018)\n4756,2.5,1,Boundaries (2018)\n4757,3.0,1,Spiral (2018)\n4758,3.75,2,Mission: Impossible - Fallout (2018)\n4759,2.5,1,SuperFly (2018)\n4760,1.0,1,Iron Soldier (2010)\n4761,2.5,1,BlacKkKlansman (2018)\n4762,3.5,1,The Darkest Minds (2018)\n4763,1.5,1,Tilt (2011)\n4764,4.0,1,Jeff Ross Roasts the Border (2017)\n4765,1.0,1,John From (2015)\n4766,1.5,1,Liquid Truth (2017)\n4767,1.0,1,Hommage à Zgougou (et salut à Sabine Mamou) (2002)\n4768,4.5,1,Gintama (2017)\n4769,3.5,1,Gintama: The Movie (2010)\n4770,3.0,1,anohana: The Flower We Saw That Day - The Movie (2013)\n4771,4.0,1,Silver Spoon (2014)\n4772,4.0,1,Love Live! The School Idol Movie (2015)\n4773,3.5,1,Jon Stewart Has Left the Building (2015)\n4774,4.0,1,Black Butler: Book of the Atlantic (2017)\n4775,3.5,1,No Game No Life: Zero (2017)\n4776,3.5,1,Flint (2017)\n4777,3.5,1,Bungo Stray Dogs: Dead Apple (2018)\n"
  },
  {
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    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A1/data/small_movies_b.csv",
    "content": 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7,0.4126075,-0.36223966,-0.3201397\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A1/public_tests.py",
    "content": "import numpy as np\n\ndef test_cofi_cost_func(target):\n    num_users_r = 4\n    num_movies_r = 5 \n    num_features_r = 3\n\n    X_r = np.ones((num_movies_r, num_features_r))\n    W_r = np.ones((num_users_r, num_features_r))\n    b_r = np.zeros((1, num_users_r))\n    Y_r = np.zeros((num_movies_r, num_users_r))\n    R_r = np.zeros((num_movies_r, num_users_r))\n    \n    J = target(X_r, W_r, b_r, Y_r, R_r, 2);\n    assert not np.isclose(J, 13.5), f\"Wrong value. Got {J}. Did you multiplied the regulartization term by lambda_?\"\n    assert np.isclose(J, 27), f\"Wrong value. Expected {27}, got {J}. Check the regularization term\"\n    \n    \n    X_r = np.ones((num_movies_r, num_features_r))\n    W_r = np.ones((num_users_r, num_features_r))\n    b_r = np.ones((1, num_users_r))\n    Y_r = np.ones((num_movies_r, num_users_r))\n    R_r = np.ones((num_movies_r, num_users_r))\n\n    # Evaluate cost function\n    J = target(X_r, W_r, b_r, Y_r, R_r, 0);\n    \n    assert np.isclose(J, 90), f\"Wrong value. Expected {90}, got {J}. Check the term without the regularization\"\n    \n    \n    X_r = np.ones((num_movies_r, num_features_r))\n    W_r = np.ones((num_users_r, num_features_r))\n    b_r = np.ones((1, num_users_r))\n    Y_r = np.zeros((num_movies_r, num_users_r))\n    R_r = np.ones((num_movies_r, num_users_r))\n\n    # Evaluate cost function\n    J = target(X_r, W_r, b_r, Y_r, R_r, 0);\n    \n    assert np.isclose(J, 160), f\"Wrong value. Expected {160}, got {J}. Check the term without the regularization\"\n    \n    X_r = np.ones((num_movies_r, num_features_r))\n    W_r = np.ones((num_users_r, num_features_r))\n    b_r = np.ones((1, num_users_r))\n    Y_r = np.ones((num_movies_r, num_users_r))\n    R_r = np.ones((num_movies_r, num_users_r))\n\n    # Evaluate cost function\n    J = target(X_r, W_r, b_r, Y_r, R_r, 1);\n    \n    assert np.isclose(J, 103.5), f\"Wrong value. Expected {103.5}, got {J}. Check the term without the regularization\"\n    \n    num_users_r = 3\n    num_movies_r = 4 \n    num_features_r = 4\n    \n    #np.random.seed(247)\n    X_r = np.array([[0.36618032, 0.9075415,  0.8310605,  0.08590986],\n                     [0.62634721, 0.38234325, 0.85624346, 0.55183039],\n                     [0.77458727, 0.35704147, 0.31003294, 0.20100006],\n                     [0.34420469, 0.46103436, 0.88638208, 0.36175401]])#np.random.rand(num_movies_r, num_features_r)\n    W_r = np.array([[0.04786854, 0.61504665, 0.06633146, 0.38298908], \n                    [0.16515965, 0.22320207, 0.89826005, 0.14373251], \n                    [0.1274051 , 0.22757303, 0.96865613, 0.70741111]])#np.random.rand(num_users_r, num_features_r)\n    b_r = np.array([[0.14246472, 0.30110933, 0.56141144]])#np.random.rand(1, num_users_r)\n    Y_r = np.array([[0.20651685, 0.60767914, 0.86344527], \n                    [0.82665019, 0.00944765, 0.4376798 ], \n                    [0.81623732, 0.26776794, 0.03757507], \n                    [0.37232161, 0.19890823, 0.13026598]])#np.random.rand(num_movies_r, num_users_r)\n    R_r = np.array([[1, 0, 1], [1, 0, 0], [1, 0, 0], [0, 1, 0]])#(np.random.rand(num_movies_r, num_users_r) > 0.4) * 1\n\n    # Evaluate cost function\n    J = target(X_r, W_r, b_r, Y_r, R_r, 3);\n    \n    assert np.isclose(J, 13.621929978531858, atol=1e-8), f\"Wrong value. Expected {13.621929978531858}, got {J}.\"\n    \n    print('\\033[92mAll tests passed!')\n    \n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A1/recsys_utils.py",
    "content": "import numpy as np\nimport pandas as pd\nfrom numpy import loadtxt\n\ndef normalizeRatings(Y, R):\n    \"\"\"\n    Preprocess data by subtracting mean rating for every movie (every row).\n    Only include real ratings R(i,j)=1.\n    [Ynorm, Ymean] = normalizeRatings(Y, R) normalized Y so that each movie\n    has a rating of 0 on average. Unrated moves then have a mean rating (0)\n    Returns the mean rating in Ymean.\n    \"\"\"\n    Ymean = (np.sum(Y*R,axis=1)/(np.sum(R, axis=1)+1e-12)).reshape(-1,1)\n    Ynorm = Y - np.multiply(Ymean, R) \n    return(Ynorm, Ymean)\n\ndef load_precalc_params_small():\n\n    file = open('./data/small_movies_X.csv', 'rb')\n    X = loadtxt(file, delimiter = \",\")\n\n    file = open('./data/small_movies_W.csv', 'rb')\n    W = loadtxt(file,delimiter = \",\")\n\n    file = open('./data/small_movies_b.csv', 'rb')\n    b = loadtxt(file,delimiter = \",\")\n    b = b.reshape(1,-1)\n    num_movies, num_features = X.shape\n    num_users,_ = W.shape\n    return(X, W, b, num_movies, num_features, num_users)\n    \ndef load_ratings_small():\n    file = open('./data/small_movies_Y.csv', 'rb')\n    Y = loadtxt(file,delimiter = \",\")\n\n    file = open('./data/small_movies_R.csv', 'rb')\n    R = loadtxt(file,delimiter = \",\")\n    return(Y,R)\n\ndef load_Movie_List_pd():\n    \"\"\" returns df with and index of movies in the order they are in in the Y matrix \"\"\"\n    df = pd.read_csv('./data/small_movie_list.csv', header=0, index_col=0,  delimiter=',', quotechar='\"')\n    mlist = df[\"title\"].to_list()\n    return(mlist, df)\n\n\n\n\n\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A2/C3_W2_RecSysNN_Assignment.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"Lzk7iX_CodX6\",\n    \"tags\": []\n   },\n   \"source\": [\n    \"# <img align=\\\"left\\\" src=\\\"./images/film_strip_vertical.png\\\"     style=\\\" width:40px;  \\\" > Practice lab: Deep Learning for Content-Based Filtering\\n\",\n    \"\\n\",\n    \"In this exercise, you will implement content-based filtering using a neural network to build a recommender system for movies. \\n\",\n    \"\\n\",\n    \"# Outline <img align=\\\"left\\\" src=\\\"./images/film_reel.png\\\"     style=\\\" width:40px;  \\\" >\\n\",\n    \"- [ 1 - Packages](#1)\\n\",\n    \"- [ 2 - Movie ratings dataset](#2)\\n\",\n    \"  - [ 2.1 Content-based filtering with a neural network](#2.1)\\n\",\n    \"  - [ 2.2 Preparing the training data](#2.2)\\n\",\n    \"- [ 3 - Neural Network for content-based filtering](#3)\\n\",\n    \"  - [ 3.1 Predictions](#3.1)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"- [ 4 - Congratulations!](#4)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Packages <img align=\\\"left\\\" src=\\\"./images/movie_camera.png\\\"     style=\\\" width:40px;  \\\">\\n\",\n    \"We will use familiar packages, NumPy, TensorFlow and helpful routines from [scikit-learn](https://scikit-learn.org/stable/). We will also use [tabulate](https://pypi.org/project/tabulate/) to neatly print tables and [Pandas](https://pandas.pydata.org/) to organize tabular data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"id\": \"Xu-w_RmNwCV5\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import numpy.ma as ma\\n\",\n    \"from numpy import genfromtxt\\n\",\n    \"from collections import defaultdict\\n\",\n    \"import pandas as pd\\n\",\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow import keras\\n\",\n    \"from sklearn.preprocessing import StandardScaler, MinMaxScaler\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"import tabulate\\n\",\n    \"from recsysNN_utils import *\\n\",\n    \"pd.set_option(\\\"display.precision\\\", 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Movie ratings dataset <img align=\\\"left\\\" src=\\\"./images/film_rating.png\\\" style=\\\" width:40px;\\\" >\\n\",\n    \"The data set is derived from the [MovieLens ml-latest-small](https://grouplens.org/datasets/movielens/latest/) dataset. \\n\",\n    \"\\n\",\n    \"[F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. <https://doi.org/10.1145/2827872>]\\n\",\n    \"\\n\",\n    \"The original dataset has 9000 movies rated by 600 users with ratings on a scale of 0.5 to 5 in 0.5 step increments. The dataset has been reduced in size to focus on movies from the years since 2000 and popular genres. The reduced dataset has $n_u = 395$ users and $n_m= 694$ movies. For each movie, the dataset provides a movie title, release date, and one or more genres. For example \\\"Toy Story 3\\\" was released in 2010 and has several genres: \\\"Adventure|Animation|Children|Comedy|Fantasy|IMAX\\\".  This dataset contains little information about users other than their ratings. This dataset is used to create training vectors for the neural networks described below. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.1\\\"></a>\\n\",\n    \"### 2.1 Content-based filtering with a neural network\\n\",\n    \"\\n\",\n    \"In the collaborative filtering lab, you generated two vectors, a user vector and an item/movie vector whose dot product would predict a rating. The vectors were derived solely from the ratings.   \\n\",\n    \"\\n\",\n    \"Content-based filtering also generates a user and movie feature vector but recognizes there may be other information available about the user and/or movie that may improve the prediction. The additional information is provided to a neural network which then generates the user and movie vector as shown below.\\n\",\n    \"<figure>\\n\",\n    \"    <center> <img src=\\\"./images/RecSysNN.png\\\"   style=\\\"width:500px;height:280px;\\\" ></center>\\n\",\n    \"</figure>\\n\",\n    \"The movie content provided to the network is a combination of the original data and some 'engineered features'. Recall the feature engineering discussion and lab from Course 1, Week 2, lab 4. The original features are the year the movie was released and the movie's genre presented as a one-hot vector. There are 14 genres. The engineered feature is an average rating derived from the user ratings. Movies with multiple genre have a training vector per genre. \\n\",\n    \"\\n\",\n    \"The user content is composed of only engineered features. A per genre average rating is computed per user. Additionally, a user id, rating count and rating average are available, but are not included in the training or prediction content. They are useful in interpreting data.\\n\",\n    \"\\n\",\n    \"The training set consists of all the ratings made by the users in the data set. The user and movie/item vectors are presented to the above network together as a training set. The user vector is the same for all the movies rated by the user. \\n\",\n    \"\\n\",\n    \"Below, let's load and display some of the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"id\": \"M5gfMLYgxCD1\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of training vectors: 58187\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Load Data, set configuration variables\\n\",\n    \"item_train, user_train, y_train, item_features, user_features, item_vecs, movie_dict, user_to_genre = load_data()\\n\",\n    \"\\n\",\n    \"num_user_features = user_train.shape[1] - 3  # remove userid, rating count and ave rating during training\\n\",\n    \"num_item_features = item_train.shape[1] - 1  # remove movie id at train time\\n\",\n    \"uvs = 3  # user genre vector start\\n\",\n    \"ivs = 3  # item genre vector start\\n\",\n    \"u_s = 3  # start of columns to use in training, user\\n\",\n    \"i_s = 1  # start of columns to use in training, items\\n\",\n    \"scaledata = True  # applies the standard scalar to data if true\\n\",\n    \"print(f\\\"Number of training vectors: {len(item_train)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Some of the user and item/movie features are not used in training. Below, the features in brackets \\\"[]\\\" such as the \\\"user id\\\", \\\"rating count\\\" and \\\"rating ave\\\" are not included when the model is trained and used. Note, the user vector is the same for all the movies rated.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table>\\n\",\n       \"<thead>\\n\",\n       \"<tr><th style=\\\"text-align: center;\\\"> [user id] </th><th style=\\\"text-align: center;\\\"> [rating count] </th><th style=\\\"text-align: center;\\\"> [rating ave] </th><th style=\\\"text-align: center;\\\"> Act ion </th><th style=\\\"text-align: center;\\\"> Adve nture </th><th style=\\\"text-align: center;\\\"> Anim ation </th><th style=\\\"text-align: center;\\\"> Chil dren </th><th style=\\\"text-align: center;\\\"> Com edy </th><th style=\\\"text-align: center;\\\"> Crime </th><th style=\\\"text-align: center;\\\"> Docum entary </th><th style=\\\"text-align: center;\\\"> Drama </th><th style=\\\"text-align: center;\\\"> Fan tasy </th><th style=\\\"text-align: center;\\\"> Hor ror </th><th style=\\\"text-align: center;\\\"> Mys tery </th><th style=\\\"text-align: center;\\\"> Rom ance </th><th style=\\\"text-align: center;\\\"> Sci -Fi </th><th style=\\\"text-align: center;\\\"> Thri ller </th></tr>\\n\",\n       \"</thead>\\n\",\n       \"<tbody>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\n\",\n       \"</tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"'<table>\\\\n<thead>\\\\n<tr><th style=\\\"text-align: center;\\\"> [user id] </th><th style=\\\"text-align: center;\\\"> [rating count] </th><th style=\\\"text-align: center;\\\"> [rating ave] </th><th style=\\\"text-align: center;\\\"> Act ion </th><th style=\\\"text-align: center;\\\"> Adve nture </th><th style=\\\"text-align: center;\\\"> Anim ation </th><th style=\\\"text-align: center;\\\"> Chil dren </th><th style=\\\"text-align: center;\\\"> Com edy </th><th style=\\\"text-align: center;\\\"> Crime </th><th style=\\\"text-align: center;\\\"> Docum entary </th><th style=\\\"text-align: center;\\\"> Drama </th><th style=\\\"text-align: center;\\\"> Fan tasy </th><th style=\\\"text-align: center;\\\"> Hor ror </th><th style=\\\"text-align: center;\\\"> Mys tery </th><th style=\\\"text-align: center;\\\"> Rom ance </th><th style=\\\"text-align: center;\\\"> Sci -Fi </th><th style=\\\"text-align: center;\\\"> Thri ller </th></tr>\\\\n</thead>\\\\n<tbody>\\\\n<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">     2     </td><td style=\\\"text-align: center;\\\">       16       </td><td style=\\\"text-align: center;\\\">     4.1      </td><td style=\\\"text-align: center;\\\">   3.9   </td><td style=\\\"text-align: center;\\\">    5.0     </td><td style=\\\"text-align: center;\\\">    0.0     </td><td style=\\\"text-align: center;\\\">    0.0    </td><td style=\\\"text-align: center;\\\">   4.0   </td><td style=\\\"text-align: center;\\\">  4.2  </td><td style=\\\"text-align: center;\\\">     4.0      </td><td style=\\\"text-align: center;\\\">  4.0  </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   3.0   </td><td style=\\\"text-align: center;\\\">   4.0    </td><td style=\\\"text-align: center;\\\">   0.0    </td><td style=\\\"text-align: center;\\\">   4.2   </td><td style=\\\"text-align: center;\\\">    3.9    </td></tr>\\\\n</tbody>\\\\n</table>'\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pprint_train(user_train, user_features, uvs,  u_s, maxcount=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table>\\n\",\n       \"<thead>\\n\",\n       \"<tr><th style=\\\"text-align: center;\\\"> [movie id] </th><th style=\\\"text-align: center;\\\"> year </th><th style=\\\"text-align: center;\\\"> ave rating </th><th style=\\\"text-align: center;\\\"> Act ion </th><th style=\\\"text-align: center;\\\"> Adve nture </th><th style=\\\"text-align: center;\\\"> Anim ation </th><th style=\\\"text-align: center;\\\"> Chil dren </th><th style=\\\"text-align: center;\\\"> Com edy </th><th style=\\\"text-align: center;\\\"> Crime </th><th style=\\\"text-align: center;\\\"> Docum entary </th><th style=\\\"text-align: center;\\\"> Drama </th><th style=\\\"text-align: center;\\\"> Fan tasy </th><th style=\\\"text-align: center;\\\"> Hor ror </th><th style=\\\"text-align: center;\\\"> Mys tery </th><th style=\\\"text-align: center;\\\"> Rom ance </th><th style=\\\"text-align: center;\\\"> Sci -Fi </th><th style=\\\"text-align: center;\\\"> Thri ller </th></tr>\\n\",\n       \"</thead>\\n\",\n       \"<tbody>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">    6874    </td><td style=\\\"text-align: center;\\\"> 2003 </td><td style=\\\"text-align: center;\\\">    4.0     </td><td style=\\\"text-align: center;\\\">    1    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0     </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">    6874    </td><td style=\\\"text-align: center;\\\"> 2003 </td><td style=\\\"text-align: center;\\\">    4.0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   1   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0     </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">    6874    </td><td style=\\\"text-align: center;\\\"> 2003 </td><td style=\\\"text-align: center;\\\">    4.0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     1     </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">    8798    </td><td style=\\\"text-align: center;\\\"> 2004 </td><td style=\\\"text-align: center;\\\">    3.8     </td><td style=\\\"text-align: center;\\\">    1    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0     </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">    8798    </td><td style=\\\"text-align: center;\\\"> 2004 </td><td style=\\\"text-align: center;\\\">    3.8     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   1   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0     </td></tr>\\n\",\n       \"</tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"'<table>\\\\n<thead>\\\\n<tr><th style=\\\"text-align: center;\\\"> [movie id] </th><th style=\\\"text-align: center;\\\"> year </th><th style=\\\"text-align: center;\\\"> ave rating </th><th style=\\\"text-align: center;\\\"> Act ion </th><th style=\\\"text-align: center;\\\"> Adve nture </th><th style=\\\"text-align: center;\\\"> Anim ation </th><th style=\\\"text-align: center;\\\"> Chil dren </th><th style=\\\"text-align: center;\\\"> Com edy </th><th style=\\\"text-align: center;\\\"> Crime </th><th style=\\\"text-align: center;\\\"> Docum entary </th><th style=\\\"text-align: center;\\\"> Drama </th><th style=\\\"text-align: center;\\\"> Fan tasy </th><th style=\\\"text-align: center;\\\"> Hor ror </th><th style=\\\"text-align: center;\\\"> Mys tery </th><th style=\\\"text-align: center;\\\"> Rom ance </th><th style=\\\"text-align: center;\\\"> Sci -Fi </th><th style=\\\"text-align: center;\\\"> Thri ller </th></tr>\\\\n</thead>\\\\n<tbody>\\\\n<tr><td style=\\\"text-align: center;\\\">    6874    </td><td style=\\\"text-align: center;\\\"> 2003 </td><td style=\\\"text-align: center;\\\">    4.0     </td><td style=\\\"text-align: center;\\\">    1    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0     </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">    6874    </td><td style=\\\"text-align: center;\\\"> 2003 </td><td style=\\\"text-align: center;\\\">    4.0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   1   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0     </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">    6874    </td><td style=\\\"text-align: center;\\\"> 2003 </td><td style=\\\"text-align: center;\\\">    4.0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     1     </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">    8798    </td><td style=\\\"text-align: center;\\\"> 2004 </td><td style=\\\"text-align: center;\\\">    3.8     </td><td style=\\\"text-align: center;\\\">    1    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0     </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">    8798    </td><td style=\\\"text-align: center;\\\"> 2004 </td><td style=\\\"text-align: center;\\\">    3.8     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0      </td><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">   1   </td><td style=\\\"text-align: center;\\\">      0       </td><td style=\\\"text-align: center;\\\">   0   </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0     </td><td style=\\\"text-align: center;\\\">    0    </td><td style=\\\"text-align: center;\\\">     0     </td></tr>\\\\n</tbody>\\\\n</table>'\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pprint_train(item_train, item_features, ivs, i_s, maxcount=5, user=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"y_train[:5]: [4.  4.  4.  3.5 3.5]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"y_train[:5]: {y_train[:5]}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Above, we can see that movie 6874 is an action movie released in 2003. User 2 rates action movies as 3.9 on average. Further, movie 6874 was also listed in the Crime and Thriller genre. MovieLens users gave the movie an average rating of 4. A training example consists of a row from both tables and a rating from y_train.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2.2\\\"></a>\\n\",\n    \"### 2.2 Preparing the training data\\n\",\n    \"Recall in Course 1, Week 2, you explored feature scaling as a means of improving convergence. We'll scale the input features using the [scikit learn StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). This was used in Course 1, Week 2, Lab 5.  Below, the inverse_transform is also shown to produce the original inputs.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"True\\n\",\n      \"True\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# scale training data\\n\",\n    \"if scaledata:\\n\",\n    \"    item_train_save = item_train\\n\",\n    \"    user_train_save = user_train\\n\",\n    \"\\n\",\n    \"    scalerItem = StandardScaler()\\n\",\n    \"    scalerItem.fit(item_train)\\n\",\n    \"    item_train = scalerItem.transform(item_train)\\n\",\n    \"\\n\",\n    \"    scalerUser = StandardScaler()\\n\",\n    \"    scalerUser.fit(user_train)\\n\",\n    \"    user_train = scalerUser.transform(user_train)\\n\",\n    \"\\n\",\n    \"    print(np.allclose(item_train_save, scalerItem.inverse_transform(item_train)))\\n\",\n    \"    print(np.allclose(user_train_save, scalerUser.inverse_transform(user_train)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To allow us to evaluate the results, we will split the data into training and test sets as was discussed in Course 2, Week 3. Here we will use [sklean train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) to split and shuffle the data. Note that setting the initial random state to the same value ensures item, user, and y are shuffled identically.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"movie/item training data shape: (46549, 17)\\n\",\n      \"movie/item test  data shape: (11638, 17)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"item_train, item_test = train_test_split(item_train, train_size=0.80, shuffle=True, random_state=1)\\n\",\n    \"user_train, user_test = train_test_split(user_train, train_size=0.80, shuffle=True, random_state=1)\\n\",\n    \"y_train, y_test       = train_test_split(y_train,    train_size=0.80, shuffle=True, random_state=1)\\n\",\n    \"print(f\\\"movie/item training data shape: {item_train.shape}\\\")\\n\",\n    \"print(f\\\"movie/item test  data shape: {item_test.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The scaled, shuffled data now has a mean of zero.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table>\\n\",\n       \"<thead>\\n\",\n       \"<tr><th style=\\\"text-align: center;\\\"> [user id] </th><th style=\\\"text-align: center;\\\"> [rating count] </th><th style=\\\"text-align: center;\\\"> [rating ave] </th><th style=\\\"text-align: center;\\\"> Act ion </th><th style=\\\"text-align: center;\\\"> Adve nture </th><th style=\\\"text-align: center;\\\"> Anim ation </th><th style=\\\"text-align: center;\\\"> Chil dren </th><th style=\\\"text-align: center;\\\"> Com edy </th><th style=\\\"text-align: center;\\\"> Crime </th><th style=\\\"text-align: center;\\\"> Docum entary </th><th style=\\\"text-align: center;\\\"> Drama </th><th style=\\\"text-align: center;\\\"> Fan tasy </th><th style=\\\"text-align: center;\\\"> Hor ror </th><th style=\\\"text-align: center;\\\"> Mys tery </th><th style=\\\"text-align: center;\\\"> Rom ance </th><th style=\\\"text-align: center;\\\"> Sci -Fi </th><th style=\\\"text-align: center;\\\"> Thri ller </th></tr>\\n\",\n       \"</thead>\\n\",\n       \"<tbody>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">     1     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     0.6      </td><td style=\\\"text-align: center;\\\">   0.7   </td><td style=\\\"text-align: center;\\\">    0.6     </td><td style=\\\"text-align: center;\\\">    0.6     </td><td style=\\\"text-align: center;\\\">    0.7    </td><td style=\\\"text-align: center;\\\">   0.7   </td><td style=\\\"text-align: center;\\\">  0.5  </td><td style=\\\"text-align: center;\\\">     0.7      </td><td style=\\\"text-align: center;\\\">  0.2  </td><td style=\\\"text-align: center;\\\">   0.3    </td><td style=\\\"text-align: center;\\\">   0.3   </td><td style=\\\"text-align: center;\\\">   0.5    </td><td style=\\\"text-align: center;\\\">   0.5    </td><td style=\\\"text-align: center;\\\">   0.8   </td><td style=\\\"text-align: center;\\\">    0.5    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     1.6      </td><td style=\\\"text-align: center;\\\">   1.5   </td><td style=\\\"text-align: center;\\\">    1.7     </td><td style=\\\"text-align: center;\\\">    0.9     </td><td style=\\\"text-align: center;\\\">    1.0    </td><td style=\\\"text-align: center;\\\">   1.4   </td><td style=\\\"text-align: center;\\\">  0.8  </td><td style=\\\"text-align: center;\\\">     -1.2     </td><td style=\\\"text-align: center;\\\">  1.2  </td><td style=\\\"text-align: center;\\\">   1.2    </td><td style=\\\"text-align: center;\\\">   1.6   </td><td style=\\\"text-align: center;\\\">   0.9    </td><td style=\\\"text-align: center;\\\">   1.4    </td><td style=\\\"text-align: center;\\\">   1.2   </td><td style=\\\"text-align: center;\\\">    1.0    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     0.8      </td><td style=\\\"text-align: center;\\\">   0.6   </td><td style=\\\"text-align: center;\\\">    0.7     </td><td style=\\\"text-align: center;\\\">    0.5     </td><td style=\\\"text-align: center;\\\">    0.6    </td><td style=\\\"text-align: center;\\\">   0.6   </td><td style=\\\"text-align: center;\\\">  0.3  </td><td style=\\\"text-align: center;\\\">     -1.2     </td><td style=\\\"text-align: center;\\\">  0.7  </td><td style=\\\"text-align: center;\\\">   0.8    </td><td style=\\\"text-align: center;\\\">   0.9   </td><td style=\\\"text-align: center;\\\">   0.6    </td><td style=\\\"text-align: center;\\\">   0.2    </td><td style=\\\"text-align: center;\\\">   0.6   </td><td style=\\\"text-align: center;\\\">    0.6    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">     1     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     -0.1     </td><td style=\\\"text-align: center;\\\">   0.2   </td><td style=\\\"text-align: center;\\\">    -0.1    </td><td style=\\\"text-align: center;\\\">    0.3     </td><td style=\\\"text-align: center;\\\">    0.7    </td><td style=\\\"text-align: center;\\\">   0.3   </td><td style=\\\"text-align: center;\\\">  0.2  </td><td style=\\\"text-align: center;\\\">     1.0      </td><td style=\\\"text-align: center;\\\"> -0.5  </td><td style=\\\"text-align: center;\\\">   -0.7   </td><td style=\\\"text-align: center;\\\">  -2.1   </td><td style=\\\"text-align: center;\\\">   0.5    </td><td style=\\\"text-align: center;\\\">   0.7    </td><td style=\\\"text-align: center;\\\">   0.3   </td><td style=\\\"text-align: center;\\\">    0.0    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: center;\\\">    -1     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     -1.3     </td><td style=\\\"text-align: center;\\\">  -0.8   </td><td style=\\\"text-align: center;\\\">    -0.8    </td><td style=\\\"text-align: center;\\\">    0.1     </td><td style=\\\"text-align: center;\\\">   -0.1    </td><td style=\\\"text-align: center;\\\">  -1.1   </td><td style=\\\"text-align: center;\\\"> -0.9  </td><td style=\\\"text-align: center;\\\">     -1.2     </td><td style=\\\"text-align: center;\\\"> -1.5  </td><td style=\\\"text-align: center;\\\">   -0.6   </td><td style=\\\"text-align: center;\\\">  -0.5   </td><td style=\\\"text-align: center;\\\">   -0.6   </td><td style=\\\"text-align: center;\\\">   -0.9   </td><td style=\\\"text-align: center;\\\">  -0.4   </td><td style=\\\"text-align: center;\\\">   -0.9    </td></tr>\\n\",\n       \"</tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"'<table>\\\\n<thead>\\\\n<tr><th style=\\\"text-align: center;\\\"> [user id] </th><th style=\\\"text-align: center;\\\"> [rating count] </th><th style=\\\"text-align: center;\\\"> [rating ave] </th><th style=\\\"text-align: center;\\\"> Act ion </th><th style=\\\"text-align: center;\\\"> Adve nture </th><th style=\\\"text-align: center;\\\"> Anim ation </th><th style=\\\"text-align: center;\\\"> Chil dren </th><th style=\\\"text-align: center;\\\"> Com edy </th><th style=\\\"text-align: center;\\\"> Crime </th><th style=\\\"text-align: center;\\\"> Docum entary </th><th style=\\\"text-align: center;\\\"> Drama </th><th style=\\\"text-align: center;\\\"> Fan tasy </th><th style=\\\"text-align: center;\\\"> Hor ror </th><th style=\\\"text-align: center;\\\"> Mys tery </th><th style=\\\"text-align: center;\\\"> Rom ance </th><th style=\\\"text-align: center;\\\"> Sci -Fi </th><th style=\\\"text-align: center;\\\"> Thri ller </th></tr>\\\\n</thead>\\\\n<tbody>\\\\n<tr><td style=\\\"text-align: center;\\\">     1     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     0.6      </td><td style=\\\"text-align: center;\\\">   0.7   </td><td style=\\\"text-align: center;\\\">    0.6     </td><td style=\\\"text-align: center;\\\">    0.6     </td><td style=\\\"text-align: center;\\\">    0.7    </td><td style=\\\"text-align: center;\\\">   0.7   </td><td style=\\\"text-align: center;\\\">  0.5  </td><td style=\\\"text-align: center;\\\">     0.7      </td><td style=\\\"text-align: center;\\\">  0.2  </td><td style=\\\"text-align: center;\\\">   0.3    </td><td style=\\\"text-align: center;\\\">   0.3   </td><td style=\\\"text-align: center;\\\">   0.5    </td><td style=\\\"text-align: center;\\\">   0.5    </td><td style=\\\"text-align: center;\\\">   0.8   </td><td style=\\\"text-align: center;\\\">    0.5    </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     1.6      </td><td style=\\\"text-align: center;\\\">   1.5   </td><td style=\\\"text-align: center;\\\">    1.7     </td><td style=\\\"text-align: center;\\\">    0.9     </td><td style=\\\"text-align: center;\\\">    1.0    </td><td style=\\\"text-align: center;\\\">   1.4   </td><td style=\\\"text-align: center;\\\">  0.8  </td><td style=\\\"text-align: center;\\\">     -1.2     </td><td style=\\\"text-align: center;\\\">  1.2  </td><td style=\\\"text-align: center;\\\">   1.2    </td><td style=\\\"text-align: center;\\\">   1.6   </td><td style=\\\"text-align: center;\\\">   0.9    </td><td style=\\\"text-align: center;\\\">   1.4    </td><td style=\\\"text-align: center;\\\">   1.2   </td><td style=\\\"text-align: center;\\\">    1.0    </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">     0     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     0.8      </td><td style=\\\"text-align: center;\\\">   0.6   </td><td style=\\\"text-align: center;\\\">    0.7     </td><td style=\\\"text-align: center;\\\">    0.5     </td><td style=\\\"text-align: center;\\\">    0.6    </td><td style=\\\"text-align: center;\\\">   0.6   </td><td style=\\\"text-align: center;\\\">  0.3  </td><td style=\\\"text-align: center;\\\">     -1.2     </td><td style=\\\"text-align: center;\\\">  0.7  </td><td style=\\\"text-align: center;\\\">   0.8    </td><td style=\\\"text-align: center;\\\">   0.9   </td><td style=\\\"text-align: center;\\\">   0.6    </td><td style=\\\"text-align: center;\\\">   0.2    </td><td style=\\\"text-align: center;\\\">   0.6   </td><td style=\\\"text-align: center;\\\">    0.6    </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">     1     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     -0.1     </td><td style=\\\"text-align: center;\\\">   0.2   </td><td style=\\\"text-align: center;\\\">    -0.1    </td><td style=\\\"text-align: center;\\\">    0.3     </td><td style=\\\"text-align: center;\\\">    0.7    </td><td style=\\\"text-align: center;\\\">   0.3   </td><td style=\\\"text-align: center;\\\">  0.2  </td><td style=\\\"text-align: center;\\\">     1.0      </td><td style=\\\"text-align: center;\\\"> -0.5  </td><td style=\\\"text-align: center;\\\">   -0.7   </td><td style=\\\"text-align: center;\\\">  -2.1   </td><td style=\\\"text-align: center;\\\">   0.5    </td><td style=\\\"text-align: center;\\\">   0.7    </td><td style=\\\"text-align: center;\\\">   0.3   </td><td style=\\\"text-align: center;\\\">    0.0    </td></tr>\\\\n<tr><td style=\\\"text-align: center;\\\">    -1     </td><td style=\\\"text-align: center;\\\">       0        </td><td style=\\\"text-align: center;\\\">     -1.3     </td><td style=\\\"text-align: center;\\\">  -0.8   </td><td style=\\\"text-align: center;\\\">    -0.8    </td><td style=\\\"text-align: center;\\\">    0.1     </td><td style=\\\"text-align: center;\\\">   -0.1    </td><td style=\\\"text-align: center;\\\">  -1.1   </td><td style=\\\"text-align: center;\\\"> -0.9  </td><td style=\\\"text-align: center;\\\">     -1.2     </td><td style=\\\"text-align: center;\\\"> -1.5  </td><td style=\\\"text-align: center;\\\">   -0.6   </td><td style=\\\"text-align: center;\\\">  -0.5   </td><td style=\\\"text-align: center;\\\">   -0.6   </td><td style=\\\"text-align: center;\\\">   -0.9   </td><td style=\\\"text-align: center;\\\">  -0.4   </td><td style=\\\"text-align: center;\\\">   -0.9    </td></tr>\\\\n</tbody>\\\\n</table>'\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"pprint_train(user_train, user_features, uvs, u_s, maxcount=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"KeoAhs95LRop\"\n   },\n   \"source\": [\n    \"Scale the target ratings using a Min Max Scaler to scale the target to be between -1 and 1. We use scikit-learn because it has an inverse_transform. [scikit learn MinMaxScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"id\": \"A8myXMxFC8lP\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(46549, 1) (11638, 1)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scaler = MinMaxScaler((-1, 1))\\n\",\n    \"scaler.fit(y_train.reshape(-1, 1))\\n\",\n    \"ynorm_train = scaler.transform(y_train.reshape(-1, 1))\\n\",\n    \"ynorm_test = scaler.transform(y_test.reshape(-1, 1))\\n\",\n    \"print(ynorm_train.shape, ynorm_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - Neural Network for content-based filtering\\n\",\n    \"Now, let's construct a neural network as described in the figure above. It will have two networks that are combined by a dot product. You will construct the two networks. In this example, they will be identical. Note that these networks do not need to be the same. If the user content was substantially larger than the movie content, you might elect to increase the complexity of the user network relative to the movie network. In this case, the content is similar, so the networks are the same.\\n\",\n    \"\\n\",\n    \"- Use a Keras sequential model\\n\",\n    \"    - The first layer is a dense layer with 256 units and a relu activation.\\n\",\n    \"    - The second layer is a dense layer with 128 units and a relu activation.\\n\",\n    \"    - The third layer is a dense layer with `num_outputs` units and a linear or no activation.   \\n\",\n    \"    \\n\",\n    \"The remainder of the network will be provided. The provided code does not use the Keras sequential model but instead uses the Keras [functional api](https://keras.io/guides/functional_api/). This format allows for more flexibility in how components are interconnected.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"id\": \"CBjZ2HhRwpa0\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"model\\\"\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"Layer (type)                    Output Shape         Param #     Connected to                     \\n\",\n      \"==================================================================================================\\n\",\n      \"input_1 (InputLayer)            [(None, 14)]         0                                            \\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"input_2 (InputLayer)            [(None, 16)]         0                                            \\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"sequential (Sequential)         (None, 32)           40864       input_1[0][0]                    \\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"sequential_1 (Sequential)       (None, 32)           41376       input_2[0][0]                    \\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize/Square [(None, 32)]         0           sequential[0][0]                 \\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_1/Squa [(None, 32)]         0           sequential_1[0][0]               \\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize/Sum (T [(None, 1)]          0           tf_op_layer_l2_normalize/Square[0\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_1/Sum  [(None, 1)]          0           tf_op_layer_l2_normalize_1/Square\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize/Maximu [(None, 1)]          0           tf_op_layer_l2_normalize/Sum[0][0\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_1/Maxi [(None, 1)]          0           tf_op_layer_l2_normalize_1/Sum[0]\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize/Rsqrt  [(None, 1)]          0           tf_op_layer_l2_normalize/Maximum[\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_1/Rsqr [(None, 1)]          0           tf_op_layer_l2_normalize_1/Maximu\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize (Tenso [(None, 32)]         0           sequential[0][0]                 \\n\",\n      \"                                                                 tf_op_layer_l2_normalize/Rsqrt[0]\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_1 (Ten [(None, 32)]         0           sequential_1[0][0]               \\n\",\n      \"                                                                 tf_op_layer_l2_normalize_1/Rsqrt[\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"dot (Dot)                       (None, 1)            0           tf_op_layer_l2_normalize[0][0]   \\n\",\n      \"                                                                 tf_op_layer_l2_normalize_1[0][0] \\n\",\n      \"==================================================================================================\\n\",\n      \"Total params: 82,240\\n\",\n      \"Trainable params: 82,240\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"__________________________________________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# GRADED_CELL\\n\",\n    \"# UNQ_C1\\n\",\n    \"\\n\",\n    \"num_outputs = 32\\n\",\n    \"tf.random.set_seed(1)\\n\",\n    \"user_NN = tf.keras.models.Sequential([\\n\",\n    \"    ### START CODE HERE ###   \\n\",\n    \"    tf.keras.layers.Dense(256, activation='relu'),\\n\",\n    \"    tf.keras.layers.Dense(128, activation='relu'),\\n\",\n    \"    tf.keras.layers.Dense(num_outputs, activation='linear'),\\n\",\n    \"    ### END CODE HERE ###  \\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"item_NN = tf.keras.models.Sequential([\\n\",\n    \"    ### START CODE HERE ###     \\n\",\n    \"    tf.keras.layers.Dense(256, activation='relu'),\\n\",\n    \"    tf.keras.layers.Dense(128, activation='relu'),\\n\",\n    \"    tf.keras.layers.Dense(num_outputs, activation='linear'),\\n\",\n    \"    ### END CODE HERE ###  \\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"# create the user input and point to the base network\\n\",\n    \"input_user = tf.keras.layers.Input(shape=(num_user_features))\\n\",\n    \"vu = user_NN(input_user)\\n\",\n    \"vu = tf.linalg.l2_normalize(vu, axis=1)\\n\",\n    \"\\n\",\n    \"# create the item input and point to the base network\\n\",\n    \"input_item = tf.keras.layers.Input(shape=(num_item_features))\\n\",\n    \"vm = item_NN(input_item)\\n\",\n    \"vm = tf.linalg.l2_normalize(vm, axis=1)\\n\",\n    \"\\n\",\n    \"# compute the dot product of the two vectors vu and vm\\n\",\n    \"output = tf.keras.layers.Dot(axes=1)([vu, vm])\\n\",\n    \"\\n\",\n    \"# specify the inputs and output of the model\\n\",\n    \"model = Model([input_user, input_item], output)\\n\",\n    \"\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Public tests\\n\",\n    \"from public_tests import *\\n\",\n    \"test_tower(user_NN)\\n\",\n    \"test_tower(item_NN)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"  You can create a dense layer with a relu activation as shown.\\n\",\n    \"    \\n\",\n    \"```python     \\n\",\n    \"user_NN = tf.keras.models.Sequential([\\n\",\n    \"    ### START CODE HERE ###     \\n\",\n    \"  tf.keras.layers.Dense(256, activation='relu'),\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ###  \\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"item_NN = tf.keras.models.Sequential([\\n\",\n    \"    ### START CODE HERE ###     \\n\",\n    \"  tf.keras.layers.Dense(256, activation='relu'),\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ###  \\n\",\n    \"])\\n\",\n    \"```    \\n\",\n    \"<details>\\n\",\n    \"    <summary><font size=\\\"2\\\" color=\\\"darkblue\\\"><b> Click for solution</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python \\n\",\n    \"user_NN = tf.keras.models.Sequential([\\n\",\n    \"    ### START CODE HERE ###     \\n\",\n    \"  tf.keras.layers.Dense(256, activation='relu'),\\n\",\n    \"  tf.keras.layers.Dense(128, activation='relu'),\\n\",\n    \"  tf.keras.layers.Dense(num_outputs),\\n\",\n    \"    ### END CODE HERE ###  \\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"item_NN = tf.keras.models.Sequential([\\n\",\n    \"    ### START CODE HERE ###     \\n\",\n    \"  tf.keras.layers.Dense(256, activation='relu'),\\n\",\n    \"  tf.keras.layers.Dense(128, activation='relu'),\\n\",\n    \"  tf.keras.layers.Dense(num_outputs),\\n\",\n    \"    ### END CODE HERE ###  \\n\",\n    \"])\\n\",\n    \"```\\n\",\n    \"</details>\\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We'll use a mean squared error loss and an Adam optimizer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"id\": \"pGK5MEUowxN4\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tf.random.set_seed(1)\\n\",\n    \"cost_fn = tf.keras.losses.MeanSquaredError()\\n\",\n    \"opt = keras.optimizers.Adam(learning_rate=0.01)\\n\",\n    \"model.compile(optimizer=opt,\\n\",\n    \"              loss=cost_fn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"id\": \"6zHf7eASw0tN\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 46549 samples\\n\",\n      \"Epoch 1/30\\n\",\n      \"46549/46549 [==============================] - 6s 133us/sample - loss: 0.1254\\n\",\n      \"Epoch 2/30\\n\",\n      \"46549/46549 [==============================] - 6s 122us/sample - loss: 0.1187\\n\",\n      \"Epoch 3/30\\n\",\n      \"46549/46549 [==============================] - 6s 121us/sample - loss: 0.1169\\n\",\n      \"Epoch 4/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1154\\n\",\n      \"Epoch 5/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1142\\n\",\n      \"Epoch 6/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1130\\n\",\n      \"Epoch 7/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1119\\n\",\n      \"Epoch 8/30\\n\",\n      \"46549/46549 [==============================] - 6s 119us/sample - loss: 0.1110\\n\",\n      \"Epoch 9/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1095\\n\",\n      \"Epoch 10/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1083\\n\",\n      \"Epoch 11/30\\n\",\n      \"46549/46549 [==============================] - 6s 122us/sample - loss: 0.1073\\n\",\n      \"Epoch 12/30\\n\",\n      \"46549/46549 [==============================] - 6s 119us/sample - loss: 0.1066\\n\",\n      \"Epoch 13/30\\n\",\n      \"46549/46549 [==============================] - 6s 121us/sample - loss: 0.1059\\n\",\n      \"Epoch 14/30\\n\",\n      \"46549/46549 [==============================] - 6s 121us/sample - loss: 0.1054\\n\",\n      \"Epoch 15/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1047\\n\",\n      \"Epoch 16/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1041\\n\",\n      \"Epoch 17/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1036\\n\",\n      \"Epoch 18/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1030\\n\",\n      \"Epoch 19/30\\n\",\n      \"46549/46549 [==============================] - 6s 119us/sample - loss: 0.1027\\n\",\n      \"Epoch 20/30\\n\",\n      \"46549/46549 [==============================] - 6s 122us/sample - loss: 0.1021\\n\",\n      \"Epoch 21/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1018\\n\",\n      \"Epoch 22/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1014\\n\",\n      \"Epoch 23/30\\n\",\n      \"46549/46549 [==============================] - 6s 119us/sample - loss: 0.1010\\n\",\n      \"Epoch 24/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1006\\n\",\n      \"Epoch 25/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.1003\\n\",\n      \"Epoch 26/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.0999\\n\",\n      \"Epoch 27/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.0997\\n\",\n      \"Epoch 28/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.0991\\n\",\n      \"Epoch 29/30\\n\",\n      \"46549/46549 [==============================] - 6s 120us/sample - loss: 0.0989\\n\",\n      \"Epoch 30/30\\n\",\n      \"46549/46549 [==============================] - 6s 121us/sample - loss: 0.0985\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tensorflow.python.keras.callbacks.History at 0x7fba9d2b5110>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"tf.random.set_seed(1)\\n\",\n    \"model.fit([user_train[:, u_s:], item_train[:, i_s:]], ynorm_train, epochs=30)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Evaluate the model to determine loss on the test data. It is comparable to the training loss indicating the model has not substantially overfit the training data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"11638/11638 [==============================] - 0s 36us/sample - loss: 0.1045\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.10449595100221243\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.evaluate([user_test[:, u_s:], item_test[:, i_s:]], ynorm_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"Xsre-gquwEls\"\n   },\n   \"source\": [\n    \"<a name=\\\"3.1\\\"></a>\\n\",\n    \"### 3.1 Predictions\\n\",\n    \"Below, you'll use your model to make predictions in a number of circumstances. \\n\",\n    \"#### Predictions for a new user\\n\",\n    \"First, we'll create a new user and have the model suggest movies for that user. After you have tried this example on the example user content, feel free to change the user content to match your own preferences and see what the model suggests. Note that ratings are between 0.5 and 5.0, inclusive, in half-step increments.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"id\": \"4_7nZyPiVJ4r\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"new_user_id = 5000\\n\",\n    \"new_rating_ave = 1.0\\n\",\n    \"new_action = 1.0\\n\",\n    \"new_adventure = 1\\n\",\n    \"new_animation = 1\\n\",\n    \"new_childrens = 1\\n\",\n    \"new_comedy = 5\\n\",\n    \"new_crime = 1\\n\",\n    \"new_documentary = 1\\n\",\n    \"new_drama = 1\\n\",\n    \"new_fantasy = 1\\n\",\n    \"new_horror = 1\\n\",\n    \"new_mystery = 1\\n\",\n    \"new_romance = 5\\n\",\n    \"new_scifi = 5\\n\",\n    \"new_thriller = 1\\n\",\n    \"new_rating_count = 3\\n\",\n    \"\\n\",\n    \"user_vec = np.array([[new_user_id, new_rating_count, new_rating_ave,\\n\",\n    \"                      new_action, new_adventure, new_animation, new_childrens,\\n\",\n    \"                      new_comedy, new_crime, new_documentary,\\n\",\n    \"                      new_drama, new_fantasy, new_horror, new_mystery,\\n\",\n    \"                      new_romance, new_scifi, new_thriller]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Let's look at the top-rated movies for the new user. Recall, the user vector had genres that favored Comedy and Romance.\\n\",\n    \"Below, we'll use a set of movie/item vectors, `item_vecs` that have a vector for each movie in the training/test set. This is matched with the user vector above and the scaled vectors are used to predict ratings for all the movies for our new user above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table>\\n\",\n       \"<thead>\\n\",\n       \"<tr><th style=\\\"text-align: right;\\\">    y_p</th><th style=\\\"text-align: right;\\\">  movie id</th><th style=\\\"text-align: right;\\\">  rating ave</th><th>title                      </th><th>genres      </th></tr>\\n\",\n       \"</thead>\\n\",\n       \"<tbody>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.86762</td><td style=\\\"text-align: right;\\\">     64969</td><td style=\\\"text-align: right;\\\">     3.61765</td><td>Yes Man (2008)             </td><td>Comedy      </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.86692</td><td style=\\\"text-align: right;\\\">     69122</td><td style=\\\"text-align: right;\\\">     3.63158</td><td>Hangover, The (2009)       </td><td>Comedy|Crime</td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.86477</td><td style=\\\"text-align: right;\\\">     63131</td><td style=\\\"text-align: right;\\\">     3.625  </td><td>Role Models (2008)         </td><td>Comedy      </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.85853</td><td style=\\\"text-align: right;\\\">     60756</td><td style=\\\"text-align: right;\\\">     3.55357</td><td>Step Brothers (2008)       </td><td>Comedy      </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.85785</td><td style=\\\"text-align: right;\\\">     68135</td><td style=\\\"text-align: right;\\\">     3.55   </td><td>17 Again (2009)            </td><td>Comedy|Drama</td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.85178</td><td style=\\\"text-align: right;\\\">     78209</td><td style=\\\"text-align: right;\\\">     3.55   </td><td>Get Him to the Greek (2010)</td><td>Comedy      </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.85138</td><td style=\\\"text-align: right;\\\">      8622</td><td style=\\\"text-align: right;\\\">     3.48649</td><td>Fahrenheit 9/11 (2004)     </td><td>Documentary </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.8505 </td><td style=\\\"text-align: right;\\\">     67087</td><td style=\\\"text-align: right;\\\">     3.52941</td><td>I Love You, Man (2009)     </td><td>Comedy      </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.85043</td><td style=\\\"text-align: right;\\\">     69784</td><td style=\\\"text-align: right;\\\">     3.65   </td><td>Brüno (Bruno) (2009)       </td><td>Comedy      </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">4.84934</td><td style=\\\"text-align: right;\\\">     89864</td><td style=\\\"text-align: right;\\\">     3.63158</td><td>50/50 (2011)               </td><td>Comedy|Drama</td></tr>\\n\",\n       \"</tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"'<table>\\\\n<thead>\\\\n<tr><th style=\\\"text-align: right;\\\">    y_p</th><th style=\\\"text-align: right;\\\">  movie id</th><th style=\\\"text-align: right;\\\">  rating ave</th><th>title                      </th><th>genres      </th></tr>\\\\n</thead>\\\\n<tbody>\\\\n<tr><td style=\\\"text-align: right;\\\">4.86762</td><td style=\\\"text-align: right;\\\">     64969</td><td style=\\\"text-align: right;\\\">     3.61765</td><td>Yes Man (2008)             </td><td>Comedy      </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">4.86692</td><td style=\\\"text-align: right;\\\">     69122</td><td style=\\\"text-align: right;\\\">     3.63158</td><td>Hangover, The (2009)       </td><td>Comedy|Crime</td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">4.86477</td><td style=\\\"text-align: right;\\\">     63131</td><td style=\\\"text-align: right;\\\">     3.625  </td><td>Role Models (2008)         </td><td>Comedy      </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">4.85853</td><td style=\\\"text-align: right;\\\">     60756</td><td style=\\\"text-align: right;\\\">     3.55357</td><td>Step Brothers (2008)       </td><td>Comedy      </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">4.85785</td><td style=\\\"text-align: right;\\\">     68135</td><td style=\\\"text-align: right;\\\">     3.55   </td><td>17 Again (2009)            </td><td>Comedy|Drama</td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">4.85178</td><td style=\\\"text-align: right;\\\">     78209</td><td style=\\\"text-align: right;\\\">     3.55   </td><td>Get Him to the Greek (2010)</td><td>Comedy      </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">4.85138</td><td style=\\\"text-align: right;\\\">      8622</td><td style=\\\"text-align: right;\\\">     3.48649</td><td>Fahrenheit 9/11 (2004)     </td><td>Documentary </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">4.8505 </td><td style=\\\"text-align: right;\\\">     67087</td><td style=\\\"text-align: right;\\\">     3.52941</td><td>I Love You, Man (2009)     </td><td>Comedy      </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">4.85043</td><td style=\\\"text-align: right;\\\">     69784</td><td style=\\\"text-align: right;\\\">     3.65   </td><td>Brüno (Bruno) (2009)       </td><td>Comedy      </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">4.84934</td><td style=\\\"text-align: right;\\\">     89864</td><td style=\\\"text-align: right;\\\">     3.63158</td><td>50/50 (2011)               </td><td>Comedy|Drama</td></tr>\\\\n</tbody>\\\\n</table>'\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# generate and replicate the user vector to match the number movies in the data set.\\n\",\n    \"user_vecs = gen_user_vecs(user_vec,len(item_vecs))\\n\",\n    \"\\n\",\n    \"# scale the vectors and make predictions for all movies. Return results sorted by rating.\\n\",\n    \"sorted_index, sorted_ypu, sorted_items, sorted_user = predict_uservec(user_vecs,  item_vecs, model, u_s, i_s, \\n\",\n    \"                                                                       scaler, scalerUser, scalerItem, scaledata=scaledata)\\n\",\n    \"\\n\",\n    \"print_pred_movies(sorted_ypu, sorted_user, sorted_items, movie_dict, maxcount = 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you do create a user above, it is worth noting that the network was trained to predict a user rating given a user vector that includes a **set** of user genre ratings.  Simply providing a maximum rating for a single genre and minimum ratings for the rest may not be meaningful to the network if there were no users with similar sets of ratings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Predictions for an existing user.\\n\",\n    \"Let's look at the predictions for \\\"user 36\\\", one of the users in the data set. We can compare the predicted ratings with the model's ratings. Note that movies with multiple genre's show up multiple times in the training data. For example,'The Time Machine' has three genre's: Adventure, Action, Sci-Fi\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table>\\n\",\n       \"<thead>\\n\",\n       \"<tr><th style=\\\"text-align: right;\\\">  y_p</th><th style=\\\"text-align: right;\\\">  y</th><th style=\\\"text-align: right;\\\">  user</th><th style=\\\"text-align: right;\\\">  user genre ave</th><th style=\\\"text-align: right;\\\">  movie rating ave</th><th>title                   </th><th>genres   </th></tr>\\n\",\n       \"</thead>\\n\",\n       \"<tbody>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">  3.1</td><td style=\\\"text-align: right;\\\">3.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            3.00</td><td style=\\\"text-align: right;\\\">              2.86</td><td>Time Machine, The (2002)</td><td>Adventure</td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">  3.0</td><td style=\\\"text-align: right;\\\">3.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            3.00</td><td style=\\\"text-align: right;\\\">              2.86</td><td>Time Machine, The (2002)</td><td>Action   </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">  2.8</td><td style=\\\"text-align: right;\\\">3.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            3.00</td><td style=\\\"text-align: right;\\\">              2.86</td><td>Time Machine, The (2002)</td><td>Sci-Fi   </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">  2.3</td><td style=\\\"text-align: right;\\\">1.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.00</td><td style=\\\"text-align: right;\\\">              4.00</td><td>Beautiful Mind, A (2001)</td><td>Romance  </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">  2.2</td><td style=\\\"text-align: right;\\\">1.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.50</td><td style=\\\"text-align: right;\\\">              4.00</td><td>Beautiful Mind, A (2001)</td><td>Drama    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">  1.6</td><td style=\\\"text-align: right;\\\">1.5</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.75</td><td style=\\\"text-align: right;\\\">              3.52</td><td>Road to Perdition (2002)</td><td>Crime    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">  1.6</td><td style=\\\"text-align: right;\\\">2.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.75</td><td style=\\\"text-align: right;\\\">              3.52</td><td>Gangs of New York (2002)</td><td>Crime    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">  1.5</td><td style=\\\"text-align: right;\\\">1.5</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.50</td><td style=\\\"text-align: right;\\\">              3.52</td><td>Road to Perdition (2002)</td><td>Drama    </td></tr>\\n\",\n       \"<tr><td style=\\\"text-align: right;\\\">  1.5</td><td style=\\\"text-align: right;\\\">2.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.50</td><td style=\\\"text-align: right;\\\">              3.52</td><td>Gangs of New York (2002)</td><td>Drama    </td></tr>\\n\",\n       \"</tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"'<table>\\\\n<thead>\\\\n<tr><th style=\\\"text-align: right;\\\">  y_p</th><th style=\\\"text-align: right;\\\">  y</th><th style=\\\"text-align: right;\\\">  user</th><th style=\\\"text-align: right;\\\">  user genre ave</th><th style=\\\"text-align: right;\\\">  movie rating ave</th><th>title                   </th><th>genres   </th></tr>\\\\n</thead>\\\\n<tbody>\\\\n<tr><td style=\\\"text-align: right;\\\">  3.1</td><td style=\\\"text-align: right;\\\">3.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            3.00</td><td style=\\\"text-align: right;\\\">              2.86</td><td>Time Machine, The (2002)</td><td>Adventure</td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">  3.0</td><td style=\\\"text-align: right;\\\">3.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            3.00</td><td style=\\\"text-align: right;\\\">              2.86</td><td>Time Machine, The (2002)</td><td>Action   </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">  2.8</td><td style=\\\"text-align: right;\\\">3.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            3.00</td><td style=\\\"text-align: right;\\\">              2.86</td><td>Time Machine, The (2002)</td><td>Sci-Fi   </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">  2.3</td><td style=\\\"text-align: right;\\\">1.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.00</td><td style=\\\"text-align: right;\\\">              4.00</td><td>Beautiful Mind, A (2001)</td><td>Romance  </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">  2.2</td><td style=\\\"text-align: right;\\\">1.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.50</td><td style=\\\"text-align: right;\\\">              4.00</td><td>Beautiful Mind, A (2001)</td><td>Drama    </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">  1.6</td><td style=\\\"text-align: right;\\\">1.5</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.75</td><td style=\\\"text-align: right;\\\">              3.52</td><td>Road to Perdition (2002)</td><td>Crime    </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">  1.6</td><td style=\\\"text-align: right;\\\">2.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.75</td><td style=\\\"text-align: right;\\\">              3.52</td><td>Gangs of New York (2002)</td><td>Crime    </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">  1.5</td><td style=\\\"text-align: right;\\\">1.5</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.50</td><td style=\\\"text-align: right;\\\">              3.52</td><td>Road to Perdition (2002)</td><td>Drama    </td></tr>\\\\n<tr><td style=\\\"text-align: right;\\\">  1.5</td><td style=\\\"text-align: right;\\\">2.0</td><td style=\\\"text-align: right;\\\">    36</td><td style=\\\"text-align: right;\\\">            1.50</td><td style=\\\"text-align: right;\\\">              3.52</td><td>Gangs of New York (2002)</td><td>Drama    </td></tr>\\\\n</tbody>\\\\n</table>'\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"uid =  36 \\n\",\n    \"# form a set of user vectors. This is the same vector, transformed and repeated.\\n\",\n    \"user_vecs, y_vecs = get_user_vecs(uid, scalerUser.inverse_transform(user_train), item_vecs, user_to_genre)\\n\",\n    \"\\n\",\n    \"# scale the vectors and make predictions for all movies. Return results sorted by rating.\\n\",\n    \"sorted_index, sorted_ypu, sorted_items, sorted_user = predict_uservec(user_vecs, item_vecs, model, u_s, i_s, scaler, \\n\",\n    \"                                                                      scalerUser, scalerItem, scaledata=scaledata)\\n\",\n    \"sorted_y = y_vecs[sorted_index]\\n\",\n    \"\\n\",\n    \"#print sorted predictions\\n\",\n    \"print_existing_user(sorted_ypu, sorted_y.reshape(-1,1), sorted_user, sorted_items, item_features, ivs, uvs, movie_dict, maxcount = 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Finding Similar Items\\n\",\n    \"The neural network above produces two feature vectors, a user feature vector $v_u$, and a movie feature vector, $v_m$. These are 32 entry vectors whose values are difficult to interpret. However, similar items will have similar vectors. This information can be used to make recommendations. For example, if a user has rated \\\"Toy Story 3\\\" highly, one could recommend similar movies by selecting movies with similar movie feature vectors.\\n\",\n    \"\\n\",\n    \"A similarity measure is the squared distance between the two vectors $ \\\\mathbf{v_m^{(k)}}$ and $\\\\mathbf{v_m^{(i)}}$ :\\n\",\n    \"$$\\\\left\\\\Vert \\\\mathbf{v_m^{(k)}} - \\\\mathbf{v_m^{(i)}}  \\\\right\\\\Vert^2 = \\\\sum_{l=1}^{n}(v_{m_l}^{(k)} - v_{m_l}^{(i)})^2\\\\tag{1}$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"Write a function to compute the square distance.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# GRADED_FUNCTION: sq_dist\\n\",\n    \"# UNQ_C2\\n\",\n    \"def sq_dist(a,b):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Returns the squared distance between two vectors\\n\",\n    \"    Args:\\n\",\n    \"      a (ndarray (n,)): vector with n features\\n\",\n    \"      b (ndarray (n,)): vector with n features\\n\",\n    \"    Returns:\\n\",\n    \"      d (float) : distance\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    ### START CODE HERE ###     \\n\",\n    \"    d = sum(np.square(a-b))\\n\",\n    \"    ### END CODE HERE ###     \\n\",\n    \"    return (d)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Public tests\\n\",\n    \"test_sq_dist(sq_dist)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"squared distance between a1 and b1: 0.0\\n\",\n      \"squared distance between a2 and b2: 0.030000000000000054\\n\",\n      \"squared distance between a3 and b3: 2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"a1 = np.array([1.0, 2.0, 3.0]); b1 = np.array([1.0, 2.0, 3.0])\\n\",\n    \"a2 = np.array([1.1, 2.1, 3.1]); b2 = np.array([1.0, 2.0, 3.0])\\n\",\n    \"a3 = np.array([0, 1, 0]);       b3 = np.array([1, 0, 0])\\n\",\n    \"print(f\\\"squared distance between a1 and b1: {sq_dist(a1, b1)}\\\")\\n\",\n    \"print(f\\\"squared distance between a2 and b2: {sq_dist(a2, b2)}\\\")\\n\",\n    \"print(f\\\"squared distance between a3 and b3: {sq_dist(a3, b3)}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"  While a summation is often an indication a for loop should be used, here the subtraction can be element-wise in one statement. Further, you can utilized np.square to square, element-wise, the result of the subtraction. np.sum can be used to sum the squared elements.\\n\",\n    \"    \\n\",\n    \"</details>\\n\",\n    \"\\n\",\n    \"    \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A matrix of distances between movies can be computed once when the model is trained and then reused for new recommendations without retraining. The first step, once a model is trained, is to obtain the movie feature vector, $v_m$, for each of the movies. To do this, we will use the trained `item_NN` and build a small model to allow us to run the movie vectors through it to generate $v_m$.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model: \\\"model_1\\\"\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"Layer (type)                    Output Shape         Param #     Connected to                     \\n\",\n      \"==================================================================================================\\n\",\n      \"input_3 (InputLayer)            [(None, 16)]         0                                            \\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"sequential_1 (Sequential)       (None, 32)           41376       input_3[0][0]                    \\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_2/Squa [(None, 32)]         0           sequential_1[1][0]               \\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_2/Sum  [(None, 1)]          0           tf_op_layer_l2_normalize_2/Square\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_2/Maxi [(None, 1)]          0           tf_op_layer_l2_normalize_2/Sum[0]\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_2/Rsqr [(None, 1)]          0           tf_op_layer_l2_normalize_2/Maximu\\n\",\n      \"__________________________________________________________________________________________________\\n\",\n      \"tf_op_layer_l2_normalize_2 (Ten [(None, 32)]         0           sequential_1[1][0]               \\n\",\n      \"                                                                 tf_op_layer_l2_normalize_2/Rsqrt[\\n\",\n      \"==================================================================================================\\n\",\n      \"Total params: 41,376\\n\",\n      \"Trainable params: 41,376\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"__________________________________________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"input_item_m = tf.keras.layers.Input(shape=(num_item_features))    # input layer\\n\",\n    \"vm_m = item_NN(input_item_m)                                       # use the trained item_NN\\n\",\n    \"vm_m = tf.linalg.l2_normalize(vm_m, axis=1)                        # incorporate normalization as was done in the original model\\n\",\n    \"model_m = Model(input_item_m, vm_m)                                \\n\",\n    \"model_m.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once you have a movie model, you can create a set of movie feature vectors by using the model to predict using a set of item/movie vectors as input. `item_vecs` is a set of all of the movie vectors. Recall that the same movie will appear as a separate vector for each of its genres. It must be scaled to use with the trained model. The result of the prediction is a 32 entry feature vector for each movie.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"size of all predicted movie feature vectors: (1883, 32)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"scaled_item_vecs = scalerItem.transform(item_vecs)\\n\",\n    \"vms = model_m.predict(scaled_item_vecs[:,i_s:])\\n\",\n    \"print(f\\\"size of all predicted movie feature vectors: {vms.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's now compute a matrix of the squared distance between each movie feature vector and all other movie feature vectors:\\n\",\n    \"<figure>\\n\",\n    \"    <left> <img src=\\\"./images/distmatrix.PNG\\\"   style=\\\"width:400px;height:225px;\\\" ></center>\\n\",\n    \"</figure>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can then find the closest movie by finding the minimum along each row. We will make use of [numpy masked arrays](https://numpy.org/doc/1.21/user/tutorial-ma.html) to avoid selecting the same movie. The masked values along the diagonal won't be included in the computation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<table>\\n\",\n       \"<thead>\\n\",\n       \"<tr><th>movie1                                </th><th>genres   </th><th>movie2                                             </th><th>genres   </th></tr>\\n\",\n       \"</thead>\\n\",\n       \"<tbody>\\n\",\n       \"<tr><td>Save the Last Dance (2001)            </td><td>Drama    </td><td>John Q (2002)                                      </td><td>Drama    </td></tr>\\n\",\n       \"<tr><td>Save the Last Dance (2001)            </td><td>Romance  </td><td>Saving Silverman (Evil Woman) (2001)               </td><td>Romance  </td></tr>\\n\",\n       \"<tr><td>Wedding Planner, The (2001)           </td><td>Comedy   </td><td>National Lampoon&#x27;s Van Wilder (2002)               </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Wedding Planner, The (2001)           </td><td>Romance  </td><td>Mr. Deeds (2002)                                   </td><td>Romance  </td></tr>\\n\",\n       \"<tr><td>Hannibal (2001)                       </td><td>Horror   </td><td>Final Destination 2 (2003)                         </td><td>Horror   </td></tr>\\n\",\n       \"<tr><td>Hannibal (2001)                       </td><td>Thriller </td><td>Sum of All Fears, The (2002)                       </td><td>Thriller </td></tr>\\n\",\n       \"<tr><td>Saving Silverman (Evil Woman) (2001)  </td><td>Comedy   </td><td>Cats &amp; Dogs (2001)                                 </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Saving Silverman (Evil Woman) (2001)  </td><td>Romance  </td><td>Save the Last Dance (2001)                         </td><td>Romance  </td></tr>\\n\",\n       \"<tr><td>Down to Earth (2001)                  </td><td>Comedy   </td><td>Joe Dirt (2001)                                    </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Down to Earth (2001)                  </td><td>Fantasy  </td><td>Haunted Mansion, The (2003)                        </td><td>Fantasy  </td></tr>\\n\",\n       \"<tr><td>Down to Earth (2001)                  </td><td>Romance  </td><td>Joe Dirt (2001)                                    </td><td>Romance  </td></tr>\\n\",\n       \"<tr><td>Mexican, The (2001)                   </td><td>Action   </td><td>Knight&#x27;s Tale, A (2001)                            </td><td>Action   </td></tr>\\n\",\n       \"<tr><td>Mexican, The (2001)                   </td><td>Comedy   </td><td>Knight&#x27;s Tale, A (2001)                            </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>15 Minutes (2001)                     </td><td>Thriller </td><td>Final Destination 2 (2003)                         </td><td>Thriller </td></tr>\\n\",\n       \"<tr><td>Heartbreakers (2001)                  </td><td>Comedy   </td><td>Animal, The (2001)                                 </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Heartbreakers (2001)                  </td><td>Crime    </td><td>Charlie&#x27;s Angels: Full Throttle (2003)             </td><td>Crime    </td></tr>\\n\",\n       \"<tr><td>Heartbreakers (2001)                  </td><td>Romance  </td><td>Stepford Wives, The (2004)                         </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Spy Kids (2001)                       </td><td>Action   </td><td>Lara Croft: Tomb Raider (2001)                     </td><td>Action   </td></tr>\\n\",\n       \"<tr><td>Spy Kids (2001)                       </td><td>Adventure</td><td>Lara Croft: Tomb Raider (2001)                     </td><td>Adventure</td></tr>\\n\",\n       \"<tr><td>Spy Kids (2001)                       </td><td>Children </td><td>Princess Diaries, The (2001)                       </td><td>Children </td></tr>\\n\",\n       \"<tr><td>Spy Kids (2001)                       </td><td>Comedy   </td><td>Men in Black II (a.k.a. MIIB) (a.k.a. MIB 2) (2002)</td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Along Came a Spider (2001)            </td><td>Action   </td><td>Swordfish (2001)                                   </td><td>Action   </td></tr>\\n\",\n       \"<tr><td>Along Came a Spider (2001)            </td><td>Crime    </td><td>Swordfish (2001)                                   </td><td>Crime    </td></tr>\\n\",\n       \"<tr><td>Along Came a Spider (2001)            </td><td>Mystery  </td><td>Ring, The (2002)                                   </td><td>Mystery  </td></tr>\\n\",\n       \"<tr><td>Along Came a Spider (2001)            </td><td>Thriller </td><td>Signs (2002)                                       </td><td>Thriller </td></tr>\\n\",\n       \"<tr><td>Blow (2001)                           </td><td>Crime    </td><td>Training Day (2001)                                </td><td>Crime    </td></tr>\\n\",\n       \"<tr><td>Blow (2001)                           </td><td>Drama    </td><td>Training Day (2001)                                </td><td>Drama    </td></tr>\\n\",\n       \"<tr><td>Bridget Jones&#x27;s Diary (2001)          </td><td>Comedy   </td><td>Super Troopers (2001)                              </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Bridget Jones&#x27;s Diary (2001)          </td><td>Drama    </td><td>Others, The (2001)                                 </td><td>Drama    </td></tr>\\n\",\n       \"<tr><td>Bridget Jones&#x27;s Diary (2001)          </td><td>Romance  </td><td>Punch-Drunk Love (2002)                            </td><td>Romance  </td></tr>\\n\",\n       \"<tr><td>Joe Dirt (2001)                       </td><td>Adventure</td><td>Charlie&#x27;s Angels: Full Throttle (2003)             </td><td>Action   </td></tr>\\n\",\n       \"<tr><td>Joe Dirt (2001)                       </td><td>Comedy   </td><td>Dr. Dolittle 2 (2001)                              </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Joe Dirt (2001)                       </td><td>Mystery  </td><td>Doom (2005)                                        </td><td>Horror   </td></tr>\\n\",\n       \"<tr><td>Joe Dirt (2001)                       </td><td>Romance  </td><td>Down to Earth (2001)                               </td><td>Romance  </td></tr>\\n\",\n       \"<tr><td>Crocodile Dundee in Los Angeles (2001)</td><td>Comedy   </td><td>Heartbreakers (2001)                               </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Crocodile Dundee in Los Angeles (2001)</td><td>Drama    </td><td>Scary Movie 4 (2006)                               </td><td>Horror   </td></tr>\\n\",\n       \"<tr><td>Mummy Returns, The (2001)             </td><td>Action   </td><td>Swordfish (2001)                                   </td><td>Action   </td></tr>\\n\",\n       \"<tr><td>Mummy Returns, The (2001)             </td><td>Adventure</td><td>Rundown, The (2003)                                </td><td>Adventure</td></tr>\\n\",\n       \"<tr><td>Mummy Returns, The (2001)             </td><td>Comedy   </td><td>American Pie 2 (2001)                              </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Mummy Returns, The (2001)             </td><td>Thriller </td><td>Star Trek: Nemesis (2002)                          </td><td>Thriller </td></tr>\\n\",\n       \"<tr><td>Knight&#x27;s Tale, A (2001)               </td><td>Action   </td><td>Mexican, The (2001)                                </td><td>Action   </td></tr>\\n\",\n       \"<tr><td>Knight&#x27;s Tale, A (2001)               </td><td>Comedy   </td><td>Mexican, The (2001)                                </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Knight&#x27;s Tale, A (2001)               </td><td>Romance  </td><td>Bruce Almighty (2003)                              </td><td>Romance  </td></tr>\\n\",\n       \"<tr><td>Shrek (2001)                          </td><td>Adventure</td><td>Monsters, Inc. (2001)                              </td><td>Adventure</td></tr>\\n\",\n       \"<tr><td>Shrek (2001)                          </td><td>Animation</td><td>Monsters, Inc. (2001)                              </td><td>Animation</td></tr>\\n\",\n       \"<tr><td>Shrek (2001)                          </td><td>Children </td><td>Monsters, Inc. (2001)                              </td><td>Children </td></tr>\\n\",\n       \"<tr><td>Shrek (2001)                          </td><td>Comedy   </td><td>Monsters, Inc. (2001)                              </td><td>Comedy   </td></tr>\\n\",\n       \"<tr><td>Shrek (2001)                          </td><td>Fantasy  </td><td>Monsters, Inc. (2001)                              </td><td>Fantasy  </td></tr>\\n\",\n       \"<tr><td>Shrek (2001)                          </td><td>Romance  </td><td>Monsoon Wedding (2001)                             </td><td>Romance  </td></tr>\\n\",\n       \"<tr><td>Animal, The (2001)                    </td><td>Comedy   </td><td>Heartbreakers (2001)                               </td><td>Comedy   </td></tr>\\n\",\n       \"</tbody>\\n\",\n       \"</table>\"\n      ],\n      \"text/plain\": [\n       \"'<table>\\\\n<thead>\\\\n<tr><th>movie1                                </th><th>genres   </th><th>movie2                                             </th><th>genres   </th></tr>\\\\n</thead>\\\\n<tbody>\\\\n<tr><td>Save the Last Dance (2001)            </td><td>Drama    </td><td>John Q (2002)                                      </td><td>Drama    </td></tr>\\\\n<tr><td>Save the Last Dance (2001)            </td><td>Romance  </td><td>Saving Silverman (Evil Woman) (2001)               </td><td>Romance  </td></tr>\\\\n<tr><td>Wedding Planner, The (2001)           </td><td>Comedy   </td><td>National Lampoon&#x27;s Van Wilder (2002)               </td><td>Comedy   </td></tr>\\\\n<tr><td>Wedding Planner, The (2001)           </td><td>Romance  </td><td>Mr. Deeds (2002)                                   </td><td>Romance  </td></tr>\\\\n<tr><td>Hannibal (2001)                       </td><td>Horror   </td><td>Final Destination 2 (2003)                         </td><td>Horror   </td></tr>\\\\n<tr><td>Hannibal (2001)                       </td><td>Thriller </td><td>Sum of All Fears, The (2002)                       </td><td>Thriller </td></tr>\\\\n<tr><td>Saving Silverman (Evil Woman) (2001)  </td><td>Comedy   </td><td>Cats &amp; Dogs (2001)                                 </td><td>Comedy   </td></tr>\\\\n<tr><td>Saving Silverman (Evil Woman) (2001)  </td><td>Romance  </td><td>Save the Last Dance (2001)                         </td><td>Romance  </td></tr>\\\\n<tr><td>Down to Earth (2001)                  </td><td>Comedy   </td><td>Joe Dirt (2001)                                    </td><td>Comedy   </td></tr>\\\\n<tr><td>Down to Earth (2001)                  </td><td>Fantasy  </td><td>Haunted Mansion, The (2003)                        </td><td>Fantasy  </td></tr>\\\\n<tr><td>Down to Earth (2001)                  </td><td>Romance  </td><td>Joe Dirt (2001)                                    </td><td>Romance  </td></tr>\\\\n<tr><td>Mexican, The (2001)                   </td><td>Action   </td><td>Knight&#x27;s Tale, A (2001)                            </td><td>Action   </td></tr>\\\\n<tr><td>Mexican, The (2001)                   </td><td>Comedy   </td><td>Knight&#x27;s Tale, A (2001)                            </td><td>Comedy   </td></tr>\\\\n<tr><td>15 Minutes (2001)                     </td><td>Thriller </td><td>Final Destination 2 (2003)                         </td><td>Thriller </td></tr>\\\\n<tr><td>Heartbreakers (2001)                  </td><td>Comedy   </td><td>Animal, The (2001)                                 </td><td>Comedy   </td></tr>\\\\n<tr><td>Heartbreakers (2001)                  </td><td>Crime    </td><td>Charlie&#x27;s Angels: Full Throttle (2003)             </td><td>Crime    </td></tr>\\\\n<tr><td>Heartbreakers (2001)                  </td><td>Romance  </td><td>Stepford Wives, The (2004)                         </td><td>Comedy   </td></tr>\\\\n<tr><td>Spy Kids (2001)                       </td><td>Action   </td><td>Lara Croft: Tomb Raider (2001)                     </td><td>Action   </td></tr>\\\\n<tr><td>Spy Kids (2001)                       </td><td>Adventure</td><td>Lara Croft: Tomb Raider (2001)                     </td><td>Adventure</td></tr>\\\\n<tr><td>Spy Kids (2001)                       </td><td>Children </td><td>Princess Diaries, The (2001)                       </td><td>Children </td></tr>\\\\n<tr><td>Spy Kids (2001)                       </td><td>Comedy   </td><td>Men in Black II (a.k.a. MIIB) (a.k.a. MIB 2) (2002)</td><td>Comedy   </td></tr>\\\\n<tr><td>Along Came a Spider (2001)            </td><td>Action   </td><td>Swordfish (2001)                                   </td><td>Action   </td></tr>\\\\n<tr><td>Along Came a Spider (2001)            </td><td>Crime    </td><td>Swordfish (2001)                                   </td><td>Crime    </td></tr>\\\\n<tr><td>Along Came a Spider (2001)            </td><td>Mystery  </td><td>Ring, The (2002)                                   </td><td>Mystery  </td></tr>\\\\n<tr><td>Along Came a Spider (2001)            </td><td>Thriller </td><td>Signs (2002)                                       </td><td>Thriller </td></tr>\\\\n<tr><td>Blow (2001)                           </td><td>Crime    </td><td>Training Day (2001)                                </td><td>Crime    </td></tr>\\\\n<tr><td>Blow (2001)                           </td><td>Drama    </td><td>Training Day (2001)                                </td><td>Drama    </td></tr>\\\\n<tr><td>Bridget Jones&#x27;s Diary (2001)          </td><td>Comedy   </td><td>Super Troopers (2001)                              </td><td>Comedy   </td></tr>\\\\n<tr><td>Bridget Jones&#x27;s Diary (2001)          </td><td>Drama    </td><td>Others, The (2001)                                 </td><td>Drama    </td></tr>\\\\n<tr><td>Bridget Jones&#x27;s Diary (2001)          </td><td>Romance  </td><td>Punch-Drunk Love (2002)                            </td><td>Romance  </td></tr>\\\\n<tr><td>Joe Dirt (2001)                       </td><td>Adventure</td><td>Charlie&#x27;s Angels: Full Throttle (2003)             </td><td>Action   </td></tr>\\\\n<tr><td>Joe Dirt (2001)                       </td><td>Comedy   </td><td>Dr. Dolittle 2 (2001)                              </td><td>Comedy   </td></tr>\\\\n<tr><td>Joe Dirt (2001)                       </td><td>Mystery  </td><td>Doom (2005)                                        </td><td>Horror   </td></tr>\\\\n<tr><td>Joe Dirt (2001)                       </td><td>Romance  </td><td>Down to Earth (2001)                               </td><td>Romance  </td></tr>\\\\n<tr><td>Crocodile Dundee in Los Angeles (2001)</td><td>Comedy   </td><td>Heartbreakers (2001)                               </td><td>Comedy   </td></tr>\\\\n<tr><td>Crocodile Dundee in Los Angeles (2001)</td><td>Drama    </td><td>Scary Movie 4 (2006)                               </td><td>Horror   </td></tr>\\\\n<tr><td>Mummy Returns, The (2001)             </td><td>Action   </td><td>Swordfish (2001)                                   </td><td>Action   </td></tr>\\\\n<tr><td>Mummy Returns, The (2001)             </td><td>Adventure</td><td>Rundown, The (2003)                                </td><td>Adventure</td></tr>\\\\n<tr><td>Mummy Returns, The (2001)             </td><td>Comedy   </td><td>American Pie 2 (2001)                              </td><td>Comedy   </td></tr>\\\\n<tr><td>Mummy Returns, The (2001)             </td><td>Thriller </td><td>Star Trek: Nemesis (2002)                          </td><td>Thriller </td></tr>\\\\n<tr><td>Knight&#x27;s Tale, A (2001)               </td><td>Action   </td><td>Mexican, The (2001)                                </td><td>Action   </td></tr>\\\\n<tr><td>Knight&#x27;s Tale, A (2001)               </td><td>Comedy   </td><td>Mexican, The (2001)                                </td><td>Comedy   </td></tr>\\\\n<tr><td>Knight&#x27;s Tale, A (2001)               </td><td>Romance  </td><td>Bruce Almighty (2003)                              </td><td>Romance  </td></tr>\\\\n<tr><td>Shrek (2001)                          </td><td>Adventure</td><td>Monsters, Inc. (2001)                              </td><td>Adventure</td></tr>\\\\n<tr><td>Shrek (2001)                          </td><td>Animation</td><td>Monsters, Inc. (2001)                              </td><td>Animation</td></tr>\\\\n<tr><td>Shrek (2001)                          </td><td>Children </td><td>Monsters, Inc. (2001)                              </td><td>Children </td></tr>\\\\n<tr><td>Shrek (2001)                          </td><td>Comedy   </td><td>Monsters, Inc. (2001)                              </td><td>Comedy   </td></tr>\\\\n<tr><td>Shrek (2001)                          </td><td>Fantasy  </td><td>Monsters, Inc. (2001)                              </td><td>Fantasy  </td></tr>\\\\n<tr><td>Shrek (2001)                          </td><td>Romance  </td><td>Monsoon Wedding (2001)                             </td><td>Romance  </td></tr>\\\\n<tr><td>Animal, The (2001)                    </td><td>Comedy   </td><td>Heartbreakers (2001)                               </td><td>Comedy   </td></tr>\\\\n</tbody>\\\\n</table>'\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"count = 50\\n\",\n    \"dim = len(vms)\\n\",\n    \"dist = np.zeros((dim,dim))\\n\",\n    \"\\n\",\n    \"for i in range(dim):\\n\",\n    \"    for j in range(dim):\\n\",\n    \"        dist[i,j] = sq_dist(vms[i, :], vms[j, :])\\n\",\n    \"        \\n\",\n    \"m_dist = ma.masked_array(dist, mask=np.identity(dist.shape[0]))  # mask the diagonal\\n\",\n    \"\\n\",\n    \"disp = [[\\\"movie1\\\", \\\"genres\\\", \\\"movie2\\\", \\\"genres\\\"]]\\n\",\n    \"for i in range(count):\\n\",\n    \"    min_idx = np.argmin(m_dist[i])\\n\",\n    \"    movie1_id = int(item_vecs[i,0])\\n\",\n    \"    movie2_id = int(item_vecs[min_idx,0])\\n\",\n    \"    genre1,_  = get_item_genre(item_vecs[i,:], ivs, item_features)\\n\",\n    \"    genre2,_  = get_item_genre(item_vecs[min_idx,:], ivs, item_features)\\n\",\n    \"\\n\",\n    \"    disp.append( [movie_dict[movie1_id]['title'], genre1,\\n\",\n    \"                  movie_dict[movie2_id]['title'], genre2]\\n\",\n    \"               )\\n\",\n    \"table = tabulate.tabulate(disp, tablefmt='html', headers=\\\"firstrow\\\", floatfmt=[\\\".1f\\\", \\\".1f\\\", \\\".0f\\\", \\\".2f\\\", \\\".2f\\\"])\\n\",\n    \"table\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The results show the model will suggest a movie from the same genre.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Congratulations! <img align=\\\"left\\\" src=\\\"./images/film_award.png\\\" style=\\\" width:40px;\\\">\\n\",\n    \"You have completed a content-based recommender system.    \\n\",\n    \"\\n\",\n    \"This structure is the basis of many commercial recommender systems. The user content can be greatly expanded to incorporate more information about the user if it is available.  Items are not limited to movies. This can be used to recommend any item, books, cars or items that are similar to an item in your 'shopping cart'.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"authorship_tag\": \"ABX9TyOFYdA6zQJ1FpgYwYmRIeXa\",\n   \"collapsed_sections\": [],\n   \"name\": \"Recsys_NN.ipynb\",\n   \"private_outputs\": true,\n   \"provenance\": [\n    {\n     \"file_id\": \"1RO0HLb7kRE0Tj_0D4E5I-vQz2QLu3CUm\",\n     \"timestamp\": 1655169179306\n    }\n   ]\n  },\n  \"gpuClass\": \"standard\",\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A2/data/content_item_train.csv",
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  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A2/data/content_item_train_header.txt",
    "content": "movie id,year,ave rating,Action,Adventure,Animation,Children,Comedy,Crime,Documentary,Drama,Fantasy,Horror,Mystery,Romance,Sci-Fi,Thriller\n"
  },
  {
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  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A2/data/content_movie_list.csv",
    "content": "movieId,title,genres\n4054,Save the Last Dance (2001),Drama|Romance\n4069,\"Wedding Planner, The (2001)\",Comedy|Romance\n4148,Hannibal (2001),Horror|Thriller\n4149,Saving Silverman (Evil Woman) (2001),Comedy|Romance\n4153,Down to Earth (2001),Comedy|Fantasy|Romance\n4161,\"Mexican, The (2001)\",Action|Comedy\n4167,15 Minutes (2001),Thriller\n4228,Heartbreakers (2001),Comedy|Crime|Romance\n4232,Spy Kids (2001),Action|Adventure|Children|Comedy\n4238,Along Came a Spider (2001),Action|Crime|Mystery|Thriller\n4239,Blow (2001),Crime|Drama\n4246,Bridget Jones's Diary (2001),Comedy|Drama|Romance\n4247,Joe Dirt (2001),Adventure|Comedy|Mystery|Romance\n4254,Crocodile Dundee in Los Angeles (2001),Comedy|Drama\n4270,\"Mummy Returns, The (2001)\",Action|Adventure|Comedy|Thriller\n4299,\"Knight's Tale, A (2001)\",Action|Comedy|Romance\n4306,Shrek (2001),Adventure|Animation|Children|Comedy|Fantasy|Romance\n4340,\"Animal, The (2001)\",Comedy\n4343,Evolution (2001),Comedy|Sci-Fi\n4344,Swordfish (2001),Action|Crime|Drama\n4366,Atlantis: The Lost Empire (2001),Adventure|Animation|Children|Fantasy\n4367,Lara Croft: Tomb Raider (2001),Action|Adventure\n4368,Dr. Dolittle 2 (2001),Comedy\n4369,\"Fast and the Furious, The (2001)\",Action|Crime|Thriller\n4370,A.I. Artificial Intelligence (2001),Adventure|Drama|Sci-Fi\n4386,Cats & Dogs (2001),Children|Comedy\n4388,Scary Movie 2 (2001),Comedy\n4446,Final Fantasy: The Spirits Within (2001),Adventure|Animation|Fantasy|Sci-Fi\n4447,Legally Blonde (2001),Comedy|Romance\n4448,\"Score, The (2001)\",Action|Drama\n4638,Jurassic Park III (2001),Action|Adventure|Sci-Fi|Thriller\n4639,America's Sweethearts (2001),Comedy|Romance\n4641,Ghost World (2001),Comedy|Drama\n4643,Planet of the Apes (2001),Action|Adventure|Drama|Sci-Fi\n4700,\"Princess Diaries, The (2001)\",Children|Comedy|Romance\n4701,Rush Hour 2 (2001),Action|Comedy\n4718,American Pie 2 (2001),Comedy\n4720,\"Others, The (2001)\",Drama|Horror|Mystery|Thriller\n4728,Rat Race (2001),Comedy\n4734,Jay and Silent Bob Strike Back (2001),Adventure|Comedy\n4776,Training Day (2001),Crime|Drama|Thriller\n4816,Zoolander (2001),Comedy\n4823,Serendipity (2001),Comedy|Romance\n4865,From Hell (2001),Crime|Horror|Mystery|Thriller\n4873,Waking Life (2001),Animation|Drama|Fantasy\n4874,K-PAX (2001),Drama|Fantasy|Mystery|Sci-Fi\n4876,Thirteen Ghosts (a.k.a. Thir13en Ghosts) (2001),Horror|Thriller\n4878,Donnie Darko (2001),Drama|Mystery|Sci-Fi|Thriller\n4881,\"Man Who Wasn't There, The (2001)\",Crime|Drama\n4886,\"Monsters, Inc. (2001)\",Adventure|Animation|Children|Comedy|Fantasy\n4887,\"One, The (2001)\",Action|Sci-Fi|Thriller\n4890,Shallow Hal (2001),Comedy|Fantasy|Romance\n4896,Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001),Adventure|Children|Fantasy\n4901,Spy Game (2001),Action|Crime|Drama|Thriller\n4903,In the Bedroom (2001),Drama\n4963,Ocean's Eleven (2001),Crime|Thriller\n4973,\"Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)\",Comedy|Romance\n4974,Not Another Teen Movie (2001),Comedy\n4975,Vanilla Sky (2001),Mystery|Romance|Sci-Fi|Thriller\n4979,\"Royal Tenenbaums, The (2001)\",Comedy|Drama\n4992,Kate & Leopold (2001),Comedy|Romance\n4993,\"Lord of the Rings: The Fellowship of the Ring, The (2001)\",Adventure|Fantasy\n4994,\"Majestic, The (2001)\",Comedy|Drama|Romance\n4995,\"Beautiful Mind, A (2001)\",Drama|Romance\n5013,Gosford Park (2001),Comedy|Drama|Mystery\n5014,I Am Sam (2001),Drama\n5015,Monster's Ball (2001),Drama|Romance\n5025,Orange County (2002),Comedy\n5026,\"Brotherhood of the Wolf (Pacte des loups, Le) (2001)\",Action|Mystery|Thriller\n5064,The Count of Monte Cristo (2002),Action|Adventure|Drama|Thriller\n5065,\"Mothman Prophecies, The (2002)\",Drama|Fantasy|Horror|Mystery|Thriller\n5066,\"Walk to Remember, A (2002)\",Drama|Romance\n5072,Metropolis (2001),Animation|Sci-Fi\n5108,John Q (2002),Crime|Drama|Thriller\n5110,Super Troopers (2001),Comedy|Crime|Mystery\n5128,Queen of the Damned (2002),Fantasy|Horror\n5135,Monsoon Wedding (2001),Comedy|Romance\n5171,\"Time Machine, The (2002)\",Action|Adventure|Sci-Fi\n5218,Ice Age (2002),Adventure|Animation|Children|Comedy\n5219,Resident Evil (2002),Action|Horror|Sci-Fi|Thriller\n5220,Showtime (2002),Action|Comedy\n5254,Blade II (2002),Action|Horror|Thriller\n5265,Death to Smoochy (2002),Comedy|Crime|Drama\n5266,Panic Room (2002),Thriller\n5267,\"Rookie, The (2002)\",Drama\n5283,National Lampoon's Van Wilder (2002),Comedy\n5294,Frailty (2001),Crime|Drama|Thriller\n5296,\"Sweetest Thing, The (2002)\",Comedy|Romance\n5299,My Big Fat Greek Wedding (2002),Comedy|Romance\n5313,The Scorpion King (2002),Action|Adventure|Fantasy|Thriller\n5329,\"Salton Sea, The (2002)\",Crime|Drama|Thriller\n5349,Spider-Man (2002),Action|Adventure|Sci-Fi|Thriller\n5377,About a Boy (2002),Comedy|Drama|Romance\n5380,\"Importance of Being Earnest, The (2002)\",Comedy|Drama|Romance\n5388,Insomnia (2002),Action|Crime|Drama|Mystery|Thriller\n5400,\"Sum of All Fears, The (2002)\",Drama|Thriller\n5418,\"Bourne Identity, The (2002)\",Action|Mystery|Thriller\n5419,Scooby-Doo (2002),Adventure|Children|Comedy|Fantasy|Mystery\n5444,Lilo & Stitch (2002),Adventure|Animation|Children|Sci-Fi\n5445,Minority Report (2002),Action|Crime|Mystery|Sci-Fi|Thriller\n5449,Mr. Deeds (2002),Comedy|Romance\n5459,Men in Black II (a.k.a. MIIB) (a.k.a. MIB 2) (2002),Action|Comedy|Sci-Fi\n5463,Reign of Fire (2002),Action|Adventure|Fantasy\n5464,Road to Perdition (2002),Crime|Drama\n5481,Austin Powers in Goldmember (2002),Comedy\n5502,Signs (2002),Horror|Sci-Fi|Thriller\n5505,\"Good Girl, The (2002)\",Comedy|Drama\n5507,xXx (2002),Action|Crime|Thriller\n5528,One Hour Photo (2002),Drama|Thriller\n5574,\"Transporter, The (2002)\",Action|Crime\n5577,Igby Goes Down (2002),Comedy|Drama\n5608,\"Das Experiment (Experiment, The) (2001)\",Drama|Thriller\n5618,Spirited Away (Sen to Chihiro no kamikakushi) (2001),Adventure|Animation|Fantasy\n5620,Sweet Home Alabama (2002),Comedy|Romance\n5621,\"Tuxedo, The (2002)\",Action|Comedy\n5630,Red Dragon (2002),Crime|Mystery|Thriller\n5669,Bowling for Columbine (2002),Documentary\n5673,Punch-Drunk Love (2002),Comedy|Drama|Romance\n5679,\"Ring, The (2002)\",Horror|Mystery|Thriller\n5785,Jackass: The Movie (2002),Action|Comedy|Documentary\n5791,Frida (2002),Drama|Romance\n5810,8 Mile (2002),Drama\n5812,Far from Heaven (2002),Drama|Romance\n5816,Harry Potter and the Chamber of Secrets (2002),Adventure|Fantasy\n5872,Die Another Day (2002),Action|Adventure|Thriller\n5878,Talk to Her (Hable con Ella) (2002),Drama|Romance\n5881,Solaris (2002),Drama|Romance|Sci-Fi\n5902,Adaptation (2002),Comedy|Drama|Romance\n5903,Equilibrium (2002),Action|Sci-Fi|Thriller\n5943,Maid in Manhattan (2002),Comedy|Romance\n5944,Star Trek: Nemesis (2002),Action|Drama|Sci-Fi|Thriller\n5945,About Schmidt (2002),Comedy|Drama\n5952,\"Lord of the Rings: The Two Towers, The (2002)\",Adventure|Fantasy\n5954,25th Hour (2002),Crime|Drama\n5956,Gangs of New York (2002),Crime|Drama\n5957,Two Weeks Notice (2002),Comedy|Romance\n5989,Catch Me If You Can (2002),Crime|Drama\n5992,\"Hours, The (2002)\",Drama|Romance\n6003,Confessions of a Dangerous Mind (2002),Comedy|Crime|Drama|Thriller\n6016,City of God (Cidade de Deus) (2002),Action|Adventure|Crime|Drama|Thriller\n6058,Final Destination 2 (2003),Horror|Thriller\n6059,\"Recruit, The (2003)\",Action|Thriller\n6155,How to Lose a Guy in 10 Days (2003),Comedy|Romance\n6156,Shanghai Knights (2003),Action|Adventure|Comedy\n6157,Daredevil (2003),Action|Crime\n6188,Old School (2003),Comedy\n6218,Bend It Like Beckham (2002),Comedy|Drama|Romance\n6281,Phone Booth (2002),Drama|Thriller\n6283,Cowboy Bebop: The Movie (Cowboy Bebop: Tengoku no Tobira) (2001),Action|Animation|Sci-Fi|Thriller\n6287,Anger Management (2003),Comedy\n6294,Bulletproof Monk (2003),Action|Adventure|Sci-Fi\n6297,Holes (2003),Adventure|Children|Comedy|Mystery\n6323,Identity (2003),Crime|Horror|Mystery|Thriller\n6331,Spellbound (2002),Documentary\n6333,X2: X-Men United (2003),Action|Adventure|Sci-Fi|Thriller\n6367,Down with Love (2003),Comedy|Romance\n6373,Bruce Almighty (2003),Comedy|Drama|Fantasy|Romance\n6377,Finding Nemo (2003),Adventure|Animation|Children|Comedy\n6378,\"Italian Job, The (2003)\",Action|Crime\n6383,\"2 Fast 2 Furious (Fast and the Furious 2, The) (2003)\",Action|Crime|Thriller\n6385,Whale Rider (2002),Drama\n6482,Dumb and Dumberer: When Harry Met Lloyd (2003),Comedy\n6502,28 Days Later (2002),Action|Horror|Sci-Fi\n6503,Charlie's Angels: Full Throttle (2003),Action|Adventure|Comedy|Crime|Thriller\n6534,Hulk (2003),Action|Adventure|Sci-Fi\n6535,\"Legally Blonde 2: Red, White & Blonde (2003)\",Comedy\n6537,Terminator 3: Rise of the Machines (2003),Action|Adventure|Sci-Fi\n6539,Pirates of the Caribbean: The Curse of the Black Pearl (2003),Action|Adventure|Comedy|Fantasy\n6541,\"League of Extraordinary Gentlemen, The (a.k.a. LXG) (2003)\",Action|Fantasy|Sci-Fi\n6548,Bad Boys II (2003),Action|Comedy|Crime|Thriller\n6550,Johnny English (2003),Action|Comedy|Thriller\n6552,Dirty Pretty Things (2002),Crime|Drama|Thriller\n6564,Lara Croft Tomb Raider: The Cradle of Life (2003),Action|Adventure|Comedy|Romance|Thriller\n6565,Seabiscuit (2003),Drama\n6586,American Wedding (American Pie 3) (2003),Comedy\n6593,Freaky Friday (2003),Children|Comedy|Fantasy\n6595,S.W.A.T. (2003),Action|Thriller\n6618,Shaolin Soccer (Siu lam juk kau) (2001),Action|Comedy\n6620,American Splendor (2003),Comedy|Drama\n6708,Matchstick Men (2003),Comedy|Crime|Drama\n6709,Once Upon a Time in Mexico (2003),Action|Adventure|Crime|Thriller\n6711,Lost in Translation (2003),Comedy|Drama|Romance\n6753,Secondhand Lions (2003),Children|Comedy|Drama\n6754,Underworld (2003),Action|Fantasy|Horror\n6755,Bubba Ho-tep (2002),Comedy|Horror\n6764,\"Rundown, The (2003)\",Action|Adventure|Comedy\n6773,\"Triplets of Belleville, The (Les triplettes de Belleville) (2003)\",Animation|Comedy|Fantasy\n6867,\"Station Agent, The (2003)\",Comedy|Drama\n6870,Mystic River (2003),Crime|Drama|Mystery\n6873,Intolerable Cruelty (2003),Comedy|Romance\n6874,Kill Bill: Vol. 1 (2003),Action|Crime|Thriller\n6879,Runaway Jury (2003),Drama|Thriller\n6888,Scary Movie 3 (2003),Comedy|Horror\n6936,Elf (2003),Children|Comedy|Fantasy\n6942,Love Actually (2003),Comedy|Drama|Romance\n6953,21 Grams (2003),Crime|Drama|Mystery|Romance|Thriller\n6957,Bad Santa (2003),Comedy|Crime\n6958,\"Haunted Mansion, The (2003)\",Children|Comedy|Fantasy|Horror\n7090,Hero (Ying xiong) (2002),Action|Adventure|Drama\n7137,\"Cooler, The (2003)\",Comedy|Drama|Romance\n7147,Big Fish (2003),Drama|Fantasy|Romance\n7149,Something's Gotta Give (2003),Comedy|Drama|Romance\n7150,Stuck on You (2003),Comedy\n7151,Girl with a Pearl Earring (2003),Drama|Romance\n7153,\"Lord of the Rings: The Return of the King, The (2003)\",Action|Adventure|Drama|Fantasy\n7154,Mona Lisa Smile (2003),Drama|Romance\n7160,Monster (2003),Crime|Drama\n7163,Paycheck (2003),Action|Sci-Fi|Thriller\n7173,Along Came Polly (2004),Comedy|Romance\n7254,The Butterfly Effect (2004),Drama|Sci-Fi|Thriller\n7263,Miracle (2004),Drama\n7265,\"Dreamers, The (2003)\",Drama\n7293,50 First Dates (2004),Comedy|Romance\n7317,EuroTrip (2004),Adventure|Comedy\n7323,\"Good bye, Lenin! (2003)\",Comedy|Drama\n7324,Hidalgo (2004),Adventure|Drama\n7325,Starsky & Hutch (2004),Action|Comedy|Crime|Thriller\n7347,Secret Window (2004),Mystery|Thriller\n7360,Dawn of the Dead (2004),Action|Drama|Horror|Thriller\n7361,Eternal Sunshine of the Spotless Mind (2004),Drama|Romance|Sci-Fi\n7367,\"Ladykillers, The (2004)\",Comedy|Crime\n7371,Dogville (2003),Drama|Mystery|Thriller\n7373,Hellboy (2004),Action|Adventure|Fantasy|Horror\n7438,Kill Bill: Vol. 2 (2004),Action|Drama|Thriller\n7439,\"Punisher, The (2004)\",Action|Crime|Thriller\n7444,13 Going on 30 (2004),Comedy|Fantasy|Romance\n7445,Man on Fire (2004),Action|Crime|Drama|Mystery|Thriller\n7451,Mean Girls (2004),Comedy\n7454,Van Helsing (2004),Action|Adventure|Fantasy|Horror\n8014,\"Spring, Summer, Fall, Winter... and Spring (Bom yeoreum gaeul gyeoul geurigo bom) (2003)\",Drama\n8361,\"Day After Tomorrow, The (2004)\",Action|Adventure|Drama|Sci-Fi|Thriller\n8366,Saved! (2004),Comedy|Drama\n8370,\"Blind Swordsman: Zatoichi, The (Zatôichi) (2003)\",Action|Comedy|Crime|Drama\n8371,\"Chronicles of Riddick, The (2004)\",Action|Sci-Fi|Thriller\n8373,\"Stepford Wives, The (2004)\",Comedy|Fantasy|Thriller\n8376,Napoleon Dynamite (2004),Comedy\n8464,Super Size Me (2004),Comedy|Documentary|Drama\n8528,Dodgeball: A True Underdog Story (2004),Comedy\n8529,\"Terminal, The (2004)\",Comedy|Drama|Romance\n8533,\"Notebook, The (2004)\",Drama|Romance\n8622,Fahrenheit 9/11 (2004),Documentary\n8638,Before Sunset (2004),Drama|Romance\n8641,Anchorman: The Legend of Ron Burgundy (2004),Comedy\n8644,\"I, Robot (2004)\",Action|Adventure|Sci-Fi|Thriller\n8665,\"Bourne Supremacy, The (2004)\",Action|Crime|Thriller\n8781,\"Manchurian Candidate, The (2004)\",Thriller\n8783,\"Village, The (2004)\",Drama|Mystery|Thriller\n8784,Garden State (2004),Comedy|Drama|Romance\n8798,Collateral (2004),Action|Crime|Drama|Thriller\n8807,Harold and Kumar Go to White Castle (2004),Adventure|Comedy\n8810,AVP: Alien vs. Predator (2004),Action|Horror|Sci-Fi|Thriller\n8861,Resident Evil: Apocalypse (2004),Action|Horror|Sci-Fi|Thriller\n8865,Sky Captain and the World of Tomorrow (2004),Action|Adventure|Sci-Fi\n8873,\"Motorcycle Diaries, The (Diarios de motocicleta) (2004)\",Adventure|Drama\n8874,Shaun of the Dead (2004),Comedy|Horror\n8907,Shark Tale (2004),Animation|Children|Comedy\n8910,I Heart Huckabees (2004),Comedy\n8914,Primer (2004),Drama|Sci-Fi\n8917,Team America: World Police (2004),Action|Adventure|Animation|Comedy\n8947,\"Grudge, The (2004)\",Horror|Mystery|Thriller\n8949,Sideways (2004),Comedy|Drama|Romance\n8950,The Machinist (2004),Drama|Mystery|Thriller\n8957,Saw (2004),Horror|Mystery|Thriller\n8958,Ray (2004),Drama\n8961,\"Incredibles, The (2004)\",Action|Adventure|Animation|Children|Comedy\n8969,Bridget Jones: The Edge of Reason (2004),Comedy|Drama|Romance\n8970,Finding Neverland (2004),Drama\n8972,National Treasure (2004),Action|Adventure|Drama|Mystery|Thriller\n8981,Closer (2004),Drama|Romance\n8983,House of Flying Daggers (Shi mian mai fu) (2004),Action|Drama|Romance\n8984,Ocean's Twelve (2004),Action|Comedy|Crime|Thriller\n8985,Blade: Trinity (2004),Action|Fantasy|Horror|Thriller\n27660,\"Animatrix, The (2003)\",Action|Animation|Drama|Sci-Fi\n27706,Lemony Snicket's A Series of Unfortunate Events (2004),Adventure|Children|Comedy|Fantasy\n27773,Old Boy (2003),Mystery|Thriller\n27801,Ong-Bak: The Thai Warrior (Ong Bak) (2003),Action|Thriller\n27808,Spanglish (2004),Comedy|Drama|Romance\n27815,\"Chorus, The (Choristes, Les) (2004)\",Drama\n27831,Layer Cake (2004),Crime|Drama|Thriller\n27846,\"Corporation, The (2003)\",Documentary\n27904,\"Scanner Darkly, A (2006)\",Animation|Drama|Mystery|Sci-Fi|Thriller\n30707,Million Dollar Baby (2004),Drama\n30810,\"Life Aquatic with Steve Zissou, The (2004)\",Adventure|Comedy|Fantasy\n30812,\"Aviator, The (2004)\",Drama\n30822,In Good Company (2004),Comedy|Drama\n30825,Meet the Fockers (2004),Comedy\n31221,Elektra (2005),Action|Adventure|Crime|Drama\n31433,\"Wedding Date, The (2005)\",Comedy|Romance\n31658,Howl's Moving Castle (Hauru no ugoku shiro) (2004),Adventure|Animation|Fantasy|Romance\n31685,Hitch (2005),Comedy|Romance\n31696,Constantine (2005),Action|Fantasy|Horror|Thriller\n31878,Kung Fu Hustle (Gong fu) (2004),Action|Comedy\n32029,Hostage (2005),Action|Crime|Drama|Thriller\n33004,\"Hitchhiker's Guide to the Galaxy, The (2005)\",Adventure|Comedy|Sci-Fi\n33166,Crash (2004),Crime|Drama\n33493,Star Wars: Episode III - Revenge of the Sith (2005),Action|Adventure|Sci-Fi\n33615,Madagascar (2005),Adventure|Animation|Children|Comedy\n33660,Cinderella Man (2005),Drama|Romance\n33679,Mr. & Mrs. Smith (2005),Action|Adventure|Comedy|Romance\n33836,Bewitched (2005),Comedy|Fantasy|Romance\n34048,War of the Worlds (2005),Action|Adventure|Sci-Fi|Thriller\n34072,\"March of the Penguins (Marche de l'empereur, La) (2005)\",Documentary\n34150,Fantastic Four (2005),Action|Adventure|Sci-Fi\n34162,Wedding Crashers (2005),Comedy|Romance\n34319,\"Island, The (2005)\",Action|Sci-Fi|Thriller\n34405,Serenity (2005),Action|Adventure|Sci-Fi\n34437,Broken Flowers (2005),Comedy|Drama\n34520,\"Dukes of Hazzard, The (2005)\",Action|Adventure|Comedy\n35836,\"40-Year-Old Virgin, The (2005)\",Comedy|Romance\n36401,\"Brothers Grimm, The (2005)\",Comedy|Fantasy|Horror|Thriller\n36517,\"Constant Gardener, The (2005)\",Drama|Thriller\n36519,Transporter 2 (2005),Action|Crime|Thriller\n36708,Family Guy Presents Stewie Griffin: The Untold Story (2005),Adventure|Animation|Comedy\n37380,Doom (2005),Action|Horror|Sci-Fi\n37384,Waiting... (2005),Comedy\n37386,Aeon Flux (2005),Action|Sci-Fi\n37727,Flightplan (2005),Action|Drama|Thriller\n37733,\"History of Violence, A (2005)\",Action|Crime|Drama|Thriller\n37741,Capote (2005),Crime|Drama\n37830,Final Fantasy VII: Advent Children (2004),Action|Adventure|Animation|Fantasy|Sci-Fi\n38038,Wallace & Gromit in The Curse of the Were-Rabbit (2005),Adventure|Animation|Children|Comedy\n38061,Kiss Kiss Bang Bang (2005),Comedy|Crime|Mystery|Thriller\n39183,Brokeback Mountain (2005),Drama|Romance\n39292,\"Good Night, and Good Luck. (2005)\",Crime|Drama\n39446,Saw II (2005),Horror|Thriller\n40583,Syriana (2005),Drama|Thriller\n40629,Pride & Prejudice (2005),Drama|Romance\n40732,\"Descent, The (2005)\",Adventure|Drama|Horror|Thriller\n41285,Match Point (2005),Crime|Drama|Romance\n41566,\"Chronicles of Narnia: The Lion, the Witch and the Wardrobe, The (2005)\",Adventure|Children|Fantasy\n41569,King Kong (2005),Action|Adventure|Drama|Fantasy|Thriller\n41997,Munich (2005),Action|Crime|Drama|Thriller\n42011,Fun with Dick and Jane (2005),Comedy|Crime\n42723,Hostel (2005),Horror\n42738,Underworld: Evolution (2006),Action|Fantasy|Horror\n43928,Ultraviolet (2006),Action|Fantasy|Sci-Fi|Thriller\n44022,Ice Age 2: The Meltdown (2006),Adventure|Animation|Children|Comedy\n44195,Thank You for Smoking (2006),Comedy|Drama\n44199,Inside Man (2006),Crime|Drama|Thriller\n44555,\"Lives of Others, The (Das leben der Anderen) (2006)\",Drama|Romance|Thriller\n44665,Lucky Number Slevin (2006),Crime|Drama|Mystery\n44840,\"Benchwarmers, The (2006)\",Comedy\n44972,Scary Movie 4 (2006),Comedy|Horror\n44974,Hard Candy (2005),Drama|Thriller\n45081,Silent Hill (2006),Fantasy|Horror|Thriller\n45186,Mission: Impossible III (2006),Action|Adventure|Thriller\n45431,Over the Hedge (2006),Adventure|Animation|Children|Comedy\n45447,\"Da Vinci Code, The (2006)\",Drama|Mystery|Thriller\n45499,X-Men: The Last Stand (2006),Action|Sci-Fi|Thriller\n45501,\"Break-Up, The (2006)\",Comedy|Drama|Romance\n45517,Cars (2006),Animation|Children|Comedy\n45666,Nacho Libre (2006),Comedy\n45668,\"Lake House, The (2006)\",Drama|Fantasy|Romance\n45672,Click (2006),Adventure|Comedy|Drama|Fantasy|Romance\n45720,\"Devil Wears Prada, The (2006)\",Comedy|Drama\n45722,Pirates of the Caribbean: Dead Man's Chest (2006),Action|Adventure|Fantasy\n45728,Clerks II (2006),Comedy\n45880,Marie Antoinette (2006),Drama|Romance\n45950,\"Inconvenient Truth, An (2006)\",Documentary\n46335,\"Fast and the Furious: Tokyo Drift, The (Fast and the Furious 3, The) (2006)\",Action|Crime|Drama|Thriller\n46578,Little Miss Sunshine (2006),Adventure|Comedy|Drama\n46723,Babel (2006),Drama|Thriller\n46965,Snakes on a Plane (2006),Action|Comedy|Horror|Thriller\n46970,Talladega Nights: The Ballad of Ricky Bobby (2006),Action|Comedy\n46976,Stranger than Fiction (2006),Comedy|Drama|Fantasy|Romance\n47044,Miami Vice (2006),Action|Crime|Drama|Thriller\n47099,\"Pursuit of Happyness, The (2006)\",Drama\n47200,Crank (2006),Action|Thriller\n47518,Accepted (2006),Comedy\n47610,\"Illusionist, The (2006)\",Drama|Fantasy|Mystery|Romance\n47629,The Queen (2006),Drama\n47997,Idiocracy (2006),Adventure|Comedy|Sci-Fi|Thriller\n48043,\"Fountain, The (2006)\",Drama|Fantasy|Romance\n48082,\"Science of Sleep, The (La science des rêves) (2006)\",Comedy|Drama|Fantasy|Romance\n48304,Apocalypto (2006),Adventure|Drama|Thriller\n48385,Borat: Cultural Learnings of America for Make Benefit Glorious Nation of Kazakhstan (2006),Comedy\n48394,\"Pan's Labyrinth (Laberinto del fauno, El) (2006)\",Drama|Fantasy|Thriller\n48516,\"Departed, The (2006)\",Crime|Drama|Thriller\n48738,\"Last King of Scotland, The (2006)\",Drama|Thriller\n48774,Children of Men (2006),Action|Adventure|Drama|Sci-Fi|Thriller\n48780,\"Prestige, The (2006)\",Drama|Mystery|Sci-Fi|Thriller\n48877,Saw III (2006),Crime|Horror|Thriller\n49272,Casino Royale (2006),Action|Adventure|Thriller\n49278,Déjà Vu (Deja Vu) (2006),Action|Sci-Fi|Thriller\n49286,\"Holiday, The (2006)\",Comedy|Romance\n49649,Eragon (2006),Action|Adventure|Fantasy\n49651,Rocky Balboa (2006),Action|Drama\n50794,Smokin' Aces (2006),Action|Crime|Drama|Thriller\n50872,Ratatouille (2007),Animation|Children|Drama\n51077,Ghost Rider (2007),Action|Fantasy|Thriller\n51084,Music and Lyrics (2007),Comedy|Romance\n51086,\"Number 23, The (2007)\",Drama|Mystery|Thriller\n51255,Hot Fuzz (2007),Action|Comedy|Crime|Mystery\n51412,Next (2007),Action|Sci-Fi|Thriller\n51540,Zodiac (2007),Crime|Drama|Thriller\n51935,Shooter (2007),Action|Drama|Thriller\n52245,Blades of Glory (2007),Comedy|Romance\n52281,Grindhouse (2007),Action|Crime|Horror|Sci-Fi|Thriller\n52287,Meet the Robinsons (2007),Action|Adventure|Animation|Children|Comedy|Sci-Fi\n52328,Sunshine (2007),Adventure|Drama|Sci-Fi|Thriller\n52458,Disturbia (2007),Drama|Thriller\n52604,Fracture (2007),Crime|Drama|Mystery|Thriller\n52973,Knocked Up (2007),Comedy|Drama|Romance\n53000,28 Weeks Later (2007),Horror|Sci-Fi|Thriller\n53121,Shrek the Third (2007),Adventure|Animation|Children|Comedy|Fantasy\n53125,Pirates of the Caribbean: At World's End (2007),Action|Adventure|Comedy|Fantasy\n53322,Ocean's Thirteen (2007),Crime|Thriller\n53464,Fantastic Four: Rise of the Silver Surfer (2007),Action|Adventure|Sci-Fi\n53519,Death Proof (2007),Action|Adventure|Crime|Horror|Thriller\n53894,Sicko (2007),Documentary|Drama\n53953,1408 (2007),Drama|Horror|Thriller\n53972,Live Free or Die Hard (2007),Action|Adventure|Crime|Thriller\n53993,Evan Almighty (2007),Comedy|Fantasy\n54004,I Now Pronounce You Chuck and Larry (2007),Comedy|Romance\n54259,Stardust (2007),Adventure|Comedy|Fantasy|Romance\n54272,\"Simpsons Movie, The (2007)\",Animation|Comedy\n54286,\"Bourne Ultimatum, The (2007)\",Action|Crime|Thriller\n54503,Superbad (2007),Comedy\n54881,\"King of Kong, The (2007)\",Documentary\n54995,Planet Terror (2007),Action|Horror|Sci-Fi\n54999,Shoot 'Em Up (2007),Action|Comedy|Crime\n55118,Eastern Promises (2007),Crime|Drama|Thriller\n55232,Resident Evil: Extinction (2007),Action|Horror|Sci-Fi|Thriller\n55247,Into the Wild (2007),Action|Adventure|Drama\n55269,\"Darjeeling Limited, The (2007)\",Adventure|Comedy|Drama\n55276,Michael Clayton (2007),Drama|Thriller\n55282,30 Days of Night (2007),Horror|Thriller\n55290,Gone Baby Gone (2007),Crime|Drama|Mystery\n55442,Persepolis (2007),Animation|Drama\n55721,Elite Squad (Tropa de Elite) (2007),Action|Crime|Drama|Thriller\n55765,American Gangster (2007),Crime|Drama|Thriller\n55768,Bee Movie (2007),Animation|Comedy\n55820,No Country for Old Men (2007),Crime|Drama\n55830,Be Kind Rewind (2008),Comedy\n56145,\"Mist, The (2007)\",Horror|Sci-Fi\n56171,\"Golden Compass, The (2007)\",Adventure|Children|Fantasy\n56251,Futurama: Bender's Big Score (2007),Animation|Comedy|Sci-Fi\n56367,Juno (2007),Comedy|Drama|Romance\n56587,\"Bucket List, The (2007)\",Comedy|Drama\n56775,National Treasure: Book of Secrets (2007),Action|Adventure\n56941,P.S. I Love You (2007),Comedy|Drama|Romance\n56949,27 Dresses (2008),Comedy|Romance\n57368,Cloverfield (2008),Action|Mystery|Sci-Fi|Thriller\n57504,\"Girl Who Leapt Through Time, The (Toki o kakeru shôjo) (2006)\",Animation|Comedy|Drama|Romance|Sci-Fi\n57640,Hellboy II: The Golden Army (2008),Action|Adventure|Fantasy|Sci-Fi\n57669,In Bruges (2008),Comedy|Crime|Drama|Thriller\n58025,Jumper (2008),Action|Adventure|Drama|Sci-Fi|Thriller\n58047,\"Definitely, Maybe (2008)\",Comedy|Drama|Romance\n58156,Semi-Pro (2008),Comedy\n58293,\"10,000 BC (2008)\",Adventure|Romance|Thriller\n58295,\"Bank Job, The (2008)\",Action|Crime|Thriller\n58803,21 (2008),Crime|Drama|Romance|Thriller\n58998,Forgetting Sarah Marshall (2008),Comedy|Romance\n59022,Harold & Kumar Escape from Guantanamo Bay (2008),Adventure|Comedy\n59258,Baby Mama (2008),Comedy\n59315,Iron Man (2008),Action|Adventure|Sci-Fi\n59369,Taken (2008),Action|Crime|Drama|Thriller\n59387,\"Fall, The (2006)\",Adventure|Drama|Fantasy\n59501,\"Chronicles of Narnia: Prince Caspian, The (2008)\",Adventure|Children|Fantasy\n59615,Indiana Jones and the Kingdom of the Crystal Skull (2008),Action|Adventure|Comedy|Sci-Fi\n59725,Sex and the City (2008),Comedy|Romance\n59900,You Don't Mess with the Zohan (2008),Comedy\n60040,\"Incredible Hulk, The (2008)\",Action|Sci-Fi\n60069,WALL·E (2008),Adventure|Animation|Children|Romance|Sci-Fi\n60072,Wanted (2008),Action|Thriller\n60074,Hancock (2008),Action|Adventure|Comedy|Crime|Fantasy\n60126,Get Smart (2008),Action|Comedy\n60756,Step Brothers (2008),Comedy\n60950,Vicky Cristina Barcelona (2008),Comedy|Drama|Romance\n61024,Pineapple Express (2008),Action|Comedy|Crime\n61240,Let the Right One In (Låt den rätte komma in) (2008),Drama|Fantasy|Horror|Romance\n61323,Burn After Reading (2008),Comedy|Crime|Drama\n62155,Nick and Norah's Infinite Playlist (2008),Comedy|Drama|Romance\n62374,Body of Lies (2008),Action|Drama|Thriller\n62849,RocknRolla (2008),Action|Crime\n63082,Slumdog Millionaire (2008),Crime|Drama|Romance\n63113,Quantum of Solace (2008),Action|Adventure|Thriller\n63131,Role Models (2008),Comedy\n63859,Bolt (2008),Action|Adventure|Animation|Children|Comedy\n63876,Milk (2008),Drama\n63992,Twilight (2008),Drama|Fantasy|Romance|Thriller\n64614,Gran Torino (2008),Crime|Drama\n64716,Seven Pounds (2008),Drama\n64839,\"Wrestler, The (2008)\",Drama\n64957,\"Curious Case of Benjamin Button, The (2008)\",Drama|Fantasy|Mystery|Romance\n64969,Yes Man (2008),Comedy\n65088,Bedtime Stories (2008),Adventure|Children|Comedy\n65230,Marley & Me (2008),Comedy|Drama\n65261,Ponyo (Gake no ue no Ponyo) (2008),Adventure|Animation|Children|Fantasy\n66097,Coraline (2009),Animation|Fantasy|Thriller\n66203,He's Just Not That Into You (2009),Comedy|Drama|Romance\n67087,\"I Love You, Man (2009)\",Comedy\n67255,\"Girl with the Dragon Tattoo, The (Män som hatar kvinnor) (2009)\",Crime|Drama|Mystery|Thriller\n67734,Adventureland (2009),Comedy|Drama\n67923,\"Fast & Furious (Fast and the Furious 4, The) (2009)\",Action|Crime|Drama|Thriller\n68135,17 Again (2009),Comedy|Drama\n68237,Moon (2009),Drama|Mystery|Sci-Fi|Thriller\n68319,X-Men Origins: Wolverine (2009),Action|Sci-Fi|Thriller\n68554,Angels & Demons (2009),Crime|Drama|Mystery|Thriller\n68791,Terminator Salvation (2009),Action|Adventure|Sci-Fi|Thriller\n68954,Up (2009),Adventure|Animation|Children|Drama\n69122,\"Hangover, The (2009)\",Comedy|Crime\n69406,\"Proposal, The (2009)\",Comedy|Romance\n69644,Ice Age: Dawn of the Dinosaurs (2009),Action|Adventure|Animation|Children|Comedy|Romance\n69757,(500) Days of Summer (2009),Comedy|Drama|Romance\n69784,Brüno (Bruno) (2009),Comedy\n69951,\"Imaginarium of Doctor Parnassus, The (2009)\",Drama|Fantasy\n70183,\"Ugly Truth, The (2009)\",Comedy|Drama|Romance\n70286,District 9 (2009),Mystery|Sci-Fi|Thriller\n70293,Julie & Julia (2009),Comedy|Drama|Romance\n71057,9 (2009),Adventure|Animation|Sci-Fi\n71464,\"Serious Man, A (2009)\",Comedy|Drama\n71530,Surrogates (2009),Action|Sci-Fi|Thriller\n71535,Zombieland (2009),Action|Comedy|Horror\n71899,Mary and Max (2009),Animation|Comedy|Drama\n72011,Up in the Air (2009),Drama|Romance\n72226,Fantastic Mr. Fox (2009),Adventure|Animation|Children|Comedy|Crime\n72378,2012 (2009),Action|Drama|Sci-Fi|Thriller\n72641,\"Blind Side, The  (2009)\",Drama\n73017,Sherlock Holmes (2009),Action|Crime|Mystery|Thriller\n73321,\"Book of Eli, The (2010)\",Action|Adventure|Drama\n74458,Shutter Island (2010),Drama|Mystery|Thriller\n76077,Hot Tub Time Machine (2010),Comedy|Sci-Fi\n76175,Clash of the Titans (2010),Action|Adventure|Drama|Fantasy\n76251,Kick-Ass (2010),Action|Comedy\n76293,Date Night (2010),Action|Comedy|Romance\n77455,Exit Through the Gift Shop (2010),Comedy|Documentary\n78209,Get Him to the Greek (2010),Comedy\n78469,\"A-Team, The (2010)\",Action|Comedy|Thriller\n79057,Predators (2010),Action|Sci-Fi|Thriller\n79091,Despicable Me (2010),Animation|Children|Comedy|Crime\n79134,Grown Ups (2010),Comedy\n79139,\"Sorcerer's Apprentice, The (2010)\",Action|Adventure|Children|Comedy|Fantasy\n79293,Salt (2010),Action|Thriller\n79592,\"Other Guys, The (2010)\",Action|Comedy\n79695,\"Expendables, The (2010)\",Action|Adventure|Thriller\n80219,Machete (2010),Action|Adventure|Comedy|Crime|Thriller\n80463,\"Social Network, The (2010)\",Drama\n80489,\"Town, The (2010)\",Crime|Drama|Thriller\n80549,Easy A (2010),Comedy|Romance\n80906,Inside Job (2010),Documentary\n81229,Red (2010),Action|Comedy\n81537,Due Date (2010),Comedy\n81562,127 Hours (2010),Adventure|Drama|Thriller\n81591,Black Swan (2010),Drama|Thriller\n81845,\"King's Speech, The (2010)\",Drama\n81932,\"Fighter, The (2010)\",Drama\n83134,Tucker & Dale vs Evil (2010),Comedy|Horror\n84152,Limitless (2011),Sci-Fi|Thriller\n84374,No Strings Attached (2011),Comedy|Romance\n84392,\"Lincoln Lawyer, The (2011)\",Crime|Drama|Thriller\n84772,Paul (2011),Adventure|Comedy|Sci-Fi\n84954,\"Adjustment Bureau, The (2011)\",Romance|Sci-Fi|Thriller\n85414,Source Code (2011),Action|Drama|Mystery|Sci-Fi|Thriller\n86190,Hanna (2011),Action|Adventure|Mystery|Thriller\n86833,Bridesmaids (2011),Comedy\n86882,Midnight in Paris (2011),Comedy|Fantasy|Romance\n86911,\"Hangover Part II, The (2011)\",Comedy\n87430,Green Lantern (2011),Action|Adventure|Sci-Fi\n87869,Horrible Bosses (2011),Comedy|Crime\n88163,\"Crazy, Stupid, Love. (2011)\",Comedy|Drama|Romance\n88405,Friends with Benefits (2011),Comedy|Romance\n88744,Rise of the Planet of the Apes (2011),Action|Drama|Sci-Fi|Thriller\n88785,\"Change-Up, The (2011)\",Comedy\n88810,\"Help, The (2011)\",Drama\n89492,Moneyball (2011),Drama\n89774,Warrior (2011),Drama\n89864,50/50 (2011),Comedy|Drama\n89904,The Artist (2011),Comedy|Drama|Romance\n90405,In Time (2011),Crime|Sci-Fi|Thriller\n90866,Hugo (2011),Children|Drama|Mystery\n91077,\"Descendants, The (2011)\",Comedy|Drama\n91485,\"Expendables 2, The (2012)\",Action|Adventure\n91500,The Hunger Games (2012),Action|Adventure|Drama|Sci-Fi|Thriller\n91542,Sherlock Holmes: A Game of Shadows (2011),Action|Adventure|Comedy|Crime|Mystery|Thriller\n91658,\"Girl with the Dragon Tattoo, The (2011)\",Drama|Thriller\n92259,Intouchables (2011),Comedy|Drama\n92535,Louis C.K.: Live at the Beacon Theater (2011),Comedy\n93326,This Means War (2012),Action|Comedy|Romance\n93510,21 Jump Street (2012),Action|Comedy|Crime\n93840,\"Cabin in the Woods, The (2012)\",Comedy|Horror|Sci-Fi|Thriller\n94677,\"Dictator, The (2012)\",Comedy\n94959,Moonrise Kingdom (2012),Comedy|Drama|Romance\n95167,Brave (2012),Action|Adventure|Animation|Children\n95441,Ted (2012),Comedy|Fantasy\n96610,Looper (2012),Action|Crime|Sci-Fi\n96737,Dredd (2012),Action|Sci-Fi\n96821,\"Perks of Being a Wallflower, The (2012)\",Drama|Romance\n96829,\"Hunt, The (Jagten) (2012)\",Drama\n97225,Hotel Transylvania (2012),Animation|Children|Comedy\n97304,Argo (2012),Drama|Thriller\n97306,Seven Psychopaths (2012),Comedy|Crime\n97913,Wreck-It Ralph (2012),Animation|Comedy\n97921,Silver Linings Playbook (2012),Comedy|Drama\n98961,Zero Dark Thirty (2012),Action|Drama|Thriller\n99007,Warm Bodies (2013),Comedy|Horror|Romance\n99112,Jack Reacher (2012),Action|Crime|Thriller\n102123,This Is the End (2013),Action|Comedy\n102407,\"Great Gatsby, The (2013)\",Drama\n102481,\"Internship, The (2013)\",Comedy\n102903,Now You See Me (2013),Crime|Mystery|Thriller\n103141,Monsters University (2013),Adventure|Animation|Comedy\n103341,\"World's End, The (2013)\",Action|Comedy|Sci-Fi\n103772,\"Wolverine, The (2013)\",Action|Adventure|Fantasy|Sci-Fi\n104211,We're the Millers (2013),Comedy|Crime\n104241,Kick-Ass 2 (2013),Action|Comedy|Crime\n104374,About Time (2013),Drama|Fantasy|Romance\n104879,Prisoners (2013),Drama|Mystery|Thriller\n104913,Rush (2013),Action|Drama\n105844,12 Years a Slave (2013),Drama\n106100,Dallas Buyers Club (2013),Drama\n106782,\"Wolf of Wall Street, The (2013)\",Comedy|Crime|Drama\n106916,American Hustle (2013),Crime|Drama\n106918,\"Secret Life of Walter Mitty, The (2013)\",Adventure|Comedy|Drama\n106920,Her (2013),Drama|Romance|Sci-Fi\n107348,Anchorman 2: The Legend Continues (2013),Comedy\n107406,Snowpiercer (2013),Action|Drama|Sci-Fi\n108932,The Lego Movie (2014),Action|Adventure|Animation|Children|Comedy|Fantasy\n109374,\"Grand Budapest Hotel, The (2014)\",Comedy|Drama\n111113,Neighbors (2014),Comedy\n111360,Lucy (2014),Action|Sci-Fi\n111362,X-Men: Days of Future Past (2014),Action|Adventure|Sci-Fi\n111781,Mission: Impossible - Rogue Nation (2015),Action|Adventure|Thriller\n112138,22 Jump Street (2014),Action|Comedy|Crime\n112175,How to Train Your Dragon 2 (2014),Action|Adventure|Animation\n112183,Birdman: Or (The Unexpected Virtue of Ignorance) (2014),Comedy|Drama\n112290,Boyhood (2014),Drama\n112552,Whiplash (2014),Drama\n112556,Gone Girl (2014),Drama|Thriller\n112623,Dawn of the Planet of the Apes (2014),Sci-Fi\n112852,Guardians of the Galaxy (2014),Action|Adventure|Sci-Fi\n114180,\"Maze Runner, The (2014)\",Action|Mystery|Sci-Fi\n114935,Predestination (2014),Action|Mystery|Sci-Fi|Thriller\n115149,John Wick (2014),Action|Thriller\n115569,Nightcrawler (2014),Crime|Drama|Thriller\n115617,Big Hero 6 (2014),Action|Animation|Comedy\n115713,Ex Machina (2015),Drama|Sci-Fi|Thriller\n116823,The Hunger Games: Mockingjay - Part 1 (2014),Adventure|Sci-Fi|Thriller\n116897,Wild Tales (2014),Comedy|Drama|Thriller\n117176,The Theory of Everything (2014),Drama|Romance\n117529,Jurassic World (2015),Action|Adventure|Drama|Sci-Fi|Thriller\n117590,Horrible Bosses 2 (2014),Comedy|Crime\n118696,The Hobbit: The Battle of the Five Armies (2014),Adventure|Fantasy\n119141,The Interview (2014),Action|Comedy\n119145,Kingsman: The Secret Service (2015),Action|Adventure|Comedy|Crime\n120466,Chappie (2015),Action|Thriller\n122882,Mad Max: Fury Road (2015),Action|Adventure|Sci-Fi|Thriller\n122892,Avengers: Age of Ultron (2015),Action|Adventure|Sci-Fi\n122900,Ant-Man (2015),Action|Adventure|Sci-Fi\n122904,Deadpool (2016),Action|Adventure|Comedy|Sci-Fi\n122906,Black Panther (2017),Action|Adventure|Sci-Fi\n122912,Avengers: Infinity War - Part I (2018),Action|Adventure|Sci-Fi\n122916,Thor: Ragnarok (2017),Action|Adventure|Sci-Fi\n122918,Guardians of the Galaxy 2 (2017),Action|Adventure|Sci-Fi\n122920,Captain America: Civil War (2016),Action|Sci-Fi|Thriller\n122922,Doctor Strange (2016),Action|Adventure|Sci-Fi\n122924,X-Men: Apocalypse (2016),Action|Adventure|Fantasy|Sci-Fi\n122926,Untitled Spider-Man Reboot (2017),Action|Adventure|Fantasy\n129354,Focus (2015),Comedy|Crime|Drama|Romance\n134130,The Martian (2015),Adventure|Drama|Sci-Fi\n134368,Spy (2015),Action|Comedy|Crime\n134393,Trainwreck (2015),Comedy|Romance\n134853,Inside Out (2015),Adventure|Animation|Children|Comedy|Drama|Fantasy\n135133,The Hunger Games: Mockingjay - Part 2 (2015),Adventure|Sci-Fi\n135143,Fantastic Beasts and Where to Find Them (2016),Fantasy\n135536,Suicide Squad (2016),Action|Crime|Sci-Fi\n135569,Star Trek Beyond (2016),Action|Adventure|Sci-Fi\n135887,Minions (2015),Adventure|Animation|Children|Comedy\n136020,Spectre (2015),Action|Adventure|Crime\n136864,Batman v Superman: Dawn of Justice (2016),Action|Adventure|Fantasy|Sci-Fi\n137857,The Jungle Book (2016),Adventure|Drama|Fantasy\n138036,The Man from U.N.C.L.E. (2015),Action|Adventure|Comedy\n139385,The Revenant (2015),Adventure|Drama\n139644,Sicario (2015),Crime|Drama|Mystery\n140110,The Intern (2015),Comedy\n142488,Spotlight (2015),Thriller\n143355,Wonder Woman (2017),Action|Adventure|Fantasy\n148626,\"Big Short, The (2015)\",Drama\n150548,Sherlock: The Abominable Bride (2016),Action|Crime|Drama|Mystery|Thriller\n152077,10 Cloverfield Lane (2016),Thriller\n152081,Zootopia (2016),Action|Adventure|Animation|Children|Comedy\n157296,Finding Dory (2016),Adventure|Animation|Comedy\n158238,The Nice Guys (2016),Crime|Mystery|Thriller\n159093,Now You See Me 2 (2016),Action|Comedy|Thriller\n164179,Arrival (2016),Sci-Fi\n166461,Moana (2016),Adventure|Animation|Children|Comedy|Fantasy\n166528,Rogue One: A Star Wars Story (2016),Action|Adventure|Fantasy|Sci-Fi\n166643,Hidden Figures (2016),Drama\n168250,Get Out (2017),Horror\n168252,Logan (2017),Action|Sci-Fi\n176371,Blade Runner 2049 (2017),Sci-Fi\n177765,Coco (2017),Adventure|Animation|Children\n179819,Star Wars: The Last Jedi (2017),Action|Adventure|Fantasy|Sci-Fi\n187593,Deadpool 2 (2018),Action|Comedy|Sci-Fi\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A2/data/content_user_train_header.txt",
    "content": "user id,rating count,rating ave,Action,Adventure,Animation,Children,Comedy,Crime,Documentary,Drama,Fantasy,Horror,Mystery,Romance,Sci-Fi,Thriller\n"
  },
  {
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n3.0\n3.5\n3.5\n3.5\n5.0\n5.0\n5.0\n4.0\n4.0\n4.0\n4.0\n3.0\n3.0\n3.0\n4.0\n4.0\n4.0\n4.0\n4.0\n4.0\n3.5\n3.5\n4.0\n4.5\n4.5\n4.5\n4.0\n4.0\n4.0\n4.0\n4.0\n5.0\n5.0\n5.0\n3.5\n5.0\n5.0\n5.0\n4.0\n4.0\n4.0\n4.0\n4.0\n4.0\n4.5\n4.5\n3.0\n3.0\n4.0\n4.0\n4.0\n4.0\n4.0\n4.0\n4.5\n4.5\n4.5\n5.0\n5.0\n5.0\n5.0\n5.0\n5.0\n4.0\n4.0\n4.5\n4.5\n4.5\n4.5\n3.5\n3.5\n3.5\n4.5\n4.5\n4.5\n4.5\n5.0\n5.0\n4.5\n4.5\n4.5\n4.0\n4.0\n4.0\n4.0\n4.0\n4.0\n3.5\n3.5\n3.5\n3.5\n3.5\n4.5\n4.5\n4.5\n4.5\n3.5\n3.5\n5.0\n5.0\n5.0\n5.0\n4.0\n4.0\n4.0\n3.5\n3.5\n3.5\n3.0\n3.0\n3.0\n3.0\n5.0\n5.0\n5.0\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n4.0\n4.0\n4.0\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n4.0\n4.0\n2.5\n2.5\n2.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n3.5\n4.0\n4.0\n4.0\n3.5\n3.5\n3.5\n4.5\n4.5\n4.5\n4.5\n4.5\n3.5\n4.0\n4.0\n4.0\n4.0\n4.0\n4.0\n4.0\n4.0\n4.0\n4.0\n5.0\n5.0\n5.0\n3.0\n3.0\n3.0\n5.0\n4.0\n4.0\n4.0\n4.0\n5.0\n5.0\n5.0\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A2/public_tests.py",
    "content": "from tensorflow.keras.activations import relu, linear\nfrom tensorflow.keras.layers import Dense\n\nimport numpy as np\n\ndef test_tower(target):\n    num_outputs = 32\n    i = 0\n    assert len(target.layers) == 3, f\"Wrong number of layers. Expected 3 but got {len(target.layers)}\"\n    expected = [[Dense, [None, 256], relu],\n                [Dense, [None, 128], relu],\n                [Dense, [None, num_outputs], linear]]\n\n    for layer in target.layers:\n        assert type(layer) == expected[i][0], \\\n            f\"Wrong type in layer {i}. Expected {expected[i][0]} but got {type(layer)}\"\n        assert layer.output.shape.as_list() == expected[i][1], \\\n            f\"Wrong number of units in layer {i}. Expected {expected[i][1]} but got {layer.output.shape.as_list()}\"\n        assert layer.activation == expected[i][2], \\\n            f\"Wrong activation in layer {i}. Expected {expected[i][2]} but got {layer.activation}\"\n        i = i + 1\n\n    print(\"\\033[92mAll tests passed!\")\n\n\ndef test_sq_dist(target):\n    a1 = np.array([1.0, 2.0, 3.0]); b1 = np.array([1.0, 2.0, 3.0])\n    c1 = target(a1, b1)\n    a2 = np.array([1.1, 2.1, 3.1]); b2 = np.array([1.0, 2.0, 3.0])\n    c2 = target(a2, b2)\n    a3 = np.array([0, 1]);          b3 = np.array([1, 0])\n    c3 = target(a3, b3)\n    a4 = np.array([1, 1, 1, 1, 1]); b4 = np.array([0, 0, 0, 0, 0])\n    c4 = target(a4, b4)\n    \n    assert np.isclose(c1, 0), f\"Wrong value. Expected {0}, got {c1}\"\n    assert np.isclose(c2, 0.03), f\"Wrong value. Expected {0.03}, got {c2}\" \n    assert np.isclose(c3, 2), f\"Wrong value. Expected {2}, got {c3}\" \n    assert np.isclose(c4, 5), f\"Wrong value. Expected {5}, got {c4}\" \n    \n    print('\\033[92mAll tests passed!')\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/C3W2/C3W2A2/recsysNN_utils.py",
    "content": "import pickle5 as pickle\nimport numpy as np\nfrom numpy import genfromtxt\nfrom collections import defaultdict\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.model_selection import train_test_split\nimport csv\nimport re\nimport tabulate\n\n\ndef load_data():\n    item_train = genfromtxt('./data/content_item_train.csv', delimiter=',')\n    user_train = genfromtxt('./data/content_user_train.csv', delimiter=',')\n    y_train    = genfromtxt('./data/content_y_train.csv', delimiter=',')\n    with open('./data/content_item_train_header.txt', newline='') as f:    #csv reader handles quoted strings better\n        item_features = list(csv.reader(f))[0]\n    with open('./data/content_user_train_header.txt', newline='') as f:\n        user_features = list(csv.reader(f))[0]\n    item_vecs = genfromtxt('./data/content_item_vecs.csv', delimiter=',')\n       \n    movie_dict = defaultdict(dict)\n    count = 0\n#    with open('./data/movies.csv', newline='') as csvfile:\n    with open('./data/content_movie_list.csv', newline='') as csvfile:\n        reader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n        for line in reader:\n            if count == 0: \n                count +=1  #skip header\n                #print(line) \n            else:\n                count +=1\n                movie_id = int(line[0])  \n                movie_dict[movie_id][\"title\"] = line[1]  \n                movie_dict[movie_id][\"genres\"] =line[2]  \n\n    with open('./data/content_user_to_genre.pickle', 'rb') as f:\n        user_to_genre = pickle.load(f)\n\n    return(item_train, user_train, y_train, item_features, user_features, item_vecs, movie_dict, user_to_genre)\n\n\ndef pprint_train(x_train, features,  vs, u_s, maxcount = 5, user=True):\n    \"\"\" Prints user_train or item_train nicely \"\"\"\n    if user:\n        flist = [\".0f\",\".0f\",\".1f\", \n                 \".1f\", \".1f\", \".1f\", \".1f\",\".1f\",\".1f\", \".1f\",\".1f\",\".1f\", \".1f\",\".1f\",\".1f\",\".1f\",\".1f\"]\n    else:\n        flist = [\".0f\",\".0f\",\".1f\", \n                 \".0f\",\".0f\",\".0f\", \".0f\",\".0f\",\".0f\", \".0f\",\".0f\",\".0f\", \".0f\",\".0f\",\".0f\",\".0f\",\".0f\"]\n\n    head = features[:vs]\n    if vs < u_s: print(\"error, vector start {vs} should be greater then user start {u_s}\")\n    for i in range(u_s):\n        head[i] = \"[\" + head[i] + \"]\"\n    genres = features[vs:]\n    hdr = head + genres\n    disp = [split_str(hdr, 5)]\n    count = 0\n    for i in range(0,x_train.shape[0]):\n        if count == maxcount: break\n        count += 1\n        disp.append( [ \n                      x_train[i,0].astype(int),  \n                      x_train[i,1].astype(int),   \n                      x_train[i,2].astype(float), \n                      *x_train[i,3:].astype(float)\n                    ])\n    table = tabulate.tabulate(disp, tablefmt='html',headers=\"firstrow\", floatfmt=flist, numalign='center')\n    return(table)\n\n\ndef pprint_data(y_p, user_train, item_train, printfull=False):\n    np.set_printoptions(precision=1)\n\n    for i in range(0,1000):\n        #print(f\"{y_p[i,0]: 0.2f}, {ynorm_train.numpy()[i].item(): 0.2f}\")\n        print(f\"{y_pu[i,0]: 0.2f}, {y_train[i]: 0.2f}, \", end='') \n        print(f\"{user_train[i,0].astype(int):d}, \",  end='')   # userid\n        print(f\"{user_train[i,1].astype(int):d}, \", end=''),  #  rating cnt\n        print(f\"{user_train[i,2].astype(float): 0.2f}, \",  end='')       # rating ave\n        print(\": \", end = '')\n        print(f\"{item_train[i,0].astype(int):d}, \",  end='')   # movie id\n        print(f\"{item_train[i,2].astype(float):0.1f}, \", end='')   # ave movie rating    \n        if printfull:\n          for j in range(8, user_train.shape[1]):\n            print(f\"{user_train[i,j].astype(float):0.1f}, \", end='')   # rating\n          print(\":\", end='')\n          for j in range(3, item_train.shape[1]):\n            print(f\"{item_train[i,j].astype(int):d}, \", end='')   # rating\n          print()\n        else:\n          a = user_train[i, uvs:user_train.shape[1]]\n          b = item_train[i, ivs:item_train.shape[1]]\n          c = np.multiply(a,b)\n          print(c)\n\ndef split_str(ifeatures, smax):\n    ofeatures = []\n    for s in ifeatures:\n        if ' ' not in s:  # skip string that already have a space            \n            if len(s) > smax:\n                mid = int(len(s)/2)\n                s = s[:mid] + \" \" + s[mid:]\n        ofeatures.append(s)\n    return(ofeatures)\n    \ndef pprint_data_tab(y_p, user_train, item_train, uvs, ivs, user_features, item_features, maxcount = 20, printfull=False):\n    flist = [\".1f\", \".1f\", \".0f\", \".1f\", \".0f\", \".0f\", \".0f\",\n             \".1f\",\".1f\",\".1f\",\".1f\",\".1f\",\".1f\",\".1f\",\".1f\",\".1f\",\".1f\",\".1f\",\".1f\",\".1f\",\".1f\"]\n    user_head = user_features[:uvs]\n    genres = user_features[uvs:]\n    item_head = item_features[:ivs]\n    hdr = [\"y_p\", \"y\"] + user_head + item_head + genres\n    disp = [split_str(hdr, 5)]\n    count = 0\n    for i in range(0,y_p.shape[0]):\n        if count == maxcount: break\n        count += 1\n        a = user_train[i, uvs:user_train.shape[1]]\n        b = item_train[i, ivs:item_train.shape[1]]\n        c = np.multiply(a,b)\n\n        disp.append( [ y_p[i,0], y_train[i], \n                      user_train[i,0].astype(int),   # user id\n                      user_train[i,1].astype(int),   # rating cnt\n                      user_train[i,2].astype(float), # user rating ave\n                      item_train[i,0].astype(int),   # movie id\n                      item_train[i,1].astype(int),   # year\n                      item_train[i,2].astype(float),  # ave movie rating \n                      *c\n                     ])\n    table = tabulate.tabulate(disp, tablefmt='html',headers=\"firstrow\", floatfmt=flist, numalign='center')\n    return(table)\n\n\n\n\ndef print_pred_movies(y_p, user, item, movie_dict, maxcount=10):\n    \"\"\" print results of prediction of a new user. inputs are expected to be in\n        sorted order, unscaled. \"\"\"\n    count = 0\n    movies_listed = defaultdict(int)\n    disp = [[\"y_p\", \"movie id\", \"rating ave\", \"title\", \"genres\"]]\n\n    for i in range(0, y_p.shape[0]):\n        if count == maxcount:\n            break\n        count += 1\n        movie_id = item[i, 0].astype(int)\n        if movie_id in movies_listed:\n            continue\n        movies_listed[movie_id] = 1\n        disp.append([y_p[i, 0], item[i, 0].astype(int), item[i, 2].astype(float),\n                    movie_dict[movie_id]['title'], movie_dict[movie_id]['genres']])\n\n    table = tabulate.tabulate(disp, tablefmt='html',headers=\"firstrow\")\n    return(table)\n\ndef gen_user_vecs(user_vec, num_items):\n    \"\"\" given a user vector return:\n        user predict maxtrix to match the size of item_vecs \"\"\"\n    user_vecs = np.tile(user_vec, (num_items, 1))\n    return(user_vecs)\n\n# predict on  everything, filter on print/use\ndef predict_uservec(user_vecs, item_vecs, model, u_s, i_s, scaler, ScalerUser, ScalerItem, scaledata=False):\n    \"\"\" given a user vector, does the prediction on all movies in item_vecs returns\n        an array predictions sorted by predicted rating,\n        arrays of user and item, sorted by predicted rating sorting index\n    \"\"\"\n    if scaledata:\n        scaled_user_vecs = ScalerUser.transform(user_vecs)\n        scaled_item_vecs = ScalerItem.transform(item_vecs)\n        y_p = model.predict([scaled_user_vecs[:, u_s:], scaled_item_vecs[:, i_s:]])\n    else:\n        y_p = model.predict([user_vecs[:, u_s:], item_vecs[:, i_s:]])\n    y_pu = scaler.inverse_transform(y_p)\n\n    if np.any(y_pu < 0) : \n        print(\"Error, expected all positive predictions\")\n    sorted_index = np.argsort(-y_pu,axis=0).reshape(-1).tolist()  #negate to get largest rating first\n    sorted_ypu   = y_pu[sorted_index]\n    sorted_items = item_vecs[sorted_index]\n    sorted_user  = user_vecs[sorted_index]\n    return(sorted_index, sorted_ypu, sorted_items, sorted_user)\n\n\ndef print_pred_debug(y_p, y, user, item, maxcount=10, onlyrating=False,  printfull=False):\n    \"\"\" hopefully reusable print. Keep for debug \"\"\"\n    count = 0\n    for i in range(0, y_p.shape[0]):\n        if onlyrating == False or (onlyrating == True and y[i,0] != 0):\n            if count == maxcount: break\n            count += 1\n            print(f\"{y_p[i, 0]: 0.2f}, {y[i,0]: 0.2f}, \", end='') \n            print(f\"{user[i, 0].astype(int):d}, \",  end='')       # userid\n            print(f\"{user[i, 1].astype(int):d}, \", end=''),       #  rating cnt\n            print(f\"{user[i, 2].astype(float):0.1f}, \", end=''),       #  rating ave\n            print(\": \", end = '')\n            print(f\"{item[i, 0].astype(int):d}, \",  end='')       # movie id\n            print(f\"{item[i, 2].astype(float):0.1f}, \", end='')   # ave movie rating    \n            print(\": \", end = '')\n            if printfull:\n                for j in range(uvs, user.shape[1]):\n                    print(f\"{user[i, j].astype(float):0.1f}, \", end='') # rating\n                print(\":\", end='')\n                for j in range(ivs, item.shape[1]):\n                    print(f\"{item[i, j].astype(int):d}, \", end='')    # rating\n                print()\n            else:\n                a = user[i, uvs:user.shape[1]]\n                b = item[i, ivs:item.shape[1]]\n                c = np.multiply(a,b)\n                print(c)    \n                \n                \ndef get_user_vecs(user_id, user_train, item_vecs, user_to_genre):\n    \"\"\" given a user_id, return:\n        user train/predict matrix to match the size of item_vecs\n        y vector with ratings for all rated movies and 0 for others of size item_vecs \"\"\"\n\n    if user_id not in user_to_genre:\n        print(\"error: unknown user id\")\n        return(None)\n    else:\n        user_vec_found = False\n        for i in range(len(user_train)):\n            if user_train[i, 0] == user_id:\n                user_vec = user_train[i]\n                user_vec_found = True\n                break\n        if not user_vec_found:\n            print(\"error in get_user_vecs, did not find uid in user_train\")\n        num_items = len(item_vecs)\n        user_vecs = np.tile(user_vec, (num_items, 1))\n\n        y = np.zeros(num_items)\n        for i in range(num_items):  # walk through movies in item_vecs and get the movies, see if user has rated them\n            movie_id = item_vecs[i, 0]\n            if movie_id in user_to_genre[user_id]['movies']:\n                rating = user_to_genre[user_id]['movies'][movie_id]\n            else:\n                rating = 0\n            y[i] = rating\n    return(user_vecs, y)\n\n\ndef get_item_genre(item, ivs, item_features):\n    offset = np.where(item[ivs:] == 1)[0][0]\n    genre = item_features[ivs + offset]\n    return(genre, offset)\n\n\ndef print_existing_user(y_p, y, user, items, item_features, ivs, uvs, movie_dict, maxcount=10):\n    \"\"\" print results of prediction a user who was in the datatbase. inputs are expected to be in sorted order, unscaled. \"\"\"\n    count = 0\n    movies_listed = defaultdict(int)\n    disp = [[\"y_p\", \"y\", \"user\", \"user genre ave\", \"movie rating ave\", \"title\", \"genres\"]]\n    listed = []\n    count = 0\n    for i in range(0, y.shape[0]):\n        if y[i, 0] != 0:\n            if count == maxcount:\n                break\n            count += 1\n            movie_id = items[i, 0].astype(int)\n\n            offset = np.where(items[i, ivs:] == 1)[0][0]\n            genre_rating = user[i, uvs + offset]\n            genre = item_features[ivs + offset]\n            disp.append([y_p[i, 0], y[i, 0],\n                        user[i, 0].astype(int),      # userid\n                        genre_rating.astype(float),\n                        items[i, 2].astype(float),    # movie average rating\n                        movie_dict[movie_id]['title'], genre])\n\n    table = tabulate.tabulate(disp, tablefmt='html', headers=\"firstrow\", floatfmt=[\".1f\", \".1f\", \".0f\", \".2f\", \".2f\"])\n    return(table)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/Practice Quiz - Collaborative Filtering/Readme.md",
    "content": "\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/64a19b1c162b79e9d4e70fa25eeb265a6d31137f/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Collaborative%20Filtering/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/64a19b1c162b79e9d4e70fa25eeb265a6d31137f/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Collaborative%20Filtering/ss2.png)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/Practice Quiz - Content-based filtering/Readme.md",
    "content": "\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/64a19b1c162b79e9d4e70fa25eeb265a6d31137f/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Content-based%20filtering/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/64a19b1c162b79e9d4e70fa25eeb265a6d31137f/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Content-based%20filtering/ss2.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/64a19b1c162b79e9d4e70fa25eeb265a6d31137f/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Content-based%20filtering/ss3.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/64a19b1c162b79e9d4e70fa25eeb265a6d31137f/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Content-based%20filtering/ss4.png)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/Practice Quiz - Recommender systems implementation/Readme.md",
    "content": "![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/64a19b1c162b79e9d4e70fa25eeb265a6d31137f/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Recommender%20systems%20implementation/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/64a19b1c162b79e9d4e70fa25eeb265a6d31137f/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Recommender%20systems%20implementation/ss2.png)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week2/Readme.md",
    "content": " ### C3 - Week 2 Solutions\n \n - [Practice Quiz : Collaborative Filtering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Collaborative%20Filtering)\n - [Practice Quiz : Recommender systems implementation](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Recommender%20systems%20implementation)\n - [Practice Quiz : Content-based filtering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/Practice%20Quiz%20:%20Content-based%20filtering)\n - [Programming Assignments](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/C3W2)\n     - [Collaborative Filtering RecSys](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/C3W2/C3W2A1/C3_W2_Collaborative_RecSys_Assignment.ipynb)\n     - [RecSys using Neural Networks](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/1d85288d0d29a33b780f7529f6e72837be7ad188/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week2/C3W2/C3W2A2/C3_W2_RecSysNN_Assignment.ipynb)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/C3W3A1/C3_W3_A1_Assignment.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Deep Q-Learning - Lunar Lander\\n\",\n    \"\\n\",\n    \"In this assignment, you will train an agent to land a lunar lander safely on a landing pad on the surface of the moon.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Outline\\n\",\n    \"- [ 1 - Import Packages <img align=\\\"Right\\\" src=\\\"./images/lunar_lander.gif\\\" width = 60% >](#1)\\n\",\n    \"- [ 2 - Hyperparameters](#2)\\n\",\n    \"- [ 3 - The Lunar Lander Environment](#3)\\n\",\n    \"  - [ 3.1 Action Space](#3.1)\\n\",\n    \"  - [ 3.2 Observation Space](#3.2)\\n\",\n    \"  - [ 3.3 Rewards](#3.3)\\n\",\n    \"  - [ 3.4 Episode Termination](#3.4)\\n\",\n    \"- [ 4 - Load the Environment](#4)\\n\",\n    \"- [ 5 - Interacting with the Gym Environment](#5)\\n\",\n    \"    - [ 5.1 Exploring the Environment's Dynamics](#5.1)\\n\",\n    \"- [ 6 - Deep Q-Learning](#6)\\n\",\n    \"  - [ 6.1 Target Network](#6.1)\\n\",\n    \"    - [ Exercise 1](#ex01)\\n\",\n    \"  - [ 6.2 Experience Replay](#6.2)\\n\",\n    \"- [ 7 - Deep Q-Learning Algorithm with Experience Replay](#7)\\n\",\n    \"  - [ Exercise 2](#ex02)\\n\",\n    \"- [ 8 - Update the Network Weights](#8)\\n\",\n    \"- [ 9 - Train the Agent](#9)\\n\",\n    \"- [ 10 - See the Trained Agent In Action](#10)\\n\",\n    \"- [ 11 - Congratulations!](#11)\\n\",\n    \"- [ 12 - References](#12)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"1\\\"></a>\\n\",\n    \"## 1 - Import Packages\\n\",\n    \"\\n\",\n    \"We'll make use of the following packages:\\n\",\n    \"- `numpy` is a package for scientific computing in python.\\n\",\n    \"- `deque` will be our data structure for our memory buffer.\\n\",\n    \"- `namedtuple` will be used to store the experience tuples.\\n\",\n    \"- The `gym` toolkit is a collection of environments that can be used to test reinforcement learning algorithms. We should note that in this notebook we are using `gym` version `0.24.0`.\\n\",\n    \"- `PIL.Image` and `pyvirtualdisplay` are needed to render the Lunar Lander environment.\\n\",\n    \"- We will use several modules from the `tensorflow.keras` framework for building deep learning models.\\n\",\n    \"- `utils` is a module that contains helper functions for this assignment. You do not need to modify the code in this file.\\n\",\n    \"\\n\",\n    \"Run the cell below to import all the necessary packages.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"id\": \"KYbOPKRtfQOr\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"from collections import deque, namedtuple\\n\",\n    \"\\n\",\n    \"import gym\\n\",\n    \"import numpy as np\\n\",\n    \"import PIL.Image\\n\",\n    \"import tensorflow as tf\\n\",\n    \"import utils\\n\",\n    \"\\n\",\n    \"from pyvirtualdisplay import Display\\n\",\n    \"from tensorflow.keras import Sequential\\n\",\n    \"from tensorflow.keras.layers import Dense, Input\\n\",\n    \"from tensorflow.keras.losses import MSE\\n\",\n    \"from tensorflow.keras.optimizers import Adam\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Set up a virtual display to render the Lunar Lander environment.\\n\",\n    \"Display(visible=0, size=(840, 480)).start();\\n\",\n    \"\\n\",\n    \"# Set the random seed for TensorFlow\\n\",\n    \"tf.random.set_seed(utils.SEED)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"2\\\"></a>\\n\",\n    \"## 2 - Hyperparameters\\n\",\n    \"\\n\",\n    \"Run the cell below to set the hyperparameters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"MEMORY_SIZE = 100_000     # size of memory buffer\\n\",\n    \"GAMMA = 0.995             # discount factor\\n\",\n    \"ALPHA = 1e-3              # learning rate  \\n\",\n    \"NUM_STEPS_FOR_UPDATE = 4  # perform a learning update every C time steps\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"3\\\"></a>\\n\",\n    \"## 3 - The Lunar Lander Environment\\n\",\n    \"\\n\",\n    \"In this notebook we will be using [OpenAI's Gym Library](https://www.gymlibrary.ml/). The Gym library provides a wide variety of environments for reinforcement learning. To put it simply, an environment represents a problem or task to be solved. In this notebook, we will try to solve the Lunar Lander environment using reinforcement learning.\\n\",\n    \"\\n\",\n    \"The goal of the Lunar Lander environment is to land the lunar lander safely on the landing pad on the surface of the moon. The landing pad is designated by two flag poles and it is always at coordinates `(0,0)` but the lander is also allowed to land outside of the landing pad. The lander starts at the top center of the environment with a random initial force applied to its center of mass and has infinite fuel. The environment is considered solved if you get `200` points. \\n\",\n    \"\\n\",\n    \"<br>\\n\",\n    \"<br>\\n\",\n    \"<figure>\\n\",\n    \"  <img src = \\\"images/lunar_lander.gif\\\" width = 40%>\\n\",\n    \"      <figcaption style = \\\"text-align: center; font-style: italic\\\">Fig 1. Lunar Lander Environment.</figcaption>\\n\",\n    \"</figure>\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.1\\\"></a>\\n\",\n    \"### 3.1 Action Space\\n\",\n    \"\\n\",\n    \"The agent has four discrete actions available:\\n\",\n    \"\\n\",\n    \"* Do nothing.\\n\",\n    \"* Fire right engine.\\n\",\n    \"* Fire main engine.\\n\",\n    \"* Fire left engine.\\n\",\n    \"\\n\",\n    \"Each action has a corresponding numerical value:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"Do nothing = 0\\n\",\n    \"Fire right engine = 1\\n\",\n    \"Fire main engine = 2\\n\",\n    \"Fire left engine = 3\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.2\\\"></a>\\n\",\n    \"### 3.2 Observation Space\\n\",\n    \"\\n\",\n    \"The agent's observation space consists of a state vector with 8 variables:\\n\",\n    \"\\n\",\n    \"* Its $(x,y)$ coordinates. The landing pad is always at coordinates $(0,0)$.\\n\",\n    \"* Its linear velocities $(\\\\dot x,\\\\dot y)$.\\n\",\n    \"* Its angle $\\\\theta$.\\n\",\n    \"* Its angular velocity $\\\\dot \\\\theta$.\\n\",\n    \"* Two booleans, $l$ and $r$, that represent whether each leg is in contact with the ground or not.\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.3\\\"></a>\\n\",\n    \"### 3.3 Rewards\\n\",\n    \"\\n\",\n    \"The Lunar Lander environment has the following reward system:\\n\",\n    \"\\n\",\n    \"* Landing on the landing pad and coming to rest is about 100-140 points.\\n\",\n    \"* If the lander moves away from the landing pad, it loses reward. \\n\",\n    \"* If the lander crashes, it receives -100 points.\\n\",\n    \"* If the lander comes to rest, it receives +100 points.\\n\",\n    \"* Each leg with ground contact is +10 points.\\n\",\n    \"* Firing the main engine is -0.3 points each frame.\\n\",\n    \"* Firing the side engine is -0.03 points each frame.\\n\",\n    \"\\n\",\n    \"<a name=\\\"3.4\\\"></a>\\n\",\n    \"### 3.4 Episode Termination\\n\",\n    \"\\n\",\n    \"An episode ends (i.e the environment enters a terminal state) if:\\n\",\n    \"\\n\",\n    \"* The lunar lander crashes (i.e if the body of the lunar lander comes in contact with the surface of the moon).\\n\",\n    \"\\n\",\n    \"* The lander's $x$-coordinate is greater than 1.\\n\",\n    \"\\n\",\n    \"You can check out the [Open AI Gym documentation](https://www.gymlibrary.ml/environments/box2d/lunar_lander/) for a full description of the environment. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"4\\\"></a>\\n\",\n    \"## 4 - Load the Environment\\n\",\n    \"\\n\",\n    \"We start by loading the `LunarLander-v2` environment from the `gym` library by using the `.make()` method. `LunarLander-v2` is the latest version of the Lunar Lander environment and you can read about its version history in the [Open AI Gym documentation](https://www.gymlibrary.ml/environments/box2d/lunar_lander/#version-history).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"id\": \"ILVMYKewfR0n\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"env = gym.make('LunarLander-v2')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once we load the environment we use the `.reset()` method to reset the environment to the initial state. The lander starts at the top center of the environment and we can render the first frame of the environment by using the `.render()` method.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<PIL.Image.Image image mode=RGB size=600x400 at 0x7F0D44CD7FD0>\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"env.reset()\\n\",\n    \"PIL.Image.fromarray(env.render(mode='rgb_array'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In order to build our neural network later on we need to know the size of the state vector and the number of valid actions. We can get this information from our environment by using the `.observation_space.shape` and `action_space.n` methods, respectively.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"id\": \"x3fdqdG4CUu2\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"State Shape: (8,)\\n\",\n      \"Number of actions: 4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"state_size = env.observation_space.shape\\n\",\n    \"num_actions = env.action_space.n\\n\",\n    \"\\n\",\n    \"print('State Shape:', state_size)\\n\",\n    \"print('Number of actions:', num_actions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"5\\\"></a>\\n\",\n    \"## 5 - Interacting with the Gym Environment\\n\",\n    \"\\n\",\n    \"The Gym library implements the standard “agent-environment loop” formalism:\\n\",\n    \"\\n\",\n    \"<br>\\n\",\n    \"<center>\\n\",\n    \"<video src = \\\"./videos/rl_formalism.m4v\\\" width=\\\"840\\\" height=\\\"480\\\" controls autoplay loop poster=\\\"./images/rl_formalism.png\\\"> </video>\\n\",\n    \"<figcaption style = \\\"text-align:center; font-style:italic\\\">Fig 2. Agent-environment Loop Formalism.</figcaption>\\n\",\n    \"</center>\\n\",\n    \"<br>\\n\",\n    \"\\n\",\n    \"In the standard “agent-environment loop” formalism, an agent interacts with the environment in discrete time steps $t=0,1,2,...$. At each time step $t$, the agent uses a policy $\\\\pi$ to select an action $A_t$ based on its observation of the environment's state $S_t$. The agent receives a numerical reward $R_t$ and on the next time step, moves to a new state $S_{t+1}$.\\n\",\n    \"\\n\",\n    \"<a name=\\\"5.1\\\"></a>\\n\",\n    \"### 5.1 Exploring the Environment's Dynamics\\n\",\n    \"\\n\",\n    \"In Open AI's Gym environments, we use the `.step()` method to run a single time step of the environment's dynamics. In the version of `gym` that we are using the `.step()` method accepts an action and returns four values:\\n\",\n    \"\\n\",\n    \"* `observation` (**object**): an environment-specific object representing your observation of the environment. In the Lunar Lander environment this corresponds to a numpy array containing the positions and velocities of the lander as described in section [3.2 Observation Space](#3.2).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* `reward` (**float**): amount of reward returned as a result of taking the given action. In the Lunar Lander environment this corresponds to a float of type `numpy.float64` as described in section [3.3 Rewards](#3.3).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* `done` (**boolean**): When done is `True`, it indicates the episode has terminated and it’s time to reset the environment. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* `info` (**dictionary**): diagnostic information useful for debugging. We won't be using this variable in this notebook but it is shown here for completeness.\\n\",\n    \"\\n\",\n    \"To begin an episode, we need to reset the environment to an initial state. We do this by using the `.reset()` method. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Reset the environment and get the initial state.\\n\",\n    \"initial_state = env.reset()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once the environment is reset, the agent can start taking actions in the environment by using the `.step()` method. Note that the agent can only take one action per time step. \\n\",\n    \"\\n\",\n    \"In the cell below you can select different actions and see how the returned values change depending on the action taken. Remember that in this environment the agent has four discrete actions available and we specify them in code by using their corresponding numerical value:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"Do nothing = 0\\n\",\n    \"Fire right engine = 1\\n\",\n    \"Fire main engine = 2\\n\",\n    \"Fire left engine = 3\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Initial State: [0.002 1.422 0.194 0.506 -0.002 -0.044 0.000 0.000]\\n\",\n      \"Action: 0\\n\",\n      \"Next State: [0.004 1.433 0.194 0.480 -0.004 -0.044 0.000 0.000]\\n\",\n      \"Reward Received: 1.1043263227541047\\n\",\n      \"Episode Terminated: False\\n\",\n      \"Info: {}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Select an action\\n\",\n    \"action = 0\\n\",\n    \"\\n\",\n    \"# Run a single time step of the environment's dynamics with the given action.\\n\",\n    \"next_state, reward, done, info = env.step(action)\\n\",\n    \"\\n\",\n    \"with np.printoptions(formatter={'float': '{:.3f}'.format}):\\n\",\n    \"    print(\\\"Initial State:\\\", initial_state)\\n\",\n    \"    print(\\\"Action:\\\", action)\\n\",\n    \"    print(\\\"Next State:\\\", next_state)\\n\",\n    \"    print(\\\"Reward Received:\\\", reward)\\n\",\n    \"    print(\\\"Episode Terminated:\\\", done)\\n\",\n    \"    print(\\\"Info:\\\", info)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In practice, when we train the agent we use a loop to allow the agent to take many consecutive actions during an episode.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"6\\\"></a>\\n\",\n    \"## 6 - Deep Q-Learning\\n\",\n    \"\\n\",\n    \"In cases where both the state and action space are discrete we can estimate the action-value function iteratively by using the Bellman equation:\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"Q_{i+1}(s,a) = R + \\\\gamma \\\\max_{a'}Q_i(s',a')\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"This iterative method converges to the optimal action-value function $Q^*(s,a)$ as $i\\\\to\\\\infty$. This means that the agent just needs to gradually explore the state-action space and keep updating the estimate of $Q(s,a)$ until it converges to the optimal action-value function $Q^*(s,a)$. However, in cases where the state space is continuous it becomes practically impossible to explore the entire state-action space. Consequently, this also makes it practically impossible to gradually estimate $Q(s,a)$ until it converges to $Q^*(s,a)$.\\n\",\n    \"\\n\",\n    \"In the Deep $Q$-Learning, we solve this problem by using a neural network to estimate the action-value function $Q(s,a)\\\\approx Q^*(s,a)$. We call this neural network a $Q$-Network and it can be trained by adjusting its weights at each iteration to minimize the mean-squared error in the Bellman equation.\\n\",\n    \"\\n\",\n    \"Unfortunately, using neural networks in reinforcement learning to estimate action-value functions has proven to be highly unstable. Luckily, there's a couple of techniques that can be employed to avoid instabilities. These techniques consist of using a ***Target Network*** and ***Experience Replay***. We will explore these two techniques in the following sections.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"6.1\\\"></a>\\n\",\n    \"### 6.1 Target Network\\n\",\n    \"\\n\",\n    \"We can train the $Q$-Network by adjusting it's weights at each iteration to minimize the mean-squared error in the Bellman equation, where the target values are given by:\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"y = R + \\\\gamma \\\\max_{a'}Q(s',a';w)\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"where $w$ are the weights of the $Q$-Network. This means that we are adjusting the weights $w$ at each iteration to minimize the following error:\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\overbrace{\\\\underbrace{R + \\\\gamma \\\\max_{a'}Q(s',a'; w)}_{\\\\rm {y~target}} - Q(s,a;w)}^{\\\\rm {Error}}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"Notice that this forms a problem because the $y$ target is changing on every iteration. Having a constantly moving target can lead to oscillations and instabilities. To avoid this, we can create\\n\",\n    \"a separate neural network for generating the $y$ targets. We call this separate neural network the **target $\\\\hat Q$-Network** and it will have the same architecture as the original $Q$-Network. By using the target $\\\\hat Q$-Network, the above error becomes:\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\overbrace{\\\\underbrace{R + \\\\gamma \\\\max_{a'}\\\\hat{Q}(s',a'; w^-)}_{\\\\rm {y~target}} - Q(s,a;w)}^{\\\\rm {Error}}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"where $w^-$ and $w$ are the weights the target $\\\\hat Q$-Network and $Q$-Network, respectively.\\n\",\n    \"\\n\",\n    \"In practice, we will use the following algorithm: every $C$ time steps we will use the $\\\\hat Q$-Network to generate the $y$ targets and update the weights of the target $\\\\hat Q$-Network using the weights of the $Q$-Network. We will update the weights $w^-$ of the the target $\\\\hat Q$-Network using a **soft update**. This means that we will update the weights $w^-$ using the following rule:\\n\",\n    \" \\n\",\n    \"$$\\n\",\n    \"w^-\\\\leftarrow \\\\tau w + (1 - \\\\tau) w^-\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"where $\\\\tau\\\\ll 1$. By using the soft update, we are ensuring that the target values, $y$, change slowly, which greatly improves the stability of our learning algorithm.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex01\\\"></a>\\n\",\n    \"### Exercise 1\\n\",\n    \"\\n\",\n    \"In this exercise you will create the $Q$ and target $\\\\hat Q$ networks and set the optimizer. Remember that the Deep $Q$-Network (DQN) is a neural network that approximates the action-value function $Q(s,a)\\\\approx Q^*(s,a)$. It does this by learning how to map states to $Q$ values.\\n\",\n    \"\\n\",\n    \"To solve the Lunar Lander environment, we are going to employ a DQN with the following architecture:\\n\",\n    \"\\n\",\n    \"* An `Input` layer that takes `state_size` as input.\\n\",\n    \"\\n\",\n    \"* A `Dense` layer with `64` units and a `relu` activation function.\\n\",\n    \"\\n\",\n    \"* A `Dense` layer with `64` units and a `relu` activation function.\\n\",\n    \"\\n\",\n    \"* A `Dense` layer with `num_actions` units and a `linear` activation function. This will be the output layer of our network.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"In the cell below you should create the $Q$-Network and the target $\\\\hat Q$-Network using the model architecture described above. Remember that both the $Q$-Network and the target $\\\\hat Q$-Network have the same architecture.\\n\",\n    \"\\n\",\n    \"Lastly, you should set `Adam` as the optimizer with a learning rate equal to `ALPHA`. Recall that `ALPHA` was defined in the [Hyperparameters](#2) section. We should note that for this exercise you should use the already imported packages:\\n\",\n    \"```python\\n\",\n    \"from tensorflow.keras.layers import Dense, Input\\n\",\n    \"from tensorflow.keras.optimizers import Adam\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C1\\n\",\n    \"# GRADED CELL\\n\",\n    \"\\n\",\n    \"# Create the Q-Network\\n\",\n    \"q_network = Sequential([\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    Input(shape=state_size),                      \\n\",\n    \"    Dense(units=64, activation='relu'),            \\n\",\n    \"    Dense(units=64, activation='relu'),            \\n\",\n    \"    Dense(units=num_actions, activation='linear'),\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"# Create the target Q^-Network\\n\",\n    \"target_q_network = Sequential([\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    Input(shape=state_size),                      \\n\",\n    \"    Dense(units=64, activation='relu'),            \\n\",\n    \"    Dense(units=64, activation='relu'),            \\n\",\n    \"    Dense(units=num_actions, activation='linear'),\\n\",\n    \"    ### END CODE HERE ###\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"### START CODE HERE ### \\n\",\n    \"optimizer = Adam(learning_rate=ALPHA)\\n\",\n    \"### END CODE HERE ###\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\",\n      \"\\u001b[92mAll tests passed!\\n\",\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# UNIT TEST\\n\",\n    \"from public_tests import *\\n\",\n    \"\\n\",\n    \"test_network(q_network)\\n\",\n    \"test_network(target_q_network)\\n\",\n    \"test_optimizer(optimizer, ALPHA) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"# Create the Q-Network\\n\",\n    \"q_network = Sequential([\\n\",\n    \"    Input(shape=state_size),                      \\n\",\n    \"    Dense(units=64, activation='relu'),            \\n\",\n    \"    Dense(units=64, activation='relu'),            \\n\",\n    \"    Dense(units=num_actions, activation='linear'),\\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"# Create the target Q^-Network\\n\",\n    \"target_q_network = Sequential([\\n\",\n    \"    Input(shape=state_size),                       \\n\",\n    \"    Dense(units=64, activation='relu'),            \\n\",\n    \"    Dense(units=64, activation='relu'),            \\n\",\n    \"    Dense(units=num_actions, activation='linear'), \\n\",\n    \"    ])\\n\",\n    \"\\n\",\n    \"optimizer = Adam(learning_rate=ALPHA)                                  \\n\",\n    \"``` \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"6.2\\\"></a>\\n\",\n    \"### 6.2 Experience Replay\\n\",\n    \"\\n\",\n    \"When an agent interacts with the environment, the states, actions, and rewards the agent experiences are sequential by nature. If the agent tries to learn from these consecutive experiences it can run into problems due to the strong correlations between them. To avoid this, we employ a technique known as **Experience Replay** to generate uncorrelated experiences for training our agent. Experience replay consists of storing the agent's experiences (i.e the states, actions, and rewards the agent receives) in a memory buffer and then sampling a random mini-batch of experiences from the buffer to do the learning. The experience tuples $(S_t, A_t, R_t, S_{t+1})$ will be added to the memory buffer at each time step as the agent interacts with the environment.\\n\",\n    \"\\n\",\n    \"For convenience, we will store the experiences as named tuples.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Store experiences as named tuples\\n\",\n    \"experience = namedtuple(\\\"Experience\\\", field_names=[\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By using experience replay we avoid problematic correlations, oscillations and instabilities. In addition, experience replay also allows the agent to potentially use the same experience in multiple weight updates, which increases data efficiency.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"7\\\"></a>\\n\",\n    \"## 7 - Deep Q-Learning Algorithm with Experience Replay\\n\",\n    \"\\n\",\n    \"Now that we know all the techniques that we are going to use, we can put them togther to arrive at the Deep Q-Learning Algorithm With Experience Replay.\\n\",\n    \"<br>\\n\",\n    \"<br>\\n\",\n    \"<figure>\\n\",\n    \"  <img src = \\\"images/deep_q_algorithm.png\\\" width = 90% style = \\\"border: thin silver solid; padding: 0px\\\">\\n\",\n    \"      <figcaption style = \\\"text-align: center; font-style: italic\\\">Fig 3. Deep Q-Learning with Experience Replay.</figcaption>\\n\",\n    \"</figure>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"ex02\\\"></a>\\n\",\n    \"### Exercise 2\\n\",\n    \"\\n\",\n    \"In this exercise you will implement line ***12*** of the algorithm outlined in *Fig 3* above and you will also compute the loss between the $y$ targets and the $Q(s,a)$ values. In the cell below, complete the `compute_loss` function by setting the $y$ targets equal to:\\n\",\n    \"\\n\",\n    \"$$\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"    y_j =\\n\",\n    \"    \\\\begin{cases}\\n\",\n    \"      R_j & \\\\text{if episode terminates at step  } j+1\\\\\\\\\\n\",\n    \"      R_j + \\\\gamma \\\\max_{a'}\\\\hat{Q}(s_{j+1},a') & \\\\text{otherwise}\\\\\\\\\\n\",\n    \"    \\\\end{cases}       \\n\",\n    \"\\\\end{equation}\\n\",\n    \"$$\\n\",\n    \"\\n\",\n    \"Here are a couple of things to note:\\n\",\n    \"\\n\",\n    \"* The `compute_loss` function takes in a mini-batch of experience tuples. This mini-batch of experience tuples is unpacked to extract the `states`, `actions`, `rewards`, `next_states`, and `done_vals`. You should keep in mind that these variables are *TensorFlow Tensors* whose size will depend on the mini-batch size. For example, if the mini-batch size is `64` then both `rewards` and `done_vals` will be TensorFlow Tensors with `64` elements.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* Using `if/else` statements to set the $y$ targets will not work when the variables are tensors with many elements. However, notice that you can use the `done_vals` to implement the above in a single line of code. To do this, recall that the `done` variable is a Boolean variable that takes the value `True` when an episode terminates at step $j+1$ and it is `False` otherwise. Taking into account that a Boolean value of `True` has the numerical value of `1` and a Boolean value of `False` has the numerical value of `0`, you can use the factor `(1 - done_vals)` to implement the above in a single line of code. Here's a hint: notice that `(1 - done_vals)` has a value of `0` when `done_vals` is `True` and a value of `1` when `done_vals` is `False`. \\n\",\n    \"\\n\",\n    \"Lastly, compute the loss by calculating the Mean-Squared Error (`MSE`) between the `y_targets` and the `q_values`. To calculate the mean-squared error you should use the already imported package `MSE`:\\n\",\n    \"```python\\n\",\n    \"from tensorflow.keras.losses import MSE\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNQ_C2\\n\",\n    \"# GRADED FUNCTION: calculate_loss\\n\",\n    \"\\n\",\n    \"def compute_loss(experiences, gamma, q_network, target_q_network):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculates the loss.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      experiences: (tuple) tuple of [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"] namedtuples\\n\",\n    \"      gamma: (float) The discount factor.\\n\",\n    \"      q_network: (tf.keras.Sequential) Keras model for predicting the q_values\\n\",\n    \"      target_q_network: (tf.keras.Sequential) Karas model for predicting the targets\\n\",\n    \"          \\n\",\n    \"    Returns:\\n\",\n    \"      loss: (TensorFlow Tensor(shape=(0,), dtype=int32)) the Mean-Squared Error between\\n\",\n    \"            the y targets and the Q(s,a) values.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Unpack the mini-batch of experience tuples\\n\",\n    \"    states, actions, rewards, next_states, done_vals = experiences\\n\",\n    \"    \\n\",\n    \"    # Compute max Q^(s,a)\\n\",\n    \"    max_qsa = tf.reduce_max(target_q_network(next_states), axis=-1)\\n\",\n    \"    \\n\",\n    \"    # Set y = R if episode terminates, otherwise set y = R + γ max Q^(s,a).\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    y_targets = rewards + (gamma * max_qsa * (1 - done_vals))\\n\",\n    \"    ### END CODE HERE ###\\n\",\n    \"    \\n\",\n    \"    # Get the q_values\\n\",\n    \"    q_values = q_network(states)\\n\",\n    \"    q_values = tf.gather_nd(q_values, tf.stack([tf.range(q_values.shape[0]),\\n\",\n    \"                                                tf.cast(actions, tf.int32)], axis=1))\\n\",\n    \"        \\n\",\n    \"    # Compute the loss\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \"    loss = MSE(y_targets, q_values)\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\u001b[92mAll tests passed!\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# UNIT TEST    \\n\",\n    \"test_compute_loss(compute_loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<details>\\n\",\n    \"  <summary><font size=\\\"3\\\" color=\\\"darkgreen\\\"><b>Click for hints</b></font></summary>\\n\",\n    \"    \\n\",\n    \"```python\\n\",\n    \"def compute_loss(experiences, gamma, q_network, target_q_network):\\n\",\n    \"    \\\"\\\"\\\" \\n\",\n    \"    Calculates the loss.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      experiences: (tuple) tuple of [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"] namedtuples\\n\",\n    \"      gamma: (float) The discount factor.\\n\",\n    \"      q_network: (tf.keras.Sequential) Keras model for predicting the q_values\\n\",\n    \"      target_q_network: (tf.keras.Sequential) Karas model for predicting the targets\\n\",\n    \"          \\n\",\n    \"    Returns:\\n\",\n    \"      loss: (TensorFlow Tensor(shape=(0,), dtype=int32)) the Mean-Squared Error between\\n\",\n    \"            the y targets and the Q(s,a) values.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"    # Unpack the mini-batch of experience tuples\\n\",\n    \"    states, actions, rewards, next_states, done_vals = experiences\\n\",\n    \"    \\n\",\n    \"    # Compute max Q^(s,a)\\n\",\n    \"    max_qsa = tf.reduce_max(target_q_network(next_states), axis=-1)\\n\",\n    \"    \\n\",\n    \"    # Set y = R if episode terminates, otherwise set y = R + γ max Q^(s,a).\\n\",\n    \"    y_targets = rewards + (gamma * max_qsa * (1 - done_vals))\\n\",\n    \"    \\n\",\n    \"    # Get the q_values\\n\",\n    \"    q_values = q_network(states)\\n\",\n    \"    q_values = tf.gather_nd(q_values, tf.stack([tf.range(q_values.shape[0]),\\n\",\n    \"                                                tf.cast(actions, tf.int32)], axis=1))\\n\",\n    \"    \\n\",\n    \"    # Calculate the loss\\n\",\n    \"    loss = MSE(y_targets, q_values)\\n\",\n    \"    \\n\",\n    \"    return loss\\n\",\n    \"\\n\",\n    \"``` \\n\",\n    \"    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"8\\\"></a>\\n\",\n    \"## 8 - Update the Network Weights\\n\",\n    \"\\n\",\n    \"We will use the `agent_learn` function below to implement lines ***12 -14*** of the algorithm outlined in [Fig 3](#7). The `agent_learn` function will update the weights of the $Q$ and target $\\\\hat Q$ networks using a custom training loop. Because we are using a custom training loop we need to retrieve the gradients via a `tf.GradientTape` instance, and then call `optimizer.apply_gradients()` to update the weights of our $Q$-Network. Note that we are also using the `@tf.function` decorator to increase performance. Without this decorator our training will take twice as long. If you would like to know more about how to increase performance with `@tf.function` take a look at the [TensorFlow documentation](https://www.tensorflow.org/guide/function).\\n\",\n    \"\\n\",\n    \"The last line of this function updates the weights of the target $\\\\hat Q$-Network using a [soft update](#6.1). If you want to know how this is implemented in code we encourage you to take a look at the `utils.update_target_network` function in the `utils` module.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"@tf.function\\n\",\n    \"def agent_learn(experiences, gamma):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Updates the weights of the Q networks.\\n\",\n    \"    \\n\",\n    \"    Args:\\n\",\n    \"      experiences: (tuple) tuple of [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"] namedtuples\\n\",\n    \"      gamma: (float) The discount factor.\\n\",\n    \"    \\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # Calculate the loss\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        loss = compute_loss(experiences, gamma, q_network, target_q_network)\\n\",\n    \"\\n\",\n    \"    # Get the gradients of the loss with respect to the weights.\\n\",\n    \"    gradients = tape.gradient(loss, q_network.trainable_variables)\\n\",\n    \"    \\n\",\n    \"    # Update the weights of the q_network.\\n\",\n    \"    optimizer.apply_gradients(zip(gradients, q_network.trainable_variables))\\n\",\n    \"\\n\",\n    \"    # update the weights of target q_network\\n\",\n    \"    utils.update_target_network(q_network, target_q_network)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"9\\\"></a>\\n\",\n    \"## 9 - Train the Agent\\n\",\n    \"\\n\",\n    \"We are now ready to train our agent to solve the Lunar Lander environment. In the cell below we will implement the algorithm in [Fig 3](#7) line by line (please note that we have included the same algorithm below for easy reference. This will prevent you from scrolling up and down the notebook):\\n\",\n    \"\\n\",\n    \"* **Line 1**: We initialize the `memory_buffer` with a capacity of $N =$ `MEMORY_SIZE`. Notice that we are using a `deque` as the data structure for our `memory_buffer`.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 2**: We skip this line since we already initialized the `q_network` in [Exercise 1](#ex01).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 3**: We initialize the `target_q_network` by setting its weights to be equal to those of the `q_network`.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 4**: We start the outer loop. Notice that we have set $M =$ `num_episodes = 2000`. This number is reasonable because the agent should be able to solve the Lunar Lander environment in less than `2000` episodes using this notebook's default parameters.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 5**: We use the `.reset()` method to reset the environment to the initial state and get the initial state.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 6**: We start the inner loop. Notice that we have set $T =$ `max_num_timesteps = 1000`. This means that the episode will automatically terminate if the episode hasn't terminated after `1000` time steps.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 7**: The agent observes the current `state` and chooses an `action` using an $\\\\epsilon$-greedy policy. Our agent starts out using a value of $\\\\epsilon =$ `epsilon = 1` which yields an $\\\\epsilon$-greedy policy that is equivalent to the equiprobable random policy. This means that at the beginning of our training, the agent is just going to take random actions regardless of the observed `state`. As training progresses we will decrease the value of $\\\\epsilon$ slowly towards a minimum value using a given $\\\\epsilon$-decay rate. We want this minimum value to be close to zero because a value of $\\\\epsilon = 0$ will yield an $\\\\epsilon$-greedy policy that is equivalent to the greedy policy. This means that towards the end of training, the agent will lean towards selecting the `action` that it believes (based on its past experiences) will maximize $Q(s,a)$. We will set the minimum $\\\\epsilon$ value to be `0.01` and not exactly 0 because we always want to keep a little bit of exploration during training. If you want to know how this is implemented in code we encourage you to take a look at the `utils.get_action` function in the `utils` module.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 8**: We use the `.step()` method to take the given `action` in the environment and get the `reward` and the `next_state`. \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 9**: We store the `experience(state, action, reward, next_state, done)` tuple in our `memory_buffer`. Notice that we also store the `done` variable so that we can keep track of when an episode terminates. This allowed us to set the $y$ targets in [Exercise 2](#ex02).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 10**: We check if the conditions are met to perform a learning update. We do this by using our custom `utils.check_update_conditions` function. This function checks if $C =$ `NUM_STEPS_FOR_UPDATE = 4` time steps have occured and if our `memory_buffer` has enough experience tuples to fill a mini-batch. For example, if the mini-batch size is `64`, then our `memory_buffer` should have at least `64` experience tuples in order to pass the latter condition. If the conditions are met, then the `utils.check_update_conditions` function will return a value of `True`, otherwise it will return a value of `False`.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Lines 11 - 14**: If the `update` variable is `True` then we perform a learning update. The learning update consists of sampling a random mini-batch of experience tuples from our `memory_buffer`, setting the $y$ targets, performing gradient descent, and updating the weights of the networks. We will use the `agent_learn` function we defined in [Section 8](#8) to perform the latter 3.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 15**: At the end of each iteration of the inner loop we set `next_state` as our new `state` so that the loop can start again from this new state. In addition, we check if the episode has reached a terminal state (i.e we check if `done = True`). If a terminal state has been reached, then we break out of the inner loop.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* **Line 16**: At the end of each iteration of the outer loop we update the value of $\\\\epsilon$, and check if the environment has been solved. We consider that the environment has been solved if the agent receives an average of `200` points in the last `100` episodes. If the environment has not been solved we continue the outer loop and start a new episode.\\n\",\n    \"\\n\",\n    \"Finally, we wanted to note that we have included some extra variables to keep track of the total number of points the agent received in each episode. This will help us determine if the agent has solved the environment and it will also allow us to see how our agent performed during training. We also use the `time` module to measure how long the training takes. \\n\",\n    \"\\n\",\n    \"<br>\\n\",\n    \"<br>\\n\",\n    \"<figure>\\n\",\n    \"  <img src = \\\"images/deep_q_algorithm.png\\\" width = 90% style = \\\"border: thin silver solid; padding: 0px\\\">\\n\",\n    \"      <figcaption style = \\\"text-align: center; font-style: italic\\\">Fig 4. Deep Q-Learning with Experience Replay.</figcaption>\\n\",\n    \"</figure>\\n\",\n    \"<br>\\n\",\n    \"\\n\",\n    \"**Note:** With this notebook's default parameters, the following cell takes between 10 to 15 minutes to run. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Episode 100 | Total point average of the last 100 episodes: -150.85\\n\",\n      \"Episode 200 | Total point average of the last 100 episodes: -106.11\\n\",\n      \"Episode 300 | Total point average of the last 100 episodes: -77.256\\n\",\n      \"Episode 400 | Total point average of the last 100 episodes: -25.01\\n\",\n      \"Episode 500 | Total point average of the last 100 episodes: 159.91\\n\",\n      \"Episode 534 | Total point average of the last 100 episodes: 201.37\\n\",\n      \"\\n\",\n      \"Environment solved in 534 episodes!\\n\",\n      \"\\n\",\n      \"Total Runtime: 722.17 s (12.04 min)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start = time.time()\\n\",\n    \"\\n\",\n    \"num_episodes = 2000\\n\",\n    \"max_num_timesteps = 1000\\n\",\n    \"\\n\",\n    \"total_point_history = []\\n\",\n    \"\\n\",\n    \"num_p_av = 100    # number of total points to use for averaging\\n\",\n    \"epsilon = 1.0     # initial ε value for ε-greedy policy\\n\",\n    \"\\n\",\n    \"# Create a memory buffer D with capacity N\\n\",\n    \"memory_buffer = deque(maxlen=MEMORY_SIZE)\\n\",\n    \"\\n\",\n    \"# Set the target network weights equal to the Q-Network weights\\n\",\n    \"target_q_network.set_weights(q_network.get_weights())\\n\",\n    \"\\n\",\n    \"for i in range(num_episodes):\\n\",\n    \"    \\n\",\n    \"    # Reset the environment to the initial state and get the initial state\\n\",\n    \"    state = env.reset()\\n\",\n    \"    total_points = 0\\n\",\n    \"    \\n\",\n    \"    for t in range(max_num_timesteps):\\n\",\n    \"        \\n\",\n    \"        # From the current state S choose an action A using an ε-greedy policy\\n\",\n    \"        state_qn = np.expand_dims(state, axis=0)  # state needs to be the right shape for the q_network\\n\",\n    \"        q_values = q_network(state_qn)\\n\",\n    \"        action = utils.get_action(q_values, epsilon)\\n\",\n    \"        \\n\",\n    \"        # Take action A and receive reward R and the next state S'\\n\",\n    \"        next_state, reward, done, _ = env.step(action)\\n\",\n    \"        \\n\",\n    \"        # Store experience tuple (S,A,R,S') in the memory buffer.\\n\",\n    \"        # We store the done variable as well for convenience.\\n\",\n    \"        memory_buffer.append(experience(state, action, reward, next_state, done))\\n\",\n    \"        \\n\",\n    \"        # Only update the network every NUM_STEPS_FOR_UPDATE time steps.\\n\",\n    \"        update = utils.check_update_conditions(t, NUM_STEPS_FOR_UPDATE, memory_buffer)\\n\",\n    \"        \\n\",\n    \"        if update:\\n\",\n    \"            # Sample random mini-batch of experience tuples (S,A,R,S') from D\\n\",\n    \"            experiences = utils.get_experiences(memory_buffer)\\n\",\n    \"            \\n\",\n    \"            # Set the y targets, perform a gradient descent step,\\n\",\n    \"            # and update the network weights.\\n\",\n    \"            agent_learn(experiences, GAMMA)\\n\",\n    \"        \\n\",\n    \"        state = next_state.copy()\\n\",\n    \"        total_points += reward\\n\",\n    \"        \\n\",\n    \"        if done:\\n\",\n    \"            break\\n\",\n    \"            \\n\",\n    \"    total_point_history.append(total_points)\\n\",\n    \"    av_latest_points = np.mean(total_point_history[-num_p_av:])\\n\",\n    \"    \\n\",\n    \"    # Update the ε value\\n\",\n    \"    epsilon = utils.get_new_eps(epsilon)\\n\",\n    \"\\n\",\n    \"    print(f\\\"\\\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\\\", end=\\\"\\\")\\n\",\n    \"\\n\",\n    \"    if (i+1) % num_p_av == 0:\\n\",\n    \"        print(f\\\"\\\\rEpisode {i+1} | Total point average of the last {num_p_av} episodes: {av_latest_points:.2f}\\\")\\n\",\n    \"\\n\",\n    \"    # We will consider that the environment is solved if we get an\\n\",\n    \"    # average of 200 points in the last 100 episodes.\\n\",\n    \"    if av_latest_points >= 200.0:\\n\",\n    \"        print(f\\\"\\\\n\\\\nEnvironment solved in {i+1} episodes!\\\")\\n\",\n    \"        q_network.save('lunar_lander_model.h5')\\n\",\n    \"        break\\n\",\n    \"        \\n\",\n    \"tot_time = time.time() - start\\n\",\n    \"\\n\",\n    \"print(f\\\"\\\\nTotal Runtime: {tot_time:.2f} s ({(tot_time/60):.2f} min)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can plot the point history to see how our agent improved during training.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"id\": \"E_EUXxurfe8m\",\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x504 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Plot the point history\\n\",\n    \"utils.plot_history(total_point_history)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"id\": \"c_xwgaX5MnYt\"\n   },\n   \"source\": [\n    \"<a name=\\\"10\\\"></a>\\n\",\n    \"## 10 - See the Trained Agent In Action\\n\",\n    \"\\n\",\n    \"Now that we have trained our agent, we can see it in action. We will use the `utils.create_video` function to create a video of our agent interacting with the environment using the trained $Q$-Network. The `utils.create_video` function uses the `imageio` library to create the video. This library produces some warnings that can be distracting, so, to suppress these warnings we run the code below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Suppress warnings from imageio\\n\",\n    \"import logging\\n\",\n    \"logging.getLogger().setLevel(logging.ERROR)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In the cell below we create a video of our agent interacting with the Lunar Lander environment using the trained `q_network`. The video is saved to the `videos` folder with the given `filename`. We use the `utils.embed_mp4` function to embed the video in the Jupyter Notebook so that we can see it here directly without having to download it.\\n\",\n    \"\\n\",\n    \"We should note that since the lunar lander starts with a random initial force applied to its center of mass, every time you run the cell below you will see a different video. If the agent was trained properly, it should be able to land the lunar lander in the landing pad every time, regardless of the initial force applied to its center of mass.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"id\": \"3Ttb_zLeJKiG\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"\\n\",\n       \"    <video width=\\\"840\\\" height=\\\"480\\\" controls>\\n\",\n       \"    <source 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type=\\\"video/mp4\\\">\\n\",\n       \"    Your browser does not support the video tag.\\n\",\n       \"    </video>\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"filename = \\\"./videos/lunar_lander.mp4\\\"\\n\",\n    \"\\n\",\n    \"utils.create_video(filename, env, q_network)\\n\",\n    \"utils.embed_mp4(filename)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"11\\\"></a>\\n\",\n    \"## 11 - Congratulations!\\n\",\n    \"\\n\",\n    \"You have successfully used Deep Q-Learning with Experience Replay to train an agent to land a lunar lander safely on a landing pad on the surface of the moon. Congratulations!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a name=\\\"12\\\"></a>\\n\",\n    \"## 12 - References\\n\",\n    \"\\n\",\n    \"If you would like to learn more about Deep Q-Learning, we recommend you check out the following papers.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* [Human-level Control Through Deep Reinforcement Learning](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* [Continuous Control with Deep Reinforcement Learning](https://arxiv.org/pdf/1509.02971.pdf)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"TensorFlow - Lunar Lander.ipynb\",\n   \"provenance\": []\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/C3W3A1/public_tests.py",
    "content": "from tensorflow.keras.activations import relu, linear\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.optimizers import Adam\n\nimport numpy as np\n\ndef test_network(target):\n    num_actions = 4\n    state_size = 8\n    i = 0\n    assert len(target.layers) == 3, f\"Wrong number of layers. Expected 3 but got {len(target.layers)}\"\n    assert target.input.shape.as_list() == [None, state_size], \\\n        f\"Wrong input shape. Expected [None,  400] but got {target.input.shape.as_list()}\" \n    expected = [[Dense, [None, 64], relu],\n                [Dense, [None, 64], relu],\n                [Dense, [None, num_actions], linear]]\n\n    for layer in target.layers:\n        assert type(layer) == expected[i][0], \\\n            f\"Wrong type in layer {i}. Expected {expected[i][0]} but got {type(layer)}\"\n        assert layer.output.shape.as_list() == expected[i][1], \\\n            f\"Wrong number of units in layer {i}. Expected {expected[i][1]} but got {layer.output.shape.as_list()}\"\n        assert layer.activation == expected[i][2], \\\n            f\"Wrong activation in layer {i}. Expected {expected[i][2]} but got {layer.activation}\"\n        i = i + 1\n\n    print(\"\\033[92mAll tests passed!\")\n    \ndef test_optimizer(target, ALPHA):\n    assert type(target) == Adam, f\"Wrong optimizer. Expected: {Adam}, got: {target}\"\n    assert np.isclose(target.learning_rate.numpy(), ALPHA), f\"Wrong alpha. Expected: {ALPHA}, got: {target.learning_rate.numpy()}\"\n    print(\"\\033[92mAll tests passed!\")\n    \n    \ndef test_compute_loss(target):\n    num_actions = 4\n    def target_q_network_random(inputs):\n        return np.float32(np.random.rand(inputs.shape[0],num_actions))\n    \n    def q_network_random(inputs):\n        return np.float32(np.random.rand(inputs.shape[0],num_actions))\n    \n    def target_q_network_ones(inputs):\n        return np.float32(np.ones((inputs.shape[0], num_actions)))\n    \n    def q_network_ones(inputs):\n        return np.float32(np.ones((inputs.shape[0], num_actions)))\n    \n    np.random.seed(1)\n    states = np.float32(np.random.rand(64, 8))\n    actions = np.float32(np.floor(np.random.uniform(0, 1, (64, )) * 4))\n    rewards = np.float32(np.random.rand(64, ))\n    next_states = np.float32(np.random.rand(64, 8))\n    done_vals = np.float32((np.random.uniform(0, 1, size=(64,)) > 0.96) * 1)\n\n    loss = target((states, actions, rewards, next_states, done_vals), 0.995, q_network_random, target_q_network_random)\n    \n\n    assert np.isclose(loss, 0.6991737), f\"Wrong value. Expected {0.6991737}, got {loss}\"\n\n    # Test when episode terminates\n    done_vals = np.float32(np.ones((64,)))\n    loss = target((states, actions, rewards, next_states, done_vals), 0.995, q_network_ones, target_q_network_ones)\n    assert np.isclose(loss, 0.343270182), f\"Wrong value. Expected {0.343270182}, got {loss}\"\n      \n    # Test MSE with parameters A = B\n    done_vals = np.float32((np.random.uniform(0, 1, size=(64,)) > 0.96) * 1)\n    rewards = np.float32(np.ones((64, )))\n    loss = target((states, actions, rewards, next_states, done_vals), 0, q_network_ones, target_q_network_ones)\n    assert np.isclose(loss, 0), f\"Wrong value. Expected {0}, got {loss}\"\n \n    # Test MSE with parameters A = 0 and B = 1\n    done_vals = np.float32((np.random.uniform(0, 1, size=(64,)) > 0.96) * 1)\n    rewards = np.float32(np.zeros((64, )))\n    loss = target((states, actions, rewards, next_states, done_vals), 0, q_network_ones, target_q_network_ones)\n    assert np.isclose(loss, 1), f\"Wrong value. Expected {1}, got {loss}\"\n\n    print(\"\\033[92mAll tests passed!\")\n    "
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/C3W3A1/utils.py",
    "content": "import base64\nimport random\nfrom itertools import zip_longest\n\nimport imageio\nimport IPython\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as mticker\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom statsmodels.iolib.table import SimpleTable\n\n\nSEED = 0              # seed for pseudo-random number generator\nMINIBATCH_SIZE = 64   # mini-batch size\nTAU = 1e-3            # soft update parameter\nE_DECAY = 0.995       # ε decay rate for ε-greedy policy\nE_MIN = 0.01          # minimum ε value for ε-greedy policy\n\n\nrandom.seed(SEED)\n\n\ndef get_experiences(memory_buffer):\n    experiences = random.sample(memory_buffer, k=MINIBATCH_SIZE)\n    states = tf.convert_to_tensor(np.array([e.state for e in experiences if e is not None]),dtype=tf.float32)\n    actions = tf.convert_to_tensor(np.array([e.action for e in experiences if e is not None]), dtype=tf.float32)\n    rewards = tf.convert_to_tensor(np.array([e.reward for e in experiences if e is not None]), dtype=tf.float32)\n    next_states = tf.convert_to_tensor(np.array([e.next_state for e in experiences if e is not None]),dtype=tf.float32)\n    done_vals = tf.convert_to_tensor(np.array([e.done for e in experiences if e is not None]).astype(np.uint8),\n                                     dtype=tf.float32)\n    return (states, actions, rewards, next_states, done_vals)\n\n\ndef check_update_conditions(t, num_steps_upd, memory_buffer):\n    if (t + 1) % num_steps_upd == 0 and len(memory_buffer) > MINIBATCH_SIZE:\n        return True\n    else:\n        return False\n    \n    \ndef get_new_eps(epsilon):\n    return max(E_MIN, E_DECAY*epsilon)\n\n\ndef get_action(q_values, epsilon=0):\n    if random.random() > epsilon:\n        return np.argmax(q_values.numpy()[0])\n    else:\n        return random.choice(np.arange(4))\n    \n    \ndef update_target_network(q_network, target_q_network):\n    for target_weights, q_net_weights in zip(target_q_network.weights, q_network.weights):\n        target_weights.assign(TAU * q_net_weights + (1.0 - TAU) * target_weights)\n    \n\ndef plot_history(reward_history, rolling_window=20, lower_limit=None,\n                 upper_limit=None, plot_rw=True, plot_rm=True):\n    \n    if lower_limit is None or upper_limit is None:\n        rh = reward_history\n        xs = [x for x in range(len(reward_history))]\n    else:\n        rh = reward_history[lower_limit:upper_limit]\n        xs = [x for x in range(lower_limit,upper_limit)]\n    \n    df = pd.DataFrame(rh)\n    rollingMean = df.rolling(rolling_window).mean()\n\n    plt.figure(figsize=(10,7), facecolor='white')\n    \n    if plot_rw:\n        plt.plot(xs, rh, linewidth=1, color='cyan')\n    if plot_rm:\n        plt.plot(xs, rollingMean, linewidth=2, color='magenta')\n\n    text_color = 'black'\n        \n    ax = plt.gca()\n    ax.set_facecolor('black')\n    plt.grid()\n#     plt.title(\"Total Point History\", color=text_color, fontsize=40)\n    plt.xlabel('Episode', color=text_color, fontsize=30)\n    plt.ylabel('Total Points', color=text_color, fontsize=30)\n    yNumFmt = mticker.StrMethodFormatter('{x:,}')\n    ax.yaxis.set_major_formatter(yNumFmt)\n    ax.tick_params(axis='x', colors=text_color)\n    ax.tick_params(axis='y', colors=text_color)\n    plt.show()\n    \n    \ndef display_table(initial_state, action, next_state, reward, done):\n\n    action_labels = [\"Do nothing\", \"Fire right engine\", \"Fire main engine\", \"Fire left engine\"]\n    \n    # Do not use column headers\n    column_headers = None\n\n    with np.printoptions(formatter={'float': '{:.3f}'.format}):\n        table_info = [(\"Initial State:\", [f\"{initial_state}\"]),\n                      (\"Action:\", [f\"{action_labels[action]}\"]),\n                      (\"Next State:\", [f\"{next_state}\"]),\n                      (\"Reward Received:\", [f\"{reward:.3f}\"]),\n                      (\"Episode Terminated:\", [f\"{done}\"])]\n\n    # Generate table  \n    row_labels, data = zip_longest(*table_info)\n    table = SimpleTable(data, column_headers, row_labels)\n\n    return table\n\n\ndef embed_mp4(filename):\n    \"\"\"Embeds an mp4 file in the notebook.\"\"\"\n    video = open(filename,'rb').read()\n    b64 = base64.b64encode(video)\n    tag = '''\n    <video width=\"840\" height=\"480\" controls>\n    <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\">\n    Your browser does not support the video tag.\n    </video>'''.format(b64.decode())\n    return IPython.display.HTML(tag)\n        \n        \ndef create_video(filename, env, q_network, fps=30):\n    with imageio.get_writer(filename, fps=fps) as video:\n        done = False\n        state = env.reset()\n        frame = env.render(mode=\"rgb_array\")\n        video.append_data(frame)\n        while not done:    \n            state = np.expand_dims(state, axis=0)\n            q_values = q_network(state)\n            action = np.argmax(q_values.numpy()[0])\n            state, _, done, _ = env.step(action)\n            frame = env.render(mode=\"rgb_array\")\n            video.append_data(frame)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/Practice Quiz - Continuous state spaces/Readme.md",
    "content": "\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/7ed64bc6aacf3c7dfb98464e0df9428f1726c3f5/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20Quiz%20:%20Continuous%20state%20spaces/ss1.png)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/Practice Quiz - Reinforcement learning introduction/Readme.md",
    "content": "\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/045f46e1e29d1a901c9ae94c7bbb448b89056e91/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20quiz%20:%20Reinforcement%20learning%20introduction/ss1.png)\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/045f46e1e29d1a901c9ae94c7bbb448b89056e91/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20quiz%20:%20Reinforcement%20learning%20introduction/ss2.png)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/Practice Quiz - State-action value function/Readme.md",
    "content": "\n![](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/513dbb63423fbc3033a47525470c62a21205aed1/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20Quiz%20:%20State-action%20value%20function/ss1.png)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/Readme.md",
    "content": " ### C3 - Week 3 Solutions\n \n- [Practice quiz : Reinforcement learning introduction](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/eb7aab8b6964336d3d8569f6e9380ca83775969e/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20quiz%20:%20Reinforcement%20learning%20introduction)\n- [Practice Quiz : State-action value function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/eb7aab8b6964336d3d8569f6e9380ca83775969e/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20Quiz%20:%20State-action%20value%20function)\n- [Practice Quiz : Continuous state spaces](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/eb7aab8b6964336d3d8569f6e9380ca83775969e/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/Practice%20Quiz%20:%20Continuous%20state%20spaces)\n- [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/84846129ed17898a3542fd1e5abc7605679fcfd8/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/C3W3A1)\n    - [Deep Q-Learning - Lunar Lander](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/84846129ed17898a3542fd1e5abc7605679fcfd8/C3%20-%20Unsupervised%20Learning,%20Recommenders,%20Reinforcement%20Learning/week3/C3W3A1/C3_W3_A1_Assignment.ipynb)"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/optional-labs/.ipynb_checkpoints/State-action value function example-checkpoint.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# State Action Value Function Example\\n\",\n    \"\\n\",\n    \"In this Jupyter notebook, you can modify the mars rover example to see how the values of Q(s,a) will change depending on the rewards and discount factor changing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from utils import *\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Do not modify\\n\",\n    \"num_states = 6\\n\",\n    \"num_actions = 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"terminal_left_reward = 100\\n\",\n    \"terminal_right_reward = 40\\n\",\n    \"each_step_reward = 0\\n\",\n    \"\\n\",\n    \"# Discount factor\\n\",\n    \"gamma = 0.5\\n\",\n    \"\\n\",\n    \"# Probability of going in the wrong direction\\n\",\n    \"misstep_prob = 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x144 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x144 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"generate_visualization(terminal_left_reward, terminal_right_reward, each_step_reward, gamma, misstep_prob)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/optional-labs/State-action value function example.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# State Action Value Function Example\\n\",\n    \"\\n\",\n    \"In this Jupyter notebook, you can modify the mars rover example to see how the values of Q(s,a) will change depending on the rewards and discount factor changing.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from utils import *\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Do not modify\\n\",\n    \"num_states = 6\\n\",\n    \"num_actions = 2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"terminal_left_reward = 100\\n\",\n    \"terminal_right_reward = 40\\n\",\n    \"each_step_reward = 0\\n\",\n    \"\\n\",\n    \"# Discount factor\\n\",\n    \"gamma = 0.5\\n\",\n    \"\\n\",\n    \"# Probability of going in the wrong direction\\n\",\n    \"misstep_prob = 0.4\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x144 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x144 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"generate_visualization(terminal_left_reward, terminal_right_reward, each_step_reward, gamma, misstep_prob)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "C3 - Unsupervised Learning, Recommenders, Reinforcement Learning/week3/optional-labs/utils.py",
    "content": "import numpy as np\nimport matplotlib.pyplot as plt\n\ndef generate_rewards(num_states, each_step_reward, terminal_left_reward, terminal_right_reward):\n\n    rewards = [each_step_reward] * num_states\n    rewards[0] = terminal_left_reward\n    rewards[-1] = terminal_right_reward\n    \n    return rewards \n\ndef generate_transition_prob(num_states, num_actions, misstep_prob = 0):\n    # 0 is left, 1 is right \n    \n    p = np.zeros((num_states, num_actions, num_states))\n    \n    for i in range(num_states):        \n        if i != 0:\n            p[i, 0, i-1] = 1 - misstep_prob\n            p[i, 1, i-1] = misstep_prob\n            \n        if i != num_states - 1:\n            p[i, 1, i+1] = 1  - misstep_prob\n            p[i, 0, i+1] = misstep_prob\n        \n    # Terminal States    \n    p[0] = np.zeros((num_actions, num_states))\n    p[-1] = np.zeros((num_actions, num_states))\n    \n    return p\n\ndef calculate_Q_value(num_states, rewards, transition_prob, gamma, V_states, state, action):\n    q_sa = rewards[state] + gamma * sum([transition_prob[state, action, sp] * V_states[sp] for sp in range(num_states)])\n    return q_sa\n\ndef evaluate_policy(num_states, rewards, transition_prob, gamma, policy):\n    max_policy_eval = 10000 \n    threshold = 1e-10\n    \n    V = np.zeros(num_states)\n    \n    for i in range(max_policy_eval):\n        delta = 0\n        for s in range(num_states):\n            v = V[s]\n            V[s] = calculate_Q_value(num_states, rewards, transition_prob, gamma, V, s, policy[s])\n            delta = max(delta, abs(v - V[s]))\n                       \n        if delta < threshold:\n            break\n            \n    return V\n\ndef improve_policy(num_states, num_actions, rewards, transition_prob, gamma, V, policy):\n    policy_stable = True\n    \n    for s in range(num_states):\n        q_best = V[s]\n        for a in range(num_actions):\n            q_sa = calculate_Q_value(num_states, rewards, transition_prob, gamma, V, s, a)\n            if q_sa > q_best and policy[s] != a:\n                policy[s] = a\n                q_best = q_sa\n                policy_stable = False\n    \n    return policy, policy_stable\n\n\ndef get_optimal_policy(num_states, num_actions, rewards, transition_prob, gamma):\n    optimal_policy = np.zeros(num_states, dtype=int)\n    max_policy_iter = 10000 \n\n    for i in range(max_policy_iter):\n        policy_stable = True\n\n        V = evaluate_policy(num_states, rewards, transition_prob, gamma, optimal_policy)\n        optimal_policy, policy_stable = improve_policy(num_states, num_actions, rewards, transition_prob, gamma, V, optimal_policy)\n\n        if policy_stable:\n            break\n            \n    return optimal_policy, V\n\ndef calculate_Q_values(num_states, rewards, transition_prob, gamma, optimal_policy):\n    # Left and then optimal policy\n    q_left_star = np.zeros(num_states)\n\n    # Right and optimal policy\n    q_right_star = np.zeros(num_states)\n    \n    V_star =  evaluate_policy(num_states, rewards, transition_prob, gamma, optimal_policy)\n\n    for s in range(num_states):\n        q_left_star[s] = calculate_Q_value(num_states, rewards, transition_prob, gamma, V_star, s, 0)\n        q_right_star[s] = calculate_Q_value(num_states, rewards, transition_prob, gamma, V_star, s, 1)\n        \n    return q_left_star, q_right_star\n\n\ndef plot_optimal_policy_return(num_states, optimal_policy, rewards, V):\n    actions = [r\"$\\leftarrow$\" if a == 0 else r\"$\\rightarrow$\" for a in optimal_policy]\n    actions[0] = \"\"\n    actions[-1] = \"\"\n    \n    fig, ax = plt.subplots(figsize=(2*num_states,2))\n\n    for i in range(num_states):\n        ax.text(i+0.5, 0.5, actions[i], fontsize=32, ha=\"center\", va=\"center\", color=\"orange\")\n        ax.text(i+0.5, 0.25, rewards[i], fontsize=16, ha=\"center\", va=\"center\", color=\"black\")\n        ax.text(i+0.5, 0.75, round(V[i],2), fontsize=16, ha=\"center\", va=\"center\", color=\"firebrick\")\n        ax.axvline(i, color=\"black\")\n    ax.set_xlim([0, num_states])\n    ax.set_ylim([0, 1])\n\n    ax.set_xticklabels([])\n    ax.set_yticklabels([])\n    ax.tick_params(axis='both', which='both', length=0)\n    ax.set_title(\"Optimal policy\",fontsize = 16)\n\ndef plot_q_values(num_states, q_left_star, q_right_star, rewards):\n    fig, ax = plt.subplots(figsize=(3*num_states,2))\n\n    for i in range(num_states):\n        ax.text(i+0.2, 0.6, round(q_left_star[i],2), fontsize=16, ha=\"center\", va=\"center\", color=\"firebrick\")\n        ax.text(i+0.8, 0.6, round(q_right_star[i],2), fontsize=16, ha=\"center\", va=\"center\", color=\"firebrick\")\n\n        ax.text(i+0.5, 0.25, rewards[i], fontsize=20, ha=\"center\", va=\"center\", color=\"black\")\n        ax.axvline(i, color=\"black\")\n    ax.set_xlim([0, num_states])\n    ax.set_ylim([0, 1])\n\n    ax.set_xticklabels([])\n    ax.set_yticklabels([])\n    ax.tick_params(axis='both', which='both', length=0)\n    ax.set_title(\"Q(s,a)\",fontsize = 16)\n\ndef generate_visualization(terminal_left_reward, terminal_right_reward, each_step_reward, gamma, misstep_prob):\n    num_states = 6\n    num_actions = 2\n    \n    rewards = generate_rewards(num_states, each_step_reward, terminal_left_reward, terminal_right_reward)\n    transition_prob = generate_transition_prob(num_states, num_actions, misstep_prob)\n    \n    optimal_policy, V = get_optimal_policy(num_states, num_actions, rewards, transition_prob, gamma)\n    q_left_star, q_right_star = calculate_Q_values(num_states, rewards, transition_prob, gamma, optimal_policy)\n    \n    plot_optimal_policy_return(num_states, optimal_policy, rewards, V)\n    plot_q_values(num_states, q_left_star, q_right_star, rewards)\n    "
  },
  {
    "path": "CODE_OF_CONDUCT.md",
    "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nWe as members, contributors, and leaders pledge to make participation in our\ncommunity a harassment-free experience for everyone, regardless of age, body\nsize, visible or invisible disability, ethnicity, sex characteristics, gender\nidentity and expression, level of experience, education, socio-economic status,\nnationality, personal appearance, race, religion, or sexual identity\nand orientation.\n\nWe pledge to act and interact in ways that contribute to an open, welcoming,\ndiverse, inclusive, and healthy community.\n\n## Our Standards\n\nExamples of behavior that contributes to a positive environment for our\ncommunity include:\n\n* Demonstrating empathy and kindness toward other people\n* Being respectful of differing opinions, viewpoints, and experiences\n* Giving and gracefully accepting constructive feedback\n* Accepting responsibility and apologizing to those affected by our mistakes,\n  and learning from the experience\n* Focusing on what is best not just for us as individuals, but for the\n  overall community\n\nExamples of unacceptable behavior include:\n\n* The use of sexualized language or imagery, and sexual attention or\n  advances of any kind\n* Trolling, insulting or derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or email\n  address, without their explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\n  professional setting\n\n## Enforcement Responsibilities\n\nCommunity leaders are responsible for clarifying and enforcing our standards of\nacceptable behavior and will take appropriate and fair corrective action in\nresponse to any behavior that they deem inappropriate, threatening, offensive,\nor harmful.\n\nCommunity leaders have the right and responsibility to remove, edit, or reject\ncomments, commits, code, wiki edits, issues, and other contributions that are\nnot aligned to this Code of Conduct, and will communicate reasons for moderation\ndecisions when appropriate.\n\n## Scope\n\nThis Code of Conduct applies within all community spaces, and also applies when\nan individual is officially representing the community in public spaces.\nExamples of representing our community include using an official e-mail address,\nposting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported to the community leaders responsible for enforcement at\n.\nAll complaints will be reviewed and investigated promptly and fairly.\n\nAll community leaders are obligated to respect the privacy and security of the\nreporter of any incident.\n\n## Enforcement Guidelines\n\nCommunity leaders will follow these Community Impact Guidelines in determining\nthe consequences for any action they deem in violation of this Code of Conduct:\n\n### 1. Correction\n\n**Community Impact**: Use of inappropriate language or other behavior deemed\nunprofessional or unwelcome in the community.\n\n**Consequence**: A private, written warning from community leaders, providing\nclarity around the nature of the violation and an explanation of why the\nbehavior was inappropriate. A public apology may be requested.\n\n### 2. Warning\n\n**Community Impact**: A violation through a single incident or series\nof actions.\n\n**Consequence**: A warning with consequences for continued behavior. No\ninteraction with the people involved, including unsolicited interaction with\nthose enforcing the Code of Conduct, for a specified period of time. This\nincludes avoiding interactions in community spaces as well as external channels\nlike social media. Violating these terms may lead to a temporary or\npermanent ban.\n\n### 3. Temporary Ban\n\n**Community Impact**: A serious violation of community standards, including\nsustained inappropriate behavior.\n\n**Consequence**: A temporary ban from any sort of interaction or public\ncommunication with the community for a specified period of time. No public or\nprivate interaction with the people involved, including unsolicited interaction\nwith those enforcing the Code of Conduct, is allowed during this period.\nViolating these terms may lead to a permanent ban.\n\n### 4. Permanent Ban\n\n**Community Impact**: Demonstrating a pattern of violation of community\nstandards, including sustained inappropriate behavior,  harassment of an\nindividual, or aggression toward or disparagement of classes of individuals.\n\n**Consequence**: A permanent ban from any sort of public interaction within\nthe community.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage],\nversion 2.0, available at\nhttps://www.contributor-covenant.org/version/2/0/code_of_conduct.html.\n\nCommunity Impact Guidelines were inspired by [Mozilla's code of conduct\nenforcement ladder](https://github.com/mozilla/diversity).\n\n[homepage]: https://www.contributor-covenant.org\n\nFor answers to common questions about this code of conduct, see the FAQ at\nhttps://www.contributor-covenant.org/faq. Translations are available at\nhttps://www.contributor-covenant.org/translations.\n"
  },
  {
    "path": "CONTRIBUTING.md",
    "content": "If you find it useful,\n\nDropping a *star* would be *motivational* !\n"
  },
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2022 Ritvik\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "# Machine Learning Specialization Coursera\n\n\n![](/resources/title-head.png)\n\nContains Solutions and Notes for the [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction/?utm_medium=coursera&utm_source=home-page&utm_campaign=mlslaunch2022IN) by Andrew NG on Coursera \n\n**Note : If you would like to have a deeper understanding of the concepts by understanding all the math required, have a look at [Mathematics for Machine Learning and Data Science](https://github.com/greyhatguy007/Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera)**\n\n<hr/>\n\n## Course 1 : [Supervised Machine Learning: Regression and Classification ](https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction)\n\n- [Week 1](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1)\n\n    - [Practice quiz: Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Practice%20quiz%20-%20Regression)\n    - [Practice quiz: Supervised vs unsupervised learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Practice%20quiz%20-%20Supervised%20vs%20unsupervised%20learning)\n    - [Practice quiz: Train the model with gradient descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Practice%20quiz%20-%20Train%20the%20model%20with%20gradient%20descent)\n  - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs)\n    - [Model Representation](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs/C1_W1_Lab03_Model_Representation_Soln.ipynb)\n    - [Cost Function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs/C1_W1_Lab04_Cost_function_Soln.ipynb)\n    - [Gradient Descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week1/Optional%20Labs/C1_W1_Lab05_Gradient_Descent_Soln.ipynb)\n\n<br/>\n\n- [Week 2](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2) \n\n    - [Practice quiz: Gradient descent in practice](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Practice%20quiz%20-%20Gradient%20descent%20in%20practice)\n    - [Practice quiz: Multiple linear regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Practice%20quiz%20-%20Multiple%20linear%20regression)\n    - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs)\n      - [Numpy Vectorization](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb)\n      - [Multi Variate Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab02_Multiple_Variable_Soln.ipynb)\n      - [Feature Scaling](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb)\n      - [Feature Engineering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb)\n      - [Sklearn Gradient Descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n      - [Sklearn Normal Method](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/Optional%20Labs/C1_W2_Lab05_Sklearn_GD_Soln.ipynb)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/C1W2A1)\n      - [Linear Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week2/C1W2A1/C1_W2_Linear_Regression.ipynb)\n\n<br/>\n\n- [Week 3](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3)\n\n    - [Practice quiz: Cost function for logistic regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Practice%20quiz%20-%20Cost%20function%20for%20logistic%20regression)\n    - [Practice quiz: Gradient descent for logistic regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Practice%20quiz%20-%20Gradient%20descent%20for%20logistic%20regression)\n    - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs)\n        - [Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab01_Classification_Soln.ipynb)\n        - [Sigmoid Function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab02_Sigmoid_function_Soln.ipynb)\n        - [Decision Boundary](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab03_Decision_Boundary_Soln.ipynb)\n        - [Logistic Loss](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab04_LogisticLoss_Soln.ipynb)\n        - [Cost Function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab05_Cost_Function_Soln.ipynb)\n        - [Gradient Descent](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab06_Gradient_Descent_Soln.ipynb)\n        - [Scikit Learn - Logistic Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab07_Scikit_Learn_Soln.ipynb)\n        - [Overfitting](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab08_Overfitting_Soln.ipynb)\n        - [Regularization](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/Optional%20Labs/C1_W3_Lab09_Regularization_Soln.ipynb)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/C1W3A1)\n      - [Logistic Regression](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C1%20-%20Supervised%20Machine%20Learning%20-%20Regression%20and%20Classification/week3/C1W3A1/C1_W3_Logistic_Regression.ipynb)\n\n#### [Certificate Of Completion](https://coursera.org/share/195768f3c1a83e42298d3f61dae99d01)\n\n<br/>\n\n## Course 2 : [Advanced Learning Algorithms](https://www.coursera.org/learn/advanced-learning-algorithms?specialization=machine-learning-introduction)\n\n- [Week 1](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1)\n    - [Practice quiz: Neural networks intuition](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz%20-%20Neural%20networks%20intuition)\n    - [Practice quiz: Neural network model](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz%20-%20Neural%20network%20model)\n    - [Practice quiz: TensorFlow implementation](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice%20quiz%20-%20TensorFlow%20implementation)\n    - [Practice quiz : Neural Networks Implementation in Numpy](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/Practice-Quiz-Neural-Networks-Implementation-in-python)\n    - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs)\n      - [Neurons and Layers](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs/C2_W1_Lab01_Neurons_and_Layers.ipynb)\n      - [Coffee Roasting](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs/C2_W1_Lab02_CoffeeRoasting_TF.ipynb)\n      - [Coffee Roasting Using Numpy](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/optional-labs/C2_W1_Lab02_CoffeeRoasting_TF.ipynb)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/C2W1A1)\n      - [Neural Networks for Binary Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week1/C2W1A1/C2_W1_Assignment.ipynb)\n  \n\n  <br/>\n\n- [Week 2](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week2)\n    - [Practice quiz : Neural Networks Training](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Neural-Network-Training)\n    - [Practice quiz : Activation Functions](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Activation-Functions)\n    - [Practice quiz : Multiclass Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-quiz-Multiclass-Classification)\n    - [Practice quiz : Additional Neural Networks Concepts](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/Practice-Quiz-Additional-Neural-Network-Concepts)\n    - [Optional Labs](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs)\n        - [RElu](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs/C2_W2_Relu.ipynb)\n        - [Softmax](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs/C2_W2_SoftMax.ipynb)\n        - [Multiclass Classification](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/optional-labs/C2_W2_Multiclass_TF.ipynb)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/C2W2A1)\n      - [Neural Networks For Handwritten Digit Recognition - Multiclass](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week2/C2W2A1/C2_W2_Assignment.ipynb)\n    \n\n<br/>\n\n- [Week 3](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week3)\n    - [Practice quiz : Advice for Applying Machine Learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week3/Practice-Quiz-Advice-for-applying-machine-learning)    \n    - [Practice quiz : Bias and Variance](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-bias-and-variance)\n    - [Practice quiz : Machine Learning Development Process](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week3/practice-quiz-machine-learning-development-process)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week3/C2W3A1)\n        - [Advice for Applied Machine Learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week3/C2W3A1/C2_W3_Assignment.ipynb)\n\n<br/>\n\n\n- [Week 4](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week4)\n    - [Practice quiz : Decision Trees](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-trees)\n    - [Practice quiz : Decision Trees Learning](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-decision-tree-learning)\n    - [Practice quiz : Decision Trees Ensembles](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week4/practice-quiz-tree-ensembles)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C2%20-%20Advanced%20Learning%20Algorithms/week4/C2W4A1)\n        - [Decision Trees](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C2%20-%20Advanced%20Learning%20Algorithms/week4/C2W4A1/C2_W4_Decision_Tree_with_Markdown.ipynb)\n\n#### [Certificate of Completion](https://coursera.org/share/c9a7766b0c6eab27db2e955376d29bf7)        \n\n<br/>\n\n## Course 3 : [Unsupervised Learning, Recommenders, Reinforcement Learning](https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning?specialization=machine-learning-introduction)\n\n<br/>\n\n- [Week 1](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week1)\n    - [Practice quiz : Clustering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week1/Practice%20Quiz%20-%20Clustering)\n    - [Practice quiz : Anomaly Detection](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week1/Practice%20Quiz%20-%20Anomaly%20Detection)\n    - [Programming Assignments](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week1/C3W1A)\n        - [K means](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week1/C3W1A/C3W1A1/C3_W1_KMeans_Assignment.ipynb)\n        - [Anomaly Detection](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week1/C3W1A/C3W1A2/C3_W1_Anomaly_Detection.ipynb)\n\n<br/>\n\n- [Week 2](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week2)\n    - [Practice quiz : Collaborative Filtering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week2/Practice%20Quiz%20-%20Collaborative%20Filtering)\n    - [Practice quiz : Recommender systems implementation](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week2/Practice%20Quiz%20-%20Recommender%20systems%20implementation)\n    - [Practice quiz : Content-based filtering](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week2/Practice%20Quiz%20-%20Content-based%20filtering)\n    - [Programming Assignments](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week2/C3W2)\n        - [Collaborative Filtering RecSys](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week2/C3W2/C3W2A1/C3_W2_Collaborative_RecSys_Assignment.ipynb)\n        - [RecSys using Neural Networks](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week2/C3W2/C3W2A2/C3_W2_RecSysNN_Assignment.ipynb)\n\n<br/>\n\n- [Week 3](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week3)\n    - [Practice quiz : Reinforcement learning introduction](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week3/Practice%20Quiz%20-%20Reinforcement%20learning%20introduction)\n    - [Practice Quiz : State-action value function](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week3/Practice%20Quiz%20-%20State-action%20value%20function)\n    - [Practice Quiz : Continuous state spaces](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week3/Practice%20Quiz%20-%20Continuous%20state%20spaces)\n    - [Programming Assignment](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/tree/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week3/C3W3A1)\n        - [Deep Q-Learning - Lunar Lander](https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera/blob/main/C3%20-%20Unsupervised%20Learning%2C%20Recommenders%2C%20Reinforcement%20Learning/week3/C3W3A1/C3_W3_A1_Assignment.ipynb)\n#### [Certificate of Completion](https://coursera.org/share/5bf5ee456b0c806df9b8622067b47ca6)\n\n\n### [Specialization Certificate](https://coursera.org/share/a15ac6426f90924491a542850700a759)\n\n<br/>\n\n<br/>\n\n<hr/>\n\n<div align=\"center\">\n\n                        \n### Stargazers over time\n[![Stargazers over time](https://starchart.cc/greyhatguy007/Machine-Learning-Specialization-Coursera.svg?variant=adaptive)](https://starchart.cc/greyhatguy007/Machine-Learning-Specialization-Coursera)                 \n\n[![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fgreyhatguy007%2FMachine-Learning-Specialization-Coursera&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com)\n\n</div>\n\n### Course Review :\n\nThis Course is a best place towards becoming a Machine Learning Engineer. Even if you're an expert, many algorithms are covered in depth such as decision trees which may help in further improvement of skills.\n\n**Special thanks to [Professor Andrew Ng](https://www.andrewng.org/) for structuring and tailoring this Course.**\n\n<br/>\n\n<hr/>\n\n#### An insight of what you might be able to accomplish at the end of this specialization :\n\n* <i>Write an unsupervised learning algorithm to **Land the Lunar Lander** Using Deep Q-Learning</i>\n\n    - The Rover was trained to land correctly on the surface, correctly between the flags as indicators after many unsuccessful attempts in learning how to do it.\n    - The final landing after training the agent using appropriate parameters : \n\nhttps://user-images.githubusercontent.com/77543865/182395635-703ae199-ba79-4940-86eb-23dd90093ab3.mp4\n\n* <i>Write an algorithm for a **Movie Recommender System**</i>\n    \n    - A movie database is collected based on its genre.\n    - A content based filtering and collaborative filtering algorithm is trained and the movie recommender system is implemented.\n    - It gives movie recommendentations based on the movie genre.\n\n![movie_recommendation](https://user-images.githubusercontent.com/77543865/182398093-c7387754-34a9-4044-b842-0085060c3525.png)\n\n* <i> And Much More !! </i>\n\n\nConcluding, this is a course which I would recommend everyone to take. Not just because you learn many new stuffs, but also the assignments are real life examples which are *exciting to complete*. \n\n<br/>\n\n**Happy Learning :))**\n\n\n \n \n"
  }
]